llama.cpp/src/llama.cpp

#include "llama-impl.h"

#include "llama-chat.h"
#include "llama-mmap.h"
#include "llama-context.h"
#include "llama-vocab.h"
#include "llama-sampling.h"
#include "llama-kv-cache.h"
#include "llama-model-loader.h"
#include "llama-model.h"

#include "ggml.h"
#include "ggml-alloc.h"
#include "ggml-backend.h"
#include "ggml-cpp.h"

#include <algorithm>
#include <array>
#include <cassert>
#include <cfloat>
#include <cmath>
#include <cstddef>
#include <cstdint>
#include <cstdio>
#include <cstring>
#include <ctime>
#include <functional>
#include <initializer_list>
#include <map>

#if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data
#endif

//
// tensor loading (TODO: add llama_tesor_loader?)
//

static int llama_get_device_count(const llama_model & model) {
    return (int) model.devices.size();
}

// checks if the weight tensor can be used with the specified buffer type and device
static bool weight_buft_supported(const llama_hparams & hparams, ggml_tensor * w, ggml_op op, ggml_backend_buffer_type_t buft, ggml_backend_dev_t dev) {
    GGML_ASSERT(w != nullptr);

    if (op == GGML_OP_NONE) {
        return true;
    }

    ggml_init_params params = {
        /*.mem_size   =*/ ggml_tensor_overhead()*8,
        /*.mem_buffer =*/ NULL,
        /*.no_alloc   =*/ true,
    };
    ggml_context_ptr ctx_ptr { ggml_init(params) };
    if (!ctx_ptr) {
        throw std::runtime_error(format("failed to create ggml context"));
    }
    ggml_context * ctx = ctx_ptr.get();

    ggml_tensor * op_tensor = nullptr;

    switch (op) {
        case GGML_OP_GET_ROWS:
            {
                ggml_tensor * b = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 512);
                op_tensor = ggml_get_rows(ctx, w, b);
            } break;
        case GGML_OP_MUL_MAT:
            {
                ggml_tensor * b = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, w->ne[0], 512, w->ne[2], w->ne[3]);
                op_tensor = ggml_mul_mat(ctx, w, b);
            } break;
        case GGML_OP_MUL_MAT_ID:
            {
                int n_expert_used = hparams.n_expert_used;
                ggml_tensor * b = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, w->ne[0], n_expert_used, 512);
                ggml_tensor * ids = ggml_new_tensor_2d(ctx, GGML_TYPE_I32, n_expert_used, 512);
                op_tensor = ggml_mul_mat_id(ctx, w, b, ids);
            } break;
        case GGML_OP_ADD:
            {
                ggml_tensor * a = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, w->ne[0], w->ne[1], w->ne[2], w->ne[3]);
                op_tensor = ggml_add(ctx, a, w);
            } break;
        case GGML_OP_MUL:
            {
                ggml_tensor * a = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, w->ne[0], w->ne[1], w->ne[2], w->ne[3]);
                op_tensor = ggml_mul(ctx, a, w);
            } break;
        case GGML_OP_DIV:
            {
                ggml_tensor * a = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, w->ne[0]);
                op_tensor = ggml_div(ctx, a, w);
            } break;
        case GGML_OP_ROPE:
            {
                int n_embd_head = hparams.n_embd_head_v;
                int n_head = hparams.n_head();
                ggml_tensor * a = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, n_embd_head, n_head, 512);
                ggml_tensor * b = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 512);
                op_tensor = ggml_rope_ext(
                    ctx, a, b, w,
                    0, 0, 0, 0, 0,
                    0, 0, 0, 0
                );

            } break;
        case GGML_OP_SSM_CONV:
            {
                // FIXME
                ggml_tensor * conv_x = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, 12345, w->ne[1], 6789);
                op_tensor = ggml_ssm_conv(ctx, conv_x, w);
            } break;
        case GGML_OP_SSM_SCAN:
            {
                // FIXME
                const int64_t d_state      = w->ne[0];
                const int64_t d_inner      = w->ne[1];
                const int64_t n_seq_tokens = 512;
                const int64_t n_seqs       = 1;
                ggml_tensor * s  = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, d_state, d_inner, n_seqs);
                ggml_tensor * x = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, d_inner, n_seq_tokens, n_seqs);
                ggml_tensor * dt = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, d_inner, n_seq_tokens, n_seqs);
                ggml_tensor * B = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, d_state, n_seq_tokens, n_seqs);
                ggml_tensor * C = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, d_state, n_seq_tokens, n_seqs);
                op_tensor = ggml_ssm_scan(ctx, s, x, dt, w, B, C);
            } break;
        case GGML_OP_RWKV_WKV6:
            {
                // FIXME
                const int64_t S = 123;
                const int64_t H = 123;
                const int64_t n_tokens = 123;
                const int64_t n_seqs = 123;
                ggml_tensor  * k = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, S, 1, H, n_tokens);
                ggml_tensor  * v = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, 1, S, H, n_tokens);
                ggml_tensor  * r = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, 1, S, H, n_tokens);
                ggml_tensor  * tf = w;
                ggml_tensor  * td = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, 1, S, H, n_tokens);
                ggml_tensor  * state = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, S, n_seqs, S, H);
                op_tensor = ggml_rwkv_wkv6(ctx, k, v, r, tf, td, state);
            } break;
        case GGML_OP_IM2COL:
            {
                const int n_embd = hparams.n_embd;
                ggml_tensor * b = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, n_embd, w->ne[1], 1, 1);
                op_tensor = ggml_im2col(ctx, w, b, 1, 0, 0, 0, 1, 0, false, GGML_TYPE_F16);
            } break;
        default:
            GGML_ABORT("%s: missing test for op %s for tensor %s", __func__, ggml_op_name(op), w->name);
    }

    // create a temporary dummy buffer for the weight so that supports_op can check the buffer type
    GGML_ASSERT(w->buffer == nullptr);
    w->buffer = ggml_backend_buft_alloc_buffer(buft, 0);
    bool op_supported = ggml_backend_dev_supports_op(dev, op_tensor);
    ggml_backend_buffer_free(w->buffer);
    w->buffer = nullptr;

    return op_supported;
}

// find the first buffer type in the list that can use the tensor
static ggml_backend_buffer_type_t select_weight_buft(const llama_model & model, ggml_tensor * tensor, ggml_op op, const llama_model::buft_list_t & buft_list) {
    GGML_ASSERT(!buft_list.empty());
    for (const auto & cur : buft_list) {
        ggml_backend_dev_t cur_dev = cur.first;
        ggml_backend_buffer_type_t cur_buft = cur.second;
        if (weight_buft_supported(model.hparams, tensor, op, cur_buft, cur_dev)) {
            return cur_buft;
        }
    }
    return nullptr;
}

// CPU: ACCEL -> CPU extra -> GPU host -> CPU
static llama_model::buft_list_t make_cpu_buft_list(llama_model & model) {
    llama_model::buft_list_t buft_list;

    // add ACCEL buffer types
    for (size_t i = 0; i < ggml_backend_dev_count(); ++i) {
        ggml_backend_dev_t dev = ggml_backend_dev_get(i);
        if (ggml_backend_dev_type(dev) == GGML_BACKEND_DEVICE_TYPE_ACCEL) {
            auto * buft = ggml_backend_dev_buffer_type(dev);
            // skip
            if (buft != ggml_backend_cpu_buffer_type()) {
                buft_list.emplace_back(dev, buft);
            }
        }
    }

    // add extra buffer types
    auto * cpu_dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU);
    auto * cpu_reg = ggml_backend_dev_backend_reg(cpu_dev);
    auto ggml_backend_dev_get_extra_bufts_fn = (ggml_backend_dev_get_extra_bufts_t)
        ggml_backend_reg_get_proc_address(cpu_reg, "ggml_backend_dev_get_extra_bufts");
    if (ggml_backend_dev_get_extra_bufts_fn) {
        ggml_backend_buffer_type_t * extra_bufts = ggml_backend_dev_get_extra_bufts_fn(cpu_dev);
        while (extra_bufts && *extra_bufts) {
            buft_list.emplace_back(cpu_dev, *extra_bufts);
            ++extra_bufts;
        }
    }

    // add a host buffer type
    // storing the tensors in a host buffer is useful when the processing of large batches
    // is offloaded to a GPU device, since it reduces the time spent on data transfers
    // generally, this will be done using the first device in the list
    // a better approach would be to handle this on a weight-by-weight basis using the offload_op
    // function of the device to determine if it would benefit from being stored in a host buffer
    for (auto * dev : model.devices) {
        ggml_backend_buffer_type_t buft = ggml_backend_dev_host_buffer_type(dev);
        if (buft) {
            buft_list.emplace_back(dev, buft);
            break;
        }
    }

    // add the CPU buffer type
    for (size_t i = 0; i < ggml_backend_dev_count(); ++i) {
        ggml_backend_dev_t dev = ggml_backend_dev_get(i);
        if (ggml_backend_dev_type(dev) == GGML_BACKEND_DEVICE_TYPE_CPU) {
            buft_list.emplace_back(dev, ggml_backend_dev_buffer_type(dev));
        }
    }

    return buft_list;
}

// GPU: split if LLAMA_SPLIT_MODE_ROW -> GPU
static llama_model::buft_list_t make_gpu_buft_list(ggml_backend_dev_t dev, enum llama_split_mode split_mode, const float * tensor_split) {
    llama_model::buft_list_t buft_list;

    // add the device split buffer type if requested and available
    if (split_mode == LLAMA_SPLIT_MODE_ROW) {
        ggml_backend_reg_t reg = ggml_backend_dev_backend_reg(dev);
        auto ggml_backend_split_buffer_type_fn = (ggml_backend_split_buffer_type_t)
            ggml_backend_reg_get_proc_address(reg, "ggml_backend_split_buffer_type");
        if (ggml_backend_split_buffer_type_fn) {
            size_t dev_index = [&]() {
                auto * reg = ggml_backend_dev_backend_reg(dev);
                for (size_t i = 0; i < ggml_backend_reg_dev_count(reg); ++i) {
                    if (ggml_backend_reg_dev_get(reg, i) == dev) {
                        return i;
                    }
                }
                throw std::runtime_error(format("device %s not found in its backend reg", ggml_backend_dev_name(dev)));
            }();
            auto * buft = ggml_backend_split_buffer_type_fn(dev_index, tensor_split);
            if (buft != nullptr) {
                buft_list.emplace_back(dev, buft);
            }
        }
    }

    // add the device default buffer type
    buft_list.emplace_back(dev, ggml_backend_dev_buffer_type(dev));

    return buft_list;
}

// Returns false if cancelled by progress_callback
static bool llm_load_tensors(
        llama_model_loader & ml,
        llama_model & model,
        int n_gpu_layers,
        enum llama_split_mode split_mode,
        int main_gpu,
        const float * tensor_split,
        bool use_mlock,
        llama_progress_callback progress_callback,
        void * progress_callback_user_data) {
    auto & hparams = model.hparams;

    model.split_mode   = split_mode;
    model.main_gpu     = main_gpu;
    model.n_gpu_layers = n_gpu_layers;

    const int n_layer = hparams.n_layer;

    bool use_mmap_buffer = true;

    // build a list of buffer types for the CPU and GPU devices
    model.cpu_buft_list = make_cpu_buft_list(model);
    for (auto * dev : model.devices) {
        llama_model::buft_list_t buft_list = make_gpu_buft_list(dev, split_mode, tensor_split);
        // add CPU buffer types as a fallback
        buft_list.insert(buft_list.end(), model.cpu_buft_list.begin(), model.cpu_buft_list.end());
        model.gpu_buft_list.emplace(dev, std::move(buft_list));
    }

    // calculate the split points
    int device_count = llama_get_device_count(model);
    bool all_zero = tensor_split == nullptr || std::all_of(tensor_split, tensor_split + device_count, [](float x) { return x == 0.0f; });
    std::vector<float> splits(device_count);
    if (all_zero) {
        // default split, by free memory
        for (int i = 0; i < device_count; ++i) {
            ggml_backend_dev_t dev = model.devices[i];
            size_t total;
            size_t free;
            ggml_backend_dev_memory(dev, &free, &total);
            splits[i] = free;
        }
    } else {
        std::copy(tensor_split, tensor_split + device_count, splits.begin());
    }

    // sum and normalize the splits to get the split points
    float split_sum = 0.0f;
    for (int i = 0; i < device_count; ++i) {
        split_sum += splits[i];
        splits[i] = split_sum;
    }
    for (int i = 0; i < device_count; ++i) {
        splits[i] /= split_sum;
    }

    ggml_backend_dev_t cpu_dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU);
    const int i_gpu_start = std::max((int) hparams.n_layer - n_gpu_layers, (int) 0);
    const int act_gpu_layers = model.devices.empty() ? 0 : std::min(n_gpu_layers, (int)n_layer + 1);
    auto get_layer_buft_list = [&](int il) -> llama_model::layer_dev {
        if (il < i_gpu_start || (il - i_gpu_start) >= act_gpu_layers) {
            return {cpu_dev, &model.cpu_buft_list};
        }
        int layer_gpu = std::upper_bound(splits.begin(), splits.begin() + device_count, float(il - i_gpu_start)/act_gpu_layers) - splits.begin();
        auto * dev = model.devices.at(layer_gpu);
        return {dev, &model.gpu_buft_list.at(dev)};
    };

    // assign the input layer
    // there is very little benefit to offloading the input layer, so always keep it on the CPU
    model.dev_input = { cpu_dev, &model.cpu_buft_list };

    // assign the repeating layers to the devices according to the splits
    model.dev_layer.resize(n_layer);
    for (int il = 0; il < n_layer; ++il) {
        model.dev_layer[il] = get_layer_buft_list(il);
    }
    // assign the output layer
    model.dev_output = get_layer_buft_list(n_layer);

    // one ggml context per buffer type
    int max_n_tensors = ml.n_tensors;
    max_n_tensors += 1;         // duplicated output tensor
    max_n_tensors += n_layer*2; // duplicated rope freq tensors
    const size_t ctx_size = ggml_tensor_overhead()*max_n_tensors;

    std::map<ggml_backend_buffer_type_t, ggml_context *> ctx_map;
    auto ctx_for_buft = [&](ggml_backend_buffer_type_t buft) -> ggml_context * {
        auto it = ctx_map.find(buft);
        if (it == ctx_map.end()) {
            ggml_init_params params = {
                /*.mem_size   =*/ ctx_size,
                /*.mem_buffer =*/ NULL,
                /*.no_alloc   =*/ true,
            };
            ggml_context * ctx = ggml_init(params);
            if (!ctx) {
                throw std::runtime_error(format("failed to create ggml context"));
            }
            ctx_map[buft] = ctx;
            model.ctxs.emplace_back(ctx);
            return ctx;
        }
        return it->second;
    };

    // create tensors for the weights
    {
        // note: cast to int64_t since we will use these for the tensor dimensions
        const int64_t n_head        = hparams.n_head();
        const int64_t n_head_kv     = hparams.n_head_kv();
        const int64_t n_embd        = hparams.n_embd;
        const int64_t n_embd_k_gqa  = hparams.n_embd_k_gqa();
        const int64_t n_embd_v_gqa  = hparams.n_embd_v_gqa();
        const int64_t n_embd_head_k = hparams.n_embd_head_k;
        const int64_t n_embd_head_v = hparams.n_embd_head_v;
        const int64_t n_ff          = hparams.n_ff();
        const int64_t n_embd_gqa    = n_embd_v_gqa;
        const int64_t n_vocab       = hparams.n_vocab;
        const int64_t n_vocab_type  = hparams.n_vocab_type;
        const int64_t n_rot         = hparams.n_rot;
        const int64_t n_expert      = hparams.n_expert;
        const int64_t n_expert_used = hparams.n_expert_used;
        const int64_t n_ctx_train   = hparams.n_ctx_train;

        if (n_expert > 0 && hparams.n_expert_used == 0) {
            throw std::runtime_error("model has expert layers but no expert layers are used");
        }

        int n_moved_tensors = 0;
        ggml_tensor * first_moved_tensor = nullptr;
        ggml_backend_buffer_type_t first_moved_from_buft = nullptr;
        ggml_backend_buffer_type_t first_moved_to_buft = nullptr;

        auto create_tensor = [&](const LLM_TN_IMPL & tn, const std::initializer_list<int64_t> & ne, int flags) -> ggml_tensor * {
            ggml_tensor * t_meta = ml.get_tensor_meta(tn.str().c_str());

            if (!t_meta) {
                if (flags & llama_model_loader::TENSOR_NOT_REQUIRED) {
                    return nullptr;
                }
                throw std::runtime_error(format("missing tensor '%s'", tn.str().c_str()));
            }

            // some models use the token embedding tensor as the output, but since these are used in different layers and with different ops
            // the tensor is duplicated
            // to handle this, we check if the tensor is duplicated, and if so, we assume that it is being loaded as the output tensor
            llm_tensor tn_tensor = tn.tensor;
            if (tn.tensor == LLM_TENSOR_TOKEN_EMBD && flags & llama_model_loader::TENSOR_DUPLICATED) {
                tn_tensor = LLM_TENSOR_OUTPUT;
            }

            llm_tensor_info info;
            try {
                info = llm_tensor_info_for(tn_tensor);
            } catch (const std::out_of_range & e) {
                throw std::runtime_error(format("missing tensor info mapping for %s", tn.str().c_str()));
            }

            // tensors with "bias" suffix are always used with GGML_OP_ADD
            ggml_op op;
            bool bias = tn.suffix != nullptr && strcmp(tn.suffix, "bias") == 0;
            if (bias) {
                op = GGML_OP_ADD;
            } else {
                op = info.op;
            }

            // sanity checks
            if (info.layer == LLM_TENSOR_LAYER_INPUT || info.layer == LLM_TENSOR_LAYER_OUTPUT) {
                if (tn.bid != -1) {
                    GGML_ABORT("input/output layer tensor %s used with a layer number", tn.str().c_str());
                }
            } else {
                if (tn.bid == -1) {
                    GGML_ABORT("repeating layer tensor %s used without a layer number", tn.str().c_str());
                }
            }

            // select the buffer type for this tensor
            llama_model::buft_list_t * buft_list;
            switch (info.layer) {
                case LLM_TENSOR_LAYER_INPUT:
                    buft_list = model.dev_input.buft_list;
                    break;
                case LLM_TENSOR_LAYER_OUTPUT:
                    buft_list = model.dev_output.buft_list;
                    break;
                case LLM_TENSOR_LAYER_REPEATING:
                    buft_list = model.dev_layer.at(tn.bid).buft_list;
                    break;
                default:
                    GGML_ABORT("invalid layer %d for tensor %s", info.layer, tn.str().c_str());
            }

            ggml_backend_buffer_type_t buft = select_weight_buft(model, t_meta, op, *buft_list);
            if (!buft) {
                throw std::runtime_error(format("failed to find a compatible buffer type for tensor %s", tn.str().c_str()));
            }

            // avoid using a host buffer when using mmap
            auto * buft_dev = ggml_backend_buft_get_device(buft);
            if (ml.use_mmap && buft_dev && buft == ggml_backend_dev_host_buffer_type(buft_dev)) {
                auto * cpu_dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU);
                buft = ggml_backend_dev_buffer_type(cpu_dev);
            }

            if (buft != buft_list->front().second) {
                n_moved_tensors++;
                if (!first_moved_tensor) {
                    first_moved_tensor = t_meta;
                    first_moved_from_buft = buft_list->front().second;
                    first_moved_to_buft   = buft;
                }
            }

            ggml_context * ctx = ctx_for_buft(buft);

            // if duplicated, check if the original tensor was allocated in the same buffer type context and avoid creating a new one
            if (flags & llama_model_loader::TENSOR_DUPLICATED) {
                ggml_tensor * t = ggml_get_tensor(ctx, tn.str().c_str());
                if (t) {
                    return t;
                }
            }
            return ml.create_tensor(ctx, tn, ne, flags);
        };

        model.layers.resize(n_layer);

        // TODO: move to a separate function
        const auto tn = LLM_TN(model.arch);
        switch (model.arch) {
            case LLM_ARCH_LLAMA:
            case LLM_ARCH_REFACT:
            case LLM_ARCH_MINICPM:
            case LLM_ARCH_GRANITE:
            case LLM_ARCH_GRANITE_MOE:
                {
                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);

                    // output
                    model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
                    model.output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);

                    // if output is NULL, init from the input tok embed
                    if (model.output == NULL) {
                        model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
                    }

                    for (int i = 0; i < n_layer; ++i) {
                        auto & layer = model.layers[i];

                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);

                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_k_gqa}, 0);
                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_v_gqa}, 0);
                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);

                        // optional bias tensors
                        layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "bias", i), {n_embd},     llama_model_loader::TENSOR_NOT_REQUIRED);
                        layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
                        layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
                        layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd},     llama_model_loader::TENSOR_NOT_REQUIRED);

                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);

                        if (hparams.rope_scaling_type_train == LLAMA_ROPE_SCALING_TYPE_LONGROPE) {
                            layer.rope_long  = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG,  "weight", i), {n_rot/2}, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0));
                            layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), {n_rot/2}, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0));
                        }
                        else {
                            layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0));
                        }

                        if (n_expert == 0) {
                            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
                            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
                            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);

                            // optional MLP bias
                            layer.ffn_gate_b = create_tensor(tn(LLM_TENSOR_FFN_GATE, "bias", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
                            layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
                            layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
                        } else {
                            layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), {n_embd, n_expert}, 0);
                            layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd,   n_ff, n_expert}, llama_model_loader::TENSOR_NOT_REQUIRED);
                            layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {  n_ff, n_embd, n_expert}, 0);
                            layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd,   n_ff, n_expert}, 0);
                        }
                    }
                } break;
            case LLM_ARCH_DECI:
                {
                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);

                    // output
                    model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
                    model.output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);

                    // if output is NULL, init from the input tok embed
                    if (model.output == NULL) {
                        model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
                    }

                    for (int i = 0; i < n_layer; ++i) {
                        auto & layer = model.layers[i];
                        const int64_t n_embd_k_gqa  = hparams.n_embd_k_gqa(i);
                        const int64_t n_embd_v_gqa  = hparams.n_embd_v_gqa(i);
                        const int64_t n_embd_gqa    = hparams.n_embd_v_gqa(i);
                        const int64_t n_ff          = hparams.n_ff(i);
                        const int64_t n_head        = hparams.n_head(i);
                        const int64_t n_head_kv     = hparams.n_head_kv(i);

                        if (n_head_kv == 0 && n_head > 0) {
                            // linear attention for DeciLMCausalModel
                            layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
                            layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
                        }
                        else if (n_head_kv > 0) {
                            layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);

                            layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
                            layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_k_gqa}, 0);
                            layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_v_gqa}, 0);
                            layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
                        }

                        // optional bias tensors
                        layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "bias", i), {n_embd},     llama_model_loader::TENSOR_NOT_REQUIRED);
                        layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
                        layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
                        layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd},     llama_model_loader::TENSOR_NOT_REQUIRED);

                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);

                        if (hparams.rope_scaling_type_train == LLAMA_ROPE_SCALING_TYPE_LONGROPE) {
                            layer.rope_long  = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG,  "weight", i), {n_rot/2}, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0));
                            layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), {n_rot/2}, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0));
                        }
                        else {
                            layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0));
                        }

                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);

                        // optional MLP bias
                        layer.ffn_gate_b = create_tensor(tn(LLM_TENSOR_FFN_GATE, "bias", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
                        layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
                        layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
                    }
                } break;
            case LLM_ARCH_MINICPM3:
                {
                    const int64_t n_embd_head_qk_rope = hparams.n_rot;
                    const int64_t n_embd_head_qk_nope = hparams.n_embd_head_k - hparams.n_rot;

                    const int64_t q_lora_rank  = hparams.n_lora_q;
                    const int64_t kv_lora_rank = hparams.n_lora_kv;
                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);

                    // output
                    model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
                    model.output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);

                    // if output is NULL, init from the input tok embed
                    if (model.output == NULL) {
                        model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
                    }

                    for (int i = 0; i < n_layer; ++i) {
                        auto & layer = model.layers[i];

                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
                        layer.attn_q_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_A_NORM, "weight", i), {q_lora_rank}, 0);

                        layer.attn_kv_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_NORM, "weight", i), {kv_lora_rank}, 0);

                        layer.wq_a = create_tensor(tn(LLM_TENSOR_ATTN_Q_A, "weight", i), {n_embd, q_lora_rank}, 0);
                        layer.wq_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_B, "weight", i), {q_lora_rank, n_head * n_embd_head_k}, 0);

                        layer.wkv_a_mqa = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_MQA, "weight", i), {n_embd, kv_lora_rank + (n_embd_head_qk_rope)}, 0);
                        layer.wkv_b     = create_tensor(tn(LLM_TENSOR_ATTN_KV_B,     "weight", i), {kv_lora_rank, n_head * (n_embd_head_qk_nope + n_embd_head_v)}, 0);
                        layer.wo        = create_tensor(tn(LLM_TENSOR_ATTN_OUT,      "weight", i), {              n_head * (                      n_embd_head_v), n_embd}, 0);

                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);

                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);

                        layer.rope_long  = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG,  "weight", i), { n_embd_head_qk_rope/2 }, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0));
                        layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), { n_embd_head_qk_rope/2 }, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0));
                    }
                } break;
            case LLM_ARCH_GROK:
                {
                    if (n_expert == 0) {
                        throw std::runtime_error("Grok model cannot have zero experts");
                    }

                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);

                    // output
                    model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
                    model.output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);

                    // if output is NULL, init from the input tok embed
                    if (model.output == NULL) {
                        model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
                    }

                    for (int i = 0; i < n_layer; ++i) {
                        auto & layer = model.layers[i];

                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);

                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd}, 0);
                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, 0);
                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, 0);
                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);

                        layer.attn_out_norm   = create_tensor(tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd}, 0);

                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);

                        layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), {n_embd, n_expert}, 0);
                        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, llama_model_loader::TENSOR_NOT_REQUIRED);
                        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {  n_ff, n_embd, n_expert}, 0);
                        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd,   n_ff, n_expert}, 0);

                        layer.layer_out_norm   = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd}, 0);
                    }
                } break;
            case LLM_ARCH_DBRX:
                {
                    if (n_expert == 0) {
                        throw std::runtime_error("DBRX model cannot have zero experts");
                    }

                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);

                    // output
                    model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
                    model.output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);

                    for (int i = 0; i < n_layer; ++i) {
                        auto & layer = model.layers[i];

                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);

                        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
                        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);

                        layer.attn_out_norm = create_tensor(tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd}, 0);

                        layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), {n_embd, n_expert}, 0);
                        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff,   n_expert}, 0);
                        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff,   n_embd, n_expert}, 0);
                        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd, n_ff,   n_expert}, 0);
                    }
                } break;
            case LLM_ARCH_BAICHUAN:
                {
                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
                    {
                        model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
                        model.output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
                    }

                    for (int i = 0; i < n_layer; ++i) {
                        auto & layer = model.layers[i];

                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);

                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd}, 0);
                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, 0);
                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, 0);
                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);

                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);

                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
                    }
                } break;
            case LLM_ARCH_FALCON:
                {
                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);

                    // output
                    {
                        model.output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
                        model.output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);

                        model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
                        if (!model.output) {
                            model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); // needs to be on GPU
                        }
                    }

                    for (int i = 0; i < n_layer; ++i) {
                        auto & layer = model.layers[i];

                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
                        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd}, 0);

                        layer.attn_norm_2   = create_tensor(tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
                        layer.attn_norm_2_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM_2, "bias", i),   {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);

                        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
                        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);

                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
                    }
                } break;
            case LLM_ARCH_STARCODER:
                {
                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
                    model.pos_embd = create_tensor(tn(LLM_TENSOR_POS_EMBD,   "weight"), {n_embd, n_ctx_train}, 0);

                    // output
                    {
                        model.output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
                        model.output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);
                        model.output        = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
                        if (!model.output) {
                            // needs to be on GPU
                            model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
                        }

                    }

                    for (int i = 0; i < n_layer; ++i) {
                        auto & layer = model.layers[i];

                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
                        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd}, 0);

                        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
                        layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd + 2*n_embd_gqa}, 0);

                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
                        layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd}, 0);

                        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
                        layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, 0);

                        layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
                        layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd}, 0);

                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i),   {n_embd, n_ff}, 0);
                        layer.ffn_up_b = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i),     {n_ff}, 0);
                    }
                } break;
            case LLM_ARCH_BERT:
            case LLM_ARCH_NOMIC_BERT:
                {
                    model.tok_embd     = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD,  "weight"), {n_embd, n_vocab}, 0);
                    model.type_embd    = create_tensor(tn(LLM_TENSOR_TOKEN_TYPES, "weight"), {n_embd, n_vocab_type}, 0);

                    if (model.arch == LLM_ARCH_BERT) {
                        model.pos_embd = create_tensor(tn(LLM_TENSOR_POS_EMBD,    "weight"), {n_embd, n_ctx_train}, 0);

                        model.cls   = create_tensor(tn(LLM_TENSOR_CLS, "weight"), {n_embd, n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
                        model.cls_b = create_tensor(tn(LLM_TENSOR_CLS, "bias"),   {n_embd},         llama_model_loader::TENSOR_NOT_REQUIRED);

                        model.cls_out   = create_tensor(tn(LLM_TENSOR_CLS_OUT, "weight"), {n_embd, 1}, llama_model_loader::TENSOR_NOT_REQUIRED);
                        model.cls_out_b = create_tensor(tn(LLM_TENSOR_CLS_OUT, "bias"),   {1},         llama_model_loader::TENSOR_NOT_REQUIRED);
                    }

                    model.tok_norm   = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}, 0);
                    model.tok_norm_b = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"),   {n_embd}, 0);

                    for (int i = 0; i < n_layer; ++i) {
                        auto & layer = model.layers[i];

                        if (model.arch == LLM_ARCH_BERT) {
                            layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd}, 0);
                            layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "bias", i),   {n_embd}, 0);

                            layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, 0);
                            layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "bias", i),   {n_embd_gqa}, 0);

                            layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, 0);
                            layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "bias", i),   {n_embd_gqa}, 0);
                        } else {
                            layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
                        }

                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT,      "weight", i), {n_embd, n_embd}, 0);

                        layer.attn_out_norm   = create_tensor(tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd}, 0);
                        layer.attn_out_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT_NORM, "bias", i),   {n_embd}, 0);

                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,        "weight", i), {n_embd, n_ff}, 0);
                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN,      "weight", i), {n_ff, n_embd}, 0);

                        if (model.arch == LLM_ARCH_BERT) {
                            layer.bo         = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, 0);
                            layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias", i), {n_ff}, 0);
                            layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, 0);
                        } else {
                            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
                        }

                        layer.layer_out_norm   = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd}, 0);
                        layer.layer_out_norm_b = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM, "bias", i),   {n_embd}, 0);
                    }
                } break;
            case LLM_ARCH_JINA_BERT_V2:
                {
                    model.tok_embd  = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD,  "weight"), {n_embd, n_vocab}, 0); // word_embeddings
                    model.type_embd = create_tensor(tn(LLM_TENSOR_TOKEN_TYPES, "weight"), {n_embd, n_vocab_type}, 0); // token_type_embeddings

                    model.tok_norm   = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}, 0); // LayerNorm
                    model.tok_norm_b = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"),   {n_embd}, 0); //LayerNorm bias

                    model.cls   = create_tensor(tn(LLM_TENSOR_CLS, "weight"), {n_embd, 1}, llama_model_loader::TENSOR_NOT_REQUIRED);
                    model.cls_b = create_tensor(tn(LLM_TENSOR_CLS, "bias"),   {1},         llama_model_loader::TENSOR_NOT_REQUIRED);
                    for (int i = 0; i < n_layer; ++i) {
                        auto & layer = model.layers[i]; // JinaBertLayer

                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0);
                        layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "bias", i),   {n_embd}, 0);

                        layer.attn_q_norm   = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
                        layer.attn_q_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "bias",   i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);

                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0);
                        layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K, "bias",   i), {n_embd_gqa}, 0);

                        layer.attn_k_norm   = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
                        layer.attn_k_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "bias",   i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);

                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0);
                        layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias",   i), {n_embd_gqa}, 0);

                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); //output_dens
                        layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias",   i), {n_embd}, 0); //output_dens

                        layer.attn_out_norm   = create_tensor(tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd}, 0); //output_norm
                        layer.attn_out_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT_NORM, "bias",   i), {n_embd}, 0);

                        layer.attn_norm_2   = create_tensor(tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
                        layer.attn_norm_2_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM_2, "bias",   i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);

                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff}, 0);
                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);

                        layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
                        layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias",   i), {n_embd}, 0);

                        layer.layer_out_norm   = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd}, 0);
                        layer.layer_out_norm_b = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM, "bias",   i), {n_embd}, 0);
                    }
                } break;
            case LLM_ARCH_BLOOM:
                {
                    model.tok_embd   = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD,      "weight"), {n_embd, n_vocab}, 0);
                    model.tok_norm   = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}, 0);
                    model.tok_norm_b = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"),   {n_embd}, 0);

                    // output
                    model.output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
                    model.output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);
                    model.output        = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);

                    for (int i = 0; i < n_layer; ++i) {
                        auto & layer = model.layers[i];

                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
                        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias",   i), {n_embd}, 0);

                        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
                        layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias",   i), {n_embd + 2*n_embd_gqa}, 0);

                        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
                        layer.bo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias",   i), {n_embd}, 0);

                        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
                        layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias",   i), {n_embd}, 0);

                        layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
                        layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias",   i), {n_embd}, 0);

                        layer.ffn_up     = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
                        layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias",   i), {n_ff}, 0);
                    }
                } break;
            case LLM_ARCH_MPT:
                {
                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
                    model.pos_embd = create_tensor(tn(LLM_TENSOR_POS_EMBD,   "weight"), {n_embd, n_ctx_train}, llama_model_loader::TENSOR_NOT_REQUIRED);

                    // output
                    model.output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
                    model.output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);

                    model.output        = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
                    if (!model.output) {
                        model.output    = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); // needs to be on GPU
                    }

                    for (int i = 0; i < n_layer; ++i) {
                        auto & layer = model.layers[i];

                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
                        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);

                        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
                        layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd + 2*n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);

                        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
                        layer.bo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);

                        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
                        layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);

                        layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
                        layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);

                        layer.ffn_up     = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
                        layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);

                        layer.attn_q_norm   = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
                        layer.attn_q_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "bias",   i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);

                        layer.attn_k_norm   = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
                        layer.attn_k_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "bias",   i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);

                        // AWQ ScaleActivation layer
                        layer.ffn_act = create_tensor(tn(LLM_TENSOR_FFN_ACT, "scales", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
                    }
                } break;
            case LLM_ARCH_STABLELM:
                {
                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);

                    // output
                    model.output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);
                    model.output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
                    model.output        = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);

                    for (int i = 0; i < n_layer; ++i) {
                        auto & layer = model.layers[i];

                        layer.attn_norm =   create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
                        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, 0);

                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd}, 0);
                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, 0);
                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, 0);
                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);

                        // optional bias tensors, present in Stable LM 2 1.6B
                        layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "bias", i), {n_embd},     llama_model_loader::TENSOR_NOT_REQUIRED);
                        layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
                        layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);

                        // optional q and k layernorms, present in StableLM 2 12B
                        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k, n_head},    llama_model_loader::TENSOR_NOT_REQUIRED);
                        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k, n_head_kv}, llama_model_loader::TENSOR_NOT_REQUIRED);

                        // optional FFN norm, not present in StableLM 2 12B which uses parallel residual
                        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
                        layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);

                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
                    }
                } break;
            case LLM_ARCH_QWEN:
                {
                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);

                    // output
                    model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
                    model.output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);

                    for (int i = 0; i < n_layer; ++i) {
                        auto & layer = model.layers[i];

                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);

                        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd*3}, 0);
                        layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd*3}, 0);
                        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);

                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);

                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff/2}, 0);
                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff/2, n_embd}, 0);
                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff/2}, 0);
                    }
                } break;
            case LLM_ARCH_QWEN2:
            case LLM_ARCH_QWEN2VL:
                {
                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);

                    // output
                    model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
                    model.output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
                    // if output is NULL, init from the input tok embed
                    if (model.output == NULL) {
                        model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
                    }

                    for (int i = 0; i < n_layer; ++i) {
                        auto & layer = model.layers[i];

                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);

                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd}, 0);
                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, 0);
                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, 0);
                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);

                        // optional bias tensors
                        layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "bias", i), {n_embd}, 0);
                        layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "bias", i), {n_embd_gqa}, 0);
                        layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "bias", i), {n_embd_gqa}, 0);

                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);

                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
                    }
                } break;
            case LLM_ARCH_QWEN2MOE:
                {
                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);

                    // output
                    model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
                    model.output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);

                    for (int i = 0; i < n_layer; ++i) {
                        auto & layer = model.layers[i];

                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);

                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd}, 0);
                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, 0);
                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, 0);
                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);

                        // optional bias tensors
                        layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "bias", i), {n_embd}, 0);
                        layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "bias", i), {n_embd_gqa}, 0);
                        layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "bias", i), {n_embd_gqa}, 0);

                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);

                        layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);

                        if (n_expert == 0) {
                            throw std::runtime_error("n_expert must be > 0 for QWEN2MOE");
                        }
                        if (n_expert_used == 0) {
                            throw std::runtime_error("n_expert_used must be > 0 for QWEN2MOE");
                        }

                        // MoE branch
                        const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / n_expert_used;

                        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);
                        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert}, 0);
                        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);

                        // Shared expert branch
                        const int64_t n_ff_shexp = hparams.n_ff_shexp ? hparams.n_ff_shexp : n_ff;

                        layer.ffn_gate_inp_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP_SHEXP, "weight", i), {n_embd}, 0);
                        layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {    n_embd, n_ff_shexp}, 0);
                        layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp,     n_embd}, 0);
                        layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {    n_embd, n_ff_shexp}, 0);
                    }
                } break;
            case LLM_ARCH_PHI2:
                {
                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);

                    // output
                    model.output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
                    model.output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);
                    model.output        = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
                    model.output_b      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "bias"),   {n_vocab}, 0);

                    for (int i = 0; i < n_layer; ++i) {
                        auto & layer = model.layers[i];

                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
                        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd}, 0);

                        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
                        layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd + 2*n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);

                        if (layer.wqkv == nullptr) {
                            layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0);
                            layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "bias", i),   {n_embd}, 0);

                            layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0);
                            layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K, "bias", i),   {n_embd_gqa}, 0);

                            layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0);
                            layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias", i),   {n_embd_gqa}, 0);
                        }

                        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
                        layer.bo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd}, 0);

                        layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
                        layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd}, 0);

                        layer.ffn_up     = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff}, 0);
                        layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff}, 0);
                    }
                } break;
            case LLM_ARCH_PHI3:
                {
                    const int64_t n_embd_head = n_embd / n_head;

                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0);

                    // output
                    model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd }, 0);
                    model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), { n_embd, n_vocab }, 0);

                    for (int i = 0; i < n_layer; ++i) {
                        auto & layer = model.layers[i];

                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, 0);

                        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), { n_embd, n_embd + 2 * n_embd_gqa }, llama_model_loader::TENSOR_NOT_REQUIRED);
                        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd, n_embd }, 0);

                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), { n_embd }, 0);

                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd }, 0);
                        layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), { n_embd, 2 * n_ff }, 0);

                        layer.rope_long  = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG,  "weight", i), { n_embd_head/2 }, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0));
                        layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), { n_embd_head/2 }, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0));
                    }
                } break;
            case LLM_ARCH_PLAMO:
                {
                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);

                    // output
                    model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
                    model.output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);

                    for (int i = 0; i < n_layer; ++i) {
                        auto & layer = model.layers[i];

                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);

                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd}, 0);
                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, 0);
                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, 0);
                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);

                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
                    }
                } break;
            case LLM_ARCH_GPT2:
                {
                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
                    model.pos_embd = create_tensor(tn(LLM_TENSOR_POS_EMBD,   "weight"), {n_embd, n_ctx_train}, 0);

                    // output
                    model.output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
                    model.output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);
                    model.output        = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);

                    for (int i = 0; i < n_layer; ++i) {
                        auto & layer = model.layers[i];

                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM,   "weight", i), {n_embd}, 0);
                        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM,   "bias", i),   {n_embd}, 0);

                        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
                        layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd + 2*n_embd_gqa}, 0);

                        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
                        layer.bo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd}, 0);

                        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
                        layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, 0);

                        layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
                        layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd}, 0);

                        layer.ffn_up     = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff}, 0);
                        layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff}, 0);
                    }
                } break;
            case LLM_ARCH_CODESHELL:
                {
                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);

                    // output
                    model.output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
                    model.output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);
                    model.output        = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);

                    for (int i = 0; i < n_layer; ++i) {
                        auto & layer = model.layers[i];

                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
                        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd}, 0);

                        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
                        layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd + 2*n_embd_gqa}, 0);

                        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
                        layer.bo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd}, 0);

                        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
                        layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, 0);

                        layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
                        layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd}, 0);

                        layer.ffn_up     = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i),   {n_embd, n_ff}, 0);
                        layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i),     {n_ff}, 0);
                    }
                } break;
            case LLM_ARCH_ORION:
                {
                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);

                    model.output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
                    model.output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);
                    model.output        = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);

                    for (int i = 0; i < n_layer; ++i) {
                        auto & layer = model.layers[i];

                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
                        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd}, 0);

                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd}, 0);
                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, 0);
                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, 0);
                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);

                        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
                        layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, 0);

                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
                    }
                } break;
            case LLM_ARCH_INTERNLM2:
                {
                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);

                    // output
                    model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
                    model.output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);

                    for (int i = 0; i < n_layer; ++i) {
                        auto & layer = model.layers[i];

                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
                        // layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd}, 0);
                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, 0);
                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, 0);

                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
                    }
                } break;
            case LLM_ARCH_GEMMA:
                {
                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);

                    // output
                    model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
                    model.output      = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD,  "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); // same as tok_embd, duplicated to allow offloading

                    for (int i = 0; i < n_layer; ++i) {
                        auto & layer = model.layers[i];

                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);

                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_k_gqa}, 0);
                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_v_gqa}, 0);
                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);

                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
                    }
                } break;
            case LLM_ARCH_GEMMA2:
                {
                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);

                    // output
                    model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
                    model.output      = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD,  "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); // same as tok_embd, duplicated to allow offloading

                    for (int i = 0; i < n_layer; ++i) {
                        auto & layer = model.layers[i];

                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);

                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_k_gqa}, 0);
                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_v_gqa}, 0);
                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
                        layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd}, 0);

                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
                        layer.ffn_post_norm = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd}, 0);
                    }
                } break;
            case LLM_ARCH_STARCODER2:
                {
                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);

                    // output
                    model.output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
                    model.output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);

                    model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
                    // if output is NULL, init from the input tok embed
                    if (model.output == NULL) {
                        model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
                    }

                    for (int i = 0; i < n_layer; ++i) {
                        auto & layer = model.layers[i];

                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
                        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd}, 0);

                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd}, 0);
                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, 0);
                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, 0);
                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);

                        // optional bias tensors
                        layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "bias", i), {n_embd}, 0);
                        layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "bias", i), {n_embd_gqa}, 0);
                        layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "bias", i), {n_embd_gqa}, 0);
                        layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, 0);

                        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
                        layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, 0);

                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);

                        // optional bias tensors
                        layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, 0);
                        layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP ,  "bias", i), {  n_ff}, 0);
                    }
                } break;
            case LLM_ARCH_MAMBA:
                {
                    const int64_t d_conv  = hparams.ssm_d_conv;
                    const int64_t d_inner = hparams.ssm_d_inner;
                    const int64_t d_state = hparams.ssm_d_state;
                    const int64_t dt_rank = hparams.ssm_dt_rank;

                    // only an expansion factor of 2 is supported for now
                    if (2 * n_embd != d_inner) {
                        throw std::runtime_error("only an expansion factor of 2 is supported for now");
                    }

                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);

                    // output
                    model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);

                    model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
                    // if output is NULL, init from the input tok embed, duplicated to allow offloading
                    if (model.output == NULL) {
                        model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
                    }

                    for (int i = 0; i < n_layer; ++i) {
                        auto & layer = model.layers[i];

                        // norm
                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);

                        layer.ssm_in = create_tensor(tn(LLM_TENSOR_SSM_IN, "weight", i), {n_embd, 2*d_inner}, 0);

                        layer.ssm_conv1d = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "weight", i), {d_conv, d_inner}, 0);
                        layer.ssm_conv1d_b = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "bias", i), {d_inner}, 0);

                        layer.ssm_x = create_tensor(tn(LLM_TENSOR_SSM_X, "weight", i), {d_inner, dt_rank + 2*d_state}, 0);

                        layer.ssm_dt = create_tensor(tn(LLM_TENSOR_SSM_DT, "weight", i), {dt_rank, d_inner}, 0);
                        layer.ssm_dt_b = create_tensor(tn(LLM_TENSOR_SSM_DT, "bias", i), {d_inner}, 0);

                        // no "weight" suffix for these
                        layer.ssm_a = create_tensor(tn(LLM_TENSOR_SSM_A, i), {d_state, d_inner}, 0);
                        layer.ssm_d = create_tensor(tn(LLM_TENSOR_SSM_D, i), {d_inner}, 0);

                        // out_proj
                        layer.ssm_out = create_tensor(tn(LLM_TENSOR_SSM_OUT, "weight", i), {d_inner, n_embd}, 0);
                    }
                } break;
            case LLM_ARCH_XVERSE:
                {
                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);

                    model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
                    model.output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);

                    for (int i = 0; i < n_layer; ++i) {
                        auto & layer = model.layers[i];

                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);

                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd}, 0);
                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, 0);
                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, 0);
                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);

                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
                    }
                } break;
            case LLM_ARCH_COMMAND_R:
                {
                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);

                    // output
                    model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
                    // init output from the input tok embed
                    model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);

                    for (int i = 0; i < n_layer; ++i) {
                        auto & layer = model.layers[i];

                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);

                        if (n_layer >= 64){
                            layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k, n_head}, 0);
                            layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k, n_head_kv}, 0);
                        }

                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd}, 0);
                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, 0);
                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, 0);
                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);

                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
                    }
                } break;
            case LLM_ARCH_COHERE2:
                {
                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0);

                    // output
                    model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd }, 0);
                    // init output from the input tok embed
                    model.output      = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab },
                                                      llama_model_loader::TENSOR_DUPLICATED);

                    for (int i = 0; i < n_layer; ++i) {
                        auto & layer = model.layers[i];

                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, 0);

                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), { n_embd, n_embd }, 0);
                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), { n_embd, n_embd_gqa }, 0);
                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), { n_embd, n_embd_gqa }, 0);
                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd, n_embd }, 0);

                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), { n_embd, n_ff }, 0);
                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd }, 0);
                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), { n_embd, n_ff }, 0);
                    }
                }
                break;
            case LLM_ARCH_OLMO:  // adapted from LLM_ARCH_LLAMA with norm params removed
                {
                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);

                    // output
                    model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
                    // if output is NULL, init from the input tok embed
                    if (model.output == NULL) {
                        model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
                    }

                    for (int i = 0; i < n_layer; ++i) {
                        auto & layer = model.layers[i];

                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd}, 0);
                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, 0);
                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, 0);
                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);

                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
                    }
                } break;
            case LLM_ARCH_OLMO2:
                {
                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);

                    // output
                    model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
                    model.output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);

                    for (int i = 0; i < n_layer; ++i) {
                        auto & layer = model.layers[i];

                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd}, 0);
                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, 0);
                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, 0);
                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
                        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}, 0);
                        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd}, 0);
                        layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd}, 0);

                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
                        layer.ffn_post_norm = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd}, 0);
                    }
                } break;
            case LLM_ARCH_OLMOE:
                {
                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);

                    // output
                    model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
                    model.output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);

                    for (int i = 0; i < n_layer; ++i) {
                        auto & layer = model.layers[i];

                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);

                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd}, 0);
                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, 0);
                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, 0);
                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
                        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}, 0);
                        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd}, 0);

                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);

                        layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);

                        if (n_expert == 0) {
                            throw std::runtime_error("n_expert must be > 0");
                        }
                        if (n_expert_used == 0) {
                            throw std::runtime_error("n_expert_used must be > 0");
                        }

                        // MoE branch
                        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff,   n_expert}, 0);
                        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff,   n_embd, n_expert}, 0);
                        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd, n_ff,   n_expert}, 0);
                    }
                } break;
            case LLM_ARCH_OPENELM:
                {
                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);

                    // output
                    model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
                    // init output from the input tok embed
                    model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);

                    for (int i = 0; i < n_layer; ++i) {
                        const int64_t n_head      =   hparams.n_head(i);
                        const int64_t n_head_qkv  = 2*hparams.n_head_kv(i) + n_head;
                        const int64_t n_ff        =   hparams.n_ff(i);

                        auto & layer = model.layers[i];

                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);

                        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_head_qkv*n_embd_head_k}, 0);
                        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);
                        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);
                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_head*n_embd_head_k, n_embd}, 0);

