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https://github.com/ggerganov/llama.cpp.git
synced 2025-01-29 21:34:51 +01:00
Steering
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63d20469b8
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021e6d9944
@ -344,6 +344,30 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
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break;
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break;
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}
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}
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params.input_suffix = argv[i];
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params.input_suffix = argv[i];
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} else if (arg == "--steering-add") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.steering_add = argv[i];
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} else if (arg == "--steering-sub") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.steering_sub = argv[i];
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} else if (arg == "--steering-mul") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.steering_mul = std::stof(argv[i]);
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} else if (arg == "--steering-lyr") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.steering_lyr = std::stoi(argv[i]);
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} else {
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} else {
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fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
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fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
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gpt_print_usage(argc, argv, default_params);
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gpt_print_usage(argc, argv, default_params);
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@ -72,6 +72,11 @@ struct gpt_params {
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bool use_mlock = false; // use mlock to keep model in memory
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bool use_mlock = false; // use mlock to keep model in memory
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bool mem_test = false; // compute maximum memory usage
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bool mem_test = false; // compute maximum memory usage
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bool verbose_prompt = false; // print prompt tokens before generation
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bool verbose_prompt = false; // print prompt tokens before generation
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std::string steering_add = "";
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std::string steering_sub = "";
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float steering_mul = 1.0f;
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int steering_lyr = 20;
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};
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};
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bool gpt_params_parse(int argc, char ** argv, gpt_params & params);
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bool gpt_params_parse(int argc, char ** argv, gpt_params & params);
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@ -136,6 +136,28 @@ int main(int argc, char ** argv) {
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return 0;
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return 0;
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}
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}
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if (params.steering_add.size() || params.steering_sub.size())
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{
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auto steering_add_tokens = ::llama_tokenize(ctx, params.steering_add, true);
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auto steering_sub_tokens = ::llama_tokenize(ctx, params.steering_sub, true);
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if (steering_add_tokens.size() != steering_sub_tokens.size()) {
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llama_token space;
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llama_tokenize(ctx, " ", &space, 1, 0);
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while (steering_add_tokens.size() < steering_sub_tokens.size()) steering_add_tokens.push_back(space);
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while (steering_sub_tokens.size() < steering_add_tokens.size()) steering_sub_tokens.push_back(space);
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}
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llama_set_steering_write(ctx, params.steering_lyr, params.steering_mul/2);
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llama_eval(ctx, steering_add_tokens.data(), std::min((int)steering_add_tokens.size(), params.n_ctx), 0, params.n_threads);
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llama_set_steering_write(ctx, params.steering_lyr, -params.steering_mul/2);
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llama_eval(ctx, steering_sub_tokens.data(), std::min((int)steering_sub_tokens.size(), params.n_ctx), 0, params.n_threads);
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llama_set_steering_read(ctx, params.steering_lyr, 1);
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}
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// Add a space in front of the first character to match OG llama tokenizer behavior
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// Add a space in front of the first character to match OG llama tokenizer behavior
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params.prompt.insert(0, 1, ' ');
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params.prompt.insert(0, 1, ' ');
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46
llama.cpp
46
llama.cpp
@ -229,6 +229,15 @@ struct llama_context {
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// input embedding (1-dimensional array: [n_embd])
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// input embedding (1-dimensional array: [n_embd])
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std::vector<float> embedding;
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std::vector<float> embedding;
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std::vector<float> steering_vector; // [n_ctx, n_embd]
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int steering_layer = 0;
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int steering_mode = 0;
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float steering_mul = 0.0f;
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#define STEERING_OFF 0
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#define STEERING_WRITE 2
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#define STEERING_READ 3
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// memory buffers used to evaluate the model
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// memory buffers used to evaluate the model
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// TODO: move in llama_state
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// TODO: move in llama_state
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llama_ctx_buffer buf_compute;
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llama_ctx_buffer buf_compute;
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@ -269,6 +278,17 @@ struct llama_context {
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}
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}
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};
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};
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void llama_set_steering_write(struct llama_context * ctx, int layer, float mul) {
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ctx->steering_mode = STEERING_WRITE;
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ctx->steering_mul = mul;
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ctx->steering_layer = layer;
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}
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void llama_set_steering_read(struct llama_context * ctx, int layer, float mul) {
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ctx->steering_mode = STEERING_READ;
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ctx->steering_mul = mul;
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ctx->steering_layer = layer;
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}
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template <typename T>
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template <typename T>
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static T checked_mul(T a, T b) {
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static T checked_mul(T a, T b) {
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T ret = a * b;
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T ret = a * b;
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@ -1141,6 +1161,12 @@ static bool llama_eval_internal(
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ggml_set_name(embd, "embd");
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ggml_set_name(embd, "embd");
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memcpy(embd->data, tokens, N*ggml_element_size(embd));
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memcpy(embd->data, tokens, N*ggml_element_size(embd));
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struct ggml_tensor * steer;
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if (lctx.steering_mode != STEERING_OFF) {
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steer = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_ctx, n_embd);
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memcpy(steer->data, lctx.steering_vector.data(), ggml_nbytes(steer));
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}
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struct ggml_tensor * inpL = ggml_get_rows(ctx0, model.tok_embeddings, embd);
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struct ggml_tensor * inpL = ggml_get_rows(ctx0, model.tok_embeddings, embd);
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for (int il = 0; il < n_layer; ++il) {
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for (int il = 0; il < n_layer; ++il) {
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@ -1150,6 +1176,18 @@ static bool llama_eval_internal(
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lctx.use_buf(ctx0, 0);
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lctx.use_buf(ctx0, 0);
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if (lctx.steering_mode != STEERING_OFF && il == lctx.steering_layer) {
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steer->data = lctx.steering_vector.data();
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struct ggml_tensor * src = ggml_scale(ctx0, inpL, ggml_new_f32(ctx0, lctx.steering_mul));
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struct ggml_tensor * dst = ggml_view_2d(ctx0, steer, n_embd, N, n_embd * sizeof(float), n_past * n_embd * sizeof(float));
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if (lctx.steering_mode == STEERING_WRITE) {
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ggml_build_forward_expand(&gf, ggml_cpy(ctx0, ggml_add(ctx0, src, dst), dst));
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} else {
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inpL = src;
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}
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}
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// norm
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// norm
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{
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{
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cur = ggml_rms_norm(ctx0, inpL);
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cur = ggml_rms_norm(ctx0, inpL);
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@ -1363,6 +1401,12 @@ static bool llama_eval_internal(
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memcpy(embedding_out.data(), (float *) ggml_get_data(embeddings) + (n_embd*(N - 1)), sizeof(float)*n_embd);
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memcpy(embedding_out.data(), (float *) ggml_get_data(embeddings) + (n_embd*(N - 1)), sizeof(float)*n_embd);
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}
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}
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if (lctx.steering_mode == STEERING_WRITE) {
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memcpy(lctx.steering_vector.data(), steer->data