                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff}, 0);
                    }
                } break;
            case LLM_ARCH_GPTNEOX:
                {
                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);

                    // output
                    model.output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
                    model.output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);
                    model.output        = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);

                    for (int i = 0; i < n_layer; ++i) {
                        auto & layer = model.layers[i];

                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
                        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd}, 0);

                        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
                        layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd + 2*n_embd_gqa}, 0);

                        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
                        layer.bo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd}, 0);

                        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
                        layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, 0);

                        layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
                        layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd}, 0);

                        layer.ffn_up     = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff}, 0);
                        layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff}, 0);
                    }
                } break;
            case LLM_ARCH_ARCTIC:
                {
                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);

                    // output
                    model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
                    model.output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);

                    // if output is NULL, init from the input tok embed
                    if (model.output == NULL) {
                        model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
                    }

                    for (int i = 0; i < n_layer; ++i) {
                        auto & layer = model.layers[i];

                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);

                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd}, 0);
                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, 0);
                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, 0);
                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);

                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);

                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_embd}, 0);
                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_embd, n_embd}, 0);
                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_embd}, 0);

                        layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
                        layer.ffn_norm_exps = create_tensor(tn(LLM_TENSOR_FFN_NORM_EXPS, "weight", i), {n_embd}, 0);
                        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd,   n_ff, n_expert}, false);
                        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {  n_ff, n_embd, n_expert}, 0);
                        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd,   n_ff, n_expert}, 0);
                    }
                } break;
            case LLM_ARCH_DEEPSEEK:
                {

                    const int64_t n_ff_exp        = hparams.n_ff_exp;
                    const int64_t n_expert_shared = hparams.n_expert_shared;

                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);

                    // output
                    model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
                    model.output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);

                    for (int i = 0; i < n_layer; ++i) {
                        auto & layer = model.layers[i];

                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);

                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd}, 0);
                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, 0);
                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, 0);
                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);

                        if (i < (int) hparams.n_layer_dense_lead) {
                            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
                            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
                            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
                        } else {
                            layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);

                            if (n_expert == 0) {
                                throw std::runtime_error("n_expert must be > 0");
                            }
                            if (n_expert_used == 0) {
                                throw std::runtime_error("n_expert_used must be > 0");
                            }

                            // MoE branch
                            layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);
                            layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert}, 0);
                            layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);

                            // Shared expert branch
                            layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
                            layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {        n_ff_exp * n_expert_shared, n_embd}, 0);
                            layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
                        }
                    }
                } break;
            case LLM_ARCH_DEEPSEEK2:
                {
                    const bool is_lite = (hparams.n_layer == 27);

                    const int64_t n_embd_head_qk_rope = hparams.n_rot;
                    const int64_t n_embd_head_qk_nope = hparams.n_embd_head_k - hparams.n_rot;

                    const int64_t q_lora_rank  = hparams.n_lora_q;
                    const int64_t kv_lora_rank = hparams.n_lora_kv;

                    const int64_t n_ff_exp        = hparams.n_ff_exp;
                    const int64_t n_expert_shared = hparams.n_expert_shared;

                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);

                    // output
                    model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
                    model.output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);

                    for (int i = 0; i < n_layer; ++i) {
                        auto & layer = model.layers[i];

                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
                        if (!is_lite) {
                            layer.attn_q_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_A_NORM, "weight", i), {q_lora_rank}, 0);
                        }

                        layer.attn_kv_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_NORM, "weight", i), {kv_lora_rank}, 0);

                        if (!is_lite) {
                            layer.wq_a = create_tensor(tn(LLM_TENSOR_ATTN_Q_A, "weight", i), {n_embd, q_lora_rank}, 0);
                            layer.wq_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_B, "weight", i), {q_lora_rank, n_head * n_embd_head_k}, 0);
                        } else {
                            layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_k_gqa}, 0);
                        }

                        layer.wkv_a_mqa = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_MQA, "weight", i), {n_embd, kv_lora_rank + (n_embd_head_qk_rope)}, 0);
                        layer.wkv_b     = create_tensor(tn(LLM_TENSOR_ATTN_KV_B,     "weight", i), {kv_lora_rank, n_head * (n_embd_head_qk_nope + n_embd_head_v)}, 0);
                        layer.wo        = create_tensor(tn(LLM_TENSOR_ATTN_OUT,      "weight", i), {              n_head * (                      n_embd_head_v), n_embd}, 0);

                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);

                        if (i < (int) hparams.n_layer_dense_lead) {
                            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
                            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
                            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
                        } else {
                            layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
                            layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, llama_model_loader::TENSOR_NOT_REQUIRED);

                            if (n_expert == 0) {
                                throw std::runtime_error("n_expert must be > 0");
                            }
                            if (n_expert_used == 0) {
                                throw std::runtime_error("n_expert_used must be > 0");
                            }

                            // MoE branch
                            layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);
                            layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert}, 0);
                            layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);

                            // Shared expert branch
                            layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
                            layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {        n_ff_exp * n_expert_shared, n_embd}, 0);
                            layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
                        }
                    }
                } break;
            case LLM_ARCH_BITNET:
                {
                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);

                    // output
                    model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);

                    for (int i = 0; i < n_layer; ++i) {
                        auto & layer = model.layers[i];

                        layer.attn_norm     = create_tensor(tn(LLM_TENSOR_ATTN_NORM,     "weight", i), {n_embd}, 0);
                        layer.attn_sub_norm = create_tensor(tn(LLM_TENSOR_ATTN_SUB_NORM, "weight", i), {n_embd}, 0);

                        layer.wq       = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd}, 0);
                        layer.wq_scale = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "scale",  i), {1}, llama_model_loader::TENSOR_NOT_REQUIRED);
                        layer.wk       = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, 0);
                        layer.wk_scale = create_tensor(tn(LLM_TENSOR_ATTN_K,   "scale",  i), {1}, llama_model_loader::TENSOR_NOT_REQUIRED);
                        layer.wv       = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, 0);
                        layer.wv_scale = create_tensor(tn(LLM_TENSOR_ATTN_V,   "scale",  i), {1}, llama_model_loader::TENSOR_NOT_REQUIRED);
                        layer.wo       = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
                        layer.wo_scale = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "scale",  i), {1}, llama_model_loader::TENSOR_NOT_REQUIRED);

                        layer.ffn_norm     = create_tensor(tn(LLM_TENSOR_FFN_NORM,     "weight", i), {n_embd}, 0);
                        layer.ffn_sub_norm = create_tensor(tn(LLM_TENSOR_FFN_SUB_NORM, "weight", i), {n_ff}, 0);

                        layer.ffn_gate       = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
                        layer.ffn_gate_scale = create_tensor(tn(LLM_TENSOR_FFN_GATE, "scale",  i), {1}, llama_model_loader::TENSOR_NOT_REQUIRED);
                        layer.ffn_down       = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
                        layer.ffn_down_scale = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "scale",  i), {1}, llama_model_loader::TENSOR_NOT_REQUIRED);
                        layer.ffn_up         = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff}, 0);
                        layer.ffn_up_scale   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "scale",  i), {1}, llama_model_loader::TENSOR_NOT_REQUIRED);
                    }
                } break;
            case LLM_ARCH_T5:
                {
                    const auto n_rel_attn_bkts = hparams.n_rel_attn_bkts;

                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);

                    // output
                    model.output_norm_enc = create_tensor(tn(LLM_TENSOR_ENC_OUTPUT_NORM, "weight"), {n_embd}, 0);
                    model.output_norm     = create_tensor(tn(LLM_TENSOR_DEC_OUTPUT_NORM, "weight"), {n_embd}, 0);

                    model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
                    // if output is NULL, init from the input tok embed
                    if (model.output == NULL) {
                        model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
                    }

                    for (int i = 0; i < n_layer; ++i) {
                        auto & layer = model.layers[i];

                        layer.attn_norm_enc  = create_tensor(tn(LLM_TENSOR_ENC_ATTN_NORM,  "weight", i), {n_embd}, 0);
                        layer.attn_rel_b_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_REL_B, "weight", i), {n_head, n_rel_attn_bkts}, llama_model_loader::TENSOR_NOT_REQUIRED);

                        layer.wq_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_Q,   "weight", i), {n_embd, n_embd_k_gqa}, 0);
                        layer.wk_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_K,   "weight", i), {n_embd, n_embd_k_gqa}, 0);
                        layer.wv_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_V,   "weight", i), {n_embd, n_embd_v_gqa}, 0);
                        layer.wo_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_OUT, "weight", i), {n_embd_v_gqa, n_embd}, 0);

                        layer.ffn_norm_enc = create_tensor(tn(LLM_TENSOR_ENC_FFN_NORM, "weight", i), {n_embd}, 0);
                        layer.ffn_gate_enc = create_tensor(tn(LLM_TENSOR_ENC_FFN_GATE, "weight", i), {n_embd,   n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
                        layer.ffn_down_enc = create_tensor(tn(LLM_TENSOR_ENC_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
                        layer.ffn_up_enc   = create_tensor(tn(LLM_TENSOR_ENC_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);

                        layer.attn_norm  = create_tensor(tn(LLM_TENSOR_DEC_ATTN_NORM,  "weight", i), {n_embd}, 0);
                        layer.attn_rel_b = create_tensor(tn(LLM_TENSOR_DEC_ATTN_REL_B, "weight", i), {n_head, n_rel_attn_bkts}, llama_model_loader::TENSOR_NOT_REQUIRED);

                        layer.wq = create_tensor(tn(LLM_TENSOR_DEC_ATTN_Q,   "weight", i), {n_embd, n_embd_k_gqa}, 0);
                        layer.wk = create_tensor(tn(LLM_TENSOR_DEC_ATTN_K,   "weight", i), {n_embd, n_embd_k_gqa}, 0);
                        layer.wv = create_tensor(tn(LLM_TENSOR_DEC_ATTN_V,   "weight", i), {n_embd, n_embd_v_gqa}, 0);
                        layer.wo = create_tensor(tn(LLM_TENSOR_DEC_ATTN_OUT, "weight", i), {n_embd_v_gqa, n_embd}, 0);

                        layer.attn_norm_cross  = create_tensor(tn(LLM_TENSOR_DEC_CROSS_ATTN_NORM,  "weight", i), {n_embd}, 0);
                        // this tensor seems to be unused in HF transformers implementation
                        layer.attn_rel_b_cross = create_tensor(tn(LLM_TENSOR_DEC_CROSS_ATTN_REL_B, "weight", i), {n_head, n_rel_attn_bkts}, llama_model_loader::TENSOR_NOT_REQUIRED);

                        layer.wq_cross = create_tensor(tn(LLM_TENSOR_DEC_CROSS_ATTN_Q,   "weight", i), {n_embd, n_embd_k_gqa}, 0);
                        layer.wk_cross = create_tensor(tn(LLM_TENSOR_DEC_CROSS_ATTN_K,   "weight", i), {n_embd, n_embd_k_gqa}, 0);
                        layer.wv_cross = create_tensor(tn(LLM_TENSOR_DEC_CROSS_ATTN_V,   "weight", i), {n_embd, n_embd_v_gqa}, 0);
                        layer.wo_cross = create_tensor(tn(LLM_TENSOR_DEC_CROSS_ATTN_OUT, "weight", i), {n_embd_v_gqa, n_embd}, 0);

                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_DEC_FFN_NORM, "weight", i), {n_embd}, 0);
                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_DEC_FFN_GATE, "weight", i), {n_embd,   n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_DEC_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_DEC_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
                    }
                } break;
            case LLM_ARCH_T5ENCODER:
                {
                    const auto n_rel_attn_bkts = hparams.n_rel_attn_bkts;

                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);

                    // output
                    model.output_norm_enc = create_tensor(tn(LLM_TENSOR_ENC_OUTPUT_NORM, "weight"), {n_embd}, 0);
                    model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
                    // if output is NULL, init from the input tok embed
                    if (model.output == NULL) {
                        model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
                    }

                    for (int i = 0; i < n_layer; ++i) {
                        auto & layer = model.layers[i];

                        layer.attn_norm_enc  = create_tensor(tn(LLM_TENSOR_ENC_ATTN_NORM,  "weight", i), {n_embd}, 0);
                        layer.attn_rel_b_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_REL_B, "weight", i), {n_head, n_rel_attn_bkts}, llama_model_loader::TENSOR_NOT_REQUIRED);

                        layer.wq_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_Q,   "weight", i), {n_embd, n_embd_k_gqa}, 0);
                        layer.wk_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_K,   "weight", i), {n_embd, n_embd_k_gqa}, 0);
                        layer.wv_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_V,   "weight", i), {n_embd, n_embd_v_gqa}, 0);
                        layer.wo_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_OUT, "weight", i), {n_embd_v_gqa, n_embd}, 0);

                        layer.ffn_norm_enc = create_tensor(tn(LLM_TENSOR_ENC_FFN_NORM, "weight", i), {n_embd}, 0);
                        layer.ffn_gate_enc = create_tensor(tn(LLM_TENSOR_ENC_FFN_GATE, "weight", i), {n_embd,   n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
                        layer.ffn_down_enc = create_tensor(tn(LLM_TENSOR_ENC_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
                        layer.ffn_up_enc   = create_tensor(tn(LLM_TENSOR_ENC_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
                    }
                } break;
            case LLM_ARCH_JAIS:
                {
                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);

                    // output
                    model.output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
                    model.output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);
                    model.output        = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);

                    for (int i = 0; i < n_layer; ++i) {
                        auto & layer = model.layers[i];

                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM,   "weight", i), {n_embd}, 0);
                        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM,   "bias", i),   {n_embd}, 0);

                        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
                        layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd + 2*n_embd_gqa}, 0);

                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
                        layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd}, 0);

                        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
                        layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, 0);

                        layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
                        layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd}, 0);

                        layer.ffn_gate   = create_tensor(tn(LLM_TENSOR_FFN_GATE,   "weight", i), {n_embd, n_ff}, 0);
                        layer.ffn_gate_b = create_tensor(tn(LLM_TENSOR_FFN_GATE,   "bias", i),   {n_ff}, 0);

                        layer.ffn_up     = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff}, 0);
                        layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff}, 0);
                    }
                } break;
            case LLM_ARCH_CHATGLM:
                {
                    model.tok_embd   = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD,      "weight"), {n_embd, n_vocab}, 0);

                    // output
                    model.output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
                    model.output        = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);

                    for (int i = 0; i < n_layer; ++i) {
                        auto & layer = model.layers[i];

                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);

                        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
                        layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd + 2*n_embd_gqa}, 0);

                        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);

                        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);

                        layer.ffn_up     = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff * 2}, 0);

                        layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
                    }
                } break;
            case LLM_ARCH_NEMOTRON:
                {
                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);

                    // output
                    model.output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
                    model.output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, 0);
                    model.output        = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0);

                    for (int i = 0; i < n_layer; ++i) {
                        auto & layer = model.layers[i];

                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
                        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, 0);

                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd}, 0);
                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, 0);
                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, 0);
                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);

                        // optional bias tensors
                        layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "bias", i), {n_embd},     llama_model_loader::TENSOR_NOT_REQUIRED);
                        layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
                        layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
                        layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd},     llama_model_loader::TENSOR_NOT_REQUIRED);

                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
                        layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, 0);

                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);

                        // optional MLP bias
                        layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
                        layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
                    }
                } break;
            case LLM_ARCH_EXAONE:
                {
                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);

                    // output
                    model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
                    model.output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);

                    for (int i = 0; i < n_layer; ++i) {
                        auto & layer = model.layers[i];

                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);

                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_k_gqa}, 0);
                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_v_gqa}, 0);
                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);

                        layer.ffn_norm   = create_tensor(tn(LLM_TENSOR_FFN_NORM,   "weight", i), {n_embd}, 0);
                        layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0));
                        layer.ffn_gate   = create_tensor(tn(LLM_TENSOR_FFN_GATE,   "weight", i), {n_embd,   n_ff}, 0);
                        layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN,   "weight", i), {  n_ff, n_embd}, 0);
                        layer.ffn_up     = create_tensor(tn(LLM_TENSOR_FFN_UP,     "weight", i), {n_embd,   n_ff}, 0);
                    }
                } break;
            case LLM_ARCH_RWKV6:
                {
                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);

                    // Block 0, LN0
                    model.tok_norm = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}, 0);
                    model.tok_norm_b = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"), {n_embd}, 0);

                    // output
                    model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
                    model.output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, 0);
                    model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0);

                    const int time_mix_extra_dim = hparams.time_mix_extra_dim;
                    const int time_decay_extra_dim = hparams.time_decay_extra_dim;
                    const int head_size = hparams.wkv_head_size;
                    const int attn_hidden_size = n_embd;
                    const int ffn_size = hparams.n_ff_arr[0];

                    for (int i = 0; i < n_layer; ++i) {
                        auto & layer = model.layers[i];

                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
                        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i),   {n_embd}, 0);

                        layer.attn_norm_2   = create_tensor(tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}, 0);
                        layer.attn_norm_2_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM_2, "bias", i),   {n_embd}, 0);

                        layer.time_mix_w1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_W1, "weight", i), {n_embd, time_mix_extra_dim * 5}, 0);
                        layer.time_mix_w2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_W2, "weight", i), {time_mix_extra_dim, n_embd, 5}, 0);

                        layer.time_mix_lerp_x = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_X, "weight", i), {n_embd, 1, 1}, 0);
                        layer.time_mix_lerp_w = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_W, "weight", i), {n_embd, 1, 1}, 0);
                        layer.time_mix_lerp_k = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_K, "weight", i), {n_embd, 1, 1}, 0);
                        layer.time_mix_lerp_v = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_V, "weight", i), {n_embd, 1, 1}, 0);
                        layer.time_mix_lerp_r = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_R, "weight", i), {n_embd, 1, 1}, 0);
                        layer.time_mix_lerp_g = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_G, "weight", i), {n_embd, 1, 1}, 0);

                        layer.time_mix_first = create_tensor(tn(LLM_TENSOR_TIME_MIX_FIRST, "weight", i), {head_size, n_embd / head_size}, 0);
                        layer.time_mix_decay = create_tensor(tn(LLM_TENSOR_TIME_MIX_DECAY, "weight", i), {n_embd}, 0);
                        layer.time_mix_decay_w1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_DECAY_W1, "weight", i), {n_embd, time_decay_extra_dim}, 0);
                        layer.time_mix_decay_w2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_DECAY_W2, "weight", i), {time_decay_extra_dim, attn_hidden_size}, 0);
                        layer.time_mix_key = create_tensor(tn(LLM_TENSOR_TIME_MIX_KEY, "weight", i), {attn_hidden_size, n_embd}, 0);
                        layer.time_mix_value = create_tensor(tn(LLM_TENSOR_TIME_MIX_VALUE, "weight", i), {attn_hidden_size, n_embd}, 0);
                        layer.time_mix_receptance = create_tensor(tn(LLM_TENSOR_TIME_MIX_RECEPTANCE, "weight", i), {attn_hidden_size, n_embd}, 0);
                        layer.time_mix_gate = create_tensor(tn(LLM_TENSOR_TIME_MIX_GATE, "weight", i), {attn_hidden_size, n_embd}, 0);

                        layer.time_mix_ln = create_tensor(tn(LLM_TENSOR_TIME_MIX_LN, "weight", i), {n_embd}, 0);
                        layer.time_mix_ln_b = create_tensor(tn(LLM_TENSOR_TIME_MIX_LN, "bias", i), {n_embd}, 0);
                        layer.time_mix_output = create_tensor(tn(LLM_TENSOR_TIME_MIX_OUTPUT, "weight", i), {n_embd, attn_hidden_size}, 0);

                        layer.channel_mix_lerp_k = create_tensor(tn(LLM_TENSOR_CHANNEL_MIX_LERP_K, "weight", i), {n_embd, 1, 1}, 0);
                        layer.channel_mix_lerp_r = create_tensor(tn(LLM_TENSOR_CHANNEL_MIX_LERP_R, "weight", i), {n_embd, 1, 1}, 0);

                        layer.channel_mix_key = create_tensor(tn(LLM_TENSOR_CHANNEL_MIX_KEY, "weight", i), {n_embd, ffn_size}, 0);
                        layer.channel_mix_value = create_tensor(tn(LLM_TENSOR_CHANNEL_MIX_VALUE, "weight", i), {ffn_size, n_embd}, 0);
                        layer.channel_mix_receptance = create_tensor(tn(LLM_TENSOR_CHANNEL_MIX_RECEPTANCE, "weight", i), {n_embd, n_embd}, 0);
                    }

                } break;
            case LLM_ARCH_CHAMELEON:
                {
                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);

                    // output
                    model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
                    model.output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
                    // if output is NULL, init from the input tok embed
                    if (model.output == NULL) {
                        model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
                    }

                    for (int i = 0; i < n_layer; ++i) {
                        auto & layer = model.layers[i];

                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
                        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k, n_head}, 0);
                        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k, n_head_kv}, 0);
                        layer.attn_q_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "bias", i),  {n_embd_head_k, n_head}, llama_model_loader::TENSOR_NOT_REQUIRED);
                        layer.attn_k_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "bias", i),  {n_embd_head_k, n_head_kv}, llama_model_loader::TENSOR_NOT_REQUIRED);

                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd}, 0);
                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, 0);
                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa}, 0);
                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);

                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);

                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
                    }
                } break;
            case LLM_ARCH_WAVTOKENIZER_DEC:
                {
                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {hparams.n_embd_features, n_vocab}, 0);

                    model.conv1d   = create_tensor(tn(LLM_TENSOR_CONV1D, "weight"), {7, hparams.n_embd_features, hparams.posnet.n_embd}, 0);
                    model.conv1d_b = create_tensor(tn(LLM_TENSOR_CONV1D, "bias"),   {1, hparams.posnet.n_embd}, 0);

                    // posnet
                    {
                        const int64_t n_embd = hparams.posnet.n_embd;

                        for (uint32_t i = 0; i < hparams.posnet.n_layer; ++i) {
                            auto & layer = model.layers[i].posnet;

                            // posnet:
                            //
                            //  - resnet
                            //  - resnet
                            //  - attn
                            //  - resnet
                            //  - resnet
                            //  - norm
                            //
                            switch (i) {
                                case 0:
                                case 1:
                                case 3:
                                case 4:
                                    {
                                        layer.norm1   = create_tensor(tn(LLM_TENSOR_POS_NET_NORM1, "weight", i), {1, n_embd}, 0);
                                        layer.norm1_b = create_tensor(tn(LLM_TENSOR_POS_NET_NORM1, "bias",   i), {1, n_embd}, 0);

                                        layer.conv1   = create_tensor(tn(LLM_TENSOR_POS_NET_CONV1, "weight", i), {3, n_embd, n_embd}, 0);
                                        layer.conv1_b = create_tensor(tn(LLM_TENSOR_POS_NET_CONV1, "bias",   i), {1, n_embd}, 0);

                                        layer.norm2   = create_tensor(tn(LLM_TENSOR_POS_NET_NORM2, "weight", i), {1, n_embd}, 0);
                                        layer.norm2_b = create_tensor(tn(LLM_TENSOR_POS_NET_NORM2, "bias",   i), {1, n_embd}, 0);

                                        layer.conv2   = create_tensor(tn(LLM_TENSOR_POS_NET_CONV2, "weight", i), {3, n_embd, n_embd}, 0);
                                        layer.conv2_b = create_tensor(tn(LLM_TENSOR_POS_NET_CONV2, "bias",   i), {1, n_embd}, 0);
                                    } break;
                                case 2:
                                    {
                                        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_NORM, "weight", i), {1, n_embd}, 0);
                                        layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_NORM, "bias",   i), {1, n_embd}, 0);

                                        layer.attn_q      = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_Q,    "weight", i), {1, n_embd, n_embd}, 0);
                                        layer.attn_q_b    = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_Q,    "bias",   i), {1, n_embd}, 0);

                                        layer.attn_k      = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_K,    "weight", i), {1, n_embd, n_embd}, 0);
                                        layer.attn_k_b    = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_K,    "bias",   i), {1, n_embd}, 0);

                                        layer.attn_v      = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_V,    "weight", i), {1, n_embd, n_embd}, 0);
                                        layer.attn_v_b    = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_V,    "bias",   i), {1, n_embd}, 0);

                                        layer.attn_o      = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_OUT,  "weight", i), {1, n_embd, n_embd}, 0);
                                        layer.attn_o_b    = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_OUT,  "bias",   i), {1, n_embd}, 0);
                                    } break;
                                case 5:
                                    {
                                        layer.norm   = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_NORM, "weight", i), {1, n_embd}, 0);
                                        layer.norm_b = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_NORM, "bias",   i), {1, n_embd}, 0);
                                    } break;
                                default: GGML_ABORT("unknown posnet layer");
                            };
                        }
                    }

                    GGML_ASSERT(hparams.posnet.n_embd == hparams.convnext.n_embd);

                    model.tok_norm   = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {hparams.posnet.n_embd}, 0);
                    model.tok_norm_b = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"),   {hparams.posnet.n_embd}, 0);

                    // convnext
                    {
                        const int64_t n_embd = hparams.convnext.n_embd;

                        for (uint32_t i = 0; i < hparams.convnext.n_layer; ++i) {
                            auto & layer = model.layers[i].convnext;

                            layer.dw     = create_tensor(tn(LLM_TENSOR_CONVNEXT_DW,    "weight", i), {7, 1, n_embd}, 0);
                            layer.dw_b   = create_tensor(tn(LLM_TENSOR_CONVNEXT_DW,    "bias",   i), {1, n_embd}, 0);

                            layer.norm   = create_tensor(tn(LLM_TENSOR_CONVNEXT_NORM,  "weight", i), {n_embd}, 0);
                            layer.norm_b = create_tensor(tn(LLM_TENSOR_CONVNEXT_NORM,  "bias",   i), {n_embd}, 0);

                            layer.pw1    = create_tensor(tn(LLM_TENSOR_CONVNEXT_PW1,   "weight", i), {n_embd, n_ff}, 0);
                            layer.pw1_b  = create_tensor(tn(LLM_TENSOR_CONVNEXT_PW1,   "bias",   i), {n_ff}, 0);

                            layer.pw2    = create_tensor(tn(LLM_TENSOR_CONVNEXT_PW2,   "weight", i), {n_ff, n_embd}, 0);
                            layer.pw2_b  = create_tensor(tn(LLM_TENSOR_CONVNEXT_PW2,   "bias",   i), {n_embd}, 0);

                            layer.gamma  = create_tensor(tn(LLM_TENSOR_CONVNEXT_GAMMA, "weight", i), {n_embd}, 0);
                        }

                        // output
                        model.output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
                        model.output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd}, 0);
                    }

                    model.output   = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {hparams.convnext.n_embd, n_embd}, 0);
                    model.output_b = create_tensor(tn(LLM_TENSOR_OUTPUT, "bias"),   {n_embd}, 0);
                } break;
            default:
                throw std::runtime_error("unknown architecture");
        }

        if (n_moved_tensors > 0) {
            LLAMA_LOG_DEBUG("%s: tensor '%s' (%s) (and %d others) cannot be used with preferred buffer type %s, using %s instead\n",
                __func__, first_moved_tensor->name, ggml_type_name(first_moved_tensor->type), n_moved_tensors - 1,
                ggml_backend_buft_name(first_moved_from_buft), ggml_backend_buft_name(first_moved_to_buft));
        }
    }

    ml.done_getting_tensors();

    ml.init_mappings(true, use_mlock ? &model.mlock_mmaps : nullptr);
    model.mappings.reserve(ml.mappings.size());

    // create the backend buffers
    std::vector<std::pair<ggml_context *, llama_buf_map>> ctx_bufs;
    ctx_bufs.reserve(ctx_map.size());

    // Ensure we have enough capacity for the maximum backend buffer we will potentially create
    const size_t n_max_backend_buffer = ctx_map.size() * ml.files.size();
    model.bufs.reserve(n_max_backend_buffer);

    for (auto & it : ctx_map) {
        ggml_backend_buffer_type_t buft = it.first;
        ggml_context * ctx              = it.second;

        // skip contexts without tensors
        if (ggml_get_first_tensor(ctx) == nullptr) {
            continue;
        }

        llama_buf_map bufs;
        bufs.reserve(n_max_backend_buffer);

        // check if it is possible to use buffer_from_host_ptr with this buffer type
        ggml_backend_dev_t dev = ggml_backend_buft_get_device(buft);
        if (!dev) {
            // FIXME: workaround for CPU backend buft having a NULL device
            dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU);
        }
        ggml_backend_dev_props props;
        ggml_backend_dev_get_props(dev, &props);
        bool buffer_from_host_ptr_supported = props.caps.buffer_from_host_ptr;
        bool is_default_buft = buft == ggml_backend_dev_buffer_type(dev);

        if (ml.use_mmap && use_mmap_buffer && buffer_from_host_ptr_supported && is_default_buft) {
            for (uint32_t idx = 0; idx < ml.files.size(); idx++) {
                // only the mmap region containing the tensors in the model is mapped to the backend buffer
                // this is important for metal with apple silicon: if the entire model could be mapped to a metal buffer, then we could just use metal for all layers
                // this allows using partial offloading when the model size exceeds the metal buffer size, but not the RAM size
                void * addr = nullptr;
                size_t first, last; // NOLINT
                ml.get_mapping_range(&first, &last, &addr, idx, ctx);
                if (first >= last) {
                    continue;
                }
                const size_t max_size = ggml_get_max_tensor_size(ctx);
                ggml_backend_buffer_t buf = ggml_backend_dev_buffer_from_host_ptr(dev, (char *) addr + first, last - first, max_size);
                if (buf == nullptr) {
                    throw std::runtime_error(format("unable to allocate %s buffer", ggml_backend_buft_name(buft)));
                }
                model.bufs.emplace_back(buf);
                bufs.emplace(idx, buf);
            }
        }
        else {
            ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, buft);
            if (buf == nullptr) {
                throw std::runtime_error(format("unable to allocate %s buffer", ggml_backend_buft_name(buft)));
            }
            model.bufs.emplace_back(buf);
            if (use_mlock && ggml_backend_buffer_is_host(buf)) {
                model.mlock_bufs.emplace_back(new llama_mlock);
                auto & mlock_buf = model.mlock_bufs.back();
                mlock_buf->init   (ggml_backend_buffer_get_base(buf));
                mlock_buf->grow_to(ggml_backend_buffer_get_size(buf));
            }
            for (uint32_t idx = 0; idx < ml.files.size(); idx++) {
                bufs.emplace(idx, buf);
            }
        }

        if (bufs.empty()) {
            throw std::runtime_error("failed to allocate buffer");
        }

        for (auto & buf : bufs) {
            // indicate that this buffer contains weights
            // this is used by ggml_backend_sched to improve op scheduling: ops that use a weight are preferably scheduled to the backend that contains the weight
            ggml_backend_buffer_set_usage(buf.second, GGML_BACKEND_BUFFER_USAGE_WEIGHTS);
        }

        ctx_bufs.emplace_back(ctx, bufs);
    }

    if (llama_supports_gpu_offload()) {
        const int n_gpu = std::min(n_gpu_layers, int(hparams.n_layer));

        LLAMA_LOG_INFO("%s: offloading %d repeating layers to GPU\n", __func__, n_gpu);
        if (n_gpu_layers > (int) hparams.n_layer) {
            LLAMA_LOG_INFO("%s: offloading output layer to GPU\n", __func__);
        }

        const int max_backend_supported_layers = hparams.n_layer + 1;
        const int max_offloadable_layers       = hparams.n_layer + 1;

        LLAMA_LOG_INFO("%s: offloaded %d/%d layers to GPU\n", __func__, std::min(n_gpu_layers, max_offloadable_layers), max_backend_supported_layers);
    }

    // print memory requirements per buffer type
    for (auto & buf : model.bufs) {
        LLAMA_LOG_INFO("%s: %12s model buffer size = %8.2f MiB\n", __func__, ggml_backend_buffer_name(buf.get()), ggml_backend_buffer_get_size(buf.get()) / 1024.0 / 1024.0);
    }

    // populate tensors_by_name
    for (auto & ctx : model.ctxs) {
        for (auto * cur = ggml_get_first_tensor(ctx.get()); cur != NULL; cur = ggml_get_next_tensor(ctx.get(), cur)) {
            model.tensors_by_name.emplace_back(ggml_get_name(cur), cur);
        }
    }

    // load tensor data
    for (auto & it : ctx_bufs) {
        ggml_context * ctx = it.first;
        auto & bufs = it.second;
        if (!ml.load_all_data(ctx, bufs, use_mlock ? &model.mlock_mmaps : NULL, progress_callback, progress_callback_user_data)) {
            return false;
        }
    }

    if (use_mmap_buffer) {
        for (auto & mapping : ml.mappings) {
            model.mappings.emplace_back(std::move(mapping));
        }
    }

    return true;
}

// Returns 0 on success, -1 on error, and -2 on cancellation via llama_progress_callback
static int llama_model_load(const std::string & fname, llama_model & model, llama_model_params & params) {
    model.t_start_us = ggml_time_us();

    try {
        llama_model_loader ml(fname, params.use_mmap, params.check_tensors, params.kv_overrides);

        model.hparams.vocab_only = params.vocab_only;

        try {
            llm_load_arch(ml, model);
        } catch(const std::exception & e) {
            throw std::runtime_error("error loading model architecture: " + std::string(e.what()));
        }
        try {
            llm_load_hparams(ml, model);
        } catch(const std::exception & e) {
            throw std::runtime_error("error loading model hyperparameters: " + std::string(e.what()));
        }
        try {
            llm_load_vocab(ml, model);
        } catch(const std::exception & e) {
            throw std::runtime_error("error loading model vocabulary: " + std::string(e.what()));
        }

        llm_load_stats(ml, model);
        llm_load_print_meta(ml, model);

        if (model.vocab.type != LLAMA_VOCAB_TYPE_NONE &&
            model.hparams.n_vocab != model.vocab.id_to_token.size()) {
            throw std::runtime_error("vocab size mismatch");
        }

        if (params.vocab_only) {
            LLAMA_LOG_INFO("%s: vocab only - skipping tensors\n", __func__);
            return 0;
        }

        if (!llm_load_tensors(
            ml, model, params.n_gpu_layers, params.split_mode,  params.main_gpu, params.tensor_split, params.use_mlock,
            params.progress_callback, params.progress_callback_user_data
        )) {
            return -2;
        }
    } catch (const std::exception & err) {
        LLAMA_LOG_ERROR("%s: error loading model: %s\n", __func__, err.what());
        return -1;
    }

    // loading time will be recalculate after the first eval, so
    // we take page faults deferred by mmap() into consideration
    model.t_load_us = ggml_time_us() - model.t_start_us;

    return 0;
}

//
// llm_build
//

using llm_build_cb = std::function<void(struct ggml_tensor * cur, const char * name, int nl)>;

enum llm_ffn_op_type {
    LLM_FFN_SILU,
    LLM_FFN_GELU,
    LLM_FFN_RELU,
    LLM_FFN_RELU_SQR,
    LLM_FFN_SWIGLU,
};

enum llm_ffn_gate_type {
    LLM_FFN_SEQ,
    LLM_FFN_PAR, // ffn_gate is parallel to ffn_up
};

enum llm_norm_type {
    LLM_NORM,
    LLM_NORM_RMS,
    LLM_NORM_GROUP,
};

static struct ggml_tensor * llm_build_inp_embd(
        struct ggml_context * ctx,
       struct llama_context & lctx,
        const llama_hparams & hparams,
         const llama_ubatch & ubatch,
         struct ggml_tensor * tok_embd,
         const llm_build_cb & cb) {
    const int64_t n_embd = hparams.n_embd;

    struct ggml_tensor * inpL;

    if (ubatch.token) {
        lctx.inp_tokens = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, ubatch.n_tokens);
        cb(lctx.inp_tokens, "inp_tokens", -1);
        ggml_set_input(lctx.inp_tokens);

        inpL = ggml_get_rows(ctx, tok_embd, lctx.inp_tokens);
    } else {
        lctx.inp_embd = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, ubatch.n_tokens);
        inpL = lctx.inp_embd;
        ggml_set_input(lctx.inp_embd);
    }

    // For Granite architecture
    if (hparams.f_embedding_scale != 0.0f) {
        inpL = ggml_scale(ctx, inpL, hparams.f_embedding_scale);
    }

    cb(inpL, "inp_embd", -1);

    return inpL;
}

static void llm_build_kv_store(
        struct ggml_context * ctx,
        const llama_hparams & hparams,
        const llama_cparams & cparams,
       const llama_kv_cache & kv,
         struct ggml_cgraph * graph,
         struct ggml_tensor * k_cur,
         struct ggml_tensor * v_cur,
                    int32_t   n_tokens,
                    int32_t   kv_head,
         const llm_build_cb & cb,
                    int64_t   il) {
    const int64_t n_ctx = cparams.n_ctx;

    const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(il);
    const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa(il);

    GGML_ASSERT(kv.size == n_ctx);

    struct ggml_tensor * k_cache_view = ggml_view_1d(ctx, kv.k_l[il], n_tokens*n_embd_k_gqa, ggml_row_size(kv.k_l[il]->type, n_embd_k_gqa)*kv_head);
    cb(k_cache_view, "k_cache_view", il);

    // note: storing RoPE-ed version of K in the KV cache
    ggml_build_forward_expand(graph, ggml_cpy(ctx, k_cur, k_cache_view));

    assert(v_cur->ne[0] == n_embd_v_gqa && v_cur->ne[1] == n_tokens);

    struct ggml_tensor * v_cache_view = nullptr;

    if (cparams.flash_attn) {
        v_cache_view = ggml_view_1d(ctx, kv.v_l[il], n_tokens*n_embd_v_gqa, ggml_row_size(kv.v_l[il]->type, n_embd_v_gqa)*kv_head);
    } else {
        // note: the V cache is transposed when not using flash attention
        v_cache_view = ggml_view_2d(ctx, kv.v_l[il], n_tokens, n_embd_v_gqa,
                (  n_ctx)*ggml_element_size(kv.v_l[il]),
                (kv_head)*ggml_element_size(kv.v_l[il]));

        v_cur = ggml_transpose(ctx, v_cur);
    }
    cb(v_cache_view, "v_cache_view", il);

    ggml_build_forward_expand(graph, ggml_cpy(ctx, v_cur, v_cache_view));
}

// do mat_mul, while optionally apply lora
static struct ggml_tensor * llm_build_lora_mm(
        struct llama_context & lctx,
         struct ggml_context * ctx0,
          struct ggml_tensor * w,
          struct ggml_tensor * cur) {
    struct ggml_tensor * res = ggml_mul_mat(ctx0, w, cur);
    for (auto & it : lctx.lora_adapters) {
        struct llama_lora_weight * lora = it.first->get_weight(w);
        if (lora == nullptr) {
            continue;
        }
        const float alpha = it.first->alpha;
        const float rank  = (float) lora->b->ne[0];
        const float scale = alpha ? it.second * alpha / rank : it.second;
        struct ggml_tensor * ab_cur = ggml_mul_mat(
            ctx0, lora->b,
            ggml_mul_mat(ctx0, lora->a, cur)
        );
        ab_cur = ggml_scale(ctx0, ab_cur, scale);
        res = ggml_add(ctx0, res, ab_cur);
    }
    return res;
}

// do mat_mul_id, while optionally apply lora
static struct ggml_tensor * llm_build_lora_mm_id(
        struct llama_context & lctx,
         struct ggml_context * ctx0,
          struct ggml_tensor * w,   // struct ggml_tensor * as
          struct ggml_tensor * cur, // struct ggml_tensor * b
          struct ggml_tensor * ids) {
    struct ggml_tensor * res = ggml_mul_mat_id(ctx0, w, cur, ids);
    for (auto & it : lctx.lora_adapters) {
        struct llama_lora_weight * lora = it.first->get_weight(w);
        if (lora == nullptr) {
            continue;
        }
        const float alpha = it.first->alpha;
        const float rank  = (float) lora->b->ne[0];
        const float scale = alpha ? it.second * alpha / rank : it.second;
        struct ggml_tensor * ab_cur = ggml_mul_mat_id(
            ctx0, lora->b,
            ggml_mul_mat_id(ctx0, lora->a, cur, ids),
            ids
        );
        ab_cur = ggml_scale(ctx0, ab_cur, scale);
        res = ggml_add(ctx0, res, ab_cur);
    }
    return res;
}

static struct ggml_tensor * llm_build_norm(
        struct ggml_context * ctx,
         struct ggml_tensor * cur,
        const llama_hparams & hparams,
         struct ggml_tensor * mw,
         struct ggml_tensor * mb,
              llm_norm_type   type,
         const llm_build_cb & cb,
                        int   il) {
    switch (type) {
        case LLM_NORM:       cur = ggml_norm      (ctx, cur, hparams.f_norm_eps);     break;
        case LLM_NORM_RMS:   cur = ggml_rms_norm  (ctx, cur, hparams.f_norm_rms_eps); break;
        case LLM_NORM_GROUP:
            {
                cur = ggml_reshape_3d(ctx, cur, cur->ne[0], 1, cur->ne[1]);
                cur = ggml_group_norm(ctx, cur, hparams.n_norm_groups, hparams.f_norm_group_eps);
                cur = ggml_reshape_2d(ctx, cur, cur->ne[0],    cur->ne[2]);
            } break;
    }

    if (mw || mb) {
        cb(cur, "norm", il);
    }

    if (mw) {
        cur = ggml_mul(ctx, cur, mw);
        if (mb) {
            cb(cur, "norm_w", il);
        }
    }

    if (mb) {
        cur = ggml_add(ctx, cur, mb);
    }

    return cur;
}

static struct ggml_tensor * llm_build_ffn(
        struct ggml_context * ctx,
       struct llama_context & lctx,
         struct ggml_tensor * cur,
         struct ggml_tensor * up,
         struct ggml_tensor * up_b,
         struct ggml_tensor * up_s,
         struct ggml_tensor * gate,
         struct ggml_tensor * gate_b,
         struct ggml_tensor * gate_s,
         struct ggml_tensor * down,
         struct ggml_tensor * down_b,
         struct ggml_tensor * down_s,
         struct ggml_tensor * act_scales,
            llm_ffn_op_type   type_op,
          llm_ffn_gate_type   type_gate,
         const llm_build_cb & cb,
                        int   il) {
    struct ggml_tensor * tmp = up ? llm_build_lora_mm(lctx, ctx, up, cur) : cur;
    cb(tmp, "ffn_up", il);

    if (up_b) {
        tmp = ggml_add(ctx, tmp, up_b);
        cb(tmp, "ffn_up_b", il);
    }

    if (up_s) {
        tmp = ggml_mul(ctx, tmp, up_s);
        cb(tmp, "ffn_up_s", il);
    }

    if (gate) {
        switch (type_gate) {
            case LLM_FFN_SEQ:
                {
                    cur = llm_build_lora_mm(lctx, ctx, gate, tmp);
                    cb(cur, "ffn_gate", il);
                } break;
            case LLM_FFN_PAR:
                {
                    cur = llm_build_lora_mm(lctx, ctx, gate, cur);
                    cb(cur, "ffn_gate", il);
                } break;
        }

        if (gate_b) {
            cur = ggml_add(ctx, cur, gate_b);
            cb(cur, "ffn_gate_b", il);
        }

        if (gate_s) {
            cur = ggml_mul(ctx, cur, gate_s);
            cb(cur, "ffn_gate_s", il);
        }

    } else {
        cur = tmp;
    }

    switch (type_op) {
        case LLM_FFN_SILU:
            {
                cur = ggml_silu(ctx, cur);
                cb(cur, "ffn_silu", il);
            } break;
        case LLM_FFN_GELU:
            {
                cur = ggml_gelu(ctx, cur);
                cb(cur, "ffn_gelu", il);
                if (act_scales != NULL) {
                    cur = ggml_div(ctx, cur, act_scales);
                    cb(cur, "ffn_act", il);
                }
            } break;
        case LLM_FFN_RELU:
            {
                cur = ggml_relu(ctx, cur);
                cb(cur, "ffn_relu", il);
            } break;
        case LLM_FFN_RELU_SQR:
            {
                cur = ggml_relu(ctx, cur);
                cb(cur, "ffn_relu", il);

                cur = ggml_sqr(ctx, cur);
                cb(cur, "ffn_sqr(relu)", il);
            } break;
        case LLM_FFN_SWIGLU:
            {
                // Project to 4h. If using swiglu double the output width, see https://arxiv.org/pdf/2002.05202.pdf
                int64_t split_point = cur->ne[0] / 2;
                struct ggml_tensor * x0 = ggml_cont(ctx, ggml_view_2d(ctx, cur, split_point, cur->ne[1], cur->nb[1], 0));
                struct ggml_tensor * x1 = ggml_cont(ctx, ggml_view_2d(ctx, cur, split_point, cur->ne[1], cur->nb[1], split_point * ggml_element_size(cur)));

                x0 = ggml_silu(ctx, x0);
                cb(cur, "ffn_silu", il);

                cur = ggml_mul(ctx, x0, x1);
                cb(cur, "ffn_mul", il);
            } break;
    }

    if (type_gate == LLM_FFN_PAR) {
        cur = ggml_mul(ctx, cur, tmp);
        cb(cur, "ffn_gate_par", il);
    }

    if (down) {
        cur = llm_build_lora_mm(lctx, ctx, down, cur);
    }

    if (down_b) {
        cb(cur, "ffn_down", il);
    }

    if (down_b) {
        cur = ggml_add(ctx, cur, down_b);
    }

    if (down_s) {
        cur = ggml_mul(ctx, cur, down_s);
        cb(cur, "ffn_down_s", il);
    }

    return cur;
}

static struct ggml_tensor * llm_build_moe_ffn(
        struct ggml_context * ctx,
       struct llama_context & lctx,
         struct ggml_tensor * cur,
         struct ggml_tensor * gate_inp,
         struct ggml_tensor * up_exps,
         struct ggml_tensor * gate_exps,
         struct ggml_tensor * down_exps,
         struct ggml_tensor * exp_probs_b,
                    int64_t   n_expert,
                    int64_t   n_expert_used,
            llm_ffn_op_type   type_op,
                       bool   norm_w,
                       bool   scale_w,
                      float   w_scale,
llama_expert_gating_func_type gating_op,
         const llm_build_cb & cb,
                        int   il) {
    int64_t n_embd = cur->ne[0];
    int64_t n_tokens = cur->ne[1];

    ggml_tensor * logits = llm_build_lora_mm(lctx, ctx, gate_inp, cur); // [n_expert, n_tokens]
    cb(logits, "ffn_moe_logits", il);

    ggml_tensor * probs = nullptr;
    switch (gating_op) {
        case LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX:
            {
                probs = ggml_soft_max(ctx, logits); // [n_expert, n_tokens]
            } break;
        case LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID:
            {
                probs = ggml_sigmoid(ctx, logits); // [n_expert, n_tokens]
            } break;
        default:
            GGML_ABORT("fatal error");
    }
    cb(probs, "ffn_moe_probs", il);

    // add experts selection bias - introduced in DeepSeek V3
    // leave probs unbiased as it's later used to get expert weights
    ggml_tensor * selection_probs = probs;
    if (exp_probs_b != nullptr) {
        selection_probs = ggml_add(ctx, probs, exp_probs_b);
        cb(selection_probs, "ffn_moe_probs_biased", il);
    }

    // select experts
    ggml_tensor * selected_experts = ggml_top_k(ctx, selection_probs, n_expert_used); // [n_expert_used, n_tokens]
    cb(selected_experts->src[0], "ffn_moe_argsort", il);
    cb(selected_experts, "ffn_moe_topk", il);

    ggml_tensor * weights = ggml_get_rows(ctx,
            ggml_reshape_3d(ctx, probs, 1, n_expert, n_tokens), selected_experts); // [1, n_expert_used, n_tokens]
    cb(weights, "ffn_moe_weights", il);

    if (norm_w) {
        weights = ggml_reshape_2d(ctx, weights, n_expert_used, n_tokens);

        ggml_tensor * weights_sum = ggml_sum_rows(ctx, weights); // [1, n_tokens]
        cb(weights_sum, "ffn_moe_weights_sum", il);

        weights = ggml_div(ctx, weights, weights_sum); // [n_expert_used, n_tokens]
        cb(weights, "ffn_moe_weights_norm", il);

        weights = ggml_reshape_3d(ctx, weights, 1, n_expert_used, n_tokens);
    }
    if (scale_w) {
        weights = ggml_scale(ctx, weights, w_scale);
        cb(weights, "ffn_moe_weights_scaled", il);
    }

    cur = ggml_reshape_3d(ctx, cur, n_embd, 1, n_tokens);
    ggml_tensor * up = llm_build_lora_mm_id(lctx, ctx, up_exps, cur, selected_experts); // [n_ff, n_expert_used, n_tokens]
    cb(up, "ffn_moe_up", il);

    ggml_tensor * gate = llm_build_lora_mm_id(lctx, ctx, gate_exps, cur, selected_experts); // [n_ff, n_expert_used, n_tokens]
    cb(gate, "ffn_moe_gate", il);

    switch (type_op) {
        case LLM_FFN_SILU:
            {
                gate = ggml_silu(ctx, gate);
                cb(gate, "ffn_moe_silu", il);
            } break;
        case LLM_FFN_GELU:
            {
                gate = ggml_gelu(ctx, gate);
                cb(gate, "ffn_moe_gelu", il);
            } break;
        default:
            GGML_ABORT("fatal error");
    }

    ggml_tensor * par = ggml_mul(ctx, up, gate); // [n_ff, n_expert_used, n_tokens]
    cb(par, "ffn_moe_gate_par", il);

    ggml_tensor * experts = llm_build_lora_mm_id(lctx, ctx, down_exps, par, selected_experts); // [n_embd, n_expert_used, n_tokens]
    cb(experts, "ffn_moe_down", il);

    experts = ggml_mul(ctx, experts, weights);

    // aggregate experts
    ggml_tensor * moe_out = nullptr;
    for (int i = 0; i < n_expert_used; ++i) {
        ggml_tensor * cur_expert = ggml_view_2d(ctx, experts, n_embd, n_tokens,
                experts->nb[2], i*experts->nb[1]);

        if (i == 0) {
            moe_out = cur_expert;
        } else {
            moe_out = ggml_add(ctx, moe_out, cur_expert);
        }
    }

    if (n_expert_used == 1) {
        // avoid returning a non-contiguous tensor
        moe_out = ggml_cont(ctx, moe_out);
    }

    return moe_out;
}

static struct ggml_tensor * llm_build_kqv(
        struct ggml_context * ctx,
       struct llama_context & lctx,
       const llama_kv_cache & kv,
         struct ggml_cgraph * graph,
         struct ggml_tensor * wo,
         struct ggml_tensor * wo_b,
         struct ggml_tensor * q_cur,
         struct ggml_tensor * kq_mask,
                    int32_t   n_tokens,
                    int32_t   n_kv,
                    float     kq_scale,
         const llm_build_cb & cb,
                    int       il) {
    const llama_model   & model   = lctx.model;
    const llama_hparams & hparams = lctx.model.hparams;
    const llama_cparams & cparams = lctx.cparams;

    const int64_t n_ctx         = cparams.n_ctx;
    const int64_t n_head        = hparams.n_head(il);
    const int64_t n_head_kv     = hparams.n_head_kv(il);
    const int64_t n_embd_head_k = hparams.n_embd_head_k;
    const int64_t n_embd_k_gqa  = hparams.n_embd_k_gqa(il);
    const int64_t n_embd_head_v = hparams.n_embd_head_v;
    const int64_t n_embd_v_gqa  = hparams.n_embd_v_gqa(il);

    struct ggml_tensor * q = ggml_permute(ctx, q_cur, 0, 2, 1, 3);
    cb(q, "q", il);

    struct ggml_tensor * k =
        ggml_view_3d(ctx, kv.k_l[il],
                n_embd_head_k, n_kv, n_head_kv,
                ggml_row_size(kv.k_l[il]->type, n_embd_k_gqa),
                ggml_row_size(kv.k_l[il]->type, n_embd_head_k),
                0);
    cb(k, "k", il);

    struct ggml_tensor * cur;

    if (cparams.flash_attn) {
        GGML_UNUSED(model);
        GGML_UNUSED(n_ctx);

        // split cached v into n_head heads (not transposed)
        struct ggml_tensor * v =
            ggml_view_3d(ctx, kv.v_l[il],
                    n_embd_head_v, n_kv, n_head_kv,
                    ggml_row_size(kv.v_l[il]->type, n_embd_v_gqa),
                    ggml_row_size(kv.v_l[il]->type, n_embd_head_v),
                    0);
        cb(v, "v", il);

        cur = ggml_flash_attn_ext(ctx, q, k, v, kq_mask, kq_scale, hparams.f_max_alibi_bias,
                                  hparams.attn_soft_cap ? hparams.f_attn_logit_softcapping : 0.0f);

        ggml_flash_attn_ext_set_prec(cur, GGML_PREC_F32);

        cur = ggml_reshape_2d(ctx, cur, n_embd_head_v*n_head, n_tokens);
    } else {
        struct ggml_tensor * kq = ggml_mul_mat(ctx, k, q);
        cb(kq, "kq", il);

        // note: this op tends to require high floating point range
        //       while for some models F16 is enough, for others it is not, so we default to F32 here
        ggml_mul_mat_set_prec(kq, GGML_PREC_F32);

        if (model.arch == LLM_ARCH_GROK) {
            // need to do the following:
            // multiply by attn_output_multiplyer of 0.08838834764831845
            // and then :
            // kq = 30 * tanh(kq / 30)
            // before the softmax below

            kq = ggml_tanh(ctx, ggml_scale(ctx, kq, 0.08838834764831845f/30.0f));
            kq = ggml_scale(ctx, kq, 30);
        }

        if (hparams.attn_soft_cap) {
            kq = ggml_scale(ctx, kq, 1.0f / hparams.f_attn_logit_softcapping);
            kq = ggml_tanh(ctx, kq);
            kq = ggml_scale(ctx, kq, hparams.f_attn_logit_softcapping);
        }

        kq = ggml_soft_max_ext(ctx, kq, kq_mask, kq_scale, hparams.f_max_alibi_bias);
        cb(kq, "kq_soft_max_ext", il);

        GGML_ASSERT(kv.size == n_ctx);

        // split cached v into n_head heads
        struct ggml_tensor * v =
            ggml_view_3d(ctx, kv.v_l[il],
                    n_kv, n_embd_head_v, n_head_kv,
                    ggml_element_size(kv.v_l[il])*n_ctx,
                    ggml_element_size(kv.v_l[il])*n_ctx*n_embd_head_v,
                    0);
        cb(v, "v", il);

        struct ggml_tensor * kqv = ggml_mul_mat(ctx, v, kq);
        cb(kqv, "kqv", il);

        struct ggml_tensor * kqv_merged = ggml_permute(ctx, kqv, 0, 2, 1, 3);
        cb(kqv_merged, "kqv_merged", il);

        cur = ggml_cont_2d(ctx, kqv_merged, n_embd_head_v*n_head, n_tokens);
        cb(cur, "kqv_merged_cont", il);
    }

    ggml_build_forward_expand(graph, cur);

    if (wo) {
        cur = llm_build_lora_mm(lctx, ctx, wo, cur);
    }

    if (wo_b) {
        cb(cur, "kqv_wo", il);
    }

    if (wo_b) {
        cur = ggml_add(ctx, cur, wo_b);
    }

    return cur;
}

static struct ggml_tensor * llm_build_kv(
        struct ggml_context * ctx,
       struct llama_context & lctx,
       const llama_kv_cache & kv,
         struct ggml_cgraph * graph,
         struct ggml_tensor * wo,
         struct ggml_tensor * wo_b,
         struct ggml_tensor * k_cur,
         struct ggml_tensor * v_cur,
         struct ggml_tensor * q_cur,
         struct ggml_tensor * kq_mask,
                    int32_t   n_tokens,
                    int32_t   kv_head,
                    int32_t   n_kv,
                    float     kq_scale,
         const llm_build_cb & cb,
                    int       il) {
    const llama_hparams & hparams = lctx.model.hparams;
    const llama_cparams & cparams = lctx.cparams;

    // these nodes are added to the graph together so that they are not reordered
    // by doing so, the number of splits in the graph is reduced
    ggml_build_forward_expand(graph, q_cur);
    ggml_build_forward_expand(graph, k_cur);
    ggml_build_forward_expand(graph, v_cur);

    llm_build_kv_store(ctx, hparams, cparams, kv, graph, k_cur, v_cur, n_tokens, kv_head, cb, il);

    struct ggml_tensor * cur;

    cur  = llm_build_kqv(ctx, lctx, kv, graph, wo, wo_b, q_cur, kq_mask, n_tokens, n_kv, kq_scale, cb, il);
    cb(cur, "kqv_out", il);

    return cur;
}

static struct ggml_tensor * llm_build_copy_mask_state(
        struct ggml_context * ctx,
         struct ggml_cgraph * graph,
         struct ggml_tensor * s,
         struct ggml_tensor * state_copy,
         struct ggml_tensor * state_mask,
                    int32_t   n_state,
                    int32_t   kv_size,
                    int32_t   kv_head,
                    int32_t   n_kv,
                    int32_t   n_seqs) {
    struct ggml_tensor * states = ggml_reshape_2d(ctx, s, n_state, kv_size);

    // copy states
    // NOTE: assuming the copy destinations are ALL contained between kv_head and kv_head + n_kv
    // this shrinks the tensors's ne[1] to n_kv
    states = ggml_get_rows(ctx, states, state_copy);

    // clear states of sequences which are starting at the beginning of this batch
    // FIXME: zero-out NANs?
    states = ggml_mul(ctx, states, state_mask);

    // copy states which won't be changed further (between n_seqs and n_kv)
    ggml_build_forward_expand(graph,
        ggml_cpy(ctx,
            ggml_view_1d(ctx, states, n_state*(n_kv - n_seqs), n_seqs*n_state*ggml_element_size(states)),
            ggml_view_1d(ctx, s, n_state*(n_kv - n_seqs), (kv_head + n_seqs)*n_state*ggml_element_size(s))));

    // the part of the states that will be used and modified
    return ggml_view_2d(ctx, states, n_state, n_seqs, states->nb[1], 0);
}

// TODO: split
static struct ggml_tensor * llm_build_mamba(
        struct ggml_context * ctx,
       struct llama_context & lctx,
         const llama_ubatch & ubatch,
         struct ggml_cgraph * graph,
         struct ggml_tensor * cur,
         struct ggml_tensor * state_copy,
         struct ggml_tensor * state_mask,
                    int32_t   kv_head,
                    int32_t   n_kv,
         const llm_build_cb & cb,
                    int       il) {
    const llama_model    & model   = lctx.model;
    const llama_hparams  & hparams = model.hparams;
    const llama_kv_cache & kv      = lctx.kv_self;
    const int64_t d_conv  = hparams.ssm_d_conv;
    const int64_t d_inner = hparams.ssm_d_inner;
    const int64_t d_state = hparams.ssm_d_state;
    const int64_t dt_rank = hparams.ssm_dt_rank;
    const int64_t n_seqs  = ubatch.n_seqs;
    // Some variants of Mamba arch (e.g. FalconMamba do apply layer norm on B and Dt layers)
    const bool ssm_dt_b_c_rms = hparams.ssm_dt_b_c_rms;
    // Use the same RMS norm as the final layer norm
    const float norm_rms_eps = hparams.f_norm_rms_eps;

    const int64_t n_seq_tokens = ubatch.n_seq_tokens;

    GGML_ASSERT(n_seqs != 0);
    GGML_ASSERT(ubatch.equal_seqs);
    GGML_ASSERT(ubatch.n_tokens == n_seq_tokens * n_seqs);

    struct ggml_tensor * conv_states_all = kv.k_l[il];
    struct ggml_tensor * ssm_states_all  = kv.v_l[il];

    // (ab)using the KV cache to store the states
    struct ggml_tensor * conv = llm_build_copy_mask_state(ctx,
            graph, conv_states_all, state_copy, state_mask,
            hparams.n_embd_k_s(), kv.size, kv_head, n_kv, n_seqs);
    conv = ggml_reshape_3d(ctx, conv, d_conv - 1, d_inner, n_seqs);
    struct ggml_tensor * ssm = llm_build_copy_mask_state(ctx,
            graph, ssm_states_all, state_copy, state_mask,
            hparams.n_embd_v_s(), kv.size, kv_head, n_kv, n_seqs);
    ssm = ggml_reshape_3d(ctx, ssm, d_state, d_inner, n_seqs);

    // {n_embd, n_tokens} => {n_embd, n_seq_tokens, n_seqs}
    cur = ggml_reshape_3d(ctx, cur, cur->ne[0], n_seq_tokens, n_seqs);

    // {n_embd, 2*d_inner} @ {n_embd, n_seq_tokens, n_seqs} => {2*d_inner, n_seq_tokens, n_seqs}
    struct ggml_tensor * xz = llm_build_lora_mm(lctx, ctx, model.layers[il].ssm_in, cur);
    // split the above in two
    // => {d_inner, n_seq_tokens, n_seqs}
    struct ggml_tensor * x = ggml_view_3d(ctx, xz, d_inner, xz->ne[1], xz->ne[2], xz->nb[1], xz->nb[2], 0);
    struct ggml_tensor * z = ggml_view_3d(ctx, xz, d_inner, xz->ne[1], xz->ne[2], xz->nb[1], xz->nb[2], d_inner*ggml_element_size(xz));

    // conv
    {
        // => {d_conv - 1 + n_seq_tokens, d_inner, n_seqs}
        struct ggml_tensor * conv_x = ggml_concat(ctx, conv, ggml_transpose(ctx, x), 0);

        // copy last (d_conv - 1) columns back into the state cache
        struct ggml_tensor * last_conv = ggml_view_3d(ctx, conv_x, d_conv - 1, d_inner, n_seqs, conv_x->nb[1], conv_x->nb[2], n_seq_tokens*(conv_x->nb[0]));

        ggml_build_forward_expand(graph,
            ggml_cpy(ctx, last_conv,
                ggml_view_1d(ctx, conv_states_all,
                    (d_conv - 1)*(d_inner)*(n_seqs),
                    kv_head*(d_conv - 1)*(d_inner)*ggml_element_size(conv_states_all))));

        // 1D convolution
        // The equivalent is to make a self-overlapping view of conv_x
        // over d_conv columns at each stride in the 3rd dimension,
        // then element-wise multiply that with the conv1d weight,
        // then sum the elements of each row,
        // (the last two steps are a dot product over rows (also doable with mul_mat))
        // then permute away the ne[0] dimension,
        // and then you're left with the resulting x tensor.
        // For simultaneous sequences, all sequences need to have the same length.
        x = ggml_ssm_conv(ctx, conv_x, model.layers[il].ssm_conv1d);

        // bias
        x = ggml_add(ctx, x, model.layers[il].ssm_conv1d_b);

        x = ggml_silu(ctx, x);
    }

    // ssm
    {
        // {d_inner, dt_rank + 2*d_state} @ {d_inner, n_seq_tokens, n_seqs} => {dt_rank + 2*d_state, n_seq_tokens, n_seqs}
        struct ggml_tensor * x_db = llm_build_lora_mm(lctx, ctx, model.layers[il].ssm_x, x);
        // split
        struct ggml_tensor * dt = ggml_view_3d(ctx, x_db, dt_rank, n_seq_tokens, n_seqs, x_db->nb[1], x_db->nb[2], 0);
        struct ggml_tensor * B  = ggml_view_3d(ctx, x_db, d_state, n_seq_tokens, n_seqs, x_db->nb[1], x_db->nb[2], ggml_element_size(x_db)*dt_rank);
        struct ggml_tensor * C  = ggml_view_3d(ctx, x_db, d_state, n_seq_tokens, n_seqs, x_db->nb[1], x_db->nb[2], ggml_element_size(x_db)*(dt_rank+d_state));

        // Some Mamba variants (e.g. FalconMamba) apply RMS norm in B, C & Dt layers
        if (ssm_dt_b_c_rms) {
            dt = ggml_rms_norm(ctx, dt, norm_rms_eps);
            B = ggml_rms_norm(ctx, B, norm_rms_eps);
            C = ggml_rms_norm(ctx, C, norm_rms_eps);
        }

        // {dt_rank, d_inner} @ {dt_rank, n_seq_tokens, n_seqs} => {d_inner, n_seq_tokens, n_seqs}
        dt = llm_build_lora_mm(lctx, ctx, model.layers[il].ssm_dt, dt);
        dt = ggml_add(ctx, dt, model.layers[il].ssm_dt_b);

        // Custom operator to optimize the parallel associative scan
        // as described in the Annex D of the Mamba paper.
        // => {d_inner, n_seq_tokens, n_seqs} and {d_state, d_inner, n_seqs}
        struct ggml_tensor * y_ssm = ggml_ssm_scan(ctx, ssm, x, dt, model.layers[il].ssm_a, B, C);

        // store last states
        ggml_build_forward_expand(graph,
            ggml_cpy(ctx,
                ggml_view_1d(ctx, y_ssm, d_state*d_inner*n_seqs, x->nb[3]),
                ggml_view_1d(ctx, ssm_states_all, d_state*d_inner*n_seqs, kv_head*d_state*d_inner*ggml_element_size(ssm_states_all))));

        struct ggml_tensor * y = ggml_view_3d(ctx, y_ssm, d_inner, n_seq_tokens, n_seqs, x->nb[1], x->nb[2], 0);

        // TODO: skip computing output earlier for unused tokens

        // {d_inner, n_seq_tokens, n_seqs} * {d_inner} => {d_inner, n_seq_tokens, n_seqs}
        y = ggml_add(ctx, y, ggml_mul(ctx, x, model.layers[il].ssm_d));
        y = ggml_mul(ctx, y, ggml_silu(ctx, ggml_cont(ctx, z)));

        // {d_inner, n_embd} @ {d_inner, n_seq_tokens, n_seqs} => {n_embd, n_seq_tokens, n_seqs}
        cur = llm_build_lora_mm(lctx, ctx, model.layers[il].ssm_out, y);
    }

    // {n_embd, n_seq_tokens, n_seqs} => {n_embd, n_tokens}
    cur = ggml_reshape_2d(ctx, cur, cur->ne[0], n_seq_tokens * n_seqs);
    cb(cur, "mamba_out", il);

    return cur;
}

static struct ggml_tensor * llm_build_rwkv6_time_mix(
        struct llama_context & lctx,
        struct ggml_context * ctx,
        const struct llama_layer * layer,
        struct ggml_tensor * cur,
        struct ggml_tensor * x_prev,
        struct ggml_tensor ** wkv_state) {
    size_t n_embd       = cur->ne[0];
    size_t n_seq_tokens = cur->ne[1];
    size_t n_seqs       = cur->ne[2];

    size_t head_size  = layer->time_mix_first->ne[0];
    size_t head_count = layer->time_mix_first->ne[1];

    size_t n_tokens = n_seqs * n_seq_tokens;

    struct ggml_tensor * sx = ggml_sub(ctx, x_prev, cur);

    sx  = ggml_reshape_2d(ctx, sx,  n_embd, n_tokens);
    cur = ggml_reshape_2d(ctx, cur, n_embd, n_tokens);

    struct ggml_tensor * xxx = ggml_add(ctx, ggml_mul(ctx, sx, layer->time_mix_lerp_x), cur);

    xxx = ggml_reshape_4d(
        ctx,
        ggml_tanh(
            ctx,
            ggml_mul_mat(ctx, layer->time_mix_w1, xxx)
        ),
        layer->time_mix_w1->ne[1] / 5, 1, 5, n_tokens
    );

    xxx = ggml_cont(ctx, ggml_permute(ctx, xxx, 0, 1, 3, 2));

    xxx = ggml_mul_mat(
        ctx,
        ggml_reshape_4d(
            ctx,
            layer->time_mix_w2,
            layer->time_mix_w2->ne[0], layer->time_mix_w2->ne[1], 1, 5
        ),
        xxx
    );

    struct ggml_tensor *mw = ggml_view_2d(ctx, xxx, n_embd, n_tokens, xxx->nb[1], 0);
    struct ggml_tensor *mk = ggml_view_2d(ctx, xxx, n_embd, n_tokens, xxx->nb[1], n_embd * n_tokens * sizeof(float));
    struct ggml_tensor *mv = ggml_view_2d(ctx, xxx, n_embd, n_tokens, xxx->nb[1], n_embd * n_tokens * 2 * sizeof(float));
    struct ggml_tensor *mr = ggml_view_2d(ctx, xxx, n_embd, n_tokens, xxx->nb[1], n_embd * n_tokens * 3 * sizeof(float));
    struct ggml_tensor *mg = ggml_view_2d(ctx, xxx, n_embd, n_tokens, xxx->nb[1], n_embd * n_tokens * 4 * sizeof(float));

    struct ggml_tensor * xw = ggml_add(
        ctx,
        ggml_mul(
            ctx,
            ggml_add(ctx, mw, layer->time_mix_lerp_w),
            sx
        ),
        cur
    );

    struct ggml_tensor * xk = ggml_add(
        ctx,
        ggml_mul(
            ctx,
            ggml_add(ctx, mk, layer->time_mix_lerp_k),
            sx
        ),
        cur
    );

    struct ggml_tensor * xv = ggml_add(
        ctx,
        ggml_mul(
            ctx,
            ggml_add(ctx, mv, layer->time_mix_lerp_v),
            sx
        ),
        cur
    );

    struct ggml_tensor * xr = ggml_add(
        ctx,
        ggml_mul(
            ctx,
            ggml_add(ctx, mr, layer->time_mix_lerp_r),
            sx
        ),
        cur
    );

    struct ggml_tensor * xg = ggml_add(
        ctx,
        ggml_mul(
            ctx,
            ggml_add(ctx, mg, layer->time_mix_lerp_g),
            sx
        ),
        cur
    );

    struct ggml_tensor * r = ggml_reshape_4d(ctx, llm_build_lora_mm(lctx, ctx, layer->time_mix_receptance, xr), head_size, 1,         head_count, n_tokens);
    struct ggml_tensor * k = ggml_reshape_4d(ctx, llm_build_lora_mm(lctx, ctx, layer->time_mix_key,        xk), 1,         head_size, head_count, n_tokens);
    struct ggml_tensor * v = ggml_reshape_4d(ctx, llm_build_lora_mm(lctx, ctx, layer->time_mix_value,      xv), head_size, 1,         head_count, n_tokens);
    struct ggml_tensor * g = ggml_silu(
        ctx,
        llm_build_lora_mm(lctx, ctx, layer->time_mix_gate, xg)
    );

    struct ggml_tensor * w = ggml_mul_mat(
        ctx,
        layer->time_mix_decay_w2,
        ggml_tanh(
            ctx,
            ggml_mul_mat(ctx, layer->time_mix_decay_w1, xw)
        )
    );

    w = ggml_add(ctx, w, ggml_reshape_1d(ctx, layer->time_mix_decay, n_embd));
    w = ggml_exp(ctx, ggml_neg(ctx, ggml_exp(ctx, w)));
    w = ggml_reshape_4d(ctx, w, 1, head_size, head_count, n_tokens);

    k = ggml_transpose(ctx, k);
    v = ggml_transpose(ctx, v);
    r = ggml_transpose(ctx, r);

    struct ggml_tensor * wkv_output = ggml_rwkv_wkv6(ctx, k, v, r, layer->time_mix_first, w, *wkv_state);
    cur = ggml_view_1d(ctx, wkv_output, n_embd * n_tokens, 0);
    *wkv_state = ggml_view_1d(ctx, wkv_output, n_embd * head_size * n_seqs, n_embd * n_tokens * sizeof(float));

    // group norm with head_count groups
    cur = ggml_reshape_3d(ctx, cur, n_embd / head_count, head_count, n_tokens);
    cur = ggml_norm(ctx, cur, 64e-5f);

    // Convert back to regular vectors.
    cur = ggml_reshape_2d(ctx, cur, n_embd, n_tokens);
    cur = ggml_add(ctx, ggml_mul(ctx, cur, layer->time_mix_ln), layer->time_mix_ln_b);

    cur = ggml_mul(ctx, cur, g);
    cur = llm_build_lora_mm(lctx, ctx, layer->time_mix_output, cur);

    return ggml_reshape_3d(ctx, cur, n_embd, n_seq_tokens, n_seqs);
}

static struct ggml_tensor * llm_build_rwkv6_channel_mix(
        struct llama_context & lctx,
        struct ggml_context * ctx,
        const struct llama_layer * layer,
        struct ggml_tensor * cur,
        struct ggml_tensor * x_prev) {
    struct ggml_tensor * sx = ggml_sub(ctx, x_prev, cur);
    struct ggml_tensor * xk = ggml_add(ctx, ggml_mul(ctx, sx, layer->channel_mix_lerp_k), cur);
    struct ggml_tensor * xr = ggml_add(ctx, ggml_mul(ctx, sx, layer->channel_mix_lerp_r), cur);

    struct ggml_tensor * r = ggml_sigmoid(ctx, llm_build_lora_mm(lctx, ctx, layer->channel_mix_receptance, xr));
    struct ggml_tensor * k = ggml_sqr(
        ctx,
        ggml_relu(
            ctx,
            llm_build_lora_mm(lctx, ctx, layer->channel_mix_key, xk)
        )
    );

    return ggml_mul(ctx, r, llm_build_lora_mm(lctx, ctx, layer->channel_mix_value, k));
}

struct llm_build_context {
    const llama_model    & model;
          llama_context  & lctx;
    const llama_hparams  & hparams;
    const llama_cparams  & cparams;
    const llama_ubatch   & ubatch;
    const llama_kv_cache & kv_self;

    const int64_t n_embd;
    const int64_t n_layer;
    const int64_t n_rot;
    const int64_t n_ctx;       // user-specified context size (can be different from n_ctx_train)
    const int64_t n_head;
    const int64_t n_head_kv;
    const int64_t n_embd_head_k;
    const int64_t n_embd_k_gqa;
    const int64_t n_embd_head_v;
    const int64_t n_embd_v_gqa;
    const int64_t n_expert;
    const int64_t n_expert_used;

    const float freq_base;
    const float freq_scale;
    const float ext_factor;
    const float attn_factor;
    const float beta_fast;
    const float beta_slow;
    const float norm_eps;
    const float norm_rms_eps;

    const int32_t n_tokens;
    const int32_t n_kv;     // size of KV cache to consider (n_kv <= kv_self.size)
    const int32_t n_outputs;
    const int32_t n_outputs_enc;
    const int32_t kv_head;  // index of where we store new KV data in the cache
    const int32_t n_ctx_orig;

    const bool flash_attn;

    const enum llama_pooling_type pooling_type;
    const enum llama_rope_type    rope_type;

    const llm_build_cb & cb;

    std::vector<uint8_t> & buf_compute_meta;

    struct ggml_context * ctx0 = nullptr;

    // TODO: consider making the entire interface noexcept
    llm_build_context(
        llama_context  & lctx,
    const llama_ubatch & ubatch,
    const llm_build_cb & cb,
                  bool   worst_case) :
        model            (lctx.model),
        lctx             (lctx),
        hparams          (model.hparams),
        cparams          (lctx.cparams),
        ubatch           (ubatch),
        kv_self          (lctx.kv_self),
        n_embd           (hparams.n_embd),
        n_layer          (hparams.n_layer),
        n_rot            (hparams.n_rot),
        n_ctx            (cparams.n_ctx),
        n_head           (hparams.n_head()),
        n_head_kv        (hparams.n_head_kv()),
        n_embd_head_k    (hparams.n_embd_head_k),
        n_embd_k_gqa     (hparams.n_embd_k_gqa()),
        n_embd_head_v    (hparams.n_embd_head_v),
        n_embd_v_gqa     (hparams.n_embd_v_gqa()),
        n_expert         (hparams.n_expert),
        n_expert_used    (hparams.n_expert_used),
        freq_base        (cparams.rope_freq_base),
        freq_scale       (cparams.rope_freq_scale),
        ext_factor       (cparams.yarn_ext_factor),
        attn_factor      (cparams.yarn_attn_factor),
        beta_fast        (cparams.yarn_beta_fast),
        beta_slow        (cparams.yarn_beta_slow),
        norm_eps         (hparams.f_norm_eps),
        norm_rms_eps     (hparams.f_norm_rms_eps),
        n_tokens         (ubatch.n_tokens),
        n_kv             (worst_case ? kv_self.size : kv_self.n),
        n_outputs        (worst_case ? n_tokens : lctx.n_outputs),
        n_outputs_enc    (worst_case ? n_tokens : lctx.embd_enc.size() / hparams.n_embd),
        kv_head          (worst_case ? (kv_self.recurrent ? 0 : kv_self.size - n_tokens) : kv_self.head),
        n_ctx_orig       (cparams.n_ctx_orig_yarn),
        flash_attn       (cparams.flash_attn),
        pooling_type     (cparams.pooling_type),
        rope_type        (hparams.rope_type),
        cb               (cb),
        buf_compute_meta (lctx.buf_compute_meta) {
            // all initializations should be done in init()
        }

    void init() {
        struct ggml_init_params params = {
            /*.mem_size   =*/ buf_compute_meta.size(),
            /*.mem_buffer =*/ buf_compute_meta.data(),
            /*.no_alloc   =*/ true,
        };

        ctx0 = ggml_init(params);

        lctx.inp_tokens      = nullptr;
        lctx.inp_embd        = nullptr;
        lctx.inp_pos         = nullptr;
        lctx.inp_out_ids     = nullptr;
        lctx.inp_KQ_mask     = nullptr;
        lctx.inp_KQ_mask_swa = nullptr;
        lctx.inp_K_shift     = nullptr;
        lctx.inp_mean        = nullptr;
        lctx.inp_cls         = nullptr;
        lctx.inp_s_copy      = nullptr;
        lctx.inp_s_mask      = nullptr;
        lctx.inp_s_seq       = nullptr;
        lctx.inp_pos_bucket    = nullptr;
        lctx.inp_embd_enc      = nullptr;
        lctx.inp_KQ_mask_cross = nullptr;
    }

    void free() {
        ggml_free(ctx0);
        ctx0 = nullptr;
    }

    struct ggml_cgraph * build_k_shift() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);

        GGML_ASSERT(kv_self.size == n_ctx);

        lctx.inp_K_shift = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_ctx);
        cb(lctx.inp_K_shift, "K_shift", -1);
        ggml_set_input(lctx.inp_K_shift);

        for (int il = 0; il < n_layer; ++il) {
            const int64_t n_head_kv = hparams.n_head_kv(il);
            const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(il);
            struct ggml_tensor * rope_factors = build_rope_factors(il);
            struct ggml_tensor * k =
                ggml_view_3d(ctx0, kv_self.k_l[il],
                    n_embd_head_k, n_head_kv, n_ctx,
                    ggml_row_size(kv_self.k_l[il]->type, n_embd_head_k),
                    ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa),
                    0);

            struct ggml_tensor * tmp;
            if (ggml_is_quantized(k->type)) {
                // dequantize to f32 -> RoPE -> quantize back
                tmp = ggml_cast(ctx0, k, GGML_TYPE_F32);
                cb(tmp, "K_f32", il);
                for (auto & backend : lctx.backends) {
                    // Figure out which backend KV cache belongs to
                    if (ggml_backend_supports_buft(backend.get(), ggml_backend_buffer_get_type(kv_self.k_l[il]->buffer))) {
                        ggml_backend_sched_set_tensor_backend(lctx.sched.get(), tmp, backend.get());
                        break;
                    }
                }
                tmp = ggml_rope_ext_inplace(ctx0, tmp,
                        lctx.inp_K_shift, rope_factors, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                        ext_factor, attn_factor, beta_fast, beta_slow);
                cb(tmp, "K_shifted_f32", il);
                tmp = ggml_cpy(ctx0, tmp, k);
            } else {
                // we rotate only the first n_rot dimensions
                tmp = ggml_rope_ext_inplace(ctx0, k,
                        lctx.inp_K_shift, rope_factors, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                        ext_factor, attn_factor, beta_fast, beta_slow);
            }
            cb(tmp, "K_shifted", il);
            ggml_build_forward_expand(gf, tmp);
        }

        return gf;
    }

    struct ggml_cgraph * build_defrag(const std::vector<uint32_t> & ids) {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);

        for (uint32_t i = 0; i < ids.size(); ++i) {
            const uint32_t id = ids[i];

            if (i == id || id == ids.size()) {
                continue;
            }

            uint32_t nm = 1;

            while (i + nm < ids.size() && ids[i + nm] == id + nm) {
                nm++;
            }

            for (int il = 0; il < n_layer; ++il) {
                const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(il);
                const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa(il);

                ggml_tensor * view_k_src = ggml_view_2d(ctx0, kv_self.k_l[il],
                        n_embd_k_gqa, nm,
                        ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa),
                        ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa*i));

                ggml_tensor * view_k_dst = ggml_view_2d(ctx0, kv_self.k_l[il],
                        n_embd_k_gqa, nm,
                        ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa),
                        ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa*id));

                ggml_tensor * view_v_src;
                ggml_tensor * view_v_dst;

                if (flash_attn) {
                    // NOTE: the V cache is not transposed when using flash attention
                    view_v_src = ggml_view_2d(ctx0, kv_self.v_l[il],
                            n_embd_v_gqa, nm,
                            ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa),
                            ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa*i));

                    view_v_dst = ggml_view_2d(ctx0, kv_self.v_l[il],
                            n_embd_v_gqa, nm,
                            ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa),
                            ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa*id));
                } else {
                    view_v_src = ggml_view_2d(ctx0, kv_self.v_l[il],
                            nm, n_embd_v_gqa,
                            ggml_row_size(kv_self.v_l[il]->type, kv_self.size),
                            ggml_row_size(kv_self.v_l[il]->type, i));

                    view_v_dst = ggml_view_2d(ctx0, kv_self.v_l[il],
                            nm, n_embd_v_gqa,
                            ggml_row_size(kv_self.v_l[il]->type, kv_self.size),
                            ggml_row_size(kv_self.v_l[il]->type, id));
                }

                ggml_build_forward_expand(gf, ggml_cpy(ctx0, view_k_src, view_k_dst));
                ggml_build_forward_expand(gf, ggml_cpy(ctx0, view_v_src, view_v_dst));
            }

            i += nm - 1;
        }

        //LLAMA_LOG_INFO("gf->n_nodes = %d\n", gf->n_nodes);

        return gf;
    }

    struct ggml_tensor * build_inp_pos() {
        lctx.inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
        cb(lctx.inp_pos, "inp_pos", -1);
        ggml_set_input(lctx.inp_pos);
        return lctx.inp_pos;
    }

    struct ggml_tensor * build_rope_factors(int il) {
        // choose long/short freq factors based on the context size
        const auto n_ctx_pre_seq = cparams.n_ctx / cparams.n_seq_max;

        if (model.layers[il].rope_freqs != nullptr) {
            return model.layers[il].rope_freqs;
        }

        if (n_ctx_pre_seq > hparams.n_ctx_orig_yarn) {
            return model.layers[il].rope_long;
        }

        return model.layers[il].rope_short;
    }

    struct ggml_tensor * build_inp_out_ids() {
        lctx.inp_out_ids = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_outputs);
        cb(lctx.inp_out_ids, "inp_out_ids", -1);
        ggml_set_input(lctx.inp_out_ids);
        return lctx.inp_out_ids;
    }

    struct ggml_tensor * build_inp_KQ_mask(bool causal = true) {
        lctx.inp_KQ_mask = causal
            ? ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_kv,     GGML_PAD(n_tokens, GGML_KQ_MASK_PAD))
            : ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_tokens, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD));
        cb(lctx.inp_KQ_mask, "KQ_mask", -1);
        ggml_set_input(lctx.inp_KQ_mask);

        return flash_attn ? ggml_cast(ctx0, lctx.inp_KQ_mask, GGML_TYPE_F16) : lctx.inp_KQ_mask;
    }

    struct ggml_tensor * build_inp_KQ_mask_swa(bool causal = true) {
        GGML_ASSERT(hparams.n_swa > 0);

        lctx.inp_KQ_mask_swa = causal
            ? ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_kv,     GGML_PAD(n_tokens, GGML_KQ_MASK_PAD))
            : ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_tokens, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD));
        cb(lctx.inp_KQ_mask_swa, "KQ_mask_swa", -1);
        ggml_set_input(lctx.inp_KQ_mask_swa);

        return flash_attn ? ggml_cast(ctx0, lctx.inp_KQ_mask_swa, GGML_TYPE_F16) : lctx.inp_KQ_mask_swa;
    }

    struct ggml_tensor * build_inp_mean() {
        lctx.inp_mean = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_tokens, n_tokens);
        cb(lctx.inp_mean, "inp_mean", -1);
        ggml_set_input(lctx.inp_mean);
        return lctx.inp_mean;
    }

    struct ggml_tensor * build_inp_cls() {
        lctx.inp_cls = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
        cb(lctx.inp_cls, "inp_cls", -1);
        ggml_set_input(lctx.inp_cls);
        return lctx.inp_cls;
    }

    struct ggml_tensor * build_inp_s_copy() {
        lctx.inp_s_copy = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_kv);
        cb(lctx.inp_s_copy, "inp_s_copy", -1);
        ggml_set_input(lctx.inp_s_copy);
        return lctx.inp_s_copy;
    }

    struct ggml_tensor * build_inp_s_mask() {
        lctx.inp_s_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, 1, n_kv);
        cb(lctx.inp_s_mask, "inp_s_mask", -1);
        ggml_set_input(lctx.inp_s_mask);
        return lctx.inp_s_mask;
    }

    struct ggml_cgraph * append_pooling(struct ggml_cgraph * gf) {
        // find result_norm tensor for input
        struct ggml_tensor * inp = nullptr;
        for (int i = ggml_graph_n_nodes(gf) - 1; i >= 0; --i) {
            inp = ggml_graph_node(gf, i);
            if (strcmp(inp->name, "result_norm") == 0 || strcmp(inp->name, "result_embd") == 0) {
                break;
            } else {
                inp = nullptr;
            }
        }
        GGML_ASSERT(inp != nullptr && "missing result_norm/result_embd tensor");

        struct ggml_tensor * cur;

        switch (pooling_type) {
            case LLAMA_POOLING_TYPE_NONE:
                {
                    cur = inp;
                } break;
            case LLAMA_POOLING_TYPE_MEAN:
                {
                    struct ggml_tensor * inp_mean = build_inp_mean();
                    cur = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, inp)), inp_mean);
                } break;
            case LLAMA_POOLING_TYPE_CLS:
            case LLAMA_POOLING_TYPE_LAST:
                {
                    struct ggml_tensor * inp_cls = build_inp_cls();
                    cur = ggml_get_rows(ctx0, inp, inp_cls);
                } break;
            case LLAMA_POOLING_TYPE_RANK:
                {
                    struct ggml_tensor * inp_cls = build_inp_cls();
                    inp = ggml_get_rows(ctx0, inp, inp_cls);

                    // classification head
                    // https://github.com/huggingface/transformers/blob/5af7d41e49bbfc8319f462eb45253dcb3863dfb7/src/transformers/models/roberta/modeling_roberta.py#L1566
                    GGML_ASSERT(model.cls       != nullptr);
                    GGML_ASSERT(model.cls_b     != nullptr);

                    cur = ggml_add (ctx0, ggml_mul_mat(ctx0, model.cls, inp), model.cls_b);
                    cur = ggml_tanh(ctx0, cur);

                    // some models don't have `cls_out`, for example: https://huggingface.co/jinaai/jina-reranker-v1-tiny-en
                    // https://huggingface.co/jinaai/jina-reranker-v1-tiny-en/blob/cb5347e43979c3084a890e3f99491952603ae1b7/modeling_bert.py#L884-L896
                    if (model.cls_out) {
                        GGML_ASSERT(model.cls_out_b != nullptr);

                        cur = ggml_add (ctx0, ggml_mul_mat(ctx0, model.cls_out, cur), model.cls_out_b);
                    }
                } break;
            default:
                {
                    GGML_ABORT("unknown pooling type");
                }
        }

        cb(cur, "result_embd_pooled", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    struct ggml_tensor * llm_build_pos_bucket(bool causal) {
        if (causal) {
            lctx.inp_pos_bucket = ggml_new_tensor_2d(ctx0, GGML_TYPE_I32, n_kv,     n_tokens);
        } else {
            lctx.inp_pos_bucket = ggml_new_tensor_2d(ctx0, GGML_TYPE_I32, n_tokens, n_tokens);
        }

        ggml_set_input(lctx.inp_pos_bucket);
        cb(lctx.inp_pos_bucket, "pos_bucket", -1);

        return lctx.inp_pos_bucket;
    }

    struct ggml_tensor * llm_build_pos_bias(struct ggml_tensor * pos_bucket, struct ggml_tensor * attn_rel_b) {
        struct ggml_tensor * pos_bucket_1d = ggml_view_1d(ctx0, pos_bucket, pos_bucket->ne[0] * pos_bucket->ne[1], 0);
        cb(pos_bucket_1d, "pos_bucket_1d", -1);

        struct ggml_tensor * pos_bias = ggml_get_rows(ctx0, attn_rel_b, pos_bucket_1d);
        cb(pos_bias, "pos_bias", -1);

        pos_bias = ggml_view_3d(ctx0, pos_bias, pos_bias->ne[0], lctx.inp_pos_bucket->ne[0], lctx.inp_pos_bucket->ne[1], ggml_element_size(pos_bias) * pos_bias->ne[0], ggml_element_size(pos_bias) * pos_bias->ne[0] * lctx.inp_pos_bucket->ne[0],  0);
        cb(pos_bias, "pos_bias", -1);

        pos_bias = ggml_permute(ctx0, pos_bias, 2, 0, 1, 3);
        cb(pos_bias, "pos_bias", -1);

        pos_bias = ggml_cont(ctx0, pos_bias);
        cb(pos_bias, "pos_bias", -1);

        return pos_bias;
    }

    struct ggml_tensor * llm_build_inp_embd_enc() {
        const int64_t n_embd = hparams.n_embd;
        lctx.inp_embd_enc = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_outputs_enc);
        ggml_set_input(lctx.inp_embd_enc);
        cb(lctx.inp_embd_enc, "embd_enc", -1);
        return lctx.inp_embd_enc;
    }

    struct ggml_tensor * llm_build_inp_KQ_mask_cross() {
        lctx.inp_KQ_mask_cross = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_outputs_enc, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD));
        ggml_set_input(lctx.inp_KQ_mask_cross);
        cb(lctx.inp_KQ_mask_cross, "KQ_mask_cross", -1);
        return lctx.inp_KQ_mask_cross;
    }

    struct ggml_cgraph * build_llama() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);

        // mutable variable, needed during the last layer of the computation to skip unused tokens
        int32_t n_tokens = this->n_tokens;

        const int64_t n_embd_head = hparams.n_embd_head_v;
        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
        GGML_ASSERT(n_embd_head == hparams.n_rot);

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);

        // inp_pos - contains the positions
        struct ggml_tensor * inp_pos = build_inp_pos();

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();

        const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
        for (int il = 0; il < n_layer; ++il) {
            struct ggml_tensor * inpSA = inpL;

            // norm
            cur = llm_build_norm(ctx0, inpL, hparams,
                    model.layers[il].attn_norm, NULL,
                    LLM_NORM_RMS, cb, il);
            cb(cur, "attn_norm", il);

            // self-attention
            {
                // rope freq factors for llama3; may return nullptr for llama2 and other models
                struct ggml_tensor * rope_factors = build_rope_factors(il);

                // compute Q and K and RoPE them
                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
                cb(Qcur, "Qcur", il);
                if (model.layers[il].bq) {
                    Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
                    cb(Qcur, "Qcur", il);
                }

                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
                cb(Kcur, "Kcur", il);
                if (model.layers[il].bk) {
                    Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
                    cb(Kcur, "Kcur", il);
                }

                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
                cb(Vcur, "Vcur", il);
                if (model.layers[il].bv) {
                    Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
                    cb(Vcur, "Vcur", il);
                }

                Qcur = ggml_rope_ext(
                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, rope_factors,
                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Qcur, "Qcur", il);

                Kcur = ggml_rope_ext(
                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, rope_factors,
                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Kcur, "Kcur", il);

                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                        model.layers[il].wo, model.layers[il].bo,
                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, kq_scale, cb, il);
            }

            if (il == n_layer - 1) {
                // skip computing output for unused tokens
                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
                n_tokens = n_outputs;
                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
            }

            // For Granite architecture
            if (hparams.f_residual_scale) {
                cur = ggml_scale(ctx0, cur, hparams.f_residual_scale);
            }

            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
            cb(ffn_inp, "ffn_inp", il);

            // feed-forward network
            if (model.layers[il].ffn_gate_inp == nullptr) {
                cur = llm_build_norm(ctx0, ffn_inp, hparams,
                        model.layers[il].ffn_norm, NULL,
                        LLM_NORM_RMS, cb, il);
                cb(cur, "ffn_norm", il);

                cur = llm_build_ffn(ctx0, lctx, cur,
                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,
                        model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, NULL,
                        model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
                        NULL,
                        LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
                cb(cur, "ffn_out", il);
            } else {
                // MoE branch
                cur = llm_build_norm(ctx0, ffn_inp, hparams,
                        model.layers[il].ffn_norm, NULL,
                        LLM_NORM_RMS, cb, il);
                cb(cur, "ffn_norm", il);

                cur = llm_build_moe_ffn(ctx0, lctx, cur,
                        model.layers[il].ffn_gate_inp,
                        model.layers[il].ffn_up_exps,
                        model.layers[il].ffn_gate_exps,
                        model.layers[il].ffn_down_exps,
                        nullptr,
                        n_expert, n_expert_used,
                        LLM_FFN_SILU, true,
                        false, 0.0,
                        LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
                        cb, il);
                cb(cur, "ffn_moe_out", il);
            }

            // For Granite architecture
            if (hparams.f_residual_scale) {
                cur = ggml_scale(ctx0, cur, hparams.f_residual_scale);
            }

            cur = ggml_add(ctx0, cur, ffn_inp);
            cb(cur, "ffn_out", il);

            cur = lctx.cvec.apply_to(ctx0, cur, il);
            cb(cur, "l_out", il);

            // input for next layer
            inpL = cur;
        }

        cur = inpL;

        cur = llm_build_norm(ctx0, cur, hparams,
                model.output_norm, NULL,
                LLM_NORM_RMS, cb, -1);
        cb(cur, "result_norm", -1);

        // lm_head
        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);

        // For Granite architecture
        if (hparams.f_logit_scale) {
            cur = ggml_scale(ctx0, cur, 1.0f / hparams.f_logit_scale);
        }

        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    struct ggml_cgraph * build_deci() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);

        // mutable variable, needed during the last layer of the computation to skip unused tokens
        int32_t n_tokens = this->n_tokens;

        const int64_t n_embd_head = hparams.n_embd_head_v;
        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
        GGML_ASSERT(n_embd_head == hparams.n_rot);

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);

        // inp_pos - contains the positions
        struct ggml_tensor * inp_pos = build_inp_pos();

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();

        const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
        for (int il = 0; il < n_layer; ++il) {
            struct ggml_tensor * inpSA = inpL;
            const int64_t n_head_kv = hparams.n_head_kv(il);
            const int64_t n_head    = hparams.n_head(il);

            if (n_head == 0) {
                // attention-free layer of Llama-3_1-Nemotron-51B
                cur = inpL;
            } else {
                // norm
                cur = llm_build_norm(ctx0, inpL, hparams,
                        model.layers[il].attn_norm, NULL,
                        LLM_NORM_RMS, cb, il);
                cb(cur, "attn_norm", il);
            }

            if (n_head > 0 && n_head_kv == 0) {
                // "linear attention" of Llama-3_1-Nemotron-51B
                cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wo, cur);
                cb(cur, "wo", il);
            } else if (n_head > 0) {
                // self-attention
                // rope freq factors for llama3; may return nullptr for llama2 and other models
                struct ggml_tensor * rope_factors = build_rope_factors(il);

                // compute Q and K and RoPE them
                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
                cb(Qcur, "Qcur", il);
                if (model.layers[il].bq) {
                    Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
                    cb(Qcur, "Qcur", il);
                }

                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
                cb(Kcur, "Kcur", il);
                if (model.layers[il].bk) {
                    Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
                    cb(Kcur, "Kcur", il);
                }

                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
                cb(Vcur, "Vcur", il);
                if (model.layers[il].bv) {
                    Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
                    cb(Vcur, "Vcur", il);
                }

                Qcur = ggml_rope_ext(
                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, rope_factors,
                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Qcur, "Qcur", il);

                Kcur = ggml_rope_ext(
                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, rope_factors,
                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Kcur, "Kcur", il);

                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                        model.layers[il].wo, model.layers[il].bo,
                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, kq_scale, cb, il);
            }

            if (il == n_layer - 1) {
                // skip computing output for unused tokens
                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
                n_tokens = n_outputs;
                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
            }

            // For Granite architecture
            if (hparams.f_residual_scale) {
                cur = ggml_scale(ctx0, cur, hparams.f_residual_scale);
            }

            // modified to support attention-free layer of Llama-3_1-Nemotron-51B
            struct ggml_tensor * ffn_inp = cur;
            if (n_head > 0) {
                ffn_inp = ggml_add(ctx0, cur, inpSA);
                cb(ffn_inp, "ffn_inp", il);
            }

            // feed-forward network
            if (model.layers[il].ffn_gate_inp == nullptr) {
                cur = llm_build_norm(ctx0, ffn_inp, hparams,
                        model.layers[il].ffn_norm, NULL,
                        LLM_NORM_RMS, cb, il);
                cb(cur, "ffn_norm", il);

                cur = llm_build_ffn(ctx0, lctx, cur,
                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,
                        model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, NULL,
                        model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
                        NULL,
                        LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
                cb(cur, "ffn_out", il);
            }

            // For Granite architecture
            if (hparams.f_residual_scale) {
                cur = ggml_scale(ctx0, cur, hparams.f_residual_scale);
            }

            cur = ggml_add(ctx0, cur, ffn_inp);
            cb(cur, "ffn_out", il);

            cur = lctx.cvec.apply_to(ctx0, cur, il);
            cb(cur, "l_out", il);

            // input for next layer
            inpL = cur;
        }

        cur = inpL;

        cur = llm_build_norm(ctx0, cur, hparams,
                model.output_norm, NULL,
                LLM_NORM_RMS, cb, -1);
        cb(cur, "result_norm", -1);

        // lm_head
        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);

        // For Granite architecture
        if (hparams.f_logit_scale) {
            cur = ggml_scale(ctx0, cur, 1.0f / hparams.f_logit_scale);
        }

        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    struct ggml_cgraph * build_baichuan() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);

        const int64_t n_embd_head = hparams.n_embd_head_v;
        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
        GGML_ASSERT(n_embd_head == hparams.n_rot);

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);

        // inp_pos - contains the positions
        struct ggml_tensor * inp_pos = model.type == MODEL_7B ? build_inp_pos() : nullptr;

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();

        for (int il = 0; il < n_layer; ++il) {
            struct ggml_tensor * inpSA = inpL;

            cur = llm_build_norm(ctx0, inpL, hparams,
                    model.layers[il].attn_norm, NULL,
                    LLM_NORM_RMS, cb, il);
            cb(cur, "attn_norm", il);

            // self-attention
            {
                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
                cb(Qcur, "Qcur", il);

                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
                cb(Kcur, "Kcur", il);

                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
                cb(Vcur, "Vcur", il);

                switch (model.type) {
                    case MODEL_7B:
                        Qcur = ggml_rope_ext(
                            ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
                            n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                            ext_factor, attn_factor, beta_fast, beta_slow
                        );
                        Kcur = ggml_rope_ext(
                            ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
                            n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                            ext_factor, attn_factor, beta_fast, beta_slow
                        );
                        break;
                    case MODEL_13B:
                        Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd/n_head, n_head, n_tokens);
                        Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd/n_head, n_head, n_tokens);
                        break;
                    default:
                        GGML_ABORT("fatal error");
                }
                cb(Qcur, "Qcur", il);
                cb(Kcur, "Kcur", il);

                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                        model.layers[il].wo, NULL,
                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
            }

            if (il == n_layer - 1) {
                // skip computing output for unused tokens
                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
            }

            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
            cb(ffn_inp, "ffn_inp", il);

            // feed-forward network
            {
                cur = llm_build_norm(ctx0, ffn_inp, hparams,
                        model.layers[il].ffn_norm, NULL,
                        LLM_NORM_RMS, cb, il);
                cb(cur, "ffn_norm", il);

                cur = llm_build_ffn(ctx0, lctx, cur,
                        model.layers[il].ffn_up,   NULL, NULL,
                        model.layers[il].ffn_gate, NULL, NULL,
                        model.layers[il].ffn_down, NULL, NULL,
                        NULL,
                        LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
                cb(cur, "ffn_out", il);
            }

            cur = ggml_add(ctx0, cur, ffn_inp);
            cur = lctx.cvec.apply_to(ctx0, cur, il);
            cb(cur, "l_out", il);

            // input for next layer
            inpL = cur;
        }

        cur = inpL;

        cur = llm_build_norm(ctx0, cur, hparams,
                model.output_norm, NULL,
                LLM_NORM_RMS, cb, -1);
        cb(cur, "result_norm", -1);

        // lm_head
        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    struct ggml_cgraph * build_xverse() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);

        const int64_t n_embd_head = hparams.n_embd_head_v;
        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
        GGML_ASSERT(n_embd_head == hparams.n_rot);

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);

        // inp_pos - contains the positions
        struct ggml_tensor * inp_pos = build_inp_pos();

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();

        for (int il = 0; il < n_layer; ++il) {
            struct ggml_tensor * inpSA = inpL;

            cur = llm_build_norm(ctx0, inpL, hparams,
                    model.layers[il].attn_norm, NULL,
                    LLM_NORM_RMS, cb, il);
            cb(cur, "attn_norm", il);

            // self-attention
            {
                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
                cb(Qcur, "Qcur", il);

                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
                cb(Kcur, "Kcur", il);

                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
                cb(Vcur, "Vcur", il);

                Qcur = ggml_rope_ext(
                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Qcur, "Qcur", il);

                Kcur = ggml_rope_ext(
                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Kcur, "Kcur", il);
                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                        model.layers[il].wo, NULL,
                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
            }

            if (il == n_layer - 1) {
                // skip computing output for unused tokens
                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
                cur   = ggml_get_rows(ctx0,      cur, inp_out_ids);
                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
            }

            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
            cb(ffn_inp, "ffn_inp", il);

            // feed-forward network
            {
                cur = llm_build_norm(ctx0, ffn_inp, hparams,
                        model.layers[il].ffn_norm, NULL,
                        LLM_NORM_RMS, cb, il);
                cb(cur, "ffn_norm", il);

                cur = llm_build_ffn(ctx0, lctx, cur,
                        model.layers[il].ffn_up,   NULL, NULL,
                        model.layers[il].ffn_gate, NULL, NULL,
                        model.layers[il].ffn_down, NULL, NULL,
                        NULL,
                        LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
                cb(cur, "ffn_out", il);
            }

            cur = ggml_add(ctx0, cur, ffn_inp);
            cur = lctx.cvec.apply_to(ctx0, cur, il);
            cb(cur, "l_out", il);

            // input for next layer
            inpL = cur;
        }

        cur = inpL;

        cur = llm_build_norm(ctx0, cur, hparams, model.output_norm, NULL, LLM_NORM_RMS, cb, -1);
        cb(cur, "result_norm", -1);

        // lm_head
        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    struct ggml_cgraph * build_falcon() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);

        const int64_t n_embd_head = hparams.n_embd_head_v;
        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
        GGML_ASSERT(n_embd_head == hparams.n_rot);

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);

        // inp_pos - contains the positions
        struct ggml_tensor * inp_pos = build_inp_pos();

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();

        for (int il = 0; il < n_layer; ++il) {
            struct ggml_tensor * attn_norm;

            attn_norm = llm_build_norm(ctx0, inpL, hparams,
                    model.layers[il].attn_norm,
                    model.layers[il].attn_norm_b,
                    LLM_NORM, cb, il);
            cb(attn_norm, "attn_norm", il);

            // self-attention
            {
                if (model.layers[il].attn_norm_2) {
                    // Falcon-40B
                    cur = llm_build_norm(ctx0, inpL, hparams,
                            model.layers[il].attn_norm_2,
                            model.layers[il].attn_norm_2_b,
                            LLM_NORM, cb, il);
                    cb(cur, "attn_norm_2", il);
                } else {
                    cur = attn_norm;
                }

                cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wqkv, cur);
                cb(cur, "wqkv", il);

                struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
                struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
                struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));

                cb(Qcur, "Qcur", il);
                cb(Kcur, "Kcur", il);
                cb(Vcur, "Vcur", il);

                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
                Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);

                // using mode = 2 for neox mode
                Qcur = ggml_rope_ext(
                    ctx0, Qcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig,
                    freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Qcur, "Qcur", il);

                Kcur = ggml_rope_ext(
                    ctx0, Kcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig,
                    freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Kcur, "Kcur", il);

                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                        model.layers[il].wo, NULL,
                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
            }

            if (il == n_layer - 1) {
                // skip computing output for unused tokens
                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
                cur       = ggml_get_rows(ctx0,       cur, inp_out_ids);
                inpL      = ggml_get_rows(ctx0,      inpL, inp_out_ids);
                attn_norm = ggml_get_rows(ctx0, attn_norm, inp_out_ids);
            }

            struct ggml_tensor * ffn_inp = cur;

            // feed forward
            {
                cur = llm_build_ffn(ctx0, lctx, attn_norm, // !! use the attn norm, not the result
                        model.layers[il].ffn_up,   NULL, NULL,
                        NULL,                      NULL, NULL,
                        model.layers[il].ffn_down, NULL, NULL,
                        NULL,
                        LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
                cb(cur, "ffn_out", il);
            }

            cur = ggml_add(ctx0, cur, ffn_inp);
            cur = ggml_add(ctx0, cur, inpL);
            cur = lctx.cvec.apply_to(ctx0, cur, il);
            cb(cur, "l_out", il);

            // input for next layer
            inpL = cur;
        }

        cur = inpL;

        // norm
        cur = llm_build_norm(ctx0, cur, hparams,
                model.output_norm,
                model.output_norm_b,
                LLM_NORM, cb, -1);
        cb(cur, "result_norm", -1);

        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    struct ggml_cgraph * build_grok() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);

        // mutable variable, needed during the last layer of the computation to skip unused tokens
        int32_t n_tokens = this->n_tokens;

        const int64_t n_embd_head = hparams.n_embd_head_v;
        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
        GGML_ASSERT(n_embd_head == hparams.n_rot);

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);

        // multiply by embedding_multiplier_scale of 78.38367176906169
        inpL = ggml_scale(ctx0, inpL, 78.38367176906169f);

        // inp_pos - contains the positions
        struct ggml_tensor * inp_pos = build_inp_pos();

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();

        for (int il = 0; il < n_layer; ++il) {
            struct ggml_tensor * inpSA = inpL;

            // norm
            cur = llm_build_norm(ctx0, inpL, hparams,
                    model.layers[il].attn_norm, NULL,
                    LLM_NORM_RMS, cb, il);
            cb(cur, "attn_norm", il);


            // self-attention
            {
                // compute Q and K and RoPE them
                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
                cb(Qcur, "Qcur", il);
                if (model.layers[il].bq) {
                    Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
                    cb(Qcur, "Qcur", il);
                }

                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
                cb(Kcur, "Kcur", il);
                if (model.layers[il].bk) {
                    Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
                    cb(Kcur, "Kcur", il);
                }

                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
                cb(Vcur, "Vcur", il);
                if (model.layers[il].bv) {
                    Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
                    cb(Vcur, "Vcur", il);
                }

                Qcur = ggml_rope_ext(
                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Qcur, "Qcur", il);

                Kcur = ggml_rope_ext(
                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Kcur, "Kcur", il);

                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                        model.layers[il].wo, model.layers[il].bo,
                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f, cb, il);
            }

            if (il == n_layer - 1) {
                // skip computing output for unused tokens
                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
                n_tokens = n_outputs;
                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
            }

            // Grok
            // if attn_out_norm is present then apply it before adding the input
            if (model.layers[il].attn_out_norm) {
                cur = llm_build_norm(ctx0, cur, hparams,
                        model.layers[il].attn_out_norm, NULL,
                        LLM_NORM_RMS, cb, il);
                cb(cur, "attn_out_norm", il);
            }

            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
            cb(ffn_inp, "ffn_inp", il);

            // feed-forward network
            // MoE branch
            cur = llm_build_norm(ctx0, ffn_inp, hparams,
                    model.layers[il].ffn_norm, NULL,
                    LLM_NORM_RMS, cb, il);
            cb(cur, "ffn_norm", il);

            cur = llm_build_moe_ffn(ctx0, lctx, cur,
                    model.layers[il].ffn_gate_inp,
                    model.layers[il].ffn_up_exps,
                    model.layers[il].ffn_gate_exps,
                    model.layers[il].ffn_down_exps,
                    nullptr,
                    n_expert, n_expert_used,
                    LLM_FFN_GELU, true,
                    false, 0.0,
                    LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
                    cb, il);
            cb(cur, "ffn_moe_out", il);

            // Grok
            // if layer_out_norm is present then apply it before adding the input
            // Idea: maybe ffn_out_norm is a better name
            if (model.layers[il].layer_out_norm) {
                cur = llm_build_norm(ctx0, cur, hparams,
                        model.layers[il].layer_out_norm, NULL,
                        LLM_NORM_RMS, cb, il);
                cb(cur, "layer_out_norm", il);
            }

            cur = ggml_add(ctx0, cur, ffn_inp);
            cb(cur, "ffn_out", il);

            cur = lctx.cvec.apply_to(ctx0, cur, il);
            cb(cur, "l_out", il);

            // input for next layer
            inpL = cur;
        }

        cur = inpL;

        cur = llm_build_norm(ctx0, cur, hparams,
                model.output_norm, NULL,
                LLM_NORM_RMS, cb, -1);
        cb(cur, "result_norm", -1);

        // lm_head
        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);

        // Grok
        // multiply logits by output_multiplier_scale of 0.5773502691896257

        cur = ggml_scale(ctx0, cur, 0.5773502691896257f);

        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    struct ggml_cgraph * build_dbrx() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);

        // mutable variable, needed during the last layer of the computation to skip unused tokens
        int32_t n_tokens = this->n_tokens;

        const int64_t n_embd_head = hparams.n_embd_head_v;
        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
        GGML_ASSERT(n_embd_head == hparams.n_rot);

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);

        // inp_pos - contains the positions
        struct ggml_tensor * inp_pos = build_inp_pos();

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();

        for (int il = 0; il < n_layer; ++il) {
            struct ggml_tensor * inpSA = inpL;

            // norm
            cur = llm_build_norm(ctx0, inpL, hparams,
                                 model.layers[il].attn_norm, NULL,
                                 LLM_NORM, cb, il);
            cb(cur, "attn_norm", il);

            // self-attention
            {
                struct ggml_tensor * Qcur = nullptr;
                struct ggml_tensor * Kcur = nullptr;
                struct ggml_tensor * Vcur = nullptr;

                cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wqkv, cur);
                cb(cur, "wqkv", il);

                cur = ggml_clamp(ctx0, cur, -hparams.f_clamp_kqv, hparams.f_clamp_kqv);
                cb(cur, "wqkv_clamped", il);

                Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
                Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
                Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));

                cb(Qcur, "Qcur", il);
                cb(Kcur, "Kcur", il);
                cb(Vcur, "Vcur", il);

                Qcur = ggml_rope_ext(
                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Qcur, "Qcur", il);

                Kcur = ggml_rope_ext(
                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Kcur, "Kcur", il);

                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                        model.layers[il].wo, NULL,
                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
            }

            if (il == n_layer - 1) {
                // skip computing output for unused tokens
                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
                n_tokens = n_outputs;
                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
            }

            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
            cb(ffn_inp, "ffn_inp", il);

            // feed-forward network
            // MoE branch
            cur = llm_build_norm(ctx0, ffn_inp, hparams,
                                 model.layers[il].attn_out_norm, NULL,
                                 LLM_NORM, cb, il);
            cb(cur, "attn_out_norm", il);

            cur = llm_build_moe_ffn(ctx0, lctx, cur,
                    model.layers[il].ffn_gate_inp,
                    model.layers[il].ffn_up_exps,
                    model.layers[il].ffn_gate_exps,
                    model.layers[il].ffn_down_exps,
                    nullptr,
                    n_expert, n_expert_used,
                    LLM_FFN_SILU, true,
                    false, 0.0,
                    LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
                    cb, il);
            cb(cur, "ffn_moe_out", il);

            cur = ggml_add(ctx0, cur, ffn_inp);
            cb(cur, "ffn_out", il);

            cur = lctx.cvec.apply_to(ctx0, cur, il);
            cb(cur, "l_out", il);

            // input for next layer
            inpL = cur;
        }

        cur = inpL;

        cur = llm_build_norm(ctx0, cur, hparams,
                             model.output_norm, NULL,
                             LLM_NORM, cb, -1);
        cb(cur, "result_norm", -1);

        // lm_head
        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);

        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    struct ggml_cgraph * build_starcoder() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);

        const int64_t n_embd_head = hparams.n_embd_head_v;
        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);

        // inp_pos - contains the positions
        struct ggml_tensor * inp_pos = build_inp_pos();

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();

        struct ggml_tensor * pos = ggml_get_rows(ctx0, model.pos_embd, inp_pos);
        cb(pos, "pos_embd", -1);

        inpL = ggml_add(ctx0, inpL, pos);
        cb(inpL, "inpL", -1);

        for (int il = 0; il < n_layer; ++il) {
            cur = llm_build_norm(ctx0, inpL, hparams,
                    model.layers[il].attn_norm,
                    model.layers[il].attn_norm_b,
                    LLM_NORM, cb, il);
            cb(cur, "attn_norm", il);

            // self-attention
            {
                cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wqkv, cur);
                cb(cur, "wqkv", il);

                cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
                cb(cur, "bqkv", il);

                struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
                struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
                struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));

                cb(Qcur, "Qcur", il);
                cb(Kcur, "Kcur", il);
                cb(Vcur, "Vcur", il);

                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);

                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                        model.layers[il].wo, model.layers[il].bo,
                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
            }

            if (il == n_layer - 1) {
                // skip computing output for unused tokens
                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
                cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);
                inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
            }

            // add the input
            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
            cb(ffn_inp, "ffn_inp", il);

            // FF
            {
                cur = llm_build_norm(ctx0, ffn_inp, hparams,
                        model.layers[il].ffn_norm,
                        model.layers[il].ffn_norm_b,
                        LLM_NORM, cb, il);
                cb(cur, "ffn_norm", il);

                cur = llm_build_ffn(ctx0, lctx, cur,
                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,
                        NULL,                      NULL,                        NULL,
                        model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
                        NULL,
                        LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
                cb(cur, "ffn_out", il);
            }

            cur = ggml_add(ctx0, cur, ffn_inp);
            cur = lctx.cvec.apply_to(ctx0, cur, il);
            cb(cur, "l_out", il);

            // input for next layer
            inpL = cur;
        }

        cur = llm_build_norm(ctx0, inpL, hparams,
                model.output_norm,
                model.output_norm_b,
                LLM_NORM, cb, -1);
        cb(cur, "result_norm", -1);

        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    struct ggml_cgraph * build_refact() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);

        const int64_t n_embd_head = hparams.n_embd_head_v;
        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();

        for (int il = 0; il < n_layer; ++il) {
            struct ggml_tensor * inpSA = inpL;

            cur = llm_build_norm(ctx0, inpL, hparams,
                    model.layers[il].attn_norm, NULL,
                    LLM_NORM_RMS, cb, il);
            cb(cur, "attn_norm", il);

            // self-attention
            {
                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
                cb(Qcur, "Qcur", il);

                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
                cb(Kcur, "Kcur", il);

                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
                cb(Vcur, "Vcur", il);

                Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
                cb(Kcur, "Kcur", il);

                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
                cb(Qcur, "Qcur", il);

                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                        model.layers[il].wo, NULL,
                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
            }

            if (il == n_layer - 1) {
                // skip computing output for unused tokens
                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
            }

            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
            cb(ffn_inp, "ffn_inp", il);

            // feed-forward network
            {
                cur = llm_build_norm(ctx0, ffn_inp, hparams,
                        model.layers[il].ffn_norm, NULL,
                        LLM_NORM_RMS, cb, il);
                cb(cur, "ffn_norm", il);

                cur = llm_build_ffn(ctx0, lctx, cur,
                        model.layers[il].ffn_up,   NULL, NULL,
                        model.layers[il].ffn_gate, NULL, NULL,
                        model.layers[il].ffn_down, NULL, NULL,
                        NULL,
                        LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
                cb(cur, "ffn_out", il);
            }

            cur = ggml_add(ctx0, cur, ffn_inp);
            cur = lctx.cvec.apply_to(ctx0, cur, il);
            cb(cur, "l_out", il);

            // input for next layer
            inpL = cur;
        }

        cur = inpL;

        cur = llm_build_norm(ctx0, cur, hparams,
                model.output_norm, NULL,
                LLM_NORM_RMS, cb, -1);
        cb(cur, "result_norm", -1);

        // lm_head
        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    struct ggml_cgraph * build_bert() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);

        const int64_t n_embd_head = hparams.n_embd_head_v;
        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();

        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;
        struct ggml_tensor * inp_pos = nullptr;

        if (model.arch != LLM_ARCH_JINA_BERT_V2) {
            inp_pos = build_inp_pos();
        }

        // construct input embeddings (token, type, position)
        inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);

        // token types are hardcoded to zero ("Sentence A")
        struct ggml_tensor * type_row0 = ggml_view_1d(ctx0, model.type_embd, n_embd, 0);
        inpL = ggml_add(ctx0, inpL, type_row0);
        if (model.arch == LLM_ARCH_BERT) {
            inpL = ggml_add(ctx0, ggml_get_rows(ctx0, model.pos_embd, inp_pos), inpL);
        }
        cb(inpL, "inp_embd", -1);

        // embed layer norm
        inpL = llm_build_norm(ctx0, inpL, hparams, model.tok_norm, model.tok_norm_b, LLM_NORM, cb, -1);
        cb(inpL, "inp_norm", -1);

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask = build_inp_KQ_mask(false);

        // iterate layers
        for (int il = 0; il < n_layer; ++il) {
            struct ggml_tensor * cur = inpL;

            struct ggml_tensor * Qcur;
            struct ggml_tensor * Kcur;
            struct ggml_tensor * Vcur;

            // self-attention
            if (model.arch == LLM_ARCH_BERT || model.arch == LLM_ARCH_JINA_BERT_V2) {
                Qcur = ggml_add(ctx0, llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur), model.layers[il].bq);
                cb(Qcur, "Qcur", il);

                if (model.layers[il].attn_q_norm) {
                    Qcur = llm_build_norm(ctx0, Qcur, hparams,
                            model.layers[il].attn_q_norm,
                            model.layers[il].attn_q_norm_b,
                            LLM_NORM, cb, il);
                }

                Kcur = ggml_add(ctx0, llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur), model.layers[il].bk);
                cb(Kcur, "Kcur", il);

                if (model.layers[il].attn_k_norm) {
                    Kcur = llm_build_norm(ctx0, Kcur, hparams,
                            model.layers[il].attn_k_norm,
                            model.layers[il].attn_k_norm_b,
                            LLM_NORM, cb, il);
                }
                Vcur = ggml_add(ctx0, llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur), model.layers[il].bv);
                cb(Vcur, "Vcur", il);

                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
                Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
            } else {
                // compute Q and K and RoPE them
                cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wqkv, cur);
                cb(cur, "wqkv", il);

                Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
                Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
                Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));

                cb(Qcur, "Qcur", il);
                cb(Kcur, "Kcur", il);
                cb(Vcur, "Vcur", il);

                Qcur = ggml_rope_ext(
                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens), inp_pos, nullptr,
                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Qcur, "Qcur", il);

                Kcur = ggml_rope_ext(
                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Kcur, "Kcur", il);
            }

            struct ggml_tensor * q =                 ggml_permute(ctx0, Qcur, 0, 2, 1, 3);
            struct ggml_tensor * k = ggml_cont(ctx0, ggml_permute(ctx0, Kcur, 0, 2, 1, 3));

            struct ggml_tensor * kq = ggml_mul_mat(ctx0, k, q);
            cb(kq, "kq", il);

            kq = ggml_soft_max_ext(ctx0, kq, KQ_mask, 1.0f/sqrtf(float(n_embd_head)), hparams.f_max_alibi_bias);
            cb(kq, "kq_soft_max_ext", il);

            struct ggml_tensor * v = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_reshape_2d(ctx0, Vcur, n_embd_gqa, n_tokens)));
            cb(v, "v", il);

            struct ggml_tensor * kqv = ggml_mul_mat(ctx0, ggml_reshape_3d(ctx0, v, n_tokens, n_embd_head, n_head_kv), kq);
            cb(kqv, "kqv", il);

            struct ggml_tensor * kqv_merged = ggml_permute(ctx0, kqv, 0, 2, 1, 3);
            cb(kqv_merged, "kqv_merged", il);

            cur = ggml_cont_2d(ctx0, kqv_merged, n_embd_gqa, n_tokens);
            cb(cur, "kqv_merged_cont", il);

            ggml_build_forward_expand(gf, cur);

            cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wo, cur);
            if (model.layers[il].bo) {
                cb(cur, "kqv_wo", il);
            }

            if (model.layers[il].bo) {
                cur = ggml_add(ctx0, cur, model.layers[il].bo);
            }
            cb(cur, "kqv_out", il);

            if (il == n_layer - 1 && pooling_type == LLAMA_POOLING_TYPE_NONE) {
                // skip computing output for unused tokens
                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
                cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);
                inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
            }

            // re-add the layer input
            cur = ggml_add(ctx0, cur, inpL);

            // attention layer norm
            cur = llm_build_norm(ctx0, cur, hparams, model.layers[il].attn_out_norm, model.layers[il].attn_out_norm_b, LLM_NORM, cb, il);

            if (model.layers[il].attn_norm_2 != nullptr) {
                cur = ggml_add(ctx0, cur, inpL); // re-add the layer input
                cur = llm_build_norm(ctx0, cur, hparams, model.layers[il].attn_norm_2, model.layers[il].attn_norm_2_b, LLM_NORM, cb, il);
            }

            struct ggml_tensor * ffn_inp = cur;
            cb(ffn_inp, "ffn_inp", il);

            // feed-forward network
            if (model.arch == LLM_ARCH_BERT) {
                cur = llm_build_ffn(ctx0, lctx, cur,
                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,
                        NULL,                      NULL,                        NULL,
                        model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
                        NULL,
                        LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
            } else if (model.arch == LLM_ARCH_JINA_BERT_V2) {
                cur = llm_build_ffn(ctx0, lctx, cur,
                        model.layers[il].ffn_up,   NULL,                        NULL,
                        model.layers[il].ffn_gate, NULL,                        NULL,
                        model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
                        NULL,
                        LLM_FFN_GELU, LLM_FFN_PAR, cb, il);
            } else {
                cur = llm_build_ffn(ctx0, lctx, cur,
                        model.layers[il].ffn_up,   NULL, NULL,
                        model.layers[il].ffn_gate, NULL, NULL,
                        model.layers[il].ffn_down, NULL, NULL,
                        NULL,
                        LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
            }
            cb(cur, "ffn_out", il);

            // attentions bypass the intermediate layer
            cur = ggml_add(ctx0, cur, ffn_inp);

            // output layer norm
            cur = llm_build_norm(ctx0, cur, hparams, model.layers[il].layer_out_norm, model.layers[il].layer_out_norm_b, LLM_NORM, cb, il);

            // input for next layer
            inpL = cur;
        }

        cur = inpL;

        cb(cur, "result_embd", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    struct ggml_cgraph * build_bloom() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);

        const int64_t n_embd_head = hparams.n_embd_head_v;
        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();

        inpL = llm_build_norm(ctx0, inpL, hparams,
                model.tok_norm,
                model.tok_norm_b,
                LLM_NORM, cb, -1);
        cb(inpL, "inp_norm", -1);

        for (int il = 0; il < n_layer; ++il) {
            cur = llm_build_norm(ctx0, inpL, hparams,
                    model.layers[il].attn_norm,
                    model.layers[il].attn_norm_b,
                    LLM_NORM, cb, il);
            cb(cur, "attn_norm", il);

            // self-attention
            {
                cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wqkv, cur);
                cb(cur, "wqkv", il);

                cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
                cb(cur, "bqkv", il);

                struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
                struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
                struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));

                cb(Qcur, "Qcur", il);
                cb(Kcur, "Kcur", il);
                cb(Vcur, "Vcur", il);

                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);

                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                        model.layers[il].wo, model.layers[il].bo,
                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
            }

            if (il == n_layer - 1) {
                // skip computing output for unused tokens
                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
                cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);
                inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
            }

            // Add the input
            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
            cb(ffn_inp, "ffn_inp", il);

            // FF
            {
                cur = llm_build_norm(ctx0, ffn_inp, hparams,
                        model.layers[il].ffn_norm,
                        model.layers[il].ffn_norm_b,
                        LLM_NORM, cb, il);
                cb(cur, "ffn_norm", il);

                cur = llm_build_ffn(ctx0, lctx, cur,
                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,
                        NULL,                      NULL,                        NULL,
                        model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
                        NULL,
                        LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
                cb(cur, "ffn_out", il);
            }

            cur = ggml_add(ctx0, cur, ffn_inp);
            cur = lctx.cvec.apply_to(ctx0, cur, il);
            cb(cur, "l_out", il);

            // input for next layer
            inpL = cur;
        }

        cur = llm_build_norm(ctx0, inpL, hparams,
                model.output_norm,
                model.output_norm_b,
                LLM_NORM, cb, -1);
        cb(cur, "result_norm", -1);

        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    struct ggml_cgraph * build_mpt() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);

        const int64_t n_embd_head = hparams.n_embd_head_v;
        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);

        struct ggml_tensor * cur;
        struct ggml_tensor * pos;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();

        if (model.pos_embd) {
            // inp_pos - contains the positions
            struct ggml_tensor * inp_pos = build_inp_pos();
            pos = ggml_get_rows(ctx0, model.pos_embd, inp_pos);
            cb(pos, "pos_embd", -1);

            inpL = ggml_add(ctx0, inpL, pos);
            cb(inpL, "inpL", -1);
        }

        for (int il = 0; il < n_layer; ++il) {
            struct ggml_tensor * attn_norm;

            attn_norm = llm_build_norm(ctx0, inpL, hparams,
                    model.layers[il].attn_norm,
                    model.layers[il].attn_norm_b,
                    LLM_NORM, cb, il);
            cb(attn_norm, "attn_norm", il);

            // self-attention
            {
                cur = attn_norm;

                cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wqkv, cur);
                cb(cur, "wqkv", il);

                if (model.layers[il].bqkv){
                    cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
                    cb(cur, "bqkv", il);
                }

                if (hparams.f_clamp_kqv > 0.0f) {
                    cur = ggml_clamp(ctx0, cur, -hparams.f_clamp_kqv, hparams.f_clamp_kqv);
                    cb(cur, "wqkv_clamped", il);
                }

                struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
                struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
                struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));

                cb(Qcur, "Qcur", il);
                cb(Kcur, "Kcur", il);
                cb(Vcur, "Vcur", il);

                // Q/K Layernorm
                if (model.layers[il].attn_q_norm) {
                    Qcur = llm_build_norm(ctx0, Qcur, hparams,
                            model.layers[il].attn_q_norm,
                            model.layers[il].attn_q_norm_b,
                            LLM_NORM, cb, il);
                    cb(Qcur, "Qcur", il);

                    Kcur = llm_build_norm(ctx0, Kcur, hparams,
                            model.layers[il].attn_k_norm,
                            model.layers[il].attn_k_norm_b,
                            LLM_NORM, cb, il);
                    cb(Kcur, "Kcur", il);

                    Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
                    Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);

                    cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                            model.layers[il].wo, model.layers[il].bo,
                            Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
                } else {
                    Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);

                    cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                            model.layers[il].wo, model.layers[il].bo,
                            Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
                }
            }

            if (il == n_layer - 1) {
                // skip computing output for unused tokens
                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
                cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);
                inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
            }

            // Add the input
            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
            cb(ffn_inp, "ffn_inp", il);

            // feed forward
            {
                cur = llm_build_norm(ctx0, ffn_inp, hparams,
                        model.layers[il].ffn_norm,
                        model.layers[il].ffn_norm_b,
                        LLM_NORM, cb, il);
                cb(cur, "ffn_norm", il);
                cur = llm_build_ffn(ctx0, lctx, cur,
                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,
                        NULL,                      NULL,                        NULL,
                        model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
                        model.layers[il].ffn_act,
                        LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
                cb(cur, "ffn_out", il);
            }

            cur = ggml_add(ctx0, cur, ffn_inp);
            cur = lctx.cvec.apply_to(ctx0, cur, il);
            cb(cur, "l_out", il);

            // input for next layer
            inpL = cur;
        }

        cur = inpL;

        cur = llm_build_norm(ctx0, cur, hparams,
                model.output_norm,
                model.output_norm_b,
                LLM_NORM, cb, -1);
        cb(cur, "result_norm", -1);

        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    struct ggml_cgraph * build_stablelm() {
        struct ggml_cgraph * gf = ggml_new_graph(ctx0);

        const int64_t n_embd_head = hparams.n_embd_head_v;
        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);

        // inp_pos - contains the positions
        struct ggml_tensor * inp_pos = build_inp_pos();

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();

        for (int il = 0; il < n_layer; ++il) {


            // norm
            cur = llm_build_norm(ctx0, inpL, hparams,
                    model.layers[il].attn_norm,
                    model.layers[il].attn_norm_b,
                    LLM_NORM, cb, il);
            cb(cur, "attn_norm", il);

            struct ggml_tensor * inpSA = cur;

            // self-attention
            {
                // compute Q and K and RoPE them
                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
                cb(Qcur, "Qcur", il);
                if (model.layers[il].bq) {
                    Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
                    cb(Qcur, "Qcur", il);
                }

                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
                cb(Kcur, "Kcur", il);
                if (model.layers[il].bk) {
                    Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
                    cb(Kcur, "Kcur", il);
                }

                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
                cb(Vcur, "Vcur", il);
                if (model.layers[il].bv) {
                    Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
                    cb(Vcur, "Vcur", il);
                }

                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
                cb(Qcur, "Qcur", il);
                Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
                cb(Kcur, "Kcur", il);

                if (model.layers[il].attn_q_norm) {
                    Qcur = llm_build_norm(ctx0, Qcur, hparams,
                            model.layers[il].attn_q_norm,
                            NULL,
                            LLM_NORM, cb, il);
                    cb(Qcur, "Qcur", il);
                }
                if (model.layers[il].attn_k_norm) {
                    Kcur = llm_build_norm(ctx0, Kcur, hparams,
                            model.layers[il].attn_k_norm,
                            NULL,
                            LLM_NORM, cb, il);
                    cb(Kcur, "Kcur", il);
                }


                Qcur = ggml_rope_ext(
                    ctx0, Qcur, inp_pos, nullptr,
                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Qcur, "Qcur", il);

                Kcur = ggml_rope_ext(
                    ctx0, Kcur, inp_pos, nullptr,
                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Kcur, "Kcur", il);

                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                        model.layers[il].wo, NULL,
                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
            }

            if (il == n_layer - 1) {
                // skip computing output for unused tokens
                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
                inpL  = ggml_get_rows(ctx0,  inpL, inp_out_ids);
                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
            }

            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
            cb(ffn_inp, "ffn_inp", il);

            // feed-forward network
            {
                if (model.layers[il].ffn_norm) {
                    cur = llm_build_norm(ctx0, ffn_inp, hparams,
                            model.layers[il].ffn_norm,
                            model.layers[il].ffn_norm_b,
                            LLM_NORM, cb, il);
                    cb(cur, "ffn_norm", il);
                } else {
                    // parallel residual
                    cur = inpSA;
                }
                cur = llm_build_ffn(ctx0, lctx, cur,
                        model.layers[il].ffn_up,   NULL, NULL,
                        model.layers[il].ffn_gate, NULL, NULL,
                        model.layers[il].ffn_down, NULL, NULL,
                        NULL,
                        LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
                cb(cur, "ffn_out", il);
            }

            cur = ggml_add(ctx0, cur, ffn_inp);
            cur = lctx.cvec.apply_to(ctx0, cur, il);
            cb(cur, "l_out", il);

            // input for next layer
            inpL = cur;
        }

        cur = inpL;

        cur = llm_build_norm(ctx0, cur, hparams,
                model.output_norm,
                model.output_norm_b,
                LLM_NORM, cb, -1);
        cb(cur, "result_norm", -1);

        // lm_head
        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    struct ggml_cgraph * build_qwen() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);

        const int64_t n_embd_head = hparams.n_embd_head_v;
        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);

        // inp_pos - contains the positions
        struct ggml_tensor * inp_pos = build_inp_pos();

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();

        for (int il = 0; il < n_layer; ++il) {
            struct ggml_tensor * inpSA = inpL;

            cur = llm_build_norm(ctx0, inpL, hparams,
                    model.layers[il].attn_norm, NULL,
                    LLM_NORM_RMS, cb, il);
            cb(cur, "attn_norm", il);

            // self-attention
            {
                cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wqkv, cur);
                cb(cur, "wqkv", il);

                cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
                cb(cur, "bqkv", il);

                struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
                struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
                struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 2*sizeof(float)*(n_embd)));

                cb(Qcur, "Qcur", il);
                cb(Kcur, "Kcur", il);
                cb(Vcur, "Vcur", il);

                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
                Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);

                // using mode = 2 for neox mode
                Qcur = ggml_rope_ext(
                    ctx0, Qcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig,
                    freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Qcur, "Qcur", il);

                Kcur = ggml_rope_ext(
                    ctx0, Kcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig,
                    freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Kcur, "Kcur", il);

                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                        model.layers[il].wo, NULL,
                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
            }

            if (il == n_layer - 1) {
                // skip computing output for unused tokens
                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
            }

            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
            cb(ffn_inp, "ffn_inp", il);

            // feed-forward forward
            {
                cur = llm_build_norm(ctx0, ffn_inp, hparams,
                        model.layers[il].ffn_norm, NULL,
                        LLM_NORM_RMS, cb, il);
                cb(cur, "ffn_norm", il);

                cur = llm_build_ffn(ctx0, lctx, cur,
                        model.layers[il].ffn_up,   NULL, NULL,
                        model.layers[il].ffn_gate, NULL, NULL,
                        model.layers[il].ffn_down, NULL, NULL,
                        NULL,
                        LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
                cb(cur, "ffn_out", il);
            }

            cur = ggml_add(ctx0, cur, ffn_inp);
            cur = lctx.cvec.apply_to(ctx0, cur, il);
            cb(cur, "l_out", il);

            // input for next layer
            inpL = cur;
        }

        cur = inpL;

        cur = llm_build_norm(ctx0, cur, hparams,
                model.output_norm, NULL,
                LLM_NORM_RMS, cb, -1);
        cb(cur, "result_norm", -1);

        // lm_head
        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    struct ggml_cgraph * build_qwen2() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);

        const int64_t n_embd_head = hparams.n_embd_head_v;
        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
        GGML_ASSERT(n_embd_head == hparams.n_rot);

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);

        // inp_pos - contains the positions
        struct ggml_tensor * inp_pos = build_inp_pos();

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();

        for (int il = 0; il < n_layer; ++il) {
            struct ggml_tensor * inpSA = inpL;

            // norm
            cur = llm_build_norm(ctx0, inpL, hparams,
                    model.layers[il].attn_norm, NULL,
                    LLM_NORM_RMS, cb, il);
            cb(cur, "attn_norm", il);

            // self-attention
            {
                // compute Q and K and RoPE them
                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
                cb(Qcur, "Qcur", il);
                Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
                cb(Qcur, "Qcur", il);

                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
                cb(Kcur, "Kcur", il);
                Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
                cb(Kcur, "Kcur", il);

                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
                cb(Vcur, "Vcur", il);
                Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
                cb(Vcur, "Vcur", il);

                Qcur = ggml_rope_ext(
                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens), inp_pos, nullptr,
                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Qcur, "Qcur", il);

                Kcur = ggml_rope_ext(
                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Kcur, "Kcur", il);

                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                        model.layers[il].wo, model.layers[il].bo,
                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
            }

            if (il == n_layer - 1) {
                // skip computing output for unused tokens
                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
            }

            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
            cb(ffn_inp, "ffn_inp", il);

            // feed-forward network
            cur = llm_build_norm(ctx0, ffn_inp, hparams,
                    model.layers[il].ffn_norm, NULL,
                    LLM_NORM_RMS, cb, il);
            cb(cur, "ffn_norm", il);

            cur = llm_build_ffn(ctx0, lctx, cur,
                    model.layers[il].ffn_up,   NULL, NULL,
                    model.layers[il].ffn_gate, NULL, NULL,
                    model.layers[il].ffn_down, NULL, NULL,
                    NULL,
                    LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
            cb(cur, "ffn_out", il);

            cur = ggml_add(ctx0, cur, ffn_inp);
            cur = lctx.cvec.apply_to(ctx0, cur, il);
            cb(cur, "l_out", il);

            // input for next layer
            inpL = cur;
        }

        cur = inpL;

        cur = llm_build_norm(ctx0, cur, hparams,
                model.output_norm, NULL,
                LLM_NORM_RMS, cb, -1);
        cb(cur, "result_norm", -1);

        // lm_head
        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    struct ggml_cgraph * build_qwen2vl() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
        const int64_t n_embd_head = hparams.n_embd_head_v;
        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
        GGML_ASSERT(n_embd_head == hparams.n_rot);

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);

        // inp_pos - contains the positions
        lctx.inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens * 4);
        cb(lctx.inp_pos, "inp_pos", -1);
        ggml_set_input(lctx.inp_pos);
        struct ggml_tensor * inp_pos = lctx.inp_pos;

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
        int sections[4];
        std::copy(std::begin(hparams.rope_sections), std::begin(hparams.rope_sections) + 4, sections);

        for (int il = 0; il < n_layer; ++il) {
            struct ggml_tensor * inpSA = inpL;

            // norm
            cur = llm_build_norm(ctx0, inpL, hparams,
                    model.layers[il].attn_norm, NULL,
                    LLM_NORM_RMS, cb, il);
            cb(cur, "attn_norm", il);

            // self-attention
            {
                // compute Q and K and RoPE them
                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
                cb(Qcur, "Qcur", il);
                Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
                cb(Qcur, "Qcur", il);

                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
                cb(Kcur, "Kcur", il);
                Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
                cb(Kcur, "Kcur", il);

                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
                cb(Vcur, "Vcur", il);
                Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
                cb(Vcur, "Vcur", il);

                Qcur = ggml_rope_multi(
                    ctx0,
                    ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
                    n_rot, sections, rope_type, n_ctx_orig, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Qcur, "Qcur", il);

                Kcur = ggml_rope_multi(
                    ctx0,
                    ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
                    n_rot, sections, rope_type, n_ctx_orig, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Kcur, "Kcur", il);

                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                        model.layers[il].wo, model.layers[il].bo,
                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
            }

            if (il == n_layer - 1) {
                // skip computing output for unused tokens
                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
            }

            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
            cb(ffn_inp, "ffn_inp", il);

            // feed-forward network
            cur = llm_build_norm(ctx0, ffn_inp, hparams,
                    model.layers[il].ffn_norm, NULL,
                    LLM_NORM_RMS, cb, il);
            cb(cur, "ffn_norm", il);

            cur = llm_build_ffn(ctx0, lctx, cur,
                    model.layers[il].ffn_up,   NULL, NULL,
                    model.layers[il].ffn_gate, NULL, NULL,
                    model.layers[il].ffn_down, NULL, NULL,
                    NULL,
                    LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
            cb(cur, "ffn_out", il);

            cur = ggml_add(ctx0, cur, ffn_inp);
            cur = lctx.cvec.apply_to(ctx0, cur, il);
            cb(cur, "l_out", il);

            // input for next layer
            inpL = cur;
        }

        cur = inpL;

        cur = llm_build_norm(ctx0, cur, hparams,
                model.output_norm, NULL,
                LLM_NORM_RMS, cb, -1);
        cb(cur, "result_norm", -1);

        // lm_head
        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    struct ggml_cgraph * build_qwen2moe() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);

        // mutable variable, needed during the last layer of the computation to skip unused tokens
        int32_t n_tokens = this->n_tokens;

        const int64_t n_embd_head = hparams.n_embd_head_v;
        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
        GGML_ASSERT(n_embd_head == hparams.n_rot);

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);

        // inp_pos - contains the positions
        struct ggml_tensor * inp_pos = build_inp_pos();

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();

        for (int il = 0; il < n_layer; ++il) {
            struct ggml_tensor * inpSA = inpL;

            // norm
            cur = llm_build_norm(ctx0, inpL, hparams,
                    model.layers[il].attn_norm, NULL,
                    LLM_NORM_RMS, cb, il);
            cb(cur, "attn_norm", il);

            // self_attention
            {
                // compute Q and K and RoPE them
                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
                cb(Qcur, "Qcur", il);
                Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
                cb(Qcur, "Qcur", il);

                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
                cb(Kcur, "Kcur", il);
                Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
                cb(Kcur, "Kcur", il);

                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
                cb(Vcur, "Vcur", il);
                Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
                cb(Vcur, "Vcur", il);

                Qcur = ggml_rope_ext(
                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Qcur, "Qcur", il);

                Kcur = ggml_rope_ext(
                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Kcur, "Kcur", il);

                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                        model.layers[il].wo, model.layers[il].bo,
                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
            }

            if (il == n_layer - 1) {
                // skip computing output for unused tokens
                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
                n_tokens = n_outputs;
                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
            }

            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
            cb(ffn_inp, "ffn_inp", il);

            // MoE branch
            cur = llm_build_norm(ctx0, ffn_inp, hparams,
                    model.layers[il].ffn_norm, NULL,
                    LLM_NORM_RMS, cb, il);
            cb(cur, "ffn_norm", il);

            ggml_tensor * moe_out =
                    llm_build_moe_ffn(ctx0, lctx, cur,
                        model.layers[il].ffn_gate_inp,
                        model.layers[il].ffn_up_exps,
                        model.layers[il].ffn_gate_exps,
                        model.layers[il].ffn_down_exps,
                        nullptr,
                        n_expert, n_expert_used,
                        LLM_FFN_SILU, false,
                        false, 0.0,
                        LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
                        cb, il);
            cb(cur, "ffn_moe_out", il);

            // FFN shared expert
            {
                ggml_tensor * cur_gate_inp = llm_build_lora_mm(lctx, ctx0, model.layers[il].ffn_gate_inp_shexp, cur);
                cb(cur_gate_inp, "ffn_shexp_gate_inp", il);

                // sigmoid
                ggml_tensor * cur_gate = ggml_div(ctx0, ggml_silu(ctx0, cur_gate_inp), cur_gate_inp);
                cb(cur_gate, "ffn_shexp_gate", il);

                ggml_tensor * cur_ffn = llm_build_ffn(ctx0, lctx, cur,
                        model.layers[il].ffn_up_shexp,   NULL, NULL,
                        model.layers[il].ffn_gate_shexp, NULL, NULL,
                        model.layers[il].ffn_down_shexp, NULL, NULL,
                        NULL,
                        LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
                cb(cur_ffn, "ffn_shexp", il);

                ggml_tensor * ffn_shexp_out = ggml_mul(ctx0, cur_ffn, cur_gate);
                cb(ffn_shexp_out, "ffn_shexp_out", il);

                moe_out = ggml_add(ctx0, moe_out, ffn_shexp_out);
                cb(moe_out, "ffn_out", il);

                cur = moe_out;
            }

            cur = ggml_add(ctx0, cur, ffn_inp);
            cur = lctx.cvec.apply_to(ctx0, cur, il);
            cb(cur, "l_out", il);

            // input for next layer
            inpL = cur;
        }

        cur = inpL;

        cur = llm_build_norm(ctx0, cur, hparams,
                model.output_norm, NULL,
                LLM_NORM_RMS, cb, -1);
        cb(cur, "result_norm", -1);

        // lm_head
        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    struct ggml_cgraph * build_phi2() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);

        const int64_t n_embd_head = hparams.n_embd_head_v;
        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);

        struct ggml_tensor * cur;
        struct ggml_tensor * attn_norm_output;
        struct ggml_tensor * ffn_output;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);

        // inp_pos - contains the positions
        struct ggml_tensor * inp_pos = build_inp_pos();

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();

        for (int il = 0; il < n_layer; ++il) {
            attn_norm_output = llm_build_norm(ctx0, inpL, hparams,
                    model.layers[il].attn_norm,
                    model.layers[il].attn_norm_b,
                    LLM_NORM, cb, il);
            cb(attn_norm_output, "attn_norm", il);

            // self-attention
            {
                struct ggml_tensor * Qcur = nullptr;
                struct ggml_tensor * Kcur = nullptr;
                struct ggml_tensor * Vcur = nullptr;

                if (model.layers[il].wqkv) {
                    cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wqkv, attn_norm_output);
                    cb(cur, "wqkv", il);

                    cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
                    cb(cur, "bqkv", il);

                    Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
                    Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
                    Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
                } else {
                    Qcur = ggml_add(ctx0, llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, attn_norm_output), model.layers[il].bq);
                    Kcur = ggml_add(ctx0, llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, attn_norm_output), model.layers[il].bk);
                    Vcur = ggml_add(ctx0, llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, attn_norm_output), model.layers[il].bv);
                }

                cb(Qcur, "Qcur", il);
                cb(Kcur, "Kcur", il);
                cb(Vcur, "Vcur", il);

                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
                Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);

                Qcur = ggml_rope_ext(
                    ctx0, Qcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig,
                    freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Qcur, "Qcur", il);

                // with phi2, we scale the Q to avoid precision issues
                // ref: https://github.com/ml-explore/mlx-examples/blob/08e862336ade809bc37d1035f94b359e7d1a5152/phi2/phi2.py#L64-L66
                Qcur = ggml_scale(ctx0, Qcur, 1.0f/sqrtf(float(n_embd_head)));
                cb(Qcur, "Qcur", il);

                Kcur = ggml_rope_ext(
                    ctx0, Kcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig,
                    freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Kcur, "Kcur", il);

                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                        model.layers[il].wo, model.layers[il].bo,
                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f, cb, il);
            }

            if (il == n_layer - 1) {
                // skip computing output for unused tokens
                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
                cur              = ggml_get_rows(ctx0,              cur, inp_out_ids);
                inpL             = ggml_get_rows(ctx0,             inpL, inp_out_ids);
                attn_norm_output = ggml_get_rows(ctx0, attn_norm_output, inp_out_ids);
            }

            // FF
            {
                ffn_output = llm_build_ffn(ctx0, lctx, attn_norm_output,
                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,
                        NULL,                      NULL,                        NULL,
                        model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
                        NULL,
                        LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
                cb(ffn_output, "ffn_out", il);
            }

            cur = ggml_add(ctx0, cur, ffn_output);
            cur = ggml_add(ctx0, cur, inpL);
            cur = lctx.cvec.apply_to(ctx0, cur, il);
            cb(cur, "l_out", il);

            // input for next layer
            inpL = cur;
        }

        cur = llm_build_norm(ctx0, inpL, hparams,
                model.output_norm,
                model.output_norm_b,
                LLM_NORM, cb, -1);
        cb(cur, "result_norm", -1);

        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
        cb(cur, "result_output_no_bias", -1);

        cur = ggml_add(ctx0, cur, model.output_b);
        cb(cur, "result_output", -1);
        ggml_build_forward_expand(gf, cur);
        return gf;
    }

    struct ggml_cgraph * build_phi3() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);

        const int64_t n_embd_head = hparams.n_embd_head_v;
        const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);

        // inp_pos - contains the positions
        struct ggml_tensor * inp_pos = build_inp_pos();

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask = nullptr;
        if (hparams.n_swa == 0) {
            // Phi-4 doesn't use sliding window attention
            KQ_mask = build_inp_KQ_mask();
        } else {
            KQ_mask = build_inp_KQ_mask_swa();
        }

        for (int il = 0; il < n_layer; ++il) {
            auto residual = inpL;

            // self-attention
            {
                // rope freq factors for 128k context
                struct ggml_tensor * rope_factors = build_rope_factors(il);

                struct ggml_tensor* attn_norm_output = llm_build_norm(ctx0, inpL, hparams,
                    model.layers[il].attn_norm,
                    NULL,
                    LLM_NORM_RMS, cb, il);
                cb(attn_norm_output, "attn_norm", il);

                struct ggml_tensor * Qcur = nullptr;
                struct ggml_tensor * Kcur = nullptr;
                struct ggml_tensor * Vcur = nullptr;

                if (model.layers[il].wqkv) {
                    cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wqkv, attn_norm_output);
                    cb(cur, "wqkv", il);

                    Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0 * sizeof(float) * (n_embd)));
                    Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1 * sizeof(float) * (n_embd)));
                    Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1 * sizeof(float) * (n_embd + n_embd_gqa)));
                }
                else {
                    Qcur = ggml_add(ctx0, llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, attn_norm_output), model.layers[il].bq);
                    Kcur = ggml_add(ctx0, llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, attn_norm_output), model.layers[il].bk);
                    Vcur = ggml_add(ctx0, llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, attn_norm_output), model.layers[il].bv);
                }

                cb(Qcur, "Qcur", il);
                cb(Kcur, "Kcur", il);
                cb(Vcur, "Vcur", il);

                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
                Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);

                Qcur = ggml_rope_ext(
                    ctx0, Qcur, inp_pos, rope_factors, n_rot, rope_type, n_ctx_orig,
                    freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Qcur, "Qcur", il);

                Qcur = ggml_scale(ctx0, Qcur, 1.0f / sqrtf(float(n_embd_head)));
                cb(Qcur, "Qcur", il);

                Kcur = ggml_rope_ext(
                    ctx0, Kcur, inp_pos, rope_factors, n_rot, rope_type, n_ctx_orig,
                    freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Kcur, "Kcur", il);

                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                        model.layers[il].wo, model.layers[il].bo,
                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f, cb, il);
            }

            if (il == n_layer - 1) {
                // skip computing output for unused tokens
                struct ggml_tensor* inp_out_ids = build_inp_out_ids();
                cur = ggml_get_rows(ctx0, cur, inp_out_ids);
                residual = ggml_get_rows(ctx0, residual, inp_out_ids);
            }

            cur = ggml_add(ctx0, cur, residual);
            residual = cur;

            cur = llm_build_norm(ctx0, cur, hparams,
                model.layers[il].ffn_norm, NULL,
                LLM_NORM_RMS, cb, il);
            cb(cur, "ffn_norm", il);

            // FF
            // special-case: the up and gate tensors are merged into a single tensor
            // TOOD: support into llm_build_ffn
            {
                cur = llm_build_ffn(ctx0, lctx, cur,
                        model.layers[il].ffn_up,   NULL, NULL,
                        NULL,                      NULL, NULL,
                        model.layers[il].ffn_down, NULL, NULL,
                        NULL,
                        LLM_FFN_SWIGLU, LLM_FFN_SEQ, cb, il);
                cb(cur, "ffn_out", il);
            }

            cur = ggml_add(ctx0, residual, cur);
            cur = lctx.cvec.apply_to(ctx0, cur, il);
            cb(cur, "l_out", il);

            // input for next layer
            inpL = cur;
        }

        cur = llm_build_norm(ctx0, inpL, hparams,
            model.output_norm,
            NULL,
            LLM_NORM_RMS, cb, -1);
        cb(cur, "result_norm", -1);

        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }


    struct ggml_cgraph * build_plamo() {
        struct ggml_cgraph * gf = ggml_new_graph(ctx0);

        const int64_t n_embd_head = hparams.n_embd_head_v;
        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
        GGML_ASSERT(n_embd_head == hparams.n_rot);

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);

        // inp_pos - contains the positions
        struct ggml_tensor * inp_pos = build_inp_pos();

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();

        for (int il = 0; il < n_layer; ++il) {

            // norm
            cur = llm_build_norm(ctx0, inpL, hparams,
                    model.layers[il].attn_norm, NULL,
                    LLM_NORM_RMS, cb, il);
            cb(cur, "attn_norm", il);

            struct ggml_tensor * attention_norm = cur;

            // self-attention
            {
                // compute Q and K and RoPE them
                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
                cb(Qcur, "Qcur", il);

                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
                cb(Kcur, "Kcur", il);

                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
                cb(Vcur, "Vcur", il);

                Qcur = ggml_rope_ext(
                        ctx0, ggml_reshape_3d(ctx0, Qcur, n_rot, n_head,    n_tokens), inp_pos, nullptr,
                        n_embd_head, rope_type, n_ctx_orig, freq_base, freq_scale,
                        ext_factor, attn_factor, beta_fast, beta_slow);
                cb(Qcur, "Qcur", il);

                Kcur = ggml_rope_ext(
                        ctx0, ggml_reshape_3d(ctx0, Kcur, n_rot, n_head_kv, n_tokens), inp_pos, nullptr,
                        n_embd_head, rope_type, n_ctx_orig, freq_base, freq_scale,
                        ext_factor, attn_factor, beta_fast, beta_slow);
                cb(Kcur, "Kcur", il);

                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                        model.layers[il].wo, NULL,
                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
            }
            struct ggml_tensor * sa_out = cur;

            cur = attention_norm;

            if (il == n_layer - 1) {
                // skip computing output for unused tokens
                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
                cur    = ggml_get_rows(ctx0,    cur, inp_out_ids);
                sa_out = ggml_get_rows(ctx0, sa_out, inp_out_ids);
                inpL   = ggml_get_rows(ctx0,   inpL, inp_out_ids);
            }

            // feed-forward network
            {
                cur = llm_build_ffn(ctx0, lctx, cur,
                        model.layers[il].ffn_up,   NULL, NULL,
                        model.layers[il].ffn_gate, NULL, NULL,
                        model.layers[il].ffn_down, NULL, NULL,
                        NULL,
                        LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
                cb(cur, "ffn_out", il);
            }

            cur = ggml_add(ctx0, cur, sa_out);
            cur = ggml_add(ctx0, cur, inpL);
            cur = lctx.cvec.apply_to(ctx0, cur, il);
            cb(cur, "l_out", il);

            // input for next layer
            inpL = cur;
        }

        cur = inpL;

        cur = llm_build_norm(ctx0, cur, hparams,
                model.output_norm, NULL,
                LLM_NORM_RMS, cb, -1);
        cb(cur, "result_norm", -1);

        // lm_head
        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    struct ggml_cgraph * build_gpt2() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);

        const int64_t n_embd_head = hparams.n_embd_head_v;
        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);

        struct ggml_tensor * cur;
        struct ggml_tensor * pos;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);

        // inp_pos - contains the positions
        struct ggml_tensor * inp_pos = build_inp_pos();

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();

        pos = ggml_get_rows(ctx0, model.pos_embd, inp_pos);
        cb(pos, "pos_embd", -1);

        inpL = ggml_add(ctx0, inpL, pos);
        cb(inpL, "inpL", -1);

        for (int il = 0; il < n_layer; ++il) {
            cur = llm_build_norm(ctx0, inpL, hparams,
                    model.layers[il].attn_norm,
                    model.layers[il].attn_norm_b,
                    LLM_NORM, cb, il);
            cb(cur, "attn_norm", il);

            // self-attention
            {
                cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wqkv, cur);
                cb(cur, "wqkv", il);

                cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
                cb(cur, "bqkv", il);

                struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
                struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
                struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));

                cb(Qcur, "Qcur", il);
                cb(Kcur, "Kcur", il);
                cb(Vcur, "Vcur", il);

                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);

                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                        model.layers[il].wo, model.layers[il].bo,
                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
            }

            if (il == n_layer - 1) {
                // skip computing output for unused tokens
                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
                cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);
                inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
            }

            // add the input
            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
            cb(ffn_inp, "ffn_inp", il);

            // FF
            {
                cur = llm_build_norm(ctx0, ffn_inp, hparams,
                        model.layers[il].ffn_norm,
                        model.layers[il].ffn_norm_b,
                        LLM_NORM, cb, il);
                cb(cur, "ffn_norm", il);

                cur = llm_build_ffn(ctx0, lctx, cur,
                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,
                        NULL,                      NULL,                        NULL,
                        model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
                        NULL,
                        LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
                cb(cur, "ffn_out", il);
            }

            cur = ggml_add(ctx0, cur, ffn_inp);
            cur = lctx.cvec.apply_to(ctx0, cur, il);
            cb(cur, "l_out", il);

            // input for next layer
            inpL = cur;
        }

        cur = llm_build_norm(ctx0, inpL, hparams,
                model.output_norm,
                model.output_norm_b,
                LLM_NORM, cb, -1);
        cb(cur, "result_norm", -1);

        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    struct ggml_cgraph * build_codeshell() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);

        const int64_t n_embd_head = hparams.n_embd_head_v;
        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
        GGML_ASSERT(n_embd_head == hparams.n_rot);

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);

        // inp_pos - contains the positions
        struct ggml_tensor * inp_pos = build_inp_pos();

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();

        for (int il = 0; il < n_layer; ++il) {
            cur = llm_build_norm(ctx0, inpL, hparams,
                    model.layers[il].attn_norm,
                    model.layers[il].attn_norm_b,
                    LLM_NORM, cb, il);
            cb(cur, "attn_norm", il);

            // self-attention
            {
                cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wqkv, cur);
                cb(cur, "wqkv", il);

                cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
                cb(cur, "bqkv", il);

                struct ggml_tensor * tmpq = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
                struct ggml_tensor * tmpk = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
                struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));

                cb(tmpq, "tmpq", il);
                cb(tmpk, "tmpk", il);
                cb(Vcur, "Vcur", il);

                struct ggml_tensor * Qcur = ggml_rope_ext(
                    ctx0, ggml_reshape_3d(ctx0, tmpq, n_embd_head, n_head,    n_tokens), inp_pos, nullptr,
                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Qcur, "Qcur", il);

                struct ggml_tensor * Kcur = ggml_rope_ext(
                    ctx0, ggml_reshape_3d(ctx0, tmpk, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Kcur, "Kcur", il);

                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                        model.layers[il].wo, model.layers[il].bo,
                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
            }

            if (il == n_layer - 1) {
                // skip computing output for unused tokens
                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
                cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);
                inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
            }

            // add the input
            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
            cb(ffn_inp, "ffn_inp", il);

            // FF
            {
                cur = llm_build_norm(ctx0, ffn_inp, hparams,
                        model.layers[il].ffn_norm,
                        model.layers[il].ffn_norm_b,
                        LLM_NORM, cb, il);
                cb(cur, "ffn_norm", il);

                cur = llm_build_ffn(ctx0, lctx, cur,
                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,
                        NULL,                      NULL,                        NULL,
                        model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
                        NULL,
                        LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
                cb(cur, "ffn_out", il);
            }

            cur = ggml_add(ctx0, cur, ffn_inp);
            cur = lctx.cvec.apply_to(ctx0, cur, il);
            cb(cur, "l_out", il);

            // input for next layer
            inpL = cur;
        }

        cur = llm_build_norm(ctx0, inpL, hparams,
                model.output_norm,
                model.output_norm_b,
                LLM_NORM, cb, -1);
        cb(cur, "result_norm", -1);

        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    struct ggml_cgraph * build_orion() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);

        const int64_t n_embd_head = hparams.n_embd_head_v;
        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
        GGML_ASSERT(n_embd_head == hparams.n_rot);

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);

        // inp_pos - contains the positions
        struct ggml_tensor * inp_pos = build_inp_pos();

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();

        for (int il = 0; il < n_layer; ++il) {
            struct ggml_tensor * inpSA = inpL;

            // norm
            cur = llm_build_norm(ctx0, inpL, hparams,
                    model.layers[il].attn_norm, model.layers[il].attn_norm_b,
                    LLM_NORM, cb, il);
            cb(cur, "attn_norm", il);

            // self-attention
            {
                // compute Q and K and RoPE them
                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
                cb(Qcur, "Qcur", il);
                // if (model.layers[il].bq) {
                //     Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
                //     cb(Qcur, "Qcur", il);
                // }

                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
                cb(Kcur, "Kcur", il);
                // if (model.layers[il].bk) {
                //     Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
                //     cb(Kcur, "Kcur", il);
                // }

                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
                cb(Vcur, "Vcur", il);
                // if (model.layers[il].bv) {
                //     Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
                //     cb(Vcur, "Vcur", il);
                // }

                Qcur = ggml_rope_ext(
                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens), inp_pos, nullptr,
                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Qcur, "Qcur", il);

                Kcur = ggml_rope_ext(
                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Kcur, "Kcur", il);

                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                        model.layers[il].wo, NULL,
                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
            }

            if (il == n_layer - 1) {
                // skip computing output for unused tokens
                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
            }

            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
            cb(ffn_inp, "ffn_inp", il);

            // feed-forward network
            cur = llm_build_norm(ctx0, ffn_inp, hparams,
                    model.layers[il].ffn_norm, model.layers[il].ffn_norm_b,
                    LLM_NORM, cb, il);
            cb(cur, "ffn_norm", il);

            cur = llm_build_ffn(ctx0, lctx, cur,
                    model.layers[il].ffn_up,   NULL, NULL,
                    model.layers[il].ffn_gate, NULL, NULL,
                    model.layers[il].ffn_down, NULL, NULL,
                    NULL,
                    LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
            cb(cur, "ffn_out", il);

            cur = ggml_add(ctx0, cur, ffn_inp);
            cur = lctx.cvec.apply_to(ctx0, cur, il);
            cb(cur, "l_out", il);

            // input for next layer
            inpL = cur;
        }

        cur = inpL;

        cur = llm_build_norm(ctx0, cur, hparams,
                model.output_norm, model.output_norm_b,
                LLM_NORM, cb, -1);
        cb(cur, "result_norm", -1);

        // lm_head
        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    struct ggml_cgraph * build_internlm2() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);

        const int64_t n_embd_head = hparams.n_embd_head_v;
        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
        GGML_ASSERT(n_embd_head == hparams.n_rot);

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);

        // inp_pos - contains the positions
        struct ggml_tensor * inp_pos = build_inp_pos();

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();

        for (int il = 0; il < n_layer; ++il) {
            struct ggml_tensor * inpSA = inpL;

            // norm
            cur = llm_build_norm(ctx0, inpL, hparams,
                    model.layers[il].attn_norm, NULL,
                    LLM_NORM_RMS, cb, il);
            cb(cur, "attn_norm", il);

            // self-attention
            {
                // compute Q and K and RoPE them
                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
                cb(Qcur, "Qcur", il);
                if (model.layers[il].bq) {
                    Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
                    cb(Qcur, "Qcur", il);
                }

                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
                cb(Kcur, "Kcur", il);
                if (model.layers[il].bk) {
                    Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
                    cb(Kcur, "Kcur", il);
                }

                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
                cb(Vcur, "Vcur", il);
                if (model.layers[il].bv) {
                    Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
                    cb(Vcur, "Vcur", il);
                }

                Qcur = ggml_rope_ext(
                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens), inp_pos, nullptr,
                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Qcur, "Qcur", il);

                Kcur = ggml_rope_ext(
                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Kcur, "Kcur", il);

                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                        model.layers[il].wo, model.layers[il].bo,
                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
            }

            if (il == n_layer - 1) {
                // skip computing output for unused tokens
                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
            }

            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
            cb(ffn_inp, "ffn_inp", il);

            // feed-forward network
            cur = llm_build_norm(ctx0, ffn_inp, hparams,
                    model.layers[il].ffn_norm, NULL,
                    LLM_NORM_RMS, cb, il);
            cb(cur, "ffn_norm", il);

            cur = llm_build_ffn(ctx0, lctx, cur,
                    model.layers[il].ffn_up,   NULL, NULL,
                    model.layers[il].ffn_gate, NULL, NULL,
                    model.layers[il].ffn_down, NULL, NULL,
                    NULL,
                    LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
            cb(cur, "ffn_out", il);

            cur = ggml_add(ctx0, cur, ffn_inp);
            cur = lctx.cvec.apply_to(ctx0, cur, il);
            cb(cur, "l_out", il);

            // input for next layer
            inpL = cur;
        }

        cur = inpL;

        cur = llm_build_norm(ctx0, cur, hparams,
                model.output_norm, NULL,
                LLM_NORM_RMS, cb, -1);
        cb(cur, "result_norm", -1);

        // lm_head
        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    struct ggml_cgraph * build_minicpm3() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);

        //TODO: if the model varies, these parameters need to be read from the model
        const int64_t n_embd_base = 256;
        const float scale_embd  = 12.0f;
        const float scale_depth = 1.4f;
        const float kq_scale = 1.0f / sqrtf(float(hparams.n_embd_head_k));

        const uint32_t n_embd_head_qk_rope = hparams.n_rot;
        const uint32_t n_embd_head_qk_nope = hparams.n_embd_head_k - hparams.n_rot;
        const uint32_t kv_lora_rank = hparams.n_lora_kv;

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);

        // scale the input embeddings
        inpL = ggml_scale(ctx0, inpL, scale_embd);
        cb(inpL, "inp_scaled", -1);

        // inp_pos - contains the positions
        struct ggml_tensor * inp_pos = build_inp_pos();

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();

        for (int il = 0; il < n_layer; ++il) {
            struct ggml_tensor * inpSA = inpL;

            struct ggml_tensor * rope_factors = build_rope_factors(il);
            // norm
            cur = llm_build_norm(ctx0, inpL, hparams,
                    model.layers[il].attn_norm, NULL,
                    LLM_NORM_RMS, cb, il);
            cb(cur, "attn_norm", il);

            // self_attention
            {
                struct ggml_tensor * q = NULL;
                // {n_embd, q_lora_rank} * {n_embd, n_tokens} -> {q_lora_rank, n_tokens}
                q = ggml_mul_mat(ctx0, model.layers[il].wq_a, cur);
                cb(q, "q", il);

                q = llm_build_norm(ctx0, q, hparams,
                        model.layers[il].attn_q_a_norm, NULL,
                        LLM_NORM_RMS, cb, il);
                cb(q, "q", il);

                // {q_lora_rank, n_head * hparams.n_embd_head_k} * {q_lora_rank, n_tokens} -> {n_head * hparams.n_embd_head_k, n_tokens}
                q = ggml_mul_mat(ctx0, model.layers[il].wq_b, q);
                cb(q, "q", il);

                // split into {n_head * n_embd_head_qk_nope, n_tokens}
                struct ggml_tensor * q_nope = ggml_view_3d(ctx0, q, n_embd_head_qk_nope, n_head, n_tokens,
                        ggml_row_size(q->type, hparams.n_embd_head_k),
                        ggml_row_size(q->type, hparams.n_embd_head_k * n_head),
                        0);
                cb(q_nope, "q_nope", il);

                // and {n_head * n_embd_head_qk_rope, n_tokens}
                struct ggml_tensor * q_pe = ggml_view_3d(ctx0, q, n_embd_head_qk_rope, n_head, n_tokens,
                        ggml_row_size(q->type, hparams.n_embd_head_k),
                        ggml_row_size(q->type, hparams.n_embd_head_k * n_head),
                        ggml_row_size(q->type, n_embd_head_qk_nope));
                cb(q_pe, "q_pe", il);

                // {n_embd, kv_lora_rank + n_embd_head_qk_rope} * {n_embd, n_tokens} -> {kv_lora_rank + n_embd_head_qk_rope, n_tokens}
                struct ggml_tensor * kv_pe_compresseed = ggml_mul_mat(ctx0, model.layers[il].wkv_a_mqa, cur);
                cb(kv_pe_compresseed, "kv_pe_compresseed", il);

                // split into {kv_lora_rank, n_tokens}
                struct ggml_tensor * kv_compressed = ggml_view_2d(ctx0, kv_pe_compresseed, kv_lora_rank, n_tokens,
                        kv_pe_compresseed->nb[1],
                        0);
                cb(kv_compressed, "kv_compressed", il);

                // and {n_embd_head_qk_rope, n_tokens}
                struct ggml_tensor * k_pe = ggml_view_3d(ctx0, kv_pe_compresseed, n_embd_head_qk_rope, 1, n_tokens,
                        kv_pe_compresseed->nb[1],
                        kv_pe_compresseed->nb[1],
                        ggml_row_size(kv_pe_compresseed->type, kv_lora_rank));
                cb(k_pe, "k_pe", il);

                kv_compressed = ggml_cont(ctx0, kv_compressed); // TODO: the CUDA backend does not support non-contiguous norm
                kv_compressed = llm_build_norm(ctx0, kv_compressed, hparams,
                        model.layers[il].attn_kv_a_norm, NULL,
                        LLM_NORM_RMS, cb, il);
                cb(kv_compressed, "kv_compressed", il);

                // {kv_lora_rank, n_head * (n_embd_head_qk_nope + n_embd_head_v)} * {kv_lora_rank, n_tokens} -> {n_head * (n_embd_head_qk_nope + n_embd_head_v), n_tokens}
                struct ggml_tensor * kv = ggml_mul_mat(ctx0, model.layers[il].wkv_b, kv_compressed);
                cb(kv, "kv", il);

                // split into {n_head * n_embd_head_qk_nope, n_tokens}
                struct ggml_tensor * k_nope = ggml_view_3d(ctx0, kv, n_embd_head_qk_nope, n_head, n_tokens,
                        ggml_row_size(kv->type, n_embd_head_qk_nope + hparams.n_embd_head_v),
                        ggml_row_size(kv->type, n_head * (n_embd_head_qk_nope + hparams.n_embd_head_v)),
                        0);
                cb(k_nope, "k_nope", il);

                // and {n_head * n_embd_head_v, n_tokens}
                struct ggml_tensor * v_states = ggml_view_3d(ctx0, kv, hparams.n_embd_head_v, n_head, n_tokens,
                        ggml_row_size(kv->type, (n_embd_head_qk_nope + hparams.n_embd_head_v)),
                        ggml_row_size(kv->type, (n_embd_head_qk_nope + hparams.n_embd_head_v)*n_head),
                        ggml_row_size(kv->type, (n_embd_head_qk_nope)));
                cb(v_states, "v_states", il);

                v_states = ggml_cont(ctx0, v_states);
                cb(v_states, "v_states", il);

                v_states = ggml_view_2d(ctx0, v_states, hparams.n_embd_head_v * n_head, n_tokens,
                    ggml_row_size(kv->type, hparams.n_embd_head_v * n_head),
                    0);
                cb(v_states, "v_states", il);

                q_pe = ggml_cont(ctx0, q_pe); // TODO: the CUDA backend does not support non-contiguous RoPE
                q_pe = ggml_rope_ext(
                    ctx0, q_pe, inp_pos, rope_factors,
                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(q_pe, "q_pe", il);

                // shared RoPE key
                k_pe = ggml_cont(ctx0, k_pe); // TODO: the CUDA backend does not support non-contiguous RoPE
                k_pe = ggml_rope_ext(
                    ctx0, k_pe, inp_pos, rope_factors,
                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(k_pe, "k_pe", il);

                struct ggml_tensor * q_states = ggml_concat(ctx0, q_nope, q_pe, 0);
                cb(q_states, "q_states", il);

                struct ggml_tensor * k_states = ggml_concat(ctx0, k_nope, ggml_repeat(ctx0, k_pe, q_pe), 0);
                cb(k_states, "k_states", il);

                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                        model.layers[il].wo, NULL,
                        k_states, v_states, q_states, KQ_mask, n_tokens, kv_head, n_kv, kq_scale, cb, il);
            }

            if (il == n_layer - 1) {
                // skip computing output for unused tokens
                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
            }

            // scale_res - scale the hidden states for residual connection
            const float scale_res = scale_depth/sqrtf(float(n_layer));
            cur = ggml_scale(ctx0, cur, scale_res);
            cb(cur, "hidden_scaled", il);

            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
            cb(ffn_inp, "ffn_inp", il);

            // feed-forward network
            {
                cur = llm_build_norm(ctx0, ffn_inp, hparams,
                        model.layers[il].ffn_norm, NULL,
                        LLM_NORM_RMS, cb, il);
                cb(cur, "ffn_norm", il);

                cur = llm_build_ffn(ctx0, lctx, cur,
                        model.layers[il].ffn_up,   NULL, NULL,
                        model.layers[il].ffn_gate, NULL, NULL,
                        model.layers[il].ffn_down, NULL, NULL,
                        NULL,
                        LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
                cb(cur, "ffn_out", il);
            }

            // scale the hidden states for residual connection
            cur = ggml_scale(ctx0, cur, scale_res);
            cb(cur, "hidden_scaled_ffn", il);

            cur = ggml_add(ctx0, cur, ffn_inp);
            cur = lctx.cvec.apply_to(ctx0, cur, il);
            cb(cur, "l_out", il);

            // input for next layer
            inpL = cur;
        }

        cur = inpL;

        cur = llm_build_norm(ctx0, cur, hparams,
                model.output_norm, NULL,
                LLM_NORM_RMS, cb, -1);
        cb(cur, "result_norm", -1);

        // lm_head scaling
        const float scale_lmhead = float(n_embd_base)/float(n_embd);
        cur = ggml_scale(ctx0, cur, scale_lmhead);
        cb(cur, "lmhead_scaling", -1);

        // lm_head
        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    struct ggml_cgraph * build_gemma() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);

        const int64_t n_embd_head_k = hparams.n_embd_head_k;

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);

        inpL = ggml_scale(ctx0, inpL, sqrtf(n_embd));
        cb(inpL, "inp_scaled", -1);

        // inp_pos - contains the positions
        struct ggml_tensor * inp_pos = build_inp_pos();

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();

        for (int il = 0; il < n_layer; ++il) {
            // norm
            cur = llm_build_norm(ctx0, inpL, hparams,
                    model.layers[il].attn_norm, NULL,
                    LLM_NORM_RMS, cb, il);
            cb(cur, "attn_norm", il);

            // self-attention
            {
                // compute Q and K and RoPE them
                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
                cb(Qcur, "Qcur", il);

                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
                cb(Kcur, "Kcur", il);

                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
                cb(Vcur, "Vcur", il);

                Qcur = ggml_rope_ext(
                        ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head_k, n_head,    n_tokens), inp_pos, nullptr,
                        n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                        ext_factor, attn_factor, beta_fast, beta_slow);
                cb(Qcur, "Qcur", il);

                Qcur = ggml_scale(ctx0, Qcur, 1.0f / sqrtf(float(n_embd_head_k)));
                cb(Qcur, "Qcur_scaled", il);

                Kcur = ggml_rope_ext(
                        ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head_k, n_head_kv, n_tokens), inp_pos, nullptr,
                        n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                        ext_factor, attn_factor, beta_fast, beta_slow);
                cb(Kcur, "Kcur", il);

                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                        model.layers[il].wo, NULL,
                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f, cb, il);
            }

            if (il == n_layer - 1) {
                // skip computing output for unused tokens
                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
                cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);
                inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
            }

            struct ggml_tensor * sa_out = ggml_add(ctx0, cur, inpL);
            cb(sa_out, "sa_out", il);

            cur = llm_build_norm(ctx0, sa_out, hparams,
                    model.layers[il].ffn_norm, NULL,
                    LLM_NORM_RMS, cb, il);
            cb(cur, "ffn_norm", il);

            // feed-forward network
            {
                cur = llm_build_ffn(ctx0, lctx, cur,
                        model.layers[il].ffn_up,   NULL, NULL,
                        model.layers[il].ffn_gate, NULL, NULL,
                        model.layers[il].ffn_down, NULL, NULL,
                        NULL,
                        LLM_FFN_GELU, LLM_FFN_PAR, cb, il);
                cb(cur, "ffn_out", il);
            }

            cur = ggml_add(ctx0, cur, sa_out);
            cur = lctx.cvec.apply_to(ctx0, cur, il);
            cb(cur, "l_out", il);

            // input for next layer
            inpL = cur;
        }

        cur = inpL;

        cur = llm_build_norm(ctx0, cur, hparams,
                model.output_norm, NULL,
                LLM_NORM_RMS, cb, -1);
        cb(cur, "result_norm", -1);

        // lm_head
        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    struct ggml_cgraph * build_gemma2() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);

        const int64_t n_embd_head_k = hparams.n_embd_head_k;

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);

        inpL = ggml_scale(ctx0, inpL, sqrtf(n_embd));
        cb(inpL, "inp_scaled", -1);

        // inp_pos - contains the positions
        struct ggml_tensor * inp_pos = build_inp_pos();

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        // gemma 2 requires different mask for layers using sliding window (SWA)
        struct ggml_tensor * KQ_mask     = build_inp_KQ_mask(true);
        struct ggml_tensor * KQ_mask_swa = build_inp_KQ_mask_swa(true);

        for (int il = 0; il < n_layer; ++il) {
            // (il % 2) layers use SWA
            struct ggml_tensor * KQ_mask_l = (il % 2 == 0) ? KQ_mask_swa : KQ_mask;

            // norm
            cur = llm_build_norm(ctx0, inpL, hparams,
                    model.layers[il].attn_norm, NULL,
                    LLM_NORM_RMS, cb, il);
            cb(cur, "attn_norm", il);

            // self-attention
            {
                // compute Q and K and RoPE them
                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
                cb(Qcur, "Qcur", il);

                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
                cb(Kcur, "Kcur", il);

                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
                cb(Vcur, "Vcur", il);

                Qcur = ggml_rope_ext(
                        ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head_k, n_head,    n_tokens), inp_pos, nullptr,
                        n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                        ext_factor, attn_factor, beta_fast, beta_slow);
                cb(Qcur, "Qcur", il);

                // ref: https://github.com/google/gemma_pytorch/commit/03e657582d17cb5a8617ebf333c1c16f3694670e
                switch (model.type) {
                    case llm_type::MODEL_2B:
                    case llm_type::MODEL_9B:  Qcur = ggml_scale(ctx0, Qcur, 1.0f / sqrtf(float(n_embd_head_k)));   break;
                    case llm_type::MODEL_27B: Qcur = ggml_scale(ctx0, Qcur, 1.0f / sqrtf(float(n_embd / n_head))); break;
                    default: GGML_ABORT("fatal error");
                };
                cb(Qcur, "Qcur_scaled", il);

                Kcur = ggml_rope_ext(
                        ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head_k, n_head_kv, n_tokens), inp_pos, nullptr,
                        n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                        ext_factor, attn_factor, beta_fast, beta_slow);
                cb(Kcur, "Kcur", il);

                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                        model.layers[il].wo, NULL,
                        Kcur, Vcur, Qcur, KQ_mask_l, n_tokens, kv_head, n_kv, 1.0f, cb, il);
            }

            cur = llm_build_norm(ctx0, cur, hparams,
                    model.layers[il].attn_post_norm, NULL,
                    LLM_NORM_RMS, cb, il);
            cb(cur, "attn_post_norm", il);

            if (il == n_layer - 1) {
                // skip computing output for unused tokens
                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
                cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);
                inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
            }

            struct ggml_tensor * sa_out = ggml_add(ctx0, cur, inpL);
            cb(sa_out, "sa_out", il);

            cur = llm_build_norm(ctx0, sa_out, hparams,
                    model.layers[il].ffn_norm, NULL,
                    LLM_NORM_RMS, cb, il);
            cb(cur, "ffn_norm", il);

            // feed-forward network
            {
                cur = llm_build_ffn(ctx0, lctx, cur,
                        model.layers[il].ffn_up,   NULL, NULL,
                        model.layers[il].ffn_gate, NULL, NULL,
                        model.layers[il].ffn_down, NULL, NULL,
                        NULL,
                        LLM_FFN_GELU, LLM_FFN_PAR, cb, il);
                cb(cur, "ffn_out", il);
            }

            cur = llm_build_norm(ctx0, cur, hparams,
                model.layers[il].ffn_post_norm, NULL,
                LLM_NORM_RMS, cb, -1);
            cb(cur, "ffn_post_norm", -1);

            cur = ggml_add(ctx0, cur, sa_out);
            cur = lctx.cvec.apply_to(ctx0, cur, il);
            cb(cur, "l_out", il);

            // input for next layer
            inpL = cur;
        }

        cur = inpL;

        cur = llm_build_norm(ctx0, cur, hparams,
                model.output_norm, NULL,
                LLM_NORM_RMS, cb, -1);
        cb(cur, "result_norm", -1);

        // lm_head
        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);

        // final logit soft-capping
        cur = ggml_scale(ctx0, cur, 1.0f / hparams.f_final_logit_softcapping);
        cur = ggml_tanh(ctx0, cur);
        cur = ggml_scale(ctx0, cur, hparams.f_final_logit_softcapping);

        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }


    struct ggml_cgraph * build_starcoder2() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);

        const int64_t n_embd_head = hparams.n_embd_head_v;
        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
        GGML_ASSERT(n_embd_head == hparams.n_rot);

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);

        // inp_pos - contains the positions
        struct ggml_tensor * inp_pos = build_inp_pos();

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();

        for (int il = 0; il < n_layer; ++il) {
            struct ggml_tensor * inpSA = inpL;

            // norm
            cur = llm_build_norm(ctx0, inpL, hparams,
                    model.layers[il].attn_norm, model.layers[il].attn_norm_b,
                    LLM_NORM, cb, il);
            cb(cur, "attn_norm", il);

            // self-attention
            {
                // compute Q and K and RoPE them
                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
                cb(Qcur, "Qcur", il);
                if (model.layers[il].bq) {
                    Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
                    cb(Qcur, "Qcur", il);
                }

                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
                cb(Kcur, "Kcur", il);
                if (model.layers[il].bk) {
                    Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
                    cb(Kcur, "Kcur", il);
                }

                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
                cb(Vcur, "Vcur", il);
                if (model.layers[il].bv) {
                    Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
                    cb(Vcur, "Vcur", il);
                }

                Qcur = ggml_rope_ext(
                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Qcur, "Qcur", il);

                Kcur = ggml_rope_ext(
                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Kcur, "Kcur", il);

                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                        model.layers[il].wo, model.layers[il].bo,
                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
            }

            if (il == n_layer - 1) {
                // skip computing output for unused tokens
                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
            }

            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
            cb(ffn_inp, "ffn_inp", il);

            // feed-forward network

            cur = llm_build_norm(ctx0, ffn_inp, hparams,
                    model.layers[il].ffn_norm, model.layers[il].ffn_norm_b,
                    LLM_NORM, cb, il);
            cb(cur, "ffn_norm", il);

            cur = llm_build_ffn(ctx0, lctx, cur,
                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,
                        NULL,                      NULL,                        NULL,
                        model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
                        NULL,
                        LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
            cb(cur, "ffn_out", il);

            cur = ggml_add(ctx0, cur, ffn_inp);
            cur = lctx.cvec.apply_to(ctx0, cur, il);
            cb(cur, "l_out", il);

            // input for next layer
            inpL = cur;
        }

        cur = inpL;

        cur = llm_build_norm(ctx0, cur, hparams,
                model.output_norm, model.output_norm_b,
                LLM_NORM, cb, -1);
        cb(cur, "result_norm", -1);

        // lm_head
        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    struct ggml_cgraph * build_mamba() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;

        // {n_embd, n_tokens}
        inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);

        struct ggml_tensor * state_copy = build_inp_s_copy();
        struct ggml_tensor * state_mask = build_inp_s_mask();

        for (int il = 0; il < n_layer; ++il) {
            // norm
            cur = llm_build_norm(ctx0, inpL, hparams,
                    model.layers[il].attn_norm, NULL,
                    LLM_NORM_RMS, cb, il);
            cb(cur, "attn_norm", il);

            cur = llm_build_mamba(ctx0, lctx, ubatch, gf, cur,
                    state_copy, state_mask,
                    kv_head, n_kv, cb, il);

            if (il == n_layer - 1) {
                // skip computing output for unused tokens
                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
                cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);
                inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
            }

            // residual
            cur = ggml_add(ctx0, cur, inpL);
            cur = lctx.cvec.apply_to(ctx0, cur, il);
            cb(cur, "l_out", il);

            // input for next layer
            inpL = cur;
        }

        // final rmsnorm
        cur = llm_build_norm(ctx0, inpL, hparams,
                model.output_norm, NULL,
                LLM_NORM_RMS, cb, -1);
        cb(cur, "result_norm", -1);

        // lm_head
        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    struct ggml_cgraph * build_command_r() {

        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);

        const int64_t n_embd_head = hparams.n_embd_head_v;
        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
        const float f_logit_scale = hparams.f_logit_scale;

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);

        // inp_pos - contains the positions
        struct ggml_tensor * inp_pos = build_inp_pos();

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();

        for (int il = 0; il < n_layer; ++il) {

            // norm
            cur = llm_build_norm(ctx0, inpL, hparams,
                    model.layers[il].attn_norm, NULL,
                    LLM_NORM, cb, il);
            cb(cur, "attn_norm", il);
            struct ggml_tensor * ffn_inp = cur;

            // self-attention
            {
                // compute Q and K and RoPE them
                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
                cb(Qcur, "Qcur", il);
                if (model.layers[il].bq) {
                    Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
                    cb(Qcur, "Qcur", il);
                }

                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
                cb(Kcur, "Kcur", il);
                if (model.layers[il].bk) {
                    Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
                    cb(Kcur, "Kcur", il);
                }

                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
                cb(Vcur, "Vcur", il);
                if (model.layers[il].bv) {
                    Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
                    cb(Vcur, "Vcur", il);
                }

                if (model.layers[il].attn_q_norm) {
                    Qcur = ggml_view_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens,
                                ggml_element_size(Qcur) * n_embd_head,
                                ggml_element_size(Qcur) * n_embd_head * n_head,
                                0);
                    cb(Qcur, "Qcur", il);
                    Kcur = ggml_view_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens,
                                ggml_element_size(Kcur) * n_embd_head,
                                ggml_element_size(Kcur) * n_embd_head * n_head_kv,
                                0);
                    cb(Kcur, "Kcur", il);

                    Qcur = llm_build_norm(ctx0, Qcur, hparams,
                                model.layers[il].attn_q_norm,
                                NULL,
                                LLM_NORM, cb, il);
                    cb(Qcur, "Qcur", il);

                    Kcur = llm_build_norm(ctx0, Kcur, hparams,
                            model.layers[il].attn_k_norm,
                            NULL,
                            LLM_NORM, cb, il);
                    cb(Kcur, "Kcur", il);
                }

                Qcur = ggml_rope_ext(
                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Qcur, "Qcur", il);

                Kcur = ggml_rope_ext(
                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Kcur, "Kcur", il);

                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                        model.layers[il].wo, model.layers[il].bo,
                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
            }

            if (il == n_layer - 1) {
                // skip computing output for unused tokens
                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
                cur     = ggml_get_rows(ctx0,     cur, inp_out_ids);
                inpL    = ggml_get_rows(ctx0,    inpL, inp_out_ids);
                ffn_inp = ggml_get_rows(ctx0, ffn_inp, inp_out_ids);
            }

            struct ggml_tensor * attn_out = cur;

            // feed-forward network
            {
                cur = llm_build_ffn(ctx0, lctx, ffn_inp,
                        model.layers[il].ffn_up,   NULL, NULL,
                        model.layers[il].ffn_gate, NULL, NULL,
                        model.layers[il].ffn_down, NULL, NULL,
                        NULL,
                        LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
                cb(cur, "ffn_out", il);
            }

            // add together residual + FFN + self-attention
            cur = ggml_add(ctx0, cur, inpL);
            cur = ggml_add(ctx0, cur, attn_out);
            cur = lctx.cvec.apply_to(ctx0, cur, il);
            cb(cur, "l_out", il);

            // input for next layer
            inpL = cur;
        }

        cur = inpL;

        cur = llm_build_norm(ctx0, cur, hparams,
                model.output_norm, NULL,
                LLM_NORM, cb, -1);
        cb(cur, "result_norm", -1);

        // lm_head
        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);

        if (f_logit_scale) {
            cur = ggml_scale(ctx0, cur, f_logit_scale);
        }

        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;

    }

    struct ggml_cgraph * build_cohere2() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);

        const int64_t n_embd_head = hparams.n_embd_head_v;
        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
        const float f_logit_scale = hparams.f_logit_scale;

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);

        // inp_pos - contains the positions
        struct ggml_tensor * inp_pos = build_inp_pos();

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        // cohere2 requires different mask for layers using sliding window (SWA)
        struct ggml_tensor * KQ_mask     = build_inp_KQ_mask();
        struct ggml_tensor * KQ_mask_swa = build_inp_KQ_mask_swa();

        // sliding window switch pattern
        const int32_t sliding_window_pattern = 4;

        for (int il = 0; il < n_layer; ++il) {
            // three layers sliding window attention (window size 4096) and ROPE
            // fourth layer uses global attention without positional embeddings
            const bool           is_sliding = il % sliding_window_pattern < (sliding_window_pattern - 1);
            struct ggml_tensor * KQ_mask_l = is_sliding ? KQ_mask_swa : KQ_mask;

            // norm
            cur = llm_build_norm(ctx0, inpL, hparams, model.layers[il].attn_norm, NULL, LLM_NORM, cb, il);
            cb(cur, "attn_norm", il);
            struct ggml_tensor * ffn_inp = cur;

            // self-attention
            {
                // rope freq factors for 128k context
                struct ggml_tensor * rope_factors = build_rope_factors(il);

                // compute Q and K and RoPE them
                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
                cb(Qcur, "Qcur", il);
                if (model.layers[il].bq) {
                    Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
                    cb(Qcur, "Qcur", il);
                }

                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
                cb(Kcur, "Kcur", il);
                if (model.layers[il].bk) {
                    Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
                    cb(Kcur, "Kcur", il);
                }

                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
                cb(Vcur, "Vcur", il);
                if (model.layers[il].bv) {
                    Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
                    cb(Vcur, "Vcur", il);
                }

                if (is_sliding) {
                    Qcur = ggml_rope_ext(ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, rope_factors,
                                        n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, ext_factor, attn_factor,
                                        beta_fast, beta_slow);
                    cb(Qcur, "Qcur", il);

                    Kcur = ggml_rope_ext(ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
                                        rope_factors, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, ext_factor,
                                        attn_factor, beta_fast, beta_slow);
                    cb(Kcur, "Kcur", il);
                } else {
                    // For non-sliding layers, just reshape without applying RoPE
                    Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
                    cb(Qcur, "Qcur", il);

                    Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
                    cb(Kcur, "Kcur", il);
                }

                cur = llm_build_kv(ctx0, lctx, kv_self, gf, model.layers[il].wo, model.layers[il].bo, Kcur, Vcur, Qcur,
                                   KQ_mask_l, n_tokens, kv_head, n_kv, 1.0f / sqrtf(float(n_embd_head)), cb, il);
            }

            if (il == n_layer - 1) {
                // skip computing output for unused tokens
                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
                cur                              = ggml_get_rows(ctx0, cur, inp_out_ids);
                inpL                             = ggml_get_rows(ctx0, inpL, inp_out_ids);
                ffn_inp                          = ggml_get_rows(ctx0, ffn_inp, inp_out_ids);
            }

            struct ggml_tensor * attn_out = cur;

            // feed-forward network
            {
                cur = llm_build_ffn(ctx0, lctx, ffn_inp, model.layers[il].ffn_up, NULL, NULL, model.layers[il].ffn_gate,
                                    NULL, NULL, model.layers[il].ffn_down, NULL, NULL, NULL, LLM_FFN_SILU, LLM_FFN_PAR,
                                    cb, il);
                cb(cur, "ffn_out", il);
            }

            // add together residual + FFN + self-attention
            cur = ggml_add(ctx0, cur, inpL);
            cur = ggml_add(ctx0, cur, attn_out);
            cur = lctx.cvec.apply_to(ctx0, cur, il);
            cb(cur, "l_out", il);

            // input for next layer
            inpL = cur;
        }

        cur = inpL;

        cur = llm_build_norm(ctx0, cur, hparams, model.output_norm, NULL, LLM_NORM, cb, -1);
        cb(cur, "result_norm", -1);

        // lm_head
        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);

        if (f_logit_scale) {
            cur = ggml_scale(ctx0, cur, f_logit_scale);
        }

        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    // ref: https://allenai.org/olmo
    // based on the original build_llama() function, changes:
    //   * non-parametric layer norm
    //   * clamp qkv
    //   * removed bias
    //   * removed MoE
    struct ggml_cgraph * build_olmo() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);

        // mutable variable, needed during the last layer of the computation to skip unused tokens
        int32_t n_tokens = this->n_tokens;

        const int64_t n_embd_head = hparams.n_embd_head_v;
        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
        GGML_ASSERT(n_embd_head == hparams.n_rot);

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);

        // inp_pos - contains the positions
        struct ggml_tensor * inp_pos = build_inp_pos();

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();

        for (int il = 0; il < n_layer; ++il) {
            struct ggml_tensor * inpSA = inpL;

            // norm
            cur = llm_build_norm(ctx0, inpL, hparams,
                    NULL, NULL,
                    LLM_NORM, cb, il);
            cb(cur, "attn_norm", il);

            // self-attention
            {
                // compute Q and K and RoPE them
                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
                cb(Qcur, "Qcur", il);
                if (hparams.f_clamp_kqv > 0.0f) {
                    Qcur = ggml_clamp(ctx0, Qcur, -hparams.f_clamp_kqv, hparams.f_clamp_kqv);
                    cb(Qcur, "Qcur", il);
                }

                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
                cb(Kcur, "Kcur", il);
                if (hparams.f_clamp_kqv > 0.0f) {
                    Kcur = ggml_clamp(ctx0, Kcur, -hparams.f_clamp_kqv, hparams.f_clamp_kqv);
                    cb(Kcur, "Kcur", il);
                }

                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
                cb(Vcur, "Vcur", il);
                if (hparams.f_clamp_kqv > 0.0f) {
                    Vcur = ggml_clamp(ctx0, Vcur, -hparams.f_clamp_kqv, hparams.f_clamp_kqv);
                    cb(Vcur, "Vcur", il);
                }

                Qcur = ggml_rope_ext(
                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Qcur, "Qcur", il);

                Kcur = ggml_rope_ext(
                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Kcur, "Kcur", il);

                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                        model.layers[il].wo, nullptr,
                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
            }

            if (il == n_layer - 1) {
                // skip computing output for unused tokens
                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
                n_tokens = n_outputs;
                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
            }

            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
            cb(ffn_inp, "ffn_inp", il);

            // feed-forward network
            cur = llm_build_norm(ctx0, ffn_inp, hparams,
                    NULL, NULL,
                    LLM_NORM, cb, il);
            cb(cur, "ffn_norm", il);

            cur = llm_build_ffn(ctx0, lctx, cur,
                    model.layers[il].ffn_up,   NULL, NULL,
                    model.layers[il].ffn_gate, NULL, NULL,
                    model.layers[il].ffn_down, NULL, NULL,
                    NULL,
                    LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
            cb(cur, "ffn_out", il);

            cur = ggml_add(ctx0, cur, ffn_inp);
            cb(cur, "ffn_out", il);

            cur = lctx.cvec.apply_to(ctx0, cur, il);
            cb(cur, "l_out", il);

            // input for next layer
            inpL = cur;
        }

        cur = inpL;

        cur = llm_build_norm(ctx0, cur, hparams,
                NULL, NULL,
                LLM_NORM, cb, -1);
        cb(cur, "result_norm", -1);

        // lm_head
        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    struct ggml_cgraph * build_olmo2() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);

        // mutable variable, needed during the last layer of the computation to skip unused tokens
        int32_t n_tokens = this->n_tokens;

        const int64_t n_embd_head = hparams.n_embd_head_v;
        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
        GGML_ASSERT(n_embd_head == hparams.n_rot);

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);

        // inp_pos - contains the positions
        struct ggml_tensor * inp_pos = build_inp_pos();

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();

        for (int il = 0; il < n_layer; ++il) {
            struct ggml_tensor * inpSA = inpL;

            cur = inpL;

            // self_attention
            {
                // compute Q and K and RoPE them
                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
                cb(Qcur, "Qcur", il);

                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
                cb(Kcur, "Kcur", il);

                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
                cb(Vcur, "Vcur", il);

                Qcur = llm_build_norm(ctx0, Qcur, hparams, model.layers[il].attn_q_norm, NULL,
                        LLM_NORM_RMS, cb, il);
                cb(Qcur, "Qcur_normed", il);

                Kcur = llm_build_norm(ctx0, Kcur, hparams, model.layers[il].attn_k_norm, NULL,
                        LLM_NORM_RMS, cb, il);
                cb(Kcur, "Kcur_normed", il);

                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
                Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);

                Qcur = ggml_rope_ext(
                    ctx0, Qcur, inp_pos, nullptr,
                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Qcur, "Qcur_rope", il);

                Kcur = ggml_rope_ext(
                    ctx0, Kcur, inp_pos, nullptr,
                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Kcur, "Kcur_rope", il);

                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                        model.layers[il].wo, NULL,
                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
            }

            cur = llm_build_norm(ctx0, cur, hparams,
                    model.layers[il].attn_post_norm, NULL,
                    LLM_NORM_RMS, cb, il);
            cb(cur, "attn_post_norm", il);

            if (il == n_layer - 1) {
                // skip computing output for unused tokens
                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
                n_tokens = n_outputs;
                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
            }

            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
            cb(ffn_inp, "ffn_inp", il);

            // feed-forward network
            cur = llm_build_ffn(ctx0, lctx, ffn_inp,
                    model.layers[il].ffn_up,   NULL, NULL,
                    model.layers[il].ffn_gate, NULL, NULL,
                    model.layers[il].ffn_down, NULL, NULL,
                    NULL,
                    LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
            cb(cur, "ffn_out", il);

            cur = llm_build_norm(ctx0, cur, hparams,
                model.layers[il].ffn_post_norm, NULL,
                LLM_NORM_RMS, cb, -1);
            cb(cur, "ffn_post_norm", -1);

            cur = ggml_add(ctx0, cur, ffn_inp);
            cb(cur, "ffn_out", il);

            cur = lctx.cvec.apply_to(ctx0, cur, il);
            cb(cur, "l_out", il);

            // input for next layer
            inpL = cur;
        }

        cur = inpL;

        cur = llm_build_norm(ctx0, cur, hparams,
                model.output_norm, NULL,
                LLM_NORM_RMS, cb, -1);
        cb(cur, "result_norm", -1);

        // lm_head
        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    // based on the build_qwen2moe() function, changes:
    //   * removed shared experts
    //   * removed bias
    //   * added q, k norm
    struct ggml_cgraph * build_olmoe() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);

        // mutable variable, needed during the last layer of the computation to skip unused tokens
        int32_t n_tokens = this->n_tokens;

        const int64_t n_embd_head = hparams.n_embd_head_v;
        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
        GGML_ASSERT(n_embd_head == hparams.n_rot);

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);

        // inp_pos - contains the positions
        struct ggml_tensor * inp_pos = build_inp_pos();

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();

        for (int il = 0; il < n_layer; ++il) {
            struct ggml_tensor * inpSA = inpL;

            // norm
            cur = llm_build_norm(ctx0, inpL, hparams,
                    model.layers[il].attn_norm, NULL,
                    LLM_NORM_RMS, cb, il);
            cb(cur, "attn_norm", il);

            // self_attention
            {
                // compute Q and K and RoPE them
                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
                cb(Qcur, "Qcur", il);

                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
                cb(Kcur, "Kcur", il);

                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
                cb(Vcur, "Vcur", il);

                Qcur = llm_build_norm(ctx0, Qcur, hparams, model.layers[il].attn_q_norm, NULL,
                        LLM_NORM_RMS, cb, il);
                cb(Qcur, "Qcur_normed", il);

                Kcur = llm_build_norm(ctx0, Kcur, hparams, model.layers[il].attn_k_norm, NULL,
                        LLM_NORM_RMS, cb, il);
                cb(Kcur, "Kcur_normed", il);

                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
                Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);

                Qcur = ggml_rope_ext(
                    ctx0, Qcur, inp_pos, nullptr,
                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Qcur, "Qcur_rope", il);

                Kcur = ggml_rope_ext(
                    ctx0, Kcur, inp_pos, nullptr,
                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Kcur, "Kcur_rope", il);

                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                        model.layers[il].wo, NULL,
                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
            }

            if (il == n_layer - 1) {
                // skip computing output for unused tokens
                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
                n_tokens = n_outputs;
                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
            }

            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
            cb(ffn_inp, "ffn_inp", il);

            // MoE branch
            cur = llm_build_norm(ctx0, ffn_inp, hparams,
                    model.layers[il].ffn_norm, NULL,
                    LLM_NORM_RMS, cb, il);
            cb(cur, "ffn_norm", il);

            cur = llm_build_moe_ffn(ctx0, lctx, cur,
                    model.layers[il].ffn_gate_inp,
                    model.layers[il].ffn_up_exps,
                    model.layers[il].ffn_gate_exps,
                    model.layers[il].ffn_down_exps,
                    nullptr,
                    n_expert, n_expert_used,
                    LLM_FFN_SILU, false,
                    false, 0.0,
                    LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
                    cb, il);
            cb(cur, "ffn_moe_out", il);

            cur = ggml_add(ctx0, cur, ffn_inp);
            cur = lctx.cvec.apply_to(ctx0, cur, il);
            cb(cur, "l_out", il);

            // input for next layer
            inpL = cur;
        }

        cur = inpL;

        cur = llm_build_norm(ctx0, cur, hparams,
                model.output_norm, NULL,
                LLM_NORM_RMS, cb, -1);
        cb(cur, "result_norm", -1);

        // lm_head
        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    struct ggml_cgraph * build_openelm() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);

        const int64_t n_embd_head = hparams.n_embd_head_v;
        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;
        inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);

        // inp_pos - contains the positions
        struct ggml_tensor * inp_pos = build_inp_pos();

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();

        for (int il = 0; il < n_layer; ++il) {
            const int64_t n_head    = hparams.n_head(il);
            const int64_t n_head_kv = hparams.n_head_kv(il);
            const int64_t n_head_qkv = 2*n_head_kv + n_head;

            cur = inpL;
            struct ggml_tensor * residual = cur;

            // norm
            cur = llm_build_norm(ctx0, inpL, hparams,
                    model.layers[il].attn_norm, NULL,
                    LLM_NORM_RMS, cb, il);
            cb(cur, "attn_norm", il);

            // self-attention
            {
                cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wqkv, cur);
                cb(cur, "wqkv", il);

                cur = ggml_reshape_3d(ctx0, cur, n_embd_head_k, n_head_qkv, n_tokens);

                struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_3d(ctx0, cur, n_embd_head, n_head, n_tokens, cur->nb[1], cur->nb[2], 0));
                cb(Qcur, "Qcur", il);

                struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, cur->nb[1], cur->nb[2], cur->nb[1]*n_head));
                cb(Kcur, "Kcur", il);

                struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, cur->nb[1], cur->nb[2], cur->nb[1]*(n_head+n_head_kv)));
                cb(Vcur, "Vcur", il);

                Qcur = llm_build_norm(ctx0, Qcur, hparams,
                        model.layers[il].attn_q_norm, NULL,
                        LLM_NORM_RMS, cb, il);
                cb(Qcur, "Qcur", il);

                Kcur = llm_build_norm(ctx0, Kcur, hparams,
                        model.layers[il].attn_k_norm, NULL,
                        LLM_NORM_RMS, cb, il);
                cb(Kcur, "Kcur", il);

                Qcur = ggml_rope_ext(
                    ctx0, Qcur, inp_pos, NULL, n_rot, rope_type, n_ctx_orig,
                    freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Qcur, "Qcur", il);

                Kcur = ggml_rope_ext(
                    ctx0, Kcur, inp_pos, NULL, n_rot, rope_type, n_ctx_orig,
                    freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Kcur, "Kcur", il);

                Vcur = ggml_reshape_2d(ctx0, Vcur, n_embd_head * n_head_kv, n_tokens);
                cb(Qcur, "Vcur", il);

                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                        model.layers[il].wo, NULL,
                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
            }

            if (il == n_layer - 1) {
                // skip computing output for unused tokens
                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
                residual = ggml_get_rows(ctx0, residual, inp_out_ids);
                cur = ggml_get_rows(ctx0, cur, inp_out_ids);
            }

            struct ggml_tensor * ffn_inp = ggml_add(ctx0, residual, cur);
            cb(ffn_inp, "ffn_inp", il);

            // feed-forward network
            {
                cur = llm_build_norm(ctx0, ffn_inp, hparams,
                        model.layers[il].ffn_norm, NULL,
                        LLM_NORM_RMS, cb, il);
                cb(cur, "ffn_norm", il);

                cur = llm_build_ffn(ctx0, lctx, cur,
                        model.layers[il].ffn_up,   NULL, NULL,
                        model.layers[il].ffn_gate, NULL, NULL,
                        model.layers[il].ffn_down, NULL, NULL,
                        NULL,
                        LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
                cb(cur, "ffn_out", il);
            }

            cur = ggml_add(ctx0, cur, ffn_inp);
            cur = lctx.cvec.apply_to(ctx0, cur, il);
            cb(cur, "l_out", il);

            inpL = cur;
        }

        cur = inpL;

        // norm
        cur = llm_build_norm(ctx0, cur, hparams,
                model.output_norm, NULL,
                LLM_NORM_RMS, cb, -1);
        cb(cur, "result_norm", -1);

        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    struct ggml_cgraph * build_gptneox() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);

        const int64_t n_embd_head = hparams.n_embd_head_v;
        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);

        // inp_pos - contains the positions
        struct ggml_tensor * inp_pos = build_inp_pos();

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();

        for (int il = 0; il < n_layer; ++il) {
            cur = llm_build_norm(ctx0, inpL, hparams,
                    model.layers[il].attn_norm,
                    model.layers[il].attn_norm_b,
                    LLM_NORM, cb, il);
            cb(cur, "attn_norm", il);

            // self-attention
            {
                cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wqkv, cur);
                cb(cur, "wqkv", il);

                cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
                cb(cur, "bqkv", il);

                struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
                struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
                struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));

                cb(Qcur, "Qcur", il);
                cb(Kcur, "Kcur", il);
                cb(Vcur, "Vcur", il);

                Qcur = ggml_rope_ext(
                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Qcur, "Qcur", il);

                Kcur = ggml_rope_ext(
                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Kcur, "Kcur", il);

                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                        model.layers[il].wo, model.layers[il].bo,
                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
            }

            if (il == n_layer - 1) {
                // skip computing output for unused tokens
                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
                cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);
                inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
            }

            // ffn
            if (hparams.use_par_res) {
                // attention and ffn are computed in parallel
                // x = x + attn(ln1(x)) + ffn(ln2(x))

                struct ggml_tensor * attn_out = cur;

                cur = llm_build_norm(ctx0, inpL, hparams,
                        model.layers[il].ffn_norm,
                        model.layers[il].ffn_norm_b,
                        LLM_NORM, cb, il);
                cb(cur, "ffn_norm", il);

                cur = llm_build_ffn(ctx0, lctx, cur,
                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,
                        NULL,                      NULL,                        NULL,
                        model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
                        NULL,
                        LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
                cb(cur, "ffn_out", il);

                cur = ggml_add(ctx0, cur, inpL);
                cb(cur, "ffn_out", il);

                cur = ggml_add(ctx0, cur, attn_out);
                cur = lctx.cvec.apply_to(ctx0, cur, il);
                cb(cur, "l_out", il);

                // input for next layer
                inpL = cur;
            } else {
                // attention and ffn are computed sequentially
                // x = x + attn(ln1(x))
                // x = x + ffn(ln2(x))

                struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
                cb(ffn_inp, "ffn_inp", il);

                cur = llm_build_norm(ctx0, ffn_inp, hparams,
                        model.layers[il].ffn_norm,
                        model.layers[il].ffn_norm_b,
                        LLM_NORM, cb, il);
                cb(cur, "ffn_norm", il);

                cur = llm_build_ffn(ctx0, lctx, cur,
                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,
                        NULL,                      NULL,                        NULL,
                        model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
                        NULL,
                        LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
                cb(cur, "ffn_out", il);

                cur = ggml_add(ctx0, cur, ffn_inp);
                cur = lctx.cvec.apply_to(ctx0, cur, il);
                cb(cur, "l_out", il);

                // input for next layer
                inpL = cur;
            }
        }

        cur = llm_build_norm(ctx0, inpL, hparams,
                model.output_norm,
                model.output_norm_b,
                LLM_NORM, cb, -1);
        cb(cur, "result_norm", -1);

        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    struct ggml_cgraph * build_arctic() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);

        // mutable variable, needed during the last layer of the computation to skip unused tokens
        int32_t n_tokens = this->n_tokens;

        const int64_t n_embd_head = hparams.n_embd_head_v;
        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
        GGML_ASSERT(n_embd_head == hparams.n_rot);

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);

        // inp_pos - contains the positions
        struct ggml_tensor * inp_pos = build_inp_pos();

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();

        for (int il = 0; il < n_layer; ++il) {
            struct ggml_tensor * inpSA = inpL;

            // norm
            cur = llm_build_norm(ctx0, inpL, hparams,
                    model.layers[il].attn_norm, NULL,
                    LLM_NORM_RMS, cb, il);
            cb(cur, "attn_norm", il);

            // self-attention
            {
                // compute Q and K and RoPE them
                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
                cb(Qcur, "Qcur", il);

                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
                cb(Kcur, "Kcur", il);

                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
                cb(Vcur, "Vcur", il);

                Qcur = ggml_rope_ext(
                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Qcur, "Qcur", il);

                Kcur = ggml_rope_ext(
                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Kcur, "Kcur", il);

                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                        model.layers[il].wo, NULL,
                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
            }

            if (il == n_layer - 1) {
                // skip computing output for unused tokens
                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
                n_tokens = n_outputs;
                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
            }

            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
            cb(ffn_inp, "ffn_inp", il);

            // feed-forward network
            cur = llm_build_norm(ctx0, ffn_inp, hparams,
                    model.layers[il].ffn_norm, NULL,
                    LLM_NORM_RMS, cb, il);
            cb(cur, "ffn_norm", il);

            cur = llm_build_ffn(ctx0, lctx, cur,
                    model.layers[il].ffn_up,   NULL, NULL,
                    model.layers[il].ffn_gate, NULL, NULL,
                    model.layers[il].ffn_down, NULL, NULL,
                    NULL,
                    LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
            cb(cur, "ffn_out", il);

            struct ggml_tensor * ffn_out = ggml_add(ctx0, cur, ffn_inp);
            cb(ffn_out, "ffn_out", il);

            // MoE
            cur = llm_build_norm(ctx0, inpSA, hparams,
                    model.layers[il].ffn_norm_exps, NULL,
                    LLM_NORM_RMS, cb, il);
            cb(cur, "ffn_norm_exps", il);

            cur = llm_build_moe_ffn(ctx0, lctx, cur,
                    model.layers[il].ffn_gate_inp,
                    model.layers[il].ffn_up_exps,
                    model.layers[il].ffn_gate_exps,
                    model.layers[il].ffn_down_exps,
                    nullptr,
                    n_expert, n_expert_used,
                    LLM_FFN_SILU, true,
                    false, 0.0,
                    LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
                    cb, il);
            cb(cur, "ffn_moe_out", il);

            cur = ggml_add(ctx0, cur, ffn_out);
            cb(cur, "ffn_out", il);

            cur = lctx.cvec.apply_to(ctx0, cur, il);
            cb(cur, "l_out", il);

            // input for next layer
            inpL = cur;
        }

        cur = inpL;

        cur = llm_build_norm(ctx0, cur, hparams,
                model.output_norm, NULL,
                LLM_NORM_RMS, cb, -1);
        cb(cur, "result_norm", -1);

        // lm_head
        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    struct ggml_cgraph * build_deepseek() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);

        // mutable variable, needed during the last layer of the computation to skip unused tokens
        int32_t n_tokens = this->n_tokens;

        const int64_t n_embd_head = hparams.n_embd_head_v;
        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
        GGML_ASSERT(n_embd_head == hparams.n_rot);

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);

        // inp_pos - contains the positions
        struct ggml_tensor * inp_pos = build_inp_pos();

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
        const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
        for (int il = 0; il < n_layer; ++il) {
            struct ggml_tensor * inpSA = inpL;

            // norm
            cur = llm_build_norm(ctx0, inpL, hparams,
                    model.layers[il].attn_norm, NULL,
                    LLM_NORM_RMS, cb, il);
            cb(cur, "attn_norm", il);

            // self-attention
            {
                // rope freq factors for llama3; may return nullptr for llama2 and other models
                struct ggml_tensor * rope_factors = build_rope_factors(il);

                // compute Q and K and RoPE them
                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
                cb(Qcur, "Qcur", il);
                if (model.layers[il].bq) {
                    Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
                    cb(Qcur, "Qcur", il);
                }

                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
                cb(Kcur, "Kcur", il);
                if (model.layers[il].bk) {
                    Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
                    cb(Kcur, "Kcur", il);
                }

                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
                cb(Vcur, "Vcur", il);
                if (model.layers[il].bv) {
                    Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
                    cb(Vcur, "Vcur", il);
                }

                Qcur = ggml_rope_ext(
                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, rope_factors,
                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Qcur, "Qcur", il);

                Kcur = ggml_rope_ext(
                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, rope_factors,
                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Kcur, "Kcur", il);

                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                        model.layers[il].wo, model.layers[il].bo,
                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, kq_scale, cb, il);
            }

            if (il == n_layer - 1) {
                // skip computing output for unused tokens
                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
                n_tokens = n_outputs;
                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
            }


            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
            cb(ffn_inp, "ffn_inp", il);

            cur = llm_build_norm(ctx0, ffn_inp, hparams,
                    model.layers[il].ffn_norm, NULL,
                    LLM_NORM_RMS, cb, il);
            cb(cur, "ffn_norm", il);

            if ((uint32_t) il < hparams.n_layer_dense_lead) {
                cur = llm_build_ffn(ctx0, lctx, cur,
                        model.layers[il].ffn_up,   NULL, NULL,
                        model.layers[il].ffn_gate, NULL, NULL,
                        model.layers[il].ffn_down, NULL, NULL,
                        NULL,
                        LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
                cb(cur, "ffn_out", il);
            } else {
                // MoE branch
                ggml_tensor * moe_out =
                        llm_build_moe_ffn(ctx0, lctx, cur,
                            model.layers[il].ffn_gate_inp,
                            model.layers[il].ffn_up_exps,
                            model.layers[il].ffn_gate_exps,
                            model.layers[il].ffn_down_exps,
                            nullptr,
                            n_expert, n_expert_used,
                            LLM_FFN_SILU, false,
                            false, hparams.expert_weights_scale,
                            LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
                            cb, il);
                cb(moe_out, "ffn_moe_out", il);

                // FFN shared expert
                {
                    ggml_tensor * ffn_shexp = llm_build_ffn(ctx0, lctx, cur,
                            model.layers[il].ffn_up_shexp,   NULL, NULL,
                            model.layers[il].ffn_gate_shexp, NULL, NULL,
                            model.layers[il].ffn_down_shexp, NULL, NULL,
                            NULL,
                            LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
                    cb(ffn_shexp, "ffn_shexp", il);

                    cur = ggml_add(ctx0, moe_out, ffn_shexp);
                    cb(cur, "ffn_out", il);
                }
            }

            cur = ggml_add(ctx0, cur, ffn_inp);
            cur = lctx.cvec.apply_to(ctx0, cur, il);
            cb(cur, "l_out", il);

            // input for next layer
            inpL = cur;
        }

        cur = inpL;

        cur = llm_build_norm(ctx0, cur, hparams,
                model.output_norm, NULL,
                LLM_NORM_RMS, cb, -1);
        cb(cur, "result_norm", -1);

        // lm_head
        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);

        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    struct ggml_cgraph * build_deepseek2() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);

        // mutable variable, needed during the last layer of the computation to skip unused tokens
        int32_t n_tokens = this->n_tokens;

        bool is_lite = (hparams.n_layer == 27);

        // We have to pre-scale kq_scale and attn_factor to make the YaRN RoPE work correctly.
        // See https://github.com/ggerganov/llama.cpp/discussions/7416 for detailed explanation.
        const float mscale = attn_factor * (1.0f + hparams.rope_yarn_log_mul * logf(1.0f / freq_scale));
        const float kq_scale = 1.0f*mscale*mscale/sqrtf(float(hparams.n_embd_head_k));
        const float attn_factor_scaled = 1.0f / (1.0f + 0.1f * logf(1.0f / freq_scale));

        const uint32_t n_embd_head_qk_rope = hparams.n_rot;
        const uint32_t n_embd_head_qk_nope = hparams.n_embd_head_k - hparams.n_rot;
        const uint32_t kv_lora_rank = hparams.n_lora_kv;

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;

        // {n_embd, n_tokens}
        inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);

        // inp_pos - contains the positions
        struct ggml_tensor * inp_pos = build_inp_pos();

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();

        for (int il = 0; il < n_layer; ++il) {
            struct ggml_tensor * inpSA = inpL;

            // norm
            cur = llm_build_norm(ctx0, inpL, hparams,
                    model.layers[il].attn_norm, NULL,
                    LLM_NORM_RMS, cb, il);
            cb(cur, "attn_norm", il);

            // self_attention
            {
                struct ggml_tensor * q = NULL;
                if (!is_lite) {
                    // {n_embd, q_lora_rank} * {n_embd, n_tokens} -> {q_lora_rank, n_tokens}
                    q = ggml_mul_mat(ctx0, model.layers[il].wq_a, cur);
                    cb(q, "q", il);

                    q = llm_build_norm(ctx0, q, hparams,
                            model.layers[il].attn_q_a_norm, NULL,
                            LLM_NORM_RMS, cb, il);
                    cb(q, "q", il);

                    // {q_lora_rank, n_head * hparams.n_embd_head_k} * {q_lora_rank, n_tokens} -> {n_head * hparams.n_embd_head_k, n_tokens}
                    q = ggml_mul_mat(ctx0, model.layers[il].wq_b, q);
                    cb(q, "q", il);
                } else {
                    q = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
                    cb(q, "q", il);
                }

                // split into {n_head * n_embd_head_qk_nope, n_tokens}
                struct ggml_tensor * q_nope = ggml_view_3d(ctx0, q, n_embd_head_qk_nope, n_head, n_tokens,
                        ggml_row_size(q->type, hparams.n_embd_head_k),
                        ggml_row_size(q->type, hparams.n_embd_head_k * n_head),
                        0);
                cb(q_nope, "q_nope", il);

                // and {n_head * n_embd_head_qk_rope, n_tokens}
                struct ggml_tensor * q_pe = ggml_view_3d(ctx0, q, n_embd_head_qk_rope, n_head, n_tokens,
                        ggml_row_size(q->type, hparams.n_embd_head_k),
                        ggml_row_size(q->type, hparams.n_embd_head_k * n_head),
                        ggml_row_size(q->type, n_embd_head_qk_nope));
                cb(q_pe, "q_pe", il);

                // {n_embd, kv_lora_rank + n_embd_head_qk_rope} * {n_embd, n_tokens} -> {kv_lora_rank + n_embd_head_qk_rope, n_tokens}
                struct ggml_tensor * kv_pe_compresseed = ggml_mul_mat(ctx0, model.layers[il].wkv_a_mqa, cur);
                cb(kv_pe_compresseed, "kv_pe_compresseed", il);

                // split into {kv_lora_rank, n_tokens}
                struct ggml_tensor * kv_compressed = ggml_view_2d(ctx0, kv_pe_compresseed, kv_lora_rank, n_tokens,
                        kv_pe_compresseed->nb[1],
                        0);
                cb(kv_compressed, "kv_compressed", il);

                // and {n_embd_head_qk_rope, n_tokens}
                struct ggml_tensor * k_pe = ggml_view_3d(ctx0, kv_pe_compresseed, n_embd_head_qk_rope, 1, n_tokens,
                        kv_pe_compresseed->nb[1],
                        kv_pe_compresseed->nb[1],
                        ggml_row_size(kv_pe_compresseed->type, kv_lora_rank));
                cb(k_pe, "k_pe", il);

                kv_compressed = ggml_cont(ctx0, kv_compressed); // TODO: the CUDA backend does not support non-contiguous norm
                kv_compressed = llm_build_norm(ctx0, kv_compressed, hparams,
                        model.layers[il].attn_kv_a_norm, NULL,
                        LLM_NORM_RMS, cb, il);
                cb(kv_compressed, "kv_compressed", il);

                // {kv_lora_rank, n_head * (n_embd_head_qk_nope + n_embd_head_v)} * {kv_lora_rank, n_tokens} -> {n_head * (n_embd_head_qk_nope + n_embd_head_v), n_tokens}
                struct ggml_tensor * kv = ggml_mul_mat(ctx0, model.layers[il].wkv_b, kv_compressed);
                cb(kv, "kv", il);

                // split into {n_head * n_embd_head_qk_nope, n_tokens}
                struct ggml_tensor * k_nope = ggml_view_3d(ctx0, kv, n_embd_head_qk_nope, n_head, n_tokens,
                        ggml_row_size(kv->type, n_embd_head_qk_nope + hparams.n_embd_head_v),
                        ggml_row_size(kv->type, n_head * (n_embd_head_qk_nope + hparams.n_embd_head_v)),
                        0);
                cb(k_nope, "k_nope", il);

                // and {n_head * n_embd_head_v, n_tokens}
                struct ggml_tensor * v_states = ggml_view_3d(ctx0, kv, hparams.n_embd_head_v, n_head, n_tokens,
                        ggml_row_size(kv->type, (n_embd_head_qk_nope + hparams.n_embd_head_v)),
                        ggml_row_size(kv->type, (n_embd_head_qk_nope + hparams.n_embd_head_v)*n_head),
                        ggml_row_size(kv->type, (n_embd_head_qk_nope)));
                cb(v_states, "v_states", il);

                v_states = ggml_cont(ctx0, v_states);
                cb(v_states, "v_states", il);

                v_states = ggml_view_2d(ctx0, v_states, hparams.n_embd_head_v * n_head, n_tokens,
                    ggml_row_size(kv->type, hparams.n_embd_head_v * n_head),
                    0);
                cb(v_states, "v_states", il);

                q_pe = ggml_cont(ctx0, q_pe); // TODO: the CUDA backend does not support non-contiguous RoPE
                q_pe = ggml_rope_ext(
                    ctx0, q_pe, inp_pos, nullptr,
                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                    ext_factor, attn_factor_scaled, beta_fast, beta_slow
                );
                cb(q_pe, "q_pe", il);

                // shared RoPE key
                k_pe = ggml_cont(ctx0, k_pe); // TODO: the CUDA backend does not support non-contiguous RoPE
                k_pe = ggml_rope_ext(
                    ctx0, k_pe, inp_pos, nullptr,
                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                    ext_factor, attn_factor_scaled, beta_fast, beta_slow
                );
                cb(k_pe, "k_pe", il);

                struct ggml_tensor * q_states = ggml_concat(ctx0, q_nope, q_pe, 0);
                cb(q_states, "q_states", il);

                struct ggml_tensor * k_states = ggml_concat(ctx0, k_nope, ggml_repeat(ctx0, k_pe, q_pe), 0);
                cb(k_states, "k_states", il);

                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                        model.layers[il].wo, NULL,
                        k_states, v_states, q_states, KQ_mask, n_tokens, kv_head, n_kv, kq_scale, cb, il);
            }

            if (il == n_layer - 1) {
                // skip computing output for unused tokens
                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
                n_tokens = n_outputs;
                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
            }

            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
            cb(ffn_inp, "ffn_inp", il);

            cur = llm_build_norm(ctx0, ffn_inp, hparams,
                    model.layers[il].ffn_norm, NULL,
                    LLM_NORM_RMS, cb, il);
            cb(cur, "ffn_norm", il);

            if ((uint32_t) il < hparams.n_layer_dense_lead) {
                cur = llm_build_ffn(ctx0, lctx, cur,
                        model.layers[il].ffn_up,   NULL, NULL,
                        model.layers[il].ffn_gate, NULL, NULL,
                        model.layers[il].ffn_down, NULL, NULL,
                        NULL,
                        LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
                cb(cur, "ffn_out", il);
            } else {
                // MoE branch
                ggml_tensor * moe_out =
                        llm_build_moe_ffn(ctx0, lctx, cur,
                            model.layers[il].ffn_gate_inp,
                            model.layers[il].ffn_up_exps,
                            model.layers[il].ffn_gate_exps,
                            model.layers[il].ffn_down_exps,
                            model.layers[il].ffn_exp_probs_b,
                            n_expert, n_expert_used,
                            LLM_FFN_SILU, hparams.expert_weights_norm,
                            true, hparams.expert_weights_scale,
                            (enum llama_expert_gating_func_type) hparams.expert_gating_func,
                            cb, il);
                cb(moe_out, "ffn_moe_out", il);

                // FFN shared expert
                {
                    ggml_tensor * ffn_shexp = llm_build_ffn(ctx0, lctx, cur,
                            model.layers[il].ffn_up_shexp,   NULL, NULL,
                            model.layers[il].ffn_gate_shexp, NULL, NULL,
                            model.layers[il].ffn_down_shexp, NULL, NULL,
                            NULL,
                            LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
                    cb(ffn_shexp, "ffn_shexp", il);

                    cur = ggml_add(ctx0, moe_out, ffn_shexp);
                    cb(cur, "ffn_out", il);
                }
            }

            cur = ggml_add(ctx0, cur, ffn_inp);
            cur = lctx.cvec.apply_to(ctx0, cur, il);
            cb(cur, "l_out", il);

            // input for next layer
            inpL = cur;
        }

        cur = inpL;

        cur = llm_build_norm(ctx0, cur, hparams,
                model.output_norm, NULL,
                LLM_NORM_RMS, cb, -1);
        cb(cur, "result_norm", -1);

        // lm_head
        cur = ggml_mul_mat(ctx0, model.output, cur);
        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    struct ggml_cgraph * build_bitnet() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);

        const int64_t n_embd_head = hparams.n_embd_head_v;
        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);

        // inp_pos - contains the positions
        struct ggml_tensor * inp_pos = build_inp_pos();

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();

        for (int il = 0; il < n_layer; ++il) {
            struct ggml_tensor * inpSA = inpL;

            cur = llm_build_norm(ctx0, inpL, hparams,
                    model.layers[il].attn_norm, NULL,
                    LLM_NORM_RMS, cb, il);
            cb(cur, "attn_norm", il);

            // self-attention
            {
                // compute Q and K and RoPE them
                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
                if (model.layers[il].wq_scale) {
                    Qcur = ggml_mul(ctx0, Qcur, model.layers[il].wq_scale);
                }
                cb(Qcur, "Qcur", il);
                if (model.layers[il].bq) {
                    Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
                    cb(Qcur, "Qcur", il);
                }

                // B1.K
                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
                if (model.layers[il].wk_scale) {
                    Kcur = ggml_mul(ctx0, Kcur, model.layers[il].wk_scale);
                }
                cb(Kcur, "Kcur", il);
                if (model.layers[il].bk) {
                    Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
                    cb(Kcur, "Kcur", il);
                }

                // B1.V
                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
                if (model.layers[il].wv_scale) {
                    Vcur = ggml_mul(ctx0, Vcur, model.layers[il].wv_scale);
                }
                cb(Vcur, "Vcur", il);
                if (model.layers[il].bv) {
                    Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
                    cb(Vcur, "Vcur", il);
                }

                Qcur = ggml_rope_ext(
                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Qcur, "Qcur", il);

                Kcur = ggml_rope_ext(
                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Kcur, "Kcur", il);

                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                        NULL, NULL,
                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);

                cur = llm_build_norm(ctx0, cur, hparams,
                        model.layers[il].attn_sub_norm, NULL,
                        LLM_NORM_RMS, cb, il);
                cb(cur, "attn_sub_norm", il);

                cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wo, cur);
                if (model.layers[il].wo_scale) {
                    cur = ggml_mul(ctx0, cur, model.layers[il].wo_scale);
                }
                if (model.layers[il].bo) {
                    cur = ggml_add(ctx0, cur, model.layers[il].bo);
                }
                cb(cur, "attn_o_out", il);
            }

            if (il == n_layer - 1) {
                // skip computing output for unused tokens
                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
            }

            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
            cb(ffn_inp, "ffn_inp", il);

            // feed-forward forward
            cur = llm_build_norm(ctx0, ffn_inp, hparams,
                    model.layers[il].ffn_norm, NULL,
                    LLM_NORM_RMS, cb, il);
            cb(cur, "ffn_norm", il);

            cur = llm_build_ffn(ctx0, lctx, cur,
                    model.layers[il].ffn_up,   NULL, model.layers[il].ffn_up_scale,
                    model.layers[il].ffn_gate, NULL, model.layers[il].ffn_gate_scale,
                    NULL,                      NULL, NULL,
                    NULL,
                    LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
            cb(cur, "ffn_sub_out", il);

            cur = llm_build_norm(ctx0, cur, hparams,
                            model.layers[il].ffn_sub_norm, NULL,
                            LLM_NORM_RMS, cb, il);
            cb(cur, "ffn_sub_norm", il);

            cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].ffn_down, cur);
            if (model.layers[il].ffn_down_scale) {
                cur = ggml_mul(ctx0, cur, model.layers[il].ffn_down_scale);
            }
            cb(cur, "ffn_down", il);

            cur = ggml_add(ctx0, cur, ffn_inp);
            cb(cur, "l_out", il);

            // input for next layer
            inpL = cur;
        }

        cur = inpL;

        cur = llm_build_norm(ctx0, cur, hparams,
                model.output_norm, NULL,
                LLM_NORM_RMS, cb, -1);
        cb(cur, "result_norm", -1);

        // lm_head
        // FIXME: do not use model.tok_embd directly, duplicate as model.output
        cur = llm_build_lora_mm(lctx, ctx0, model.tok_embd, cur);
        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);
        return gf;
    }

    struct ggml_cgraph * build_t5_enc() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);

        // mutable variable, needed during the last layer of the computation to skip unused tokens
        int32_t n_tokens = this->n_tokens;

        const int64_t n_embd_head = hparams.n_embd_head_v;
        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);

        GGML_ASSERT(lctx.is_encoding);
        struct ggml_tensor * pos_bucket_enc = llm_build_pos_bucket(false);

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask_enc = build_inp_KQ_mask(false);

        for (int il = 0; il < n_layer; ++il) {
            struct ggml_tensor * inpSA = inpL;

            // norm
            cur = llm_build_norm(ctx0, inpL, hparams,
                    model.layers[il].attn_norm_enc, NULL,
                    LLM_NORM_RMS, cb, il);
            cb(cur, "attn_norm", il);

            // self-attention
            {
                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq_enc, cur);
                cb(Qcur, "Qcur", il);

                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk_enc, cur);
                cb(Kcur, "Kcur", il);

                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv_enc, cur);
                cb(Vcur, "Vcur", il);

                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
                Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);

                struct ggml_tensor * q =                 ggml_permute(ctx0, Qcur, 0, 2, 1, 3);
                struct ggml_tensor * k = ggml_cont(ctx0, ggml_permute(ctx0, Kcur, 0, 2, 1, 3));

                struct ggml_tensor * kq = ggml_mul_mat(ctx0, k, q);
                cb(kq, "kq", il);

                struct ggml_tensor * attn_rel_b = model.layers[il].attn_rel_b_enc ? model.layers[il].attn_rel_b_enc : model.layers[0].attn_rel_b_enc;
                struct ggml_tensor * pos_bias = llm_build_pos_bias(pos_bucket_enc, attn_rel_b);
                struct ggml_tensor * kq_b = ggml_add(ctx0, kq, pos_bias);
                cb(kq_b, "kq_b", il);

                kq = ggml_soft_max_ext(ctx0, kq_b, KQ_mask_enc, 1.0f, hparams.f_max_alibi_bias);
                cb(kq, "kq_soft_max_ext", il);

                struct ggml_tensor * v = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_reshape_2d(ctx0, Vcur, n_embd_gqa, n_tokens)));
                cb(v, "v", il);

                struct ggml_tensor * kqv = ggml_mul_mat(ctx0, ggml_reshape_3d(ctx0, v, n_tokens, n_embd_head, n_head_kv), kq);
                cb(kqv, "kqv", il);

                struct ggml_tensor * kqv_merged = ggml_permute(ctx0, kqv, 0, 2, 1, 3);
                cb(kqv_merged, "kqv_merged", il);

                cur = ggml_cont_2d(ctx0, kqv_merged, n_embd_gqa, n_tokens);
                cb(cur, "kqv_merged_cont", il);

                ggml_build_forward_expand(gf, cur);

                cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wo_enc, cur);
                cb(cur, "kqv_out", il);
            }

            if (il == n_layer - 1) {
                // skip computing output for unused tokens
                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
                n_tokens = n_outputs;
                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
            }

            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
            cb(ffn_inp, "ffn_inp", il);

            // feed-forward network
            {
                cur = llm_build_norm(ctx0, ffn_inp, hparams,
                        model.layers[il].ffn_norm_enc, NULL,
                        LLM_NORM_RMS, cb, il);
                cb(cur, "ffn_norm", il);

                // T5 uses relu, flan-T5 uses gelu-gated
                cur = llm_build_ffn(ctx0, lctx, cur,
                        model.layers[il].ffn_up_enc,   NULL, NULL,
                        model.layers[il].ffn_gate_enc, NULL, NULL,
                        model.layers[il].ffn_down_enc, NULL, NULL,
                        NULL,
                        model.layers[il].ffn_gate_enc ? LLM_FFN_GELU : LLM_FFN_RELU,
                        model.layers[il].ffn_gate_enc ? LLM_FFN_PAR  : LLM_FFN_SEQ,
                        cb, il);
                cb(cur, "ffn_out", il);
            }

            cur = ggml_add(ctx0, cur, ffn_inp);
            cb(cur, "ffn_out", il);

            ggml_tensor * layer_dir = lctx.cvec.tensor_for(il);
            if (layer_dir != nullptr) {
                cur = ggml_add(ctx0, cur, layer_dir);
            }
            cb(cur, "l_out", il);

            // input for next layer
            inpL = cur;
        }

        cur = inpL;
        cb(cur, "result_embd", -1);

        cur = llm_build_norm(ctx0, cur, hparams,
                model.output_norm_enc, NULL,
                LLM_NORM_RMS, cb, -1);
        cb(cur, "result_norm", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    struct ggml_cgraph * build_t5_dec() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);

        // mutable variable, needed during the last layer of the computation to skip unused tokens
        int32_t n_tokens = this->n_tokens;

        const int64_t n_embd_head = hparams.n_embd_head_v;
        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);

        GGML_ASSERT(!lctx.is_encoding);
        GGML_ASSERT(n_outputs_enc > 0 && "call llama_encode() first");

        struct ggml_tensor * embd_enc       = llm_build_inp_embd_enc();
        struct ggml_tensor * pos_bucket_dec = llm_build_pos_bucket(true);

        struct ggml_tensor * KQ_mask_dec   = build_inp_KQ_mask();
        struct ggml_tensor * KQ_mask_cross = llm_build_inp_KQ_mask_cross();

        for (int il = 0; il < n_layer; ++il) {
            struct ggml_tensor * inpSA = inpL;

            // norm
            cur = llm_build_norm(ctx0, inpL, hparams,
                    model.layers[il].attn_norm, NULL,
                    LLM_NORM_RMS, cb, il);
            cb(cur, "attn_norm", il);

            // self-attention
            {
                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
                cb(Qcur, "Qcur", il);

                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
                cb(Kcur, "Kcur", il);

                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
                cb(Vcur, "Vcur", il);

                llm_build_kv_store(ctx0, hparams, cparams, kv_self, gf, Kcur, Vcur, n_tokens, kv_head, cb, il);

                struct ggml_tensor * k =
                    ggml_view_3d(ctx0, kv_self.k_l[il],
                            n_embd_head_k, n_kv, n_head_kv,
                            ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa),
                            ggml_row_size(kv_self.k_l[il]->type, n_embd_head_k),
                            0);
                cb(k, "k", il);

                struct ggml_tensor * v =
                    ggml_view_3d(ctx0, kv_self.v_l[il],
                            n_kv, n_embd_head_v, n_head_kv,
                            ggml_element_size(kv_self.v_l[il])*n_ctx,
                            ggml_element_size(kv_self.v_l[il])*n_ctx*n_embd_head_v,
                            0);
                cb(v, "v", il);

                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);

                struct ggml_tensor * q = ggml_permute(ctx0, Qcur, 0, 2, 1, 3);

                struct ggml_tensor * kq = ggml_mul_mat(ctx0, k, q);
                cb(kq, "kq", il);

                struct ggml_tensor * attn_rel_b = model.layers[il].attn_rel_b ? model.layers[il].attn_rel_b : model.layers[0].attn_rel_b;
                struct ggml_tensor * pos_bias = llm_build_pos_bias(pos_bucket_dec, attn_rel_b);
                struct ggml_tensor * kq_b = ggml_add(ctx0, kq, pos_bias);
                cb(kq_b, "kq_b", il);

                kq = ggml_soft_max_ext(ctx0, kq_b, KQ_mask_dec, 1.0f, hparams.f_max_alibi_bias);
                cb(kq, "kq_soft_max_ext", il);

                struct ggml_tensor * kqv = ggml_mul_mat(ctx0, v, kq);
                cb(kqv, "kqv", il);

                struct ggml_tensor * kqv_merged = ggml_permute(ctx0, kqv, 0, 2, 1, 3);
                cb(kqv_merged, "kqv_merged", il);

                cur = ggml_cont_2d(ctx0, kqv_merged, n_embd_gqa, n_tokens);
                cb(cur, "kqv_merged_cont", il);

                ggml_build_forward_expand(gf, cur);

                cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wo, cur);
                cb(cur, "kqv_out", il);
            }

            cur = ggml_add(ctx0, cur, inpSA);
            cb(cur, "cross_inp", il);

            struct ggml_tensor * inpCA = cur;

            // norm
            cur = llm_build_norm(ctx0, cur, hparams,
                    model.layers[il].attn_norm_cross, NULL,
                    LLM_NORM_RMS, cb, il);
            cb(cur, "attn_norm_cross", il);

            // cross-attention
            {
                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq_cross, cur);
                cb(Qcur, "Qcur", il);

                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk_cross, embd_enc);
                cb(Kcur, "Kcur", il);

                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv_cross, embd_enc);
                cb(Vcur, "Vcur", il);

                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
                Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_outputs_enc);

                struct ggml_tensor * q =                 ggml_permute(ctx0, Qcur, 0, 2, 1, 3);
                struct ggml_tensor * k = ggml_cont(ctx0, ggml_permute(ctx0, Kcur, 0, 2, 1, 3));

                struct ggml_tensor * kq = ggml_mul_mat(ctx0, k, q);
                cb(kq, "kq", il);

                kq = ggml_soft_max_ext(ctx0, kq, KQ_mask_cross, 1.0f, hparams.f_max_alibi_bias);
                cb(kq, "kq_soft_max_ext", il);

                struct ggml_tensor * v = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_reshape_2d(ctx0, Vcur, n_embd_gqa, n_outputs_enc)));
                cb(v, "v", il);

                struct ggml_tensor * kqv = ggml_mul_mat(ctx0, ggml_reshape_3d(ctx0, v, n_outputs_enc, n_embd_head, n_head_kv), kq);
                cb(kqv, "kqv", il);

                struct ggml_tensor * kqv_merged = ggml_permute(ctx0, kqv, 0, 2, 1, 3);
                cb(kqv_merged, "kqv_merged", il);

                cur = ggml_cont_2d(ctx0, kqv_merged, n_embd_gqa, n_tokens);
                cb(cur, "kqv_merged_cont", il);

                ggml_build_forward_expand(gf, cur);

                cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wo_cross, cur);
                cb(cur, "kqv_out", il);
            }

            if (il == n_layer - 1) {
                // skip computing output for unused tokens
                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
                n_tokens = n_outputs;
                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
                inpCA = ggml_get_rows(ctx0, inpCA, inp_out_ids);
            }

            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpCA);
            cb(ffn_inp, "ffn_inp", il);

            // feed-forward network
            {
                cur = llm_build_norm(ctx0, ffn_inp, hparams,
                        model.layers[il].ffn_norm, NULL,
                        LLM_NORM_RMS, cb, il);
                cb(cur, "ffn_norm", il);

                // T5 uses relu, flan-T5 uses gelu-gated
                cur = llm_build_ffn(ctx0, lctx, cur,
                        model.layers[il].ffn_up,   NULL, NULL,
                        model.layers[il].ffn_gate, NULL, NULL,
                        model.layers[il].ffn_down, NULL, NULL,
                        NULL,
                        model.layers[il].ffn_gate_enc ? LLM_FFN_GELU : LLM_FFN_RELU,
                        model.layers[il].ffn_gate_enc ? LLM_FFN_PAR : LLM_FFN_SEQ,
                        cb, il);
                cb(cur, "ffn_out", il);
            }

            cur = ggml_add(ctx0, cur, ffn_inp);
            cb(cur, "ffn_out", il);

            ggml_tensor * layer_dir = lctx.cvec.tensor_for(il);
            if (layer_dir != nullptr) {
                cur = ggml_add(ctx0, cur, layer_dir);
            }
            cb(cur, "l_out", il);

            // input for next layer
            inpL = cur;
        }

        cur = inpL;
        cb(cur, "result_embd", -1);

        cur = llm_build_norm(ctx0, cur, hparams,
                model.output_norm, NULL,
                LLM_NORM_RMS, cb, -1);
        cb(cur, "result_norm", -1);

        // lm_head
        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    struct ggml_cgraph * build_jais() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);

        const int64_t n_embd_head = hparams.n_embd_head_v;
        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();

        for (int il = 0; il < n_layer; ++il) {
            cur = llm_build_norm(ctx0, inpL, hparams,
                    model.layers[il].attn_norm,
                    model.layers[il].attn_norm_b,
                    LLM_NORM, cb, il);
            cb(cur, "attn_norm", il);

            // self-attention
            {
                cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wqkv, cur);
                cb(cur, "wqkv", il);

                cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
                cb(cur, "bqkv", il);

                struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0*cur->nb[0]*(n_embd)));
                struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*cur->nb[0]*(n_embd)));
                struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*cur->nb[0]*(n_embd + n_embd_gqa)));

                cb(Qcur, "Qcur", il);
                cb(Kcur, "Kcur", il);
                cb(Vcur, "Vcur", il);

                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);

                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                        model.layers[il].wo, model.layers[il].bo,
                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/float(n_embd_head), cb, il);
            }

            if (il == n_layer - 1) {
                // skip computing output for unused tokens
                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
                cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);
                inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
            }

            // add the input
            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
            cb(ffn_inp, "ffn_inp", il);

            // FF
            {
                cur = llm_build_norm(ctx0, ffn_inp, hparams,
                        model.layers[il].ffn_norm,
                        model.layers[il].ffn_norm_b,
                        LLM_NORM, cb, il);
                cb(cur, "ffn_norm", il);

                cur = llm_build_ffn(ctx0, lctx, cur,
                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,
                        model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, NULL,
                        model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
                        NULL,
                        LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
                cb(cur, "ffn_out", il);
            }

            inpL = ggml_add(ctx0, cur, ffn_inp);
            cb(inpL, "l_out", il);
        }

        cur = llm_build_norm(ctx0, inpL, hparams,
                model.output_norm,
                model.output_norm_b,
                LLM_NORM, cb, -1);
        cb(cur, "result_norm", -1);

        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);

        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    struct ggml_cgraph * build_chatglm() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);

        const int64_t n_embd_head = hparams.n_embd_head_v;
        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);

        // inp_pos - contains the positions
        struct ggml_tensor * inp_pos = build_inp_pos();

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();

        for (int il = 0; il < n_layer; ++il) {
            struct ggml_tensor * inpSA = inpL;

            cur = llm_build_norm(ctx0, inpL, hparams,
                    model.layers[il].attn_norm,
                    NULL,
                    LLM_NORM_RMS, cb, il);
            cb(cur, "attn_norm", il);

            // self-attention
            {
                struct ggml_tensor * Qcur = nullptr;
                struct ggml_tensor * Kcur = nullptr;
                struct ggml_tensor * Vcur = nullptr;

                cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wqkv, cur);
                cb(cur, "wqkv", il);

                cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
                cb(cur, "bqkv", il);

                Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
                Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
                Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));

                cb(Qcur, "Qcur", il);
                cb(Kcur, "Kcur", il);
                cb(Vcur, "Vcur", il);
                //printf("freq_base: %f freq_scale: %f ext_factor: %f attn_factor: %f\n", freq_base, freq_scale, ext_factor, attn_factor);
                Qcur = ggml_rope_ext(
                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Qcur, "Qcur_rope", il);

                Kcur = ggml_rope_ext(
                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Kcur, "Kcur_rope", il);

                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                        model.layers[il].wo, NULL,
                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);

            }

            if (il == n_layer - 1) {
                // skip computing output for unused tokens
                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
            }

            // Add the input
            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
            cb(ffn_inp, "ffn_inp", il);

            // FF
            {
                cur = llm_build_norm(ctx0, ffn_inp, hparams,
                        model.layers[il].ffn_norm,
                        NULL,
                        LLM_NORM_RMS, cb, il);
                cb(cur, "ffn_norm", il);

                cur = llm_build_ffn(ctx0, lctx, cur,
                        model.layers[il].ffn_up,   NULL, NULL,
                        NULL,                      NULL, NULL,
                        model.layers[il].ffn_down, NULL, NULL,
                        NULL,
                        LLM_FFN_SWIGLU, LLM_FFN_SEQ, cb, il);
                cb(cur, "ffn_out", il);

            }

            inpL = ggml_add(ctx0, cur, ffn_inp);
            cb(inpL, "l_out", il);
        }

        cur = llm_build_norm(ctx0, inpL, hparams,
                model.output_norm,
                NULL,
                LLM_NORM_RMS, cb, -1);
        cb(cur, "result_norm", -1);

        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    struct ggml_cgraph * build_nemotron() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);

        const int64_t n_embd_head = hparams.n_embd_head_v;
        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
        //GGML_ASSERT(n_embd_head == hparams.n_rot);

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);

        // inp_pos - contains the positions
        struct ggml_tensor * inp_pos = build_inp_pos();

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();

        for (int il = 0; il < n_layer; ++il) {
            struct ggml_tensor * inpSA = inpL;

            // norm
            cur = llm_build_norm(ctx0, inpL, hparams,
                    model.layers[il].attn_norm,
                    model.layers[il].attn_norm_b,
                    LLM_NORM, cb, il);
            cb(cur, "attn_norm", il);

            // self-attention
            {
                // compute Q and K and RoPE them
                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
                cb(Qcur, "Qcur", il);
                if (model.layers[il].bq) {
                    Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
                    cb(Qcur, "Qcur", il);
                }

                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
                cb(Kcur, "Kcur", il);
                if (model.layers[il].bk) {
                    Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
                    cb(Kcur, "Kcur", il);
                }

                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
                cb(Vcur, "Vcur", il);
                if (model.layers[il].bv) {
                    Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
                    cb(Vcur, "Vcur", il);
                }

                Qcur = ggml_rope_ext(
                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Qcur, "Qcur", il);

                Kcur = ggml_rope_ext(
                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Kcur, "Kcur", il);

                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                        model.layers[il].wo, model.layers[il].bo,
                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
            }

            if (il == n_layer - 1) {
                // skip computing output for unused tokens
                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
            }

            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
            cb(ffn_inp, "ffn_inp", il);

            // feed-forward network
            cur = llm_build_norm(ctx0, ffn_inp, hparams,
                    model.layers[il].ffn_norm,
                    model.layers[il].ffn_norm_b,
                    LLM_NORM, cb, il);
            cb(cur, "ffn_norm", il);

            cur = llm_build_ffn(ctx0, lctx, cur,
                    model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,
                    NULL,                      NULL,                        NULL,
                    model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
                    NULL,
                    LLM_FFN_RELU_SQR, LLM_FFN_SEQ, cb, il);

            cur = ggml_add(ctx0, cur, ffn_inp);
            cb(cur, "ffn_out", il);

            cur = lctx.cvec.apply_to(ctx0, cur, il);
            cb(cur, "l_out", il);

            // input for next layer
            inpL = cur;
        }

        cur = inpL;

        cur = llm_build_norm(ctx0, cur, hparams,
                model.output_norm, model.output_norm_b,
                LLM_NORM, cb, -1);
        cb(cur, "result_norm", -1);

        // lm_head
        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    struct ggml_cgraph * build_exaone() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);

        // mutable variable, needed during the last layer of the computation to skip unused tokens
        int32_t n_tokens = this->n_tokens;

        const int64_t n_embd_head = hparams.n_embd_head_v;
        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
        GGML_ASSERT(n_embd_head == hparams.n_rot);

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);

        // inp_pos - contains the positions
        struct ggml_tensor * inp_pos = build_inp_pos();

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();

        for (int il = 0; il < n_layer; ++il) {
            struct ggml_tensor * inpSA = inpL;

            // norm
            cur = llm_build_norm(ctx0, inpL, hparams,
                    model.layers[il].attn_norm, NULL,
                    LLM_NORM_RMS, cb, il);
            cb(cur, "attn_norm", il);

            // self-attention
            {
                // rope freq factors for llama3; may return nullptr for llama2 and other models
                struct ggml_tensor * rope_factors = build_rope_factors(il);

                // compute Q and K and RoPE them
                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
                cb(Qcur, "Qcur", il);
                if (model.layers[il].bq) {
                    Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
                    cb(Qcur, "Qcur", il);
                }

                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
                cb(Kcur, "Kcur", il);
                if (model.layers[il].bk) {
                    Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
                    cb(Kcur, "Kcur", il);
                }

                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
                cb(Vcur, "Vcur", il);
                if (model.layers[il].bv) {
                    Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
                    cb(Vcur, "Vcur", il);
                }

                Qcur = ggml_rope_ext(
                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, rope_factors,
                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Qcur, "Qcur", il);

                Kcur = ggml_rope_ext(
                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, rope_factors,
                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Kcur, "Kcur", il);

                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                        model.layers[il].wo, model.layers[il].bo,
                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
            }

            if (il == n_layer - 1) {
                // skip computing output for unused tokens
                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
                n_tokens = n_outputs;
                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
            }

            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
            cb(ffn_inp, "ffn_inp", il);

            // feed-forward network
            cur = llm_build_norm(ctx0, ffn_inp, hparams,
                    model.layers[il].ffn_norm, NULL,
                    LLM_NORM_RMS, cb, il);
            cb(cur, "ffn_norm", il);

            cur = llm_build_ffn(ctx0, lctx, cur,
                    model.layers[il].ffn_up,   NULL, NULL,
                    model.layers[il].ffn_gate, NULL, NULL,
                    model.layers[il].ffn_down, NULL, NULL,
                    NULL,
                    LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
            cb(cur, "ffn_out", il);

            cur = ggml_add(ctx0, cur, ffn_inp);
            cb(cur, "ffn_out", il);

            cur = lctx.cvec.apply_to(ctx0, cur, il);
            cb(cur, "l_out", il);

            // input for next layer
            inpL = cur;
        }

        cur = inpL;

        cur = llm_build_norm(ctx0, cur, hparams,
                model.output_norm, NULL,
                LLM_NORM_RMS, cb, -1);
        cb(cur, "result_norm", -1);

        // lm_head
        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    ggml_cgraph * build_rwkv6() {
        ggml_cgraph *gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);

        // Token shift state dimensions should be 2 * n_emb
        GGML_ASSERT(n_embd == hparams.n_embd_k_s() / 2);

        const int64_t n_seqs = ubatch.n_seqs;
        const int64_t n_seq_tokens = ubatch.n_seq_tokens;
        const int64_t n_tokens = ubatch.n_tokens;
        GGML_ASSERT(n_seqs != 0);
        GGML_ASSERT(ubatch.equal_seqs);
        GGML_ASSERT(n_tokens == n_seq_tokens * n_seqs);

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;
        struct ggml_tensor * state_copy = build_inp_s_copy();
        struct ggml_tensor * state_mask = build_inp_s_mask();

        inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);
        inpL = llm_build_norm(ctx0, inpL, hparams, model.tok_norm, model.tok_norm_b, LLM_NORM, cb, -1);

        for (int il = 0; il < n_layer; ++il) {
            const llama_layer * layer = &model.layers[il];

            // (ab)using the KV cache to store the states
            struct ggml_tensor * token_shift = llm_build_copy_mask_state(ctx0,
                    gf, kv_self.k_l[il], state_copy, state_mask,
                    hparams.n_embd_k_s(), kv_self.size, kv_head, n_kv, n_seqs);
            struct ggml_tensor * wkv_states = llm_build_copy_mask_state(ctx0,
                    gf, kv_self.v_l[il], state_copy, state_mask,
                    hparams.n_embd_v_s(), kv_self.size, kv_head, n_kv, n_seqs);

            cur = ggml_reshape_3d(ctx0, inpL, n_embd, n_seq_tokens, n_seqs);
            token_shift = ggml_reshape_3d(ctx0, token_shift, n_embd, 2, n_seqs);

            struct ggml_tensor * att_shift = ggml_view_3d(ctx0, token_shift, n_embd, 1, n_seqs, token_shift->nb[1], token_shift->nb[2], 0);
            struct ggml_tensor * ffn_shift = ggml_view_3d(ctx0, token_shift, n_embd, 1, n_seqs, token_shift->nb[1], token_shift->nb[2], n_embd * ggml_element_size(token_shift));

            struct ggml_tensor * x_norm_att = llm_build_norm(ctx0, cur, hparams, layer->attn_norm, layer->attn_norm_b, LLM_NORM, cb, il);
            struct ggml_tensor * x_prev = ggml_concat(
                ctx0,
                att_shift,
                ggml_view_3d(ctx0, x_norm_att, n_embd, n_seq_tokens - 1, n_seqs, x_norm_att->nb[1], x_norm_att->nb[2], 0),
                1
            );

            cur = ggml_add(ctx0, cur, llm_build_rwkv6_time_mix(lctx, ctx0, layer, x_norm_att, x_prev, &wkv_states));
            ggml_build_forward_expand(gf, cur);
            ggml_build_forward_expand(
                gf,
                ggml_cpy(
                    ctx0,
                    wkv_states,
                    ggml_view_1d(
                        ctx0,
                        kv_self.v_l[il],
                        hparams.n_embd_v_s() * n_seqs,
                        hparams.n_embd_v_s() * kv_head * ggml_element_size(kv_self.v_l[il])
                    )
                )
            );

            struct ggml_tensor * x_norm_ffn = llm_build_norm(ctx0, cur, hparams, layer->attn_norm_2, layer->attn_norm_2_b, LLM_NORM, cb, il);
            x_prev = ggml_concat(
                ctx0,
                ffn_shift,
                ggml_view_3d(ctx0, x_norm_ffn, n_embd, n_seq_tokens - 1, n_seqs, x_norm_ffn->nb[1], x_norm_ffn->nb[2], 0),
                1
            );
            cur = ggml_add(ctx0, cur, llm_build_rwkv6_channel_mix(lctx, ctx0, layer, x_norm_ffn, x_prev));
            ggml_build_forward_expand(gf, cur);

            struct ggml_tensor * last_norm_att = ggml_view_3d(ctx0, x_norm_att, n_embd, 1, n_seqs, x_norm_att->nb[1], x_norm_att->nb[2], (n_seq_tokens-1)*n_embd*ggml_element_size(x_norm_att));
            struct ggml_tensor * last_norm_ffn = ggml_view_3d(ctx0, x_norm_ffn, n_embd, 1, n_seqs, x_norm_ffn->nb[1], x_norm_ffn->nb[2], (n_seq_tokens-1)*n_embd*ggml_element_size(x_norm_ffn));

            token_shift = ggml_concat(ctx0, last_norm_att, last_norm_ffn, 1);

            ggml_build_forward_expand(
                gf,
                ggml_cpy(
                    ctx0,
                    ggml_view_1d(ctx0, token_shift, n_embd * n_seqs * 2, 0),
                    ggml_view_1d(ctx0, kv_self.k_l[il], hparams.n_embd_k_s() * n_seqs, hparams.n_embd_k_s() * kv_head * ggml_element_size(kv_self.k_l[il]))
                )
            );

            if (hparams.rescale_every_n_layers != 0 && (il + 1) % hparams.rescale_every_n_layers == 0) {
                cur = ggml_scale(ctx0, cur, 0.5F);
            }

            cur = lctx.cvec.apply_to(ctx0, cur, il);
            cb(cur, "l_out", il);

            // input for next layer
            inpL = cur;
        }

        cur = inpL;
        struct ggml_tensor * inp_out_ids = build_inp_out_ids();
        cur = ggml_reshape_2d(ctx0, cur, n_embd, n_tokens);
        cur = ggml_get_rows(ctx0, cur, inp_out_ids);

        cur = llm_build_norm(ctx0, cur, hparams, model.output_norm, model.output_norm_b, LLM_NORM, cb, -1);
        cb(cur, "result_norm", -1);

        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    // ref: https://github.com/facebookresearch/chameleon
    // based on the original build_llama() function, changes:
    //   * qk-norm
    //   * swin-norm
    //   * removed bias
    //   * removed MoE
    struct ggml_cgraph * build_chameleon() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);

        // mutable variable, needed during the last layer of the computation to skip unused tokens
        int32_t n_tokens = this->n_tokens;

        const int64_t n_embd_head = hparams.n_embd_head_v;
        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
        GGML_ASSERT(n_embd_head == hparams.n_rot);

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);

        // inp_pos - contains the positions
        struct ggml_tensor * inp_pos = build_inp_pos();

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();

        for (int il = 0; il < n_layer; ++il) {
            struct ggml_tensor * inpSA = inpL;

            // norm
            if (hparams.swin_norm) {
                cur = inpL;
            } else {
                cur = llm_build_norm(ctx0, inpL, hparams,
                    model.layers[il].attn_norm, NULL,
                    LLM_NORM_RMS, cb, il);
                cb(cur, "attn_norm", il);
            }

            // self-attention
            {
                // compute Q and K and RoPE them
                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
                cb(Qcur, "Qcur", il);

                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
                cb(Kcur, "Kcur", il);

                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
                cb(Vcur, "Vcur", il);

                if (model.layers[il].attn_q_norm) {
                    Qcur = ggml_view_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens,
                                ggml_element_size(Qcur) * n_embd_head,
                                ggml_element_size(Qcur) * n_embd_head * n_head,
                                0);
                    cb(Qcur, "Qcur", il);

                    Qcur = llm_build_norm(ctx0, Qcur, hparams,
                                model.layers[il].attn_q_norm,
                                model.layers[il].attn_q_norm_b,
                                LLM_NORM, cb, il);
                    cb(Qcur, "Qcur", il);
                }

                if (model.layers[il].attn_k_norm) {
                    Kcur = ggml_view_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens,
                                ggml_element_size(Kcur) * n_embd_head,
                                ggml_element_size(Kcur) * n_embd_head * n_head_kv,
                                0);
                    cb(Kcur, "Kcur", il);

                    Kcur = llm_build_norm(ctx0, Kcur, hparams,
                               model.layers[il].attn_k_norm,
                               model.layers[il].attn_k_norm_b,
                               LLM_NORM, cb, il);
                    cb(Kcur, "Kcur", il);
                }

                Qcur = ggml_rope_ext(
                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Qcur, "Qcur", il);

                Kcur = ggml_rope_ext(
                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Kcur, "Kcur", il);

                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                        model.layers[il].wo, nullptr,
                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);

                if (hparams.swin_norm) {
                    cur = llm_build_norm(ctx0, cur, hparams,
                        model.layers[il].attn_norm, NULL,
                        LLM_NORM_RMS, cb, il);
                }
            }

            if (il == n_layer - 1) {
                // skip computing output for unused tokens
                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
                n_tokens = n_outputs;
                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
            }

            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
            cb(ffn_inp, "ffn_inp", il);

            // feed-forward network
            if (!hparams.swin_norm) {
                cur = llm_build_norm(ctx0, ffn_inp, hparams,
                        model.layers[il].ffn_norm, NULL,
                        LLM_NORM_RMS, cb, il);
                cb(cur, "ffn_norm", il);
            }

            cur = llm_build_ffn(ctx0, lctx, cur,
                    model.layers[il].ffn_up,   NULL, NULL,
                    model.layers[il].ffn_gate, NULL, NULL,
                    model.layers[il].ffn_down, NULL, NULL,
                    NULL,
                    LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
            cb(cur, "ffn_out", il);

            if (hparams.swin_norm) {
                cur = llm_build_norm(ctx0, cur, hparams,
                        model.layers[il].ffn_norm, NULL,
                        LLM_NORM_RMS, cb, il);
                cb(cur, "ffn_norm", il);
            }

            cur = ggml_add(ctx0, cur, ffn_inp);
            cb(cur, "ffn_out", il);

            cur = lctx.cvec.apply_to(ctx0, cur, il);
            cb(cur, "l_out", il);

            // input for next layer
            inpL = cur;
        }

        cur = inpL;

        cur = llm_build_norm(ctx0, cur, hparams,
                model.output_norm, NULL,
                LLM_NORM_RMS, cb, -1);
        cb(cur, "result_norm", -1);

        // lm_head
        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
        cb(cur, "result_output_with_img_logits", -1);

        // TODO: this suppresses the output of image tokens, which is required to enable text-only outputs.
        // Needs to be removed once image outputs are supported.
        int img_token_end_idx = 8196;
        int img_token_start_idx = 4;
        int num_img_tokens = img_token_end_idx - img_token_start_idx;
        // creates 1d tensor of size num_img_tokens and values -FLT_MAX,
        // which ensures that text token values are always at least larger than image token values
        struct ggml_tensor * img_logits = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, num_img_tokens);
        img_logits = ggml_clamp(ctx0, img_logits, -FLT_MAX, -FLT_MAX);
        cb(img_logits, "img_logits", -1);
        cur = ggml_set_1d(ctx0, cur, img_logits, ggml_element_size(cur) * img_token_start_idx);
        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    struct ggml_cgraph * build_wavtokenizer_dec() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);

        cur = ggml_cont(ctx0, ggml_transpose(ctx0, inpL));

        cur = ggml_conv_1d_ph(ctx0, model.conv1d, cur, 1, 1);
        cur = ggml_add(ctx0, cur, model.conv1d_b);

        // posnet
        for (uint32_t il = 0; il < hparams.posnet.n_layer; ++il) {
            const auto & layer = model.layers[il].posnet;

            inpL = cur;

            switch (il) {
                case 0:
                case 1:
                case 3:
                case 4:
                    {
                        cur = llm_build_norm(ctx0, cur, hparams,
                                layer.norm1,
                                layer.norm1_b,
                                LLM_NORM_GROUP, cb, 0);

                        cur = ggml_mul(ctx0, ggml_sigmoid(ctx0, cur), cur);

                        cur = ggml_conv_1d_ph(ctx0, layer.conv1, cur, 1, 1);
                        cur = ggml_add(ctx0, cur, layer.conv1_b);

                        cur = llm_build_norm(ctx0, cur, hparams,
                                layer.norm2,
                                layer.norm2_b,
                                LLM_NORM_GROUP, cb, 0);

                        cur = ggml_mul(ctx0, ggml_sigmoid(ctx0, cur), cur);

                        cur = ggml_conv_1d_ph(ctx0, layer.conv2, cur, 1, 1);
                        cur = ggml_add(ctx0, cur, layer.conv2_b);

                        cur = ggml_add(ctx0, cur, inpL);
                    } break;
                case 2:
                    {
                        cur = llm_build_norm(ctx0, cur, hparams,
                                layer.attn_norm,
                                layer.attn_norm_b,
                                LLM_NORM_GROUP, cb, 0);

                        struct ggml_tensor * q;
                        struct ggml_tensor * k;
                        struct ggml_tensor * v;

                        q = ggml_conv_1d_ph(ctx0, layer.attn_q, cur, 1, 1);
                        k = ggml_conv_1d_ph(ctx0, layer.attn_k, cur, 1, 1);
                        v = ggml_conv_1d_ph(ctx0, layer.attn_v, cur, 1, 1);

                        q = ggml_add(ctx0, q, layer.attn_q_b);
                        k = ggml_add(ctx0, k, layer.attn_k_b);
                        v = ggml_add(ctx0, v, layer.attn_v_b);

                        q = ggml_cont(ctx0, ggml_transpose(ctx0, q));
                        k = ggml_cont(ctx0, ggml_transpose(ctx0, k));

                        struct ggml_tensor * kq = ggml_mul_mat(ctx0, k, q);

                        kq = ggml_soft_max_ext(ctx0, kq, nullptr, 1.0f/sqrtf(float(hparams.posnet.n_embd)), 0.0f);

                        cur = ggml_mul_mat(ctx0, kq, v);

                        cur = ggml_conv_1d_ph(ctx0, layer.attn_o, cur, 1, 1);
                        cur = ggml_add(ctx0, cur, layer.attn_o_b);

                        cur = ggml_add(ctx0, cur, inpL);
                    } break;
                case 5:
                    {
                        cur = llm_build_norm(ctx0, cur, hparams,
                                layer.norm,
                                layer.norm_b,
                                LLM_NORM_GROUP, cb, 0);
                    } break;
                default: GGML_ABORT("unknown posnet layer");
            };
        }

        cur = ggml_cont(ctx0, ggml_transpose(ctx0, cur));

        cur = llm_build_norm(ctx0, cur, hparams,
                model.tok_norm,
                model.tok_norm_b,
                LLM_NORM, cb, -1);

        cur = ggml_cont(ctx0, ggml_transpose(ctx0, cur));

        inpL = cur;

        // convnext
        for (uint32_t il = 0; il < hparams.convnext.n_layer; ++il) {
            const auto & layer = model.layers[il].convnext;

            cur = inpL;

            cur = ggml_conv_1d_dw_ph(ctx0, layer.dw, cur, 1, 1);
            cur = ggml_add(ctx0, cur, layer.dw_b);

            cur = ggml_cont(ctx0, ggml_transpose(ctx0, cur));

            cur = llm_build_norm(ctx0, cur, hparams,
                    layer.norm,
                    layer.norm_b,
                    LLM_NORM, cb, -1);

            cur = llm_build_ffn(ctx0, lctx, cur,
                    layer.pw1, layer.pw1_b, NULL,
                    NULL,      NULL,        NULL,
                    layer.pw2, layer.pw2_b, NULL,
                    NULL,
                    LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);

            cur = ggml_mul(ctx0, cur, layer.gamma);

            cur = ggml_cont(ctx0, ggml_transpose(ctx0, cur));

            inpL = ggml_add(ctx0, cur, inpL);
        }

        cur = inpL;

        cur = ggml_cont(ctx0, ggml_transpose(ctx0, cur));

        cur = llm_build_norm(ctx0, cur, hparams,
                model.output_norm,
                model.output_norm_b,
                LLM_NORM, cb, -1);

        // lm_head
        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);

        cur = ggml_add(ctx0, cur, model.output_b);
        cb(cur, "result_embd", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }
};

static struct ggml_cgraph * llama_build_graph_defrag(llama_context & lctx, const std::vector<uint32_t> & ids) {
    llama_ubatch dummy = {};
    dummy.equal_seqs = true;

    llm_build_cb cb = [&](struct ggml_tensor * , const char * , int ) { };

    struct llm_build_context llm(lctx, dummy, cb, false);

    llm.init();

    struct ggml_cgraph * result = llm.build_defrag(ids);

    llm.free();

    return result;
}

static struct ggml_cgraph * llama_build_graph_k_shift(llama_context & lctx) {
    llama_ubatch dummy = {};
    dummy.equal_seqs = true;

    llm_build_cb cb = [&](struct ggml_tensor * , const char * , int ) { };

    struct llm_build_context llm(lctx, dummy, cb, false);

    llm.init();

    struct ggml_cgraph * result = llm.build_k_shift();

    llm.free();

    return result;
}

static struct ggml_cgraph * llama_build_graph(
         llama_context & lctx,
    const llama_ubatch & ubatch,
                  bool   worst_case) {
    const auto & model = lctx.model;

    // this callback allows us to apply custom logic to each tensor (e.g. ggml-alloc, offloading, etc.)
    llm_build_cb cb = [&](struct ggml_tensor * cur, const char * name, int il) {
        if (il >= 0) {
            ggml_format_name(cur, "%s-%d", name, il);
        } else {
            ggml_set_name(cur, name);
        }

        if (!lctx.cparams.offload_kqv) {
            if (strcmp(name, "kqv_merged_cont") == 0) {
                // all nodes between the KV store and the attention output are run on the CPU
                ggml_backend_sched_set_tensor_backend(lctx.sched.get(), cur, lctx.backend_cpu);
            }
        }

        // norm may be automatically assigned to the backend of the previous layer, increasing data transfer between backends
        // FIXME: fix in ggml_backend_sched
        const bool full_offload = lctx.model.n_gpu_layers > (int)lctx.model.hparams.n_layer;
        if (ubatch.n_tokens < 32 || full_offload) {
            if (il != -1 && strcmp(name, "norm") == 0) {
                const auto & dev_layer = lctx.model.dev_layer.at(il);
                for (auto & backend : lctx.backends) {
                    if (ggml_backend_get_device(backend.get()) == dev_layer.dev) {
                        if (ggml_backend_supports_op(backend.get(), cur)) {
                            ggml_backend_sched_set_tensor_backend(lctx.sched.get(), cur, backend.get());
                        }
                    }
                }
            }
        }
    };

    struct ggml_cgraph * result = NULL;

    struct llm_build_context llm(lctx, ubatch, cb, worst_case);

    llm.init();

    switch (model.arch) {
        case LLM_ARCH_LLAMA:
        case LLM_ARCH_MINICPM:
        case LLM_ARCH_GRANITE:
        case LLM_ARCH_GRANITE_MOE:
            {
                result = llm.build_llama();
            } break;
        case LLM_ARCH_DECI:
            {
                result = llm.build_deci();
            } break;
        case LLM_ARCH_BAICHUAN:
            {
                result = llm.build_baichuan();
            } break;
        case LLM_ARCH_FALCON:
            {
                result = llm.build_falcon();
            } break;
        case LLM_ARCH_GROK:
            {
                result = llm.build_grok();
            } break;
        case LLM_ARCH_STARCODER:
            {
                result = llm.build_starcoder();
            } break;
        case LLM_ARCH_REFACT:
            {
                result = llm.build_refact();
            } break;
        case LLM_ARCH_BERT:
        case LLM_ARCH_JINA_BERT_V2:
        case LLM_ARCH_NOMIC_BERT:
            {
                result = llm.build_bert();
            } break;
        case LLM_ARCH_BLOOM:
            {
                result = llm.build_bloom();
            } break;
        case LLM_ARCH_MPT:
            {
                result = llm.build_mpt();
            } break;
         case LLM_ARCH_STABLELM:
            {
                result = llm.build_stablelm();
            } break;
        case LLM_ARCH_QWEN:
            {
                result = llm.build_qwen();
            } break;
        case LLM_ARCH_QWEN2:
            {
                result = llm.build_qwen2();
            } break;
        case LLM_ARCH_QWEN2VL:
            {
                lctx.n_pos_per_token = 4;
                result = llm.build_qwen2vl();
            } break;
        case LLM_ARCH_QWEN2MOE:
            {
                result = llm.build_qwen2moe();
            } break;
        case LLM_ARCH_PHI2:
            {
                result = llm.build_phi2();
            } break;
        case LLM_ARCH_PHI3:
            {
                result = llm.build_phi3();
            } break;
        case LLM_ARCH_PLAMO:
            {
                result = llm.build_plamo();
            } break;
        case LLM_ARCH_GPT2:
            {
                result = llm.build_gpt2();
            } break;
        case LLM_ARCH_CODESHELL:
            {
                result = llm.build_codeshell();
            } break;
        case LLM_ARCH_ORION:
            {
                result = llm.build_orion();
            } break;
        case LLM_ARCH_INTERNLM2:
            {
                result = llm.build_internlm2();
            } break;
        case LLM_ARCH_MINICPM3:
            {
                result = llm.build_minicpm3();
            } break;
        case LLM_ARCH_GEMMA:
            {
                result = llm.build_gemma();
            } break;
        case LLM_ARCH_GEMMA2:
            {
                result = llm.build_gemma2();
            } break;
        case LLM_ARCH_STARCODER2:
            {
                result = llm.build_starcoder2();
            } break;
        case LLM_ARCH_MAMBA:
            {
                result = llm.build_mamba();
            } break;
        case LLM_ARCH_XVERSE:
            {
                result = llm.build_xverse();
            } break;
        case LLM_ARCH_COMMAND_R:
            {
                result = llm.build_command_r();
            } break;
        case LLM_ARCH_COHERE2:
            {
                result = llm.build_cohere2();
            } break;
        case LLM_ARCH_DBRX:
            {
                result = llm.build_dbrx();
            } break;
        case LLM_ARCH_OLMO:
            {
                result = llm.build_olmo();
            } break;
        case LLM_ARCH_OLMO2:
            {
                result = llm.build_olmo2();
            } break;
        case LLM_ARCH_OLMOE:
            {
                result = llm.build_olmoe();
            } break;
        case LLM_ARCH_OPENELM:
            {
                result = llm.build_openelm();
            } break;
        case LLM_ARCH_GPTNEOX:
            {
                result = llm.build_gptneox();
            } break;
        case LLM_ARCH_ARCTIC:
            {
                result = llm.build_arctic();
            } break;
        case LLM_ARCH_DEEPSEEK:
            {
                result = llm.build_deepseek();
            } break;
        case LLM_ARCH_DEEPSEEK2:
            {
                result = llm.build_deepseek2();
            } break;
        case LLM_ARCH_CHATGLM:
            {
                result = llm.build_chatglm();
            } break;
        case LLM_ARCH_BITNET:
            {
                result = llm.build_bitnet();
            } break;
        case LLM_ARCH_T5:
            {
                if (lctx.is_encoding) {
                    result = llm.build_t5_enc();
                } else {
                    result = llm.build_t5_dec();
                }
            } break;
        case LLM_ARCH_T5ENCODER:
            {
                result = llm.build_t5_enc();
            } break;
        case LLM_ARCH_JAIS:
            {
                result = llm.build_jais();
            } break;
        case LLM_ARCH_NEMOTRON:
            {
                result = llm.build_nemotron();
            } break;
        case LLM_ARCH_EXAONE:
            {
                result = llm.build_exaone();
            } break;
        case LLM_ARCH_RWKV6:
            {
                result = llm.build_rwkv6();
            } break;
        case LLM_ARCH_CHAMELEON:
            {
                result = llm.build_chameleon();
            } break;
        case LLM_ARCH_WAVTOKENIZER_DEC:
            {
                result = llm.build_wavtokenizer_dec();
            } break;
        default:
            GGML_ABORT("fatal error");
    }

    // add on pooling layer
    if (lctx.cparams.embeddings) {
        result = llm.append_pooling(result);
    }

    llm.free();

    return result;
}

// returns the result of ggml_backend_sched_graph_compute_async execution
static enum ggml_status llama_graph_compute(
          llama_context & lctx,
            ggml_cgraph * gf,
                    int   n_threads,
        ggml_threadpool * threadpool) {
    if (lctx.backend_cpu != nullptr) {
        auto * reg = ggml_backend_dev_backend_reg(ggml_backend_get_device(lctx.backend_cpu));
        auto * set_threadpool_fn = (decltype(ggml_backend_cpu_set_threadpool) *) ggml_backend_reg_get_proc_address(reg, "ggml_backend_cpu_set_threadpool");
        set_threadpool_fn(lctx.backend_cpu, threadpool);
    }

    // set the number of threads for all the backends
    for (const auto & set_n_threads_fn : lctx.set_n_threads_fns) {
        set_n_threads_fn.second(set_n_threads_fn.first, n_threads);
    }

    auto status = ggml_backend_sched_graph_compute_async(lctx.sched.get(), gf);
    if (status != GGML_STATUS_SUCCESS) {
        LLAMA_LOG_ERROR("%s: ggml_backend_sched_graph_compute_async failed with error %d\n", __func__, status);
    }

    // fprintf(stderr, "splits: %d\n", ggml_backend_sched_get_n_splits(lctx.sched));

    return status;
}

// decode a batch of tokens by evaluating the transformer
// in case of unsuccessful decoding (error or warning),
// the kv_cache state will be returned to its original state
// (for non-recurrent models) or cleaned (for recurrent models)
//
//   - lctx:      llama context
//   - batch:     batch to evaluate
//
// return 0 on success
// return positive int on warning
// return negative int on error
//
static int llama_decode_impl(
         llama_context & lctx,
           llama_batch   inp_batch) {

    lctx.is_encoding = false;

    if (inp_batch.n_tokens == 0) {
        LLAMA_LOG_ERROR("%s: n_tokens == 0\n", __func__);
        return -1;
    }

    // temporary allocate memory for the input batch if needed
    llama_batch_allocr batch_allocr(inp_batch, inp_batch.pos ? -1 : lctx.kv_self.max_pos() + 1);

    const llama_batch & batch = batch_allocr.batch;
    const uint32_t n_tokens_all = batch.n_tokens;

    const auto & model   = lctx.model;
    const auto & hparams = model.hparams;
    const auto & cparams = lctx.cparams;

    GGML_ASSERT((!batch.token && batch.embd) || (batch.token && !batch.embd)); // NOLINT

    if (batch.token) {
        for (uint32_t i = 0; i < n_tokens_all; ++i) {
            if (batch.token[i] < 0 || (uint32_t)batch.token[i] >= model.vocab.n_vocab) {
                LLAMA_LOG_ERROR("%s: invalid token[%d] = %d\n", __func__, i, batch.token[i]);
                return -1;
            }
        }
    }

    GGML_ASSERT(n_tokens_all <= cparams.n_batch);

    GGML_ASSERT((cparams.causal_attn || cparams.n_ubatch >= n_tokens_all) && "non-causal attention requires n_ubatch >= n_tokens");

    if (lctx.t_compute_start_us == 0) {
        lctx.t_compute_start_us = ggml_time_us();
    }
    lctx.n_queued_tokens += n_tokens_all;

    auto & kv_self = lctx.kv_self;
    llama_kv_slot_restorer kv_slot_restorer(kv_self);

    const int64_t n_embd  = hparams.n_embd;
    const int64_t n_vocab = hparams.n_vocab;

    uint32_t n_outputs = 0;
    uint32_t n_outputs_prev = 0;

    const auto n_ubatch = cparams.n_ubatch;

    // this indicates we are doing pooled embedding, so we ignore batch.logits and output all tokens
    const bool embd_pooled = cparams.embeddings && cparams.pooling_type != LLAMA_POOLING_TYPE_NONE;

    lctx.embd_seq.clear();

    // count outputs
    if (batch.logits && !embd_pooled) {
        for (uint32_t i = 0; i < n_tokens_all; ++i) {
            n_outputs += batch.logits[i] != 0;
        }
    } else if (lctx.logits_all || embd_pooled) {
        n_outputs = n_tokens_all;
    } else {
        // keep last output only
        n_outputs = 1;
    }

    lctx.sbatch.from_batch(batch, n_embd,
        /* simple_split */ !kv_self.recurrent,
        /* logits_all   */ n_outputs == n_tokens_all);

    // reserve output buffer
    if (llama_output_reserve(lctx, n_outputs) < n_outputs) {
        LLAMA_LOG_ERROR("%s: could not reserve space for batch with %u outputs\n", __func__, n_outputs);
        return -2;
    };

    while (lctx.sbatch.n_tokens > 0) {
        llama_ubatch ubatch;
        if (kv_self.recurrent) {
            if (embd_pooled) {
                // Pooled embeddings cannot be split across ubatches (yet)
                ubatch = lctx.sbatch.split_seq(n_ubatch);
            } else {
                // recurrent model architectures are easier to implement
                // with equal-length sequences
                ubatch = lctx.sbatch.split_equal(n_ubatch);
            }
        } else {
            ubatch = lctx.sbatch.split_simple(n_ubatch);
        }
        const uint32_t n_tokens = ubatch.n_tokens;

        // count the outputs in this u_batch
        {
            int32_t n_outputs_new = 0;

            if (n_outputs == n_tokens_all) {
                n_outputs_new = n_tokens;
            } else {
                GGML_ASSERT(ubatch.output);
                for (uint32_t i = 0; i < n_tokens; i++) {
                    n_outputs_new += (int32_t) (ubatch.output[i] != 0);
                }
            }

            // needs to happen before the graph is built
            lctx.n_outputs = n_outputs_new;
        }

        int n_threads = n_tokens == 1 ? cparams.n_threads : cparams.n_threads_batch;
        ggml_threadpool_t threadpool = n_tokens == 1 ? lctx.threadpool : lctx.threadpool_batch;

        GGML_ASSERT(n_threads > 0);

        // non-causal masks do not use the KV cache
        if (hparams.causal_attn) {
            llama_kv_cache_update(&lctx);

            // if we have enough unused cells before the current head ->
            //   better to start searching from the beginning of the cache, hoping to fill it
            if (kv_self.head > kv_self.used + 2*n_tokens) {
                kv_self.head = 0;
            }

            const auto slot = llama_kv_cache_find_slot(kv_self, ubatch);
            if (!slot) {
                return 1;
            }
            kv_slot_restorer.save(slot);

            if (!kv_self.recurrent) {
                // a heuristic, to avoid attending the full cache if it is not yet utilized
                // after enough generations, the benefit from this heuristic disappears
                // if we start defragmenting the cache, the benefit from this will be more important
                const uint32_t pad = llama_kv_cache_get_padding(cparams);
                kv_self.n = std::min(kv_self.size, std::max(pad, GGML_PAD(llama_kv_cache_cell_max(kv_self), pad)));
                //kv_self.n = llama_kv_cache_cell_max(kv_self);
            }
        }

        //printf("kv_self.n = %5d, kv_self.used = %5d, kv_self.head = %5d\n", kv_self.n, kv_self.used, kv_self.head);

        ggml_backend_sched_reset(lctx.sched.get());
        ggml_backend_sched_set_eval_callback(lctx.sched.get(), lctx.cparams.cb_eval, lctx.cparams.cb_eval_user_data);

        ggml_cgraph * gf = llama_build_graph(lctx, ubatch, false);

        // the output is always the last tensor in the graph
        struct ggml_tensor * res  = ggml_graph_node(gf, -1);
        struct ggml_tensor * embd = ggml_graph_node(gf, -2);

        if (lctx.n_outputs == 0) {
            // no output
            res  = nullptr;
            embd = nullptr;
        } else if (cparams.embeddings) {
            res  = nullptr; // do not extract logits for embedding case
            embd = nullptr;
            for (int i = ggml_graph_n_nodes(gf) - 1; i >= 0; --i) {
                if (strcmp(ggml_graph_node(gf, i)->name, "result_embd_pooled") == 0) {
                    embd = ggml_graph_node(gf, i);
                    break;
                }
            }
            GGML_ASSERT(embd != nullptr && "missing embeddings tensor");
        } else {
            embd = nullptr; // do not extract embeddings when not needed
            GGML_ASSERT(strcmp(res->name, "result_output") == 0 && "missing result_output tensor");
        }

        // LLAMA_LOG_INFO("graph build time: %.3f ms (%d nodes, %d leafs)\n", (ggml_time_us() - t_start_us)/1000.0, gf->n_nodes, gf->n_leafs);

        ggml_backend_sched_alloc_graph(lctx.sched.get(), gf);

        llama_set_inputs(lctx, ubatch);

        const auto compute_status = llama_graph_compute(lctx, gf, n_threads, threadpool);
        if (compute_status != GGML_STATUS_SUCCESS) {
            kv_slot_restorer.restore(kv_self);
            switch (compute_status) {
                case GGML_STATUS_ABORTED:
                    return 2;
                case GGML_STATUS_ALLOC_FAILED:
                    return -2;
                case GGML_STATUS_FAILED:
                default:
                    return -3;
            }
        }

        // update the kv ring buffer
        {
            kv_self.head += n_tokens;

            // Ensure kv cache head points to a valid index.
            if (kv_self.head >= kv_self.size) {
                kv_self.head = 0;
            }
        }

        // plot the computation graph in dot format (for debugging purposes)
        //if (n_past%100 == 0) {
        //    ggml_graph_dump_dot(gf, NULL, "llama.dot");
        //}

        // extract logits
        if (res) {
            ggml_backend_t backend_res = ggml_backend_sched_get_tensor_backend(lctx.sched.get(), res);
            GGML_ASSERT(backend_res != nullptr);
            GGML_ASSERT(lctx.logits != nullptr);

            float * logits_out = lctx.logits + n_outputs_prev*n_vocab;
            const int32_t n_outputs_new = lctx.n_outputs;

            if (n_outputs_new) {
                GGML_ASSERT( n_outputs_prev + n_outputs_new <= n_outputs);
                GGML_ASSERT((n_outputs_prev + n_outputs_new)*n_vocab <= (int64_t) lctx.logits_size);
                ggml_backend_tensor_get_async(backend_res, res, logits_out, 0, n_outputs_new*n_vocab*sizeof(float));
            }
        }

        // extract embeddings
        if (embd) {
            ggml_backend_t backend_embd = ggml_backend_sched_get_tensor_backend(lctx.sched.get(), embd);
            GGML_ASSERT(backend_embd != nullptr);

            switch (cparams.pooling_type) {
                case LLAMA_POOLING_TYPE_NONE:
                    {
                        // extract token embeddings
                        GGML_ASSERT(lctx.embd != nullptr);
                        float * embd_out = lctx.embd + n_outputs_prev*n_embd;
                        const int32_t n_outputs_new = lctx.n_outputs;

                        if (n_outputs_new) {
                            GGML_ASSERT( n_outputs_prev + n_outputs_new <= n_outputs);
                            GGML_ASSERT((n_outputs_prev + n_outputs_new)*n_embd <= (int64_t) lctx.embd_size);
                            ggml_backend_tensor_get_async(backend_embd, embd, embd_out, 0, n_outputs_new*n_embd*sizeof(float));
                        }
                    } break;
                case LLAMA_POOLING_TYPE_MEAN:
                case LLAMA_POOLING_TYPE_CLS:
                case LLAMA_POOLING_TYPE_LAST:
                    {
                        // extract sequence embeddings (cleared before processing each batch)
                        auto & embd_seq_out = lctx.embd_seq;

                        for (uint32_t s = 0; s < ubatch.n_seqs; ++s) {
                            const llama_seq_id seq_id = ubatch.seq_id[s][0];
                            if (embd_seq_out.find(seq_id) != embd_seq_out.end()) {
                                continue;
                            }
                            embd_seq_out[seq_id].resize(n_embd);
                            ggml_backend_tensor_get_async(backend_embd, embd, embd_seq_out[seq_id].data(), (n_embd*seq_id)*sizeof(float), n_embd*sizeof(float));
                        }
                    } break;
                case LLAMA_POOLING_TYPE_RANK:
                    {
                        // extract the rerank score - a single float per sequence
                        auto & embd_seq_out = lctx.embd_seq;

                        for (uint32_t s = 0; s < ubatch.n_seqs; ++s) {
                            const llama_seq_id seq_id = ubatch.seq_id[s][0];
                            if (embd_seq_out.find(seq_id) != embd_seq_out.end()) {
                                continue;
                            }
                            embd_seq_out[seq_id].resize(1);
                            ggml_backend_tensor_get_async(backend_embd, embd, embd_seq_out[seq_id].data(), (seq_id)*sizeof(float), sizeof(float));
                        }
                    } break;
                case LLAMA_POOLING_TYPE_UNSPECIFIED:
                    {
                        GGML_ABORT("unknown pooling type");
                    }
            }
        }
        n_outputs_prev += lctx.n_outputs;
    }

    // set output mappings
    {
        bool sorted_output = true;

        GGML_ASSERT(lctx.sbatch.out_ids.size() == n_outputs);

        for (size_t i = 0; i < n_outputs; ++i) {
            size_t out_id = lctx.sbatch.out_ids[i];
            lctx.output_ids[out_id] = i;
            if (out_id != i) {
                sorted_output = false;
            }
        }

        if (sorted_output) {
            lctx.sbatch.out_ids.clear();
        }
    }

    // set to total number of outputs in the batch, for use in llama_get_logits_ith
    lctx.n_outputs = n_outputs;

    // wait for the computation to finish (automatically done when obtaining the model output)
    //llama_synchronize(&lctx);

    // decide if we need to defrag the kv cache
    if (cparams.causal_attn && cparams.defrag_thold >= 0.0f) {
        const float fragmentation = kv_self.n >= 128 ? 1.0f - float(kv_self.used)/float(kv_self.n) : 0.0f;

        // queue defragmentation for next llama_kv_cache_update
        if (fragmentation > cparams.defrag_thold) {
            //LLAMA_LOG_INFO("fragmentation: %.2f\n", fragmentation);

            llama_kv_cache_defrag(kv_self);
        }
    }

    // Reset state for the next token before backend sync, to allow the CPU activities in the reset to
    // overlap with device computation.
    ggml_backend_sched_reset(lctx.sched.get());

    return 0;
}

// encode a batch of tokens by evaluating the encoder part of the transformer
//
//   - lctx:      llama context
//   - batch:     batch to evaluate
//
// return 0 on success
// return positive int on warning
// return negative int on error
//
static int llama_encode_impl(
         llama_context & lctx,
           llama_batch   inp_batch) {

    lctx.is_encoding = true;

    if (inp_batch.n_tokens == 0) {
        LLAMA_LOG_ERROR("%s: n_tokens == 0\n", __func__);
        return -1;
    }

    // temporary allocate memory for the input batch if needed
    llama_batch_allocr batch_allocr(inp_batch, inp_batch.pos ? -1 : lctx.kv_self.max_pos() + 1);

    const llama_batch & batch = batch_allocr.batch;
    const uint32_t n_tokens = batch.n_tokens;

    const auto & model   = lctx.model;
    const auto & hparams = model.hparams;
    const auto & cparams = lctx.cparams;

    GGML_ASSERT((!batch.token && batch.embd) || (batch.token && !batch.embd)); // NOLINT

    if (batch.token) {
        for (uint32_t i = 0; i < n_tokens; ++i) {
            if (batch.token[i] < 0 || (uint32_t)batch.token[i] >= model.vocab.n_vocab) {
                LLAMA_LOG_ERROR("%s: invalid token[%d] = %d\n", __func__, i, batch.token[i]);
                return -1;
            }
        }
    }

    // micro-batching is not possible for non-causal encoding, so we process the batch in a single shot
    GGML_ASSERT(cparams.n_ubatch >= n_tokens && "encoder requires n_ubatch >= n_tokens");

    if (lctx.t_compute_start_us == 0) {
        lctx.t_compute_start_us = ggml_time_us();
    }

    lctx.n_queued_tokens += n_tokens;

    const int64_t n_embd = hparams.n_embd;

    lctx.sbatch.from_batch(batch, n_embd, /* simple_split */ true, /* logits_all */ true);

    const llama_ubatch ubatch = lctx.sbatch.split_simple(n_tokens);

    // reserve output buffer
    if (llama_output_reserve(lctx, n_tokens) < n_tokens) {
        LLAMA_LOG_ERROR("%s: could not reserve space for batch with %u outputs\n", __func__, n_tokens);
        return -2;
    };

    for (uint32_t i = 0; i < n_tokens; ++i) {
        lctx.output_ids[i] = i;
    }

    lctx.inp_embd_enc = NULL;
    lctx.n_outputs = n_tokens;

    int n_threads = n_tokens == 1 ? cparams.n_threads : cparams.n_threads_batch;
    ggml_threadpool_t threadpool = n_tokens == 1 ? lctx.threadpool : lctx.threadpool_batch;

    GGML_ASSERT(n_threads > 0);

    ggml_backend_sched_reset(lctx.sched.get());
    ggml_backend_sched_set_eval_callback(lctx.sched.get(), lctx.cparams.cb_eval, lctx.cparams.cb_eval_user_data);

    ggml_cgraph * gf = llama_build_graph(lctx, ubatch, false);

    // the output embeddings after the final encoder normalization
    struct ggml_tensor * embd = nullptr;

    // there are two cases here
    if (llama_model_has_decoder(&lctx.model)) {
        // first case is an encoder-decoder T5 model where embeddings are passed to decoder
        embd = ggml_graph_node(gf, -1);
        GGML_ASSERT(strcmp(embd->name, "result_norm") == 0 && "missing result_output tensor");
    } else {
        // second case is an encoder-only T5 model
        if (cparams.embeddings) {
            // only output embeddings if required
            embd = ggml_graph_node(gf, -1);
            if (strcmp(embd->name, "result_embd_pooled") != 0) {
                embd = ggml_graph_node(gf, -2);
            }
            GGML_ASSERT(strcmp(embd->name, "result_embd_pooled") == 0 && "missing embeddings tensor");
        }
    }

    ggml_backend_sched_alloc_graph(lctx.sched.get(), gf);

    llama_set_inputs(lctx, ubatch);

    const auto compute_status = llama_graph_compute(lctx, gf, n_threads, threadpool);
    switch (compute_status) {
        case GGML_STATUS_SUCCESS:
            break;
        case GGML_STATUS_ABORTED:
            return 2;
        case GGML_STATUS_ALLOC_FAILED:
            return -2;
        case GGML_STATUS_FAILED:
        default:
            return -3;
    }

    // extract embeddings
    if (embd) {
        ggml_backend_t backend_embd = ggml_backend_sched_get_tensor_backend(lctx.sched.get(), embd);
        GGML_ASSERT(backend_embd != nullptr);

        if (llama_model_has_decoder(&lctx.model)) {
            lctx.embd_enc.resize(n_tokens*n_embd);
            float * embd_out = lctx.embd_enc.data();

            ggml_backend_tensor_get_async(backend_embd, embd, embd_out, 0, n_tokens*n_embd*sizeof(float));
            GGML_ASSERT(!ubatch.equal_seqs); // TODO: handle equal splits

            // remember the sequence ids used during the encoding - needed for cross attention later
            lctx.seq_ids_enc.resize(n_tokens);
            for (uint32_t i = 0; i < n_tokens; i++) {
                for (int s = 0; s < ubatch.n_seq_id[i]; s++) {
                    llama_seq_id seq_id = ubatch.seq_id[i][s];
                    lctx.seq_ids_enc[i].insert(seq_id);
                }
            }
        } else {
            GGML_ASSERT(lctx.embd != nullptr);

            switch (cparams.pooling_type) {
                case LLAMA_POOLING_TYPE_NONE:
                    {
                        // extract token embeddings
                        GGML_ASSERT(lctx.embd != nullptr);
                        float * embd_out = lctx.embd;

                        GGML_ASSERT(n_tokens*n_embd <= (int64_t) lctx.embd_size);
                        ggml_backend_tensor_get_async(backend_embd, embd, embd_out, 0, n_tokens*n_embd*sizeof(float));
                    } break;
                case LLAMA_POOLING_TYPE_MEAN:
                case LLAMA_POOLING_TYPE_CLS:
                case LLAMA_POOLING_TYPE_LAST:
                    {
                        // extract sequence embeddings
                        auto & embd_seq_out = lctx.embd_seq;
                        embd_seq_out.clear();

                        GGML_ASSERT(!ubatch.equal_seqs); // TODO: handle equal splits

                        for (uint32_t i = 0; i < n_tokens; i++) {
                            const llama_seq_id seq_id = ubatch.seq_id[i][0];
                            if (embd_seq_out.find(seq_id) != embd_seq_out.end()) {
                                continue;
                            }
                            embd_seq_out[seq_id].resize(n_embd);
                            ggml_backend_tensor_get_async(backend_embd, embd, embd_seq_out[seq_id].data(), (n_embd*seq_id)*sizeof(float), n_embd*sizeof(float));
                        }
                    } break;
                case LLAMA_POOLING_TYPE_RANK:
                    {
                        // TODO: this likely should be the same logic as in llama_decoder_internal, but better to
                        //       wait for an encoder model that requires this pooling type in order to test it
                        //       https://github.com/ggerganov/llama.cpp/pull/9510
                        GGML_ABORT("RANK pooling not implemented yet");
                    }
                case LLAMA_POOLING_TYPE_UNSPECIFIED:
                    {
                        GGML_ABORT("unknown pooling type");
                    }
            }
        }
    }

    // Reset state for the next token before backend sync, to allow the CPU activities in the reset to
    // overlap with device computation.
    ggml_backend_sched_reset(lctx.sched.get());

    return 0;
}

// find holes from the beginning of the KV cache and fill them by moving data from the end of the cache
static void llama_kv_cache_defrag_impl(struct llama_context & lctx) {
    auto & kv_self = lctx.kv_self;

    const auto & hparams = lctx.model.hparams;

    const uint32_t n_layer = hparams.n_layer;

    const uint32_t n_kv   = llama_kv_cache_cell_max(kv_self);
    const uint32_t n_used = kv_self.used;

    assert(n_used <= n_kv);

    //const int64_t t_start = ggml_time_us();

    // number of cells moved
    uint32_t n_moves = 0;

    // each move requires 6*n_layer tensors (see build_defrag)
    //   - source view, destination view, copy operation
    //   - x2 for keys and values
    //const uint32_t max_moves = llama_model_max_nodes(model)/(6*n_layer);
    // TODO: tmp fix https://github.com/ggerganov/llama.cpp/issues/6685#issuecomment-2057579516
    const uint32_t max_moves = (llama_model_max_nodes(lctx.model) - 2*n_layer)/(6*n_layer);

    // determine which KV cells to move where
    //
    //  cell i moves to ids[i]
    //
    //  if ids[i] == i || ids[i] == n_kv, then cell i is not moved
    //
    std::vector<uint32_t> ids(n_kv, n_kv);

    for (uint32_t i0 = 0; i0 < n_used; ++i0) {
        const auto & cell0 = kv_self.cells[i0];

        if (!cell0.is_empty()) {
            ids[i0] = i0;

            continue;
        }

        // found a hole - fill it with data from the end of the cache

        uint32_t nh = 1;

        // determine the size of the hole
        while (i0 + nh < n_used && kv_self.cells[i0 + nh].is_empty()) {
            nh++;
        }

        uint32_t nf = 0;
        uint32_t is = n_kv - 1;

        // starting from the end, find nh non-empty cells
        for (; is > i0; --is) {
            const auto & cell1 = kv_self.cells[is];

            if (cell1.is_empty() || ids[is] != n_kv) {
                continue;
            }

            // non-empty cell which is not yet moved
            nf++;

            if (nf == nh) {
                break;
            }
        }

        // this can only happen if `n_used` is not accurate, which would be a bug
        GGML_ASSERT(nf == nh && "KV defrag bug: nf != nh");

        nf = 0;

        uint32_t i1 = is;

        // are we moving a continuous block of memory?
        bool cont = false;

        // should we stop searching for the next move?
        bool stop = false;

        // go back and move the nf cells to the hole
        for (; i1 < n_kv; ++i1) {
            auto & cell1 = kv_self.cells[i1];

            if (cell1.is_empty() || ids[i1] != n_kv) {
                if (n_moves == max_moves) {
                    stop = true;
                    break;
                }

                cont = false;
                continue;
            }

            // this cell goes to (i0 + nf)
            ids[i1] = i0 + nf;

            // move the cell meta data
            kv_self.cells[i0 + nf] = cell1;

            // clear the old cell and move the head there
            cell1 = llama_kv_cell();
            kv_self.head = n_used;

            if (!cont) {
                n_moves++;
                cont = true;
            }

            nf++;

            if (nf == nh) {
                break;
            }
        }

        if (stop || n_moves == max_moves) {
            break;
        }

        //LLAMA_LOG_INFO("(tmp log) KV defrag: move [%u, %u) to [%u, %u)\n", is, i1 + 1, i0, i0 + nh);

        i0 += nh - 1;
    }

    if (n_moves == 0) {
        return;
    }

    //LLAMA_LOG_INFO("(tmp log) KV defrag cell moves: %u\n", n_moves);

    //LLAMA_LOG_INFO("expected gf nodes: %u\n", 6*n_moves*n_layer);

#if 0
    // CPU defrag
    //
    // TODO: optimizations are possible:
    //       - multiple threads
    //       - avoid copying to the host memory when already there
    //
    // likely not worth the effort, as we have ggml_graph based defrag
    //

    const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa();
    const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa();

    const uint32_t kv_size = kv_self.size;

    std::vector<uint8_t> buf_k;
    std::vector<uint8_t> buf_v;

    for (uint32_t il = 0; il < n_layer; ++il) {
        const size_t k_size_row = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa);
        const size_t k_size     = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa*kv_size);

        const size_t v_size_el = ggml_type_size(kv_self.v_l[il]->type);
        const size_t v_size    = ggml_row_size (kv_self.v_l[il]->type, n_embd_v_gqa*kv_size);

        buf_k.resize(k_size);
        buf_v.resize(v_size);

        ggml_backend_tensor_get(kv_self.k_l[il], buf_k.data(), 0, buf_k.size());
        ggml_backend_tensor_get(kv_self.v_l[il], buf_v.data(), 0, buf_v.size());

        // batch move [i, i+nm) to [id, id+nm)
        // note: cells can move only to a lower index
        for (uint32_t i = 0; i < n_kv; ++i) {
            const uint32_t id = ids[i];

            if (i == id || id == n_kv) {
                continue;
            }

            uint32_t nm = 1;

            while (i + nm < n_kv && ids[i + nm] == id + nm) {
                nm++;
            }

            // move keys
            {
                const int64_t os =  i*k_size_row;
                const int64_t od = id*k_size_row;

                memcpy(buf_k.data() + od, buf_k.data() + os, nm*k_size_row);
            }

            // move values (note: they are transposed)
            {
                const int64_t os =  i;
                const int64_t od = id;

                for (uint32_t j = 0; j < n_embd_v_gqa; ++j) {
                    memcpy(buf_v.data() + (od + j*kv_size)*v_size_el, buf_v.data() + (os + j*kv_size)*v_size_el, nm*v_size_el);
                }
            }

            i += nm - 1;
        }

        ggml_backend_tensor_set(kv_self.k_l[il], buf_k.data(), 0, buf_k.size());
        ggml_backend_tensor_set(kv_self.v_l[il], buf_v.data(), 0, buf_v.size());
    }
#else
    // ggml_graph defrag

    ggml_backend_sched_reset(lctx.sched.get());

    ggml_cgraph * gf = llama_build_graph_defrag(lctx, ids);

    llama_graph_compute(lctx, gf, lctx.cparams.n_threads, lctx.threadpool);
#endif

    //const int64_t t_end = ggml_time_us();

    //LLAMA_LOG_INFO("(tmp log) KV defrag time: %.3f ms\n", (t_end - t_start)/1000.0);
}

static void llama_kv_cache_update_impl(struct llama_context & lctx) {
    bool need_reserve = false;

    if (lctx.kv_self.has_shift) {
        if (!llama_kv_cache_can_shift(&lctx)) {
            GGML_ABORT("The current context does not support K-shift");
        }

        // apply K-shift if needed
        if (lctx.model.hparams.rope_type != LLAMA_ROPE_TYPE_NONE) {
            ggml_backend_sched_reset(lctx.sched.get());

            ggml_cgraph * gf = llama_build_graph_k_shift(lctx);

            ggml_backend_sched_alloc_graph(lctx.sched.get(), gf);

            llama_set_k_shift(lctx);

            llama_graph_compute(lctx, gf, lctx.cparams.n_threads, lctx.threadpool);

            need_reserve = true;
        }

        {
            auto & kv_self = lctx.kv_self;

            kv_self.has_shift = false;

            for (uint32_t i = 0; i < kv_self.size; ++i) {
                kv_self.cells[i].delta = 0;
            }
        }
    }

    // defragment the KV cache if needed
    if (lctx.kv_self.do_defrag) {
        llama_kv_cache_defrag_impl(lctx);

        need_reserve = true;

        lctx.kv_self.do_defrag = false;
    }

    // reserve a worst case graph again
    if (need_reserve) {
        // TODO: extract to a function
        // build worst-case graph
        uint32_t n_seqs = 1; // TODO: worst-case number of sequences
        uint32_t n_tokens = std::min(lctx.cparams.n_ctx, lctx.cparams.n_ubatch);
        llama_token token = llama_token_bos(&lctx.model); // not actually used by llama_build_graph, but required to choose between token and embedding inputs graph
        llama_ubatch ubatch = { true, n_tokens, n_tokens / n_seqs, n_seqs, &token, nullptr, nullptr, nullptr, nullptr, nullptr};
        ggml_cgraph * gf = llama_build_graph(lctx, ubatch, true);

        // initialize scheduler with the worst-case graph
        ggml_backend_sched_reset(lctx.sched.get());
        if (!ggml_backend_sched_reserve(lctx.sched.get(), gf)) {
            LLAMA_LOG_ERROR("%s: failed to allocate compute buffers\n", __func__);
        }
    }
}

int32_t llama_lora_adapter_set(
            struct llama_context * ctx,
            struct llama_lora_adapter * adapter,
            float scale) {
    ctx->lora_adapters[adapter] = scale;
    return 0;
}

int32_t llama_lora_adapter_remove(
            struct llama_context * ctx,
            struct llama_lora_adapter * adapter) {
    auto pos = ctx->lora_adapters.find(adapter);
    if (pos != ctx->lora_adapters.end()) {
        ctx->lora_adapters.erase(pos);
        return 0;
    }

    return -1;
}

void llama_lora_adapter_clear(struct llama_context * ctx) {
    ctx->lora_adapters.clear();
}

// TODO: tmp
int32_t llama_control_vector_apply(
        struct llama_context * lctx,
                 const float * data,
                      size_t   len,
                     int32_t   n_embd,
                     int32_t   il_start,
                     int32_t   il_end) {
    return llama_control_vector_apply(lctx->cvec, lctx->model, data, len, n_embd, il_start, il_end);
}

//
// interface implementation
//

struct llama_context_params llama_context_default_params() {
    struct llama_context_params result = {
        /*.n_ctx                       =*/ 512,
        /*.n_batch                     =*/ 2048,
        /*.n_ubatch                    =*/ 512,
        /*.n_seq_max                   =*/ 1,
        /*.n_threads                   =*/ GGML_DEFAULT_N_THREADS, // TODO: better default
        /*.n_threads_batch             =*/ GGML_DEFAULT_N_THREADS,
        /*.rope_scaling_type           =*/ LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED,
        /*.pooling_type                =*/ LLAMA_POOLING_TYPE_UNSPECIFIED,
        /*.attention_type              =*/ LLAMA_ATTENTION_TYPE_UNSPECIFIED,
        /*.rope_freq_base              =*/ 0.0f,
        /*.rope_freq_scale             =*/ 0.0f,
        /*.yarn_ext_factor             =*/ -1.0f,
        /*.yarn_attn_factor            =*/ 1.0f,
        /*.yarn_beta_fast              =*/ 32.0f,
        /*.yarn_beta_slow              =*/ 1.0f,
        /*.yarn_orig_ctx               =*/ 0,
        /*.defrag_thold                =*/ -1.0f,
        /*.cb_eval                     =*/ nullptr,
        /*.cb_eval_user_data           =*/ nullptr,
        /*.type_k                      =*/ GGML_TYPE_F16,
        /*.type_v                      =*/ GGML_TYPE_F16,
        /*.logits_all                  =*/ false,
        /*.embeddings                  =*/ false,
        /*.offload_kqv                 =*/ true,
        /*.flash_attn                  =*/ false,
        /*.no_perf                     =*/ true,
        /*.abort_callback              =*/ nullptr,
        /*.abort_callback_data         =*/ nullptr,
    };

    return result;
}

struct llama_sampler_chain_params llama_sampler_chain_default_params() {
    struct llama_sampler_chain_params result = {
        /*.no_perf                     =*/ true,
    };

    return result;
}

size_t llama_max_devices(void) {
    return 16;
}

bool llama_supports_mmap(void) {
    return llama_mmap::SUPPORTED;
}

bool llama_supports_mlock(void) {
    return llama_mlock::SUPPORTED;
}

bool llama_supports_gpu_offload(void) {
    return ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_GPU) != nullptr ||
           llama_supports_rpc();
}

bool llama_supports_rpc(void) {
    return ggml_backend_reg_by_name("RPC") != nullptr;
}

void llama_backend_init(void) {
    ggml_time_init();

    // needed to initialize f16 tables
    {
        struct ggml_init_params params = { 0, NULL, false };
        struct ggml_context * ctx = ggml_init(params);
        ggml_free(ctx);
    }
}

void llama_numa_init(enum ggml_numa_strategy numa) {
    if (numa != GGML_NUMA_STRATEGY_DISABLED) {
        auto * dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU);
        GGML_ASSERT(dev && "CPU backend is not loaded");
        auto * reg = ggml_backend_dev_backend_reg(dev);
        auto * numa_init_fn = (decltype(ggml_numa_init) *) ggml_backend_reg_get_proc_address(reg, "ggml_backend_cpu_numa_init");
        numa_init_fn(numa);
    }
}

void llama_backend_free(void) {
    ggml_quantize_free();
}

int64_t llama_time_us(void) {
    return ggml_time_us();
}

struct llama_model * llama_load_model_from_file(
        const char * path_model,
        struct llama_model_params params) {
    return llama_model_load_from_file(path_model, params);
}

struct llama_model * llama_model_load_from_file(
        const char * path_model,
        struct llama_model_params params) {
    ggml_time_init();

    llama_model * model = new llama_model;

    unsigned cur_percentage = 0;
    if (params.progress_callback == NULL) {
        params.progress_callback_user_data = &cur_percentage;
        params.progress_callback = [](float progress, void * ctx) {
            unsigned * cur_percentage_p = (unsigned *) ctx;
            unsigned percentage = (unsigned) (100 * progress);
            while (percentage > *cur_percentage_p) {
                *cur_percentage_p = percentage;
                LLAMA_LOG_CONT(".");
                if (percentage >= 100) {
                    LLAMA_LOG_CONT("\n");
                }
            }
            return true;
        };
    }

    if (params.rpc_servers != nullptr && params.rpc_servers[0] != '\0') {
        // split the servers set them into model->rpc_servers
        std::string servers(params.rpc_servers);
        size_t pos = 0;
        while ((pos = servers.find(',')) != std::string::npos) {
            std::string server = servers.substr(0, pos);
            model->rpc_servers.push_back(server);
            servers.erase(0, pos + 1);
        }
        model->rpc_servers.push_back(servers);
    }

    // add RPC devices
    if (!model->rpc_servers.empty()) {
        ggml_backend_reg_t rpc_reg = ggml_backend_reg_by_name("RPC");
        if (!rpc_reg) {
            LLAMA_LOG_ERROR("%s: failed to find RPC backend\n", __func__);
            llama_model_free(model);
            return nullptr;
        }

        typedef ggml_backend_dev_t (*ggml_backend_rpc_add_device_t)(const char * endpoint);
        ggml_backend_rpc_add_device_t ggml_backend_rpc_add_device_fn = (ggml_backend_rpc_add_device_t) ggml_backend_reg_get_proc_address(rpc_reg, "ggml_backend_rpc_add_device");
        if (!ggml_backend_rpc_add_device_fn) {
            LLAMA_LOG_ERROR("%s: failed to find RPC device add function\n", __func__);
            llama_model_free(model);
            return nullptr;
        }

        for (const std::string & server : model->rpc_servers) {
            ggml_backend_dev_t dev = ggml_backend_rpc_add_device_fn(server.c_str());
            if (dev) {
                model->devices.push_back(dev);
            } else {
                LLAMA_LOG_ERROR("%s: failed to add RPC device for server '%s'\n", __func__, server.c_str());
                llama_model_free(model);
                return nullptr;
            }
        }
    }

    // create list of devices to use with this model
    if (params.devices) {
        for (ggml_backend_dev_t * dev = params.devices; *dev; ++dev) {
            model->devices.push_back(*dev);
        }
    } else {
        // use all available devices
        for (size_t i = 0; i < ggml_backend_dev_count(); ++i) {
            ggml_backend_dev_t dev = ggml_backend_dev_get(i);
            switch (ggml_backend_dev_type(dev)) {
                case GGML_BACKEND_DEVICE_TYPE_CPU:
                case GGML_BACKEND_DEVICE_TYPE_ACCEL:
                    // skip CPU backends since they are handled separately
                    break;

                case GGML_BACKEND_DEVICE_TYPE_GPU:
                    model->devices.push_back(dev);
                    break;
            }
        }
    }

    // if using single GPU mode, remove all except the main GPU
    if (params.split_mode == LLAMA_SPLIT_MODE_NONE) {
        if (params.main_gpu < 0 || params.main_gpu >= (int)model->devices.size()) {
            LLAMA_LOG_ERROR("%s: invalid value for main_gpu: %d (available devices: %d)\n", __func__, params.main_gpu, (int)model->devices.size());
            llama_model_free(model);
            return nullptr;
        }
        ggml_backend_dev_t main_gpu = model->devices[params.main_gpu];
        model->devices.clear();
        model->devices.push_back(main_gpu);
    }

    for (auto * dev : model->devices) {
        size_t free, total; // NOLINT
        ggml_backend_dev_memory(dev, &free, &total);
        LLAMA_LOG_INFO("%s: using device %s (%s) - %zu MiB free\n", __func__, ggml_backend_dev_name(dev), ggml_backend_dev_description(dev), free/1024/1024);
    }

    int status = llama_model_load(path_model, *model, params);
    GGML_ASSERT(status <= 0);
    if (status < 0) {
        if (status == -1) {
            LLAMA_LOG_ERROR("%s: failed to load model\n", __func__);
        } else if (status == -2) {
            LLAMA_LOG_INFO("%s: cancelled model load\n", __func__);
        }

        llama_model_free(model);
        return nullptr;
    }

    return model;
}

struct llama_context * llama_new_context_with_model(
                 struct llama_model * model,
        struct llama_context_params   params) {

    if (!model) {
        LLAMA_LOG_ERROR("%s: model cannot be NULL\n", __func__);
        return nullptr;
    }

    if (params.n_batch == 0 && params.n_ubatch == 0) {
        LLAMA_LOG_ERROR("%s: n_batch and n_ubatch cannot both be zero\n", __func__);
        return nullptr;
    }

    if (params.n_ctx == 0 && model->hparams.n_ctx_train == 0) {
        LLAMA_LOG_ERROR("%s: n_ctx and model->hparams.n_ctx_train cannot both be zero\n", __func__);
        return nullptr;
    }

    if (params.flash_attn && model->arch == LLM_ARCH_GROK) {
        LLAMA_LOG_WARN("%s: flash_attn is not compatible with Grok - forcing off\n", __func__);
        params.flash_attn = false;
    }

    if (params.flash_attn && model->hparams.n_embd_head_k != model->hparams.n_embd_head_v) {
        LLAMA_LOG_WARN("%s: flash_attn requires n_embd_head_k == n_embd_head_v - forcing off\n", __func__);
        params.flash_attn = false;
    }

    if (ggml_is_quantized(params.type_v) && !params.flash_attn) {
        LLAMA_LOG_ERROR("%s: V cache quantization requires flash_attn\n", __func__);
        return nullptr;
    }

    llama_context * ctx = new llama_context(*model);

    const auto & hparams = model->hparams;
    auto       & cparams = ctx->cparams;

    cparams.n_seq_max        = std::max(1u, params.n_seq_max);
    cparams.n_threads        = params.n_threads;
    cparams.n_threads_batch  = params.n_threads_batch;
    cparams.yarn_ext_factor  = params.yarn_ext_factor;
    cparams.yarn_attn_factor = params.yarn_attn_factor;
    cparams.yarn_beta_fast   = params.yarn_beta_fast;
    cparams.yarn_beta_slow   = params.yarn_beta_slow;
    cparams.defrag_thold     = params.defrag_thold;
    cparams.embeddings       = params.embeddings;
    cparams.offload_kqv      = params.offload_kqv;
    cparams.flash_attn       = params.flash_attn;
    cparams.no_perf          = params.no_perf;
    cparams.pooling_type     = params.pooling_type;

    cparams.n_ctx            = params.n_ctx           == 0    ? hparams.n_ctx_train           : params.n_ctx;
    cparams.rope_freq_base   = params.rope_freq_base  == 0.0f ? hparams.rope_freq_base_train  : params.rope_freq_base;
    cparams.rope_freq_scale  = params.rope_freq_scale == 0.0f ? hparams.rope_freq_scale_train : params.rope_freq_scale;

    // this is necessary due to kv_self.n being padded later during inference
    cparams.n_ctx            = GGML_PAD(cparams.n_ctx, llama_kv_cache_get_padding(cparams));

    // with causal attention, the batch size is limited by the context size
    cparams.n_batch          = hparams.causal_attn ? std::min(cparams.n_ctx, params.n_batch) : params.n_batch;

    // the batch has to be at least GGML_KQ_MASK_PAD because we will be padding the KQ_mask
    // this is required by GPU kernels in order to avoid out-of-bounds accesses (e.g. ggml_flash_attn_ext)
    // ref: https://github.com/ggerganov/llama.cpp/pull/5021
    if (cparams.n_batch < GGML_KQ_MASK_PAD) {
        LLAMA_LOG_WARN("%s: n_batch is less than GGML_KQ_MASK_PAD - increasing to %d\n", __func__, GGML_KQ_MASK_PAD);
        cparams.n_batch = GGML_KQ_MASK_PAD;
    }

    cparams.n_ubatch         = std::min(cparams.n_batch, params.n_ubatch == 0 ? params.n_batch : params.n_ubatch);

    cparams.n_ctx_orig_yarn  = params.yarn_orig_ctx    != 0 ? params.yarn_orig_ctx    :
                               hparams.n_ctx_orig_yarn != 0 ? hparams.n_ctx_orig_yarn :
                                                              hparams.n_ctx_train;

    cparams.cb_eval           = params.cb_eval;
    cparams.cb_eval_user_data = params.cb_eval_user_data;

    auto rope_scaling_type = params.rope_scaling_type;
    if (rope_scaling_type == LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED) {
        rope_scaling_type = hparams.rope_scaling_type_train;
    }

    if (rope_scaling_type == LLAMA_ROPE_SCALING_TYPE_NONE) {
        cparams.rope_freq_scale = 1.0f; // never scale if scaling type is none
    }

    if (cparams.yarn_ext_factor < 0.0f) { // negative indicates 'not set'
        cparams.yarn_ext_factor = rope_scaling_type == LLAMA_ROPE_SCALING_TYPE_YARN ? 1.0f : 0.0f;
    }

    cparams.yarn_attn_factor *= hparams.rope_attn_factor;

    if (cparams.pooling_type == LLAMA_POOLING_TYPE_UNSPECIFIED) {
        if (hparams.pooling_type == LLAMA_POOLING_TYPE_UNSPECIFIED) {
            cparams.pooling_type = LLAMA_POOLING_TYPE_NONE;
        } else {
            cparams.pooling_type = hparams.pooling_type;
        }
    }

    if (params.attention_type == LLAMA_ATTENTION_TYPE_UNSPECIFIED) {
        cparams.causal_attn = hparams.causal_attn;
    } else {
        cparams.causal_attn = params.attention_type == LLAMA_ATTENTION_TYPE_CAUSAL;
    }

    const uint32_t n_ctx_per_seq = cparams.n_ctx / cparams.n_seq_max;

    LLAMA_LOG_INFO("%s: n_seq_max     = %u\n",   __func__, cparams.n_seq_max);
    LLAMA_LOG_INFO("%s: n_ctx         = %u\n",   __func__, cparams.n_ctx);
    LLAMA_LOG_INFO("%s: n_ctx_per_seq = %u\n",   __func__, n_ctx_per_seq);
    LLAMA_LOG_INFO("%s: n_batch       = %u\n",   __func__, cparams.n_batch);
    LLAMA_LOG_INFO("%s: n_ubatch      = %u\n",   __func__, cparams.n_ubatch);
    LLAMA_LOG_INFO("%s: flash_attn    = %d\n",   __func__, cparams.flash_attn);
    LLAMA_LOG_INFO("%s: freq_base     = %.1f\n", __func__, cparams.rope_freq_base);
    LLAMA_LOG_INFO("%s: freq_scale    = %g\n",   __func__, cparams.rope_freq_scale);

    if (n_ctx_per_seq < hparams.n_ctx_train) {
        LLAMA_LOG_WARN("%s: n_ctx_per_seq (%u) < n_ctx_train (%u) -- the full capacity of the model will not be utilized\n",
                __func__, n_ctx_per_seq, hparams.n_ctx_train);
    }

    if (n_ctx_per_seq > hparams.n_ctx_train) {
        LLAMA_LOG_WARN("%s: n_ctx_pre_seq (%u) > n_ctx_train (%u) -- possible training context overflow\n",
                __func__, n_ctx_per_seq, hparams.n_ctx_train);
    }

    ctx->logits_all = params.logits_all;

    // build worst-case graph for encoder if a model contains encoder
    ctx->is_encoding = llama_model_has_encoder(model);

    uint32_t kv_size = cparams.n_ctx;
    ggml_type type_k = params.type_k;
    ggml_type type_v = params.type_v;

    // Mamba only needs a constant number of KV cache cells per sequence
    if (llama_model_is_recurrent(model)) {
        // Mamba needs at least as many KV cells as there are sequences kept at any time
        kv_size = std::max((uint32_t) 1, params.n_seq_max);
        // it's probably best to keep as much precision as possible for the states
        type_k = GGML_TYPE_F32; // required by ggml_ssm_conv for Mamba's conv_states
        type_v = GGML_TYPE_F32; // required by ggml_ssm_scan for Mamba's ssm_states
    }

    GGML_ASSERT(hparams.n_embd_head_k % ggml_blck_size(type_k) == 0);
    GGML_ASSERT(hparams.n_embd_head_v % ggml_blck_size(type_v) == 0);

    if (!hparams.vocab_only) {
        // GPU backends
        for (auto * dev : model->devices) {
            ggml_backend_t backend = ggml_backend_dev_init(dev, nullptr);
            if (backend == nullptr) {
                LLAMA_LOG_ERROR("%s: failed to initialize %s backend\n", __func__, ggml_backend_dev_name(dev));
                llama_free(ctx);
                return nullptr;
            }
            ctx->backends.emplace_back(backend);
        }

        // add ACCEL backends (such as BLAS)
        for (size_t i = 0; i < ggml_backend_dev_count(); ++i) {
            ggml_backend_dev_t dev = ggml_backend_dev_get(i);
            if (ggml_backend_dev_type(dev) == GGML_BACKEND_DEVICE_TYPE_ACCEL) {
                ggml_backend_t backend = ggml_backend_dev_init(dev, nullptr);
                if (backend == nullptr) {
                    LLAMA_LOG_ERROR("%s: failed to initialize %s backend\n", __func__, ggml_backend_dev_name(dev));
                    llama_free(ctx);
                    return nullptr;
                }
                ctx->backends.emplace_back(backend);
            }
        }

        // add CPU backend
        ctx->backend_cpu = ggml_backend_init_by_type(GGML_BACKEND_DEVICE_TYPE_CPU, nullptr);
        if (ctx->backend_cpu == nullptr) {
            LLAMA_LOG_ERROR("%s: failed to initialize CPU backend\n", __func__);
            llama_free(ctx);
            return nullptr;
        }
        ctx->backends.emplace_back(ctx->backend_cpu);

        // create a list of the set_n_threads functions in the backends
        for (auto & backend : ctx->backends) {
            ggml_backend_dev_t dev = ggml_backend_get_device(backend.get());
            ggml_backend_reg_t reg = dev ? ggml_backend_dev_backend_reg(dev) : nullptr;
            if (reg) {
                auto ggml_backend_set_n_threads_fn = (ggml_backend_set_n_threads_t) ggml_backend_reg_get_proc_address(reg, "ggml_backend_set_n_threads");
                if (ggml_backend_set_n_threads_fn) {
                    ctx->set_n_threads_fns.emplace_back(backend.get(), ggml_backend_set_n_threads_fn);
                }
            }
        }

        llama_set_abort_callback(ctx, params.abort_callback, params.abort_callback_data);

        if (!llama_kv_cache_init(ctx->kv_self, ctx->model, ctx->cparams, type_k, type_v, kv_size, cparams.offload_kqv)) {
            LLAMA_LOG_ERROR("%s: llama_kv_cache_init() failed for self-attention cache\n", __func__);
            llama_free(ctx);
            return nullptr;
        }

        {
            size_t memory_size_k = 0;
            size_t memory_size_v = 0;

            for (auto & k : ctx->kv_self.k_l) {
                memory_size_k += ggml_nbytes(k);
            }

            for (auto & v : ctx->kv_self.v_l) {
                memory_size_v += ggml_nbytes(v);
            }

            LLAMA_LOG_INFO("%s: KV self size  = %7.2f MiB, K (%s): %7.2f MiB, V (%s): %7.2f MiB\n", __func__,
                      (float)(memory_size_k + memory_size_v) / (1024.0f * 1024.0f),
                ggml_type_name(type_k), (float)memory_size_k / (1024.0f * 1024.0f),
                ggml_type_name(type_v), (float)memory_size_v / (1024.0f * 1024.0f));
        }

        // graph outputs buffer
        {
            // resized during inference when a batch uses more outputs
            if (llama_output_reserve(*ctx, params.n_seq_max) < params.n_seq_max) {
                LLAMA_LOG_ERROR("%s: failed to reserve initial output buffer\n", __func__);
                llama_free(ctx);
                return nullptr;
            }

            LLAMA_LOG_INFO("%s: %10s  output buffer size = %8.2f MiB\n", __func__,
                    ggml_backend_buffer_name(ctx->buf_output.get()),
                    ggml_backend_buffer_get_size(ctx->buf_output.get()) / 1024.0 / 1024.0);
        }

        // scheduler and compute buffers
        {
            // buffer types used for the compute buffer of each backend
            std::vector<ggml_backend_buffer_type_t> backend_buft;
            std::vector<ggml_backend_t> backend_ptrs;
            for (auto & backend : ctx->backends) {
                auto * buft = ggml_backend_get_default_buffer_type(backend.get());
                auto backend_type = ggml_backend_dev_type(ggml_backend_get_device(backend.get()));
                if (backend_type == GGML_BACKEND_DEVICE_TYPE_CPU && !model->devices.empty()) {
                    // use the host buffer of the first device CPU for faster transfer of the intermediate state
                    auto * dev = model->devices[0];
                    auto * host_buft = ggml_backend_dev_host_buffer_type(dev);
                    if (host_buft) {
                        buft = host_buft;
                    }
                }
                backend_buft.push_back(buft);
                backend_ptrs.push_back(backend.get());
            }

            const size_t max_nodes = llama_model_max_nodes(*model);

            // buffer used to store the computation graph and the tensor meta data
            ctx->buf_compute_meta.resize(ggml_tensor_overhead()*max_nodes + ggml_graph_overhead_custom(max_nodes, false));

            // TODO: move these checks to ggml_backend_sched
            // enabling pipeline parallelism in the scheduler increases memory usage, so it is only done when necessary
            bool pipeline_parallel =
                llama_get_device_count(*model) > 1 &&
                model->n_gpu_layers > (int)model->hparams.n_layer &&
                model->split_mode == LLAMA_SPLIT_MODE_LAYER &&
                params.offload_kqv;

            // pipeline parallelism requires support for async compute and events in all devices
            if (pipeline_parallel) {
                for (auto & backend : ctx->backends) {
                    auto dev_type = ggml_backend_dev_type(ggml_backend_get_device(backend.get()));
                    if (dev_type == GGML_BACKEND_DEVICE_TYPE_CPU) {
                        // ignore CPU backend
                        continue;
                    }
                    auto * dev = ggml_backend_get_device(backend.get());
                    ggml_backend_dev_props props;
                    ggml_backend_dev_get_props(dev, &props);
                    if (!props.caps.async || !props.caps.events) {
                        // device does not support async compute or events
                        pipeline_parallel = false;
                        break;
                    }
                }
            }

            ctx->sched.reset(ggml_backend_sched_new(backend_ptrs.data(), backend_buft.data(), backend_ptrs.size(), max_nodes, pipeline_parallel));

            if (pipeline_parallel) {
                LLAMA_LOG_INFO("%s: pipeline parallelism enabled (n_copies=%d)\n", __func__, ggml_backend_sched_get_n_copies(ctx->sched.get()));
            }

            // initialize scheduler with the worst-case graph
            uint32_t n_seqs = 1; // TODO: worst-case number of sequences
            uint32_t n_tokens = std::min(cparams.n_ctx, cparams.n_ubatch);
            llama_token token = llama_token_bos(&ctx->model); // not actually used by llama_build_graph, but required to choose between token and embedding inputs graph

            llama_ubatch ubatch_pp = { true, n_tokens, n_tokens / n_seqs, n_seqs, &token, nullptr, nullptr, nullptr, nullptr, nullptr};
            ggml_cgraph * gf_pp = llama_build_graph(*ctx, ubatch_pp, true);

            // reserve pp graph first so that buffers are only allocated once
            ggml_backend_sched_reserve(ctx->sched.get(), gf_pp);
            int n_splits_pp = ggml_backend_sched_get_n_splits(ctx->sched.get());
            int n_nodes_pp = ggml_graph_n_nodes(gf_pp);

            // reserve with tg graph to get the number of splits and nodes
            llama_ubatch ubatch_tg = { true, 1, 1, n_seqs, &token, nullptr, nullptr, nullptr, nullptr, nullptr};
            ggml_cgraph * gf_tg = llama_build_graph(*ctx, ubatch_tg, true);
            ggml_backend_sched_reserve(ctx->sched.get(), gf_tg);
            int n_splits_tg = ggml_backend_sched_get_n_splits(ctx->sched.get());
            int n_nodes_tg = ggml_graph_n_nodes(gf_tg);

            // reserve again with pp graph to avoid ggml-alloc reallocations during inference
            gf_pp = llama_build_graph(*ctx, ubatch_pp, true);
            if (!ggml_backend_sched_reserve(ctx->sched.get(), gf_pp)) {
                LLAMA_LOG_ERROR("%s: failed to allocate compute buffers\n", __func__);
                llama_free(ctx);
                return nullptr;
            }

            for (size_t i = 0; i < backend_ptrs.size(); ++i) {
                ggml_backend_t backend = backend_ptrs[i];
                ggml_backend_buffer_type_t buft = backend_buft[i];
                size_t size = ggml_backend_sched_get_buffer_size(ctx->sched.get(), backend);
                if (size > 1) {
                    LLAMA_LOG_INFO("%s: %10s compute buffer size = %8.2f MiB\n", __func__,
                            ggml_backend_buft_name(buft),
                            size / 1024.0 / 1024.0);
                }
            }

            if (n_nodes_pp == n_nodes_tg) {
                LLAMA_LOG_INFO("%s: graph nodes  = %d\n", __func__, n_nodes_pp);
            } else {
                LLAMA_LOG_INFO("%s: graph nodes  = %d (with bs=%d), %d (with bs=1)\n", __func__, n_nodes_pp, n_tokens, n_nodes_tg);
            }
            if (n_splits_pp == n_splits_tg) {
                LLAMA_LOG_INFO("%s: graph splits = %d\n", __func__, n_splits_pp);
            } else {
                LLAMA_LOG_INFO("%s: graph splits = %d (with bs=%d), %d (with bs=1)\n", __func__, n_splits_pp, n_tokens, n_splits_tg);
            }
        }
    }

    return ctx;
}

//
// kv cache
//

// TODO: tmp bridges below until `struct llama_kv_cache` is exposed through the public API

struct llama_kv_cache_view llama_kv_cache_view_init(const struct llama_context * ctx, int32_t n_seq_max) {
    return llama_kv_cache_view_init(ctx->kv_self, n_seq_max);
}

void llama_kv_cache_view_update(const struct llama_context * ctx, struct llama_kv_cache_view * view) {
    llama_kv_cache_view_update(view, ctx->kv_self);
}

int32_t llama_get_kv_cache_token_count(const struct llama_context * ctx) {
    return llama_get_kv_cache_token_count(ctx->kv_self);
}

int32_t llama_get_kv_cache_used_cells(const struct llama_context * ctx) {
    return llama_get_kv_cache_used_cells(ctx->kv_self);
}

void llama_kv_cache_clear(struct llama_context * ctx) {
    llama_kv_cache_clear(ctx->kv_self);
}

bool llama_kv_cache_seq_rm(struct llama_context * ctx, llama_seq_id seq_id, llama_pos p0, llama_pos p1) {
    return llama_kv_cache_seq_rm(ctx->kv_self, seq_id, p0, p1);
}

void llama_kv_cache_seq_cp(struct llama_context * ctx, llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) {
    if (seq_id_src == seq_id_dst) {
        return;
    }
    llama_kv_cache_seq_cp(ctx->kv_self, seq_id_src, seq_id_dst, p0, p1);
}

void llama_kv_cache_seq_keep(struct llama_context * ctx, llama_seq_id seq_id) {
    llama_kv_cache_seq_keep(ctx->kv_self, seq_id);
}

void llama_kv_cache_seq_add(struct llama_context * ctx, llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos delta) {
    if (delta == 0) {
        return;
    }

    llama_kv_cache_seq_add(ctx->kv_self, seq_id, p0, p1, delta);
}

void llama_kv_cache_seq_div(struct llama_context * ctx, llama_seq_id seq_id, llama_pos p0, llama_pos p1, int d) {
    if (d == 1) {
        return;
    }

    llama_kv_cache_seq_div(ctx->kv_self, seq_id, p0, p1, d);
}

llama_pos llama_kv_cache_seq_pos_max(struct llama_context * ctx, llama_seq_id seq_id) {
    return llama_kv_cache_seq_pos_max(ctx->kv_self, seq_id);
}

void llama_kv_cache_defrag(struct llama_context * ctx) {
    llama_kv_cache_defrag(ctx->kv_self);
}

void llama_kv_cache_update(struct llama_context * ctx) {
    llama_kv_cache_update_impl(*ctx);
}

bool llama_kv_cache_can_shift(struct llama_context * ctx) {
    return llama_kv_cache_can_shift(ctx->kv_self);
}

///

int32_t llama_encode(
        struct llama_context * ctx,
          struct llama_batch   batch) {
    const int ret = llama_encode_impl(*ctx, batch);
    if (ret != 0) {
        LLAMA_LOG_ERROR("%s: failed to encode, ret = %d\n", __func__, ret);
    }

    return ret;
}

int32_t llama_decode(
        struct llama_context * ctx,
          struct llama_batch   batch) {
    const int ret = llama_decode_impl(*ctx, batch);
    if (ret != 0) {
        LLAMA_LOG_ERROR("%s: failed to decode, ret = %d\n", __func__, ret);
    }

    return ret;
}

//
// vocab
//

// TODO: tmp bridges below until `struct llama_vocab` is exposed through the public API

const char * llama_token_get_text(const struct llama_model * model, llama_token token) {
    return llama_token_get_text_impl(model->vocab, token);
}

float llama_token_get_score(const struct llama_model * model, llama_token token) {
    return llama_token_get_score_impl(model->vocab, token);
}

enum llama_token_attr llama_token_get_attr(const struct llama_model * model, llama_token token) {
    return llama_token_get_attr_impl(model->vocab, token);
}

bool llama_token_is_eog(const struct llama_model * model, llama_token token) {
    return llama_token_is_eog_impl(model->vocab, token);
}

bool llama_token_is_control(const struct llama_model * model, llama_token token) {
    return llama_token_is_control_impl(model->vocab, token);
}

llama_token llama_token_bos(const struct llama_model * model) {
    return llama_token_bos_impl(model->vocab);
}

llama_token llama_token_eos(const struct llama_model * model) {
    return llama_token_eos_impl(model->vocab);
}

llama_token llama_token_eot(const struct llama_model * model) {
    return llama_token_eot_impl(model->vocab);
}

llama_token llama_token_cls(const struct llama_model * model) {
    return llama_token_cls_impl(model->vocab);
}

llama_token llama_token_sep(const struct llama_model * model) {
    return llama_token_sep_impl(model->vocab);
}

llama_token llama_token_nl (const struct llama_model * model) {
    return llama_token_nl_impl(model->vocab);
}

llama_token llama_token_pad(const struct llama_model * model) {
    return llama_token_pad_impl(model->vocab);
}

bool llama_add_bos_token(const struct llama_model * model) {
    return llama_add_bos_token_impl(model->vocab);
}

bool llama_add_eos_token(const struct llama_model * model) {
    return llama_add_eos_token_impl(model->vocab);
}

llama_token llama_token_prefix(const struct llama_model * model) {
    return llama_token_prefix_impl(model->vocab);
}

llama_token llama_token_middle(const struct llama_model * model) {
    return llama_token_middle_impl(model->vocab);
}

llama_token llama_token_suffix(const struct llama_model * model) {
    return llama_token_suffix_impl(model->vocab);
}

llama_token llama_token_fim_pre(const struct llama_model * model) {
    return llama_token_fim_pre_impl(model->vocab);
}

llama_token llama_token_fim_suf(const struct llama_model * model) {
    return llama_token_fim_suf_impl(model->vocab);
}

llama_token llama_token_fim_mid(const struct llama_model * model) {
    return llama_token_fim_mid_impl(model->vocab);
}

llama_token llama_token_fim_pad(const struct llama_model * model) {
    return llama_token_fim_pad_impl(model->vocab);
}

llama_token llama_token_fim_rep(const struct llama_model * model) {
    return llama_token_fim_rep_impl(model->vocab);
}

llama_token llama_token_fim_sep(const struct llama_model * model) {
    return llama_token_fim_sep_impl(model->vocab);
}

//
// tokenization
//

int32_t llama_tokenize(
    const struct llama_model * model,
                  const char * text,
                     int32_t   text_len,
                 llama_token * tokens,
                     int32_t   n_tokens_max,
                        bool   add_special,
                        bool   parse_special) {
    return llama_tokenize_impl(model->vocab, text, text_len, tokens, n_tokens_max, add_special, parse_special);
}

int32_t llama_token_to_piece(
    const struct llama_model * model,
                 llama_token   token,
                        char * buf,
                     int32_t   length,
                     int32_t   lstrip,
                        bool   special) {
    return llama_token_to_piece_impl(model->vocab, token, buf, length, lstrip, special);
}

int32_t llama_detokenize(
    const struct llama_model * model,
           const llama_token * tokens,
                     int32_t   n_tokens,
                        char * text,
                     int32_t   text_len_max,
                        bool   remove_special,
                        bool   unparse_special) {
    return llama_detokenize_impl(model->vocab, tokens, n_tokens, text, text_len_max, remove_special, unparse_special);
}

//
// chat templates
//

int32_t llama_chat_apply_template(
                const struct llama_model * model,
                              const char * tmpl,
         const struct llama_chat_message * chat,
                                  size_t   n_msg,
                                    bool   add_ass,
                                    char * buf,
                                 int32_t   length) {
    std::string curr_tmpl(tmpl == nullptr ? "" : tmpl);
    if (tmpl == nullptr) {
        GGML_ASSERT(model != nullptr);

        // load template from model, if available
        const auto & it = model->gguf_kv.find("tokenizer.chat_template");
        if (it != model->gguf_kv.end() && it->second.size() > 0) {
            curr_tmpl = it->second;
        }
        else {
            // worst case: there is no information about template, we will use chatml by default
            curr_tmpl = "chatml";  // see llm_chat_apply_template
        }
    }

    // format the chat to string
    std::vector<const llama_chat_message *> chat_vec;
    chat_vec.resize(n_msg);
    for (size_t i = 0; i < n_msg; i++) {
        chat_vec[i] = &chat[i];
    }

    std::string formatted_chat;
    llm_chat_template detected_tmpl = llm_chat_detect_template(curr_tmpl);
    if (detected_tmpl == LLM_CHAT_TEMPLATE_UNKNOWN) {
        return -1;
    }
    int32_t res = llm_chat_apply_template(detected_tmpl, chat_vec, formatted_chat, add_ass);
    if (res < 0) {
        return res;
    }
    if (buf && length > 0) {
        strncpy(buf, formatted_chat.c_str(), length);
    }
    return res;
}

//
// sampling
//

// TODO: remove indirection when vocab becomes accesible in llama-sampling.cpp
struct llama_sampler * llama_sampler_init_grammar(const struct llama_model * model, const char * grammar_str, const char * grammar_root) {
    return llama_sampler_init_grammar_impl(model->vocab, grammar_str, grammar_root);
}

struct llama_sampler * llama_sampler_init_infill(const struct llama_model * model) {
    return llama_sampler_init_infill_impl(model->vocab);
}

struct llama_sampler * llama_sampler_init_dry(const struct llama_model * model, float dry_multiplier, float dry_base, int32_t dry_allowed_length, int32_t dry_penalty_last_n, const char** seq_breakers, size_t num_breakers) {
    return llama_sampler_init_dry_impl(model->vocab, llama_n_ctx_train(model), dry_multiplier, dry_base, dry_allowed_length, dry_penalty_last_n, seq_breakers, num_breakers);
}

//
// model split
//

int llama_split_path(char * split_path, size_t maxlen, const char * path_prefix, int split_no, int split_count) {
    static const char * const SPLIT_PATH_FORMAT = "%s-%05d-of-%05d.gguf";
    if (snprintf(split_path, maxlen, SPLIT_PATH_FORMAT, path_prefix, split_no + 1, split_count)) {
        return strlen(split_path);
    }
    return 0;
}

int llama_split_prefix(char * split_prefix, size_t maxlen, const char * split_path, int split_no, int split_count) {
    std::string str_split_path(split_path);
    char postfix[32];
    snprintf(postfix, 32, "-%05d-of-%05d.gguf", split_no + 1, split_count);
    std::string str_postfix(postfix);

    // check if split_prefix ends with postfix
    int size_prefix = str_split_path.size() - str_postfix.size();
    if (size_prefix > 0 && str_split_path.find(str_postfix, size_prefix) != std::string::npos) {
        snprintf(split_prefix, std::min((size_t) size_prefix + 1, maxlen), "%s", split_path);
        return size_prefix;
    }

    return 0;
}

const char * llama_print_system_info(void) {
    static std::string s;
    s.clear(); // Clear the string, since it's static, otherwise it will accumulate data from previous calls.


    for (size_t i = 0; i < ggml_backend_reg_count(); i++) {
        auto * reg = ggml_backend_reg_get(i);
        auto * get_features_fn = (ggml_backend_get_features_t) ggml_backend_reg_get_proc_address(reg, "ggml_backend_get_features");
        if (get_features_fn) {
            ggml_backend_feature * features = get_features_fn(reg);
            s += ggml_backend_reg_name(reg);
            s += " : ";
            for (; features->name; features++) {
                s += features->name;
                s += " = ";
                s += features->value;
                s += " | ";
            }
        }
    }

    return s.c_str();
}

//
// perf
//

struct llama_perf_context_data llama_perf_context(const struct llama_context * ctx) {
    struct llama_perf_context_data data = {};

    if (ctx == nullptr) {
        return data;
    }

    data.t_start_ms  = 1e-3 * ctx->t_start_us;
    data.t_load_ms   = 1e-3 * ctx->t_load_us;
    data.t_p_eval_ms = 1e-3 * ctx->t_p_eval_us;
    data.t_eval_ms   = 1e-3 * ctx->t_eval_us;
    data.n_p_eval    = std::max(1, ctx->n_p_eval);
    data.n_eval      = std::max(1, ctx->n_eval);

    return data;
}

void llama_perf_context_print(const struct llama_context * ctx) {
    const auto data = llama_perf_context(ctx);

    const double t_end_ms = 1e-3 * ggml_time_us();

    LLAMA_LOG_INFO("%s:        load time = %10.2f ms\n", __func__, data.t_load_ms);
    LLAMA_LOG_INFO("%s: prompt eval time = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)\n",
            __func__, data.t_p_eval_ms, data.n_p_eval, data.t_p_eval_ms / data.n_p_eval, 1e3 / data.t_p_eval_ms * data.n_p_eval);
    LLAMA_LOG_INFO("%s:        eval time = %10.2f ms / %5d runs   (%8.2f ms per token, %8.2f tokens per second)\n",
            __func__, data.t_eval_ms, data.n_eval, data.t_eval_ms / data.n_eval, 1e3 / data.t_eval_ms * data.n_eval);
    LLAMA_LOG_INFO("%s:       total time = %10.2f ms / %5d tokens\n", __func__, (t_end_ms - data.t_start_ms), (data.n_p_eval + data.n_eval));
}

void llama_perf_context_reset(struct llama_context * ctx) {
    ctx->t_start_us  = ggml_time_us();
    ctx->t_eval_us   = ctx->n_eval = 0;
    ctx->t_p_eval_us = ctx->n_p_eval = 0;
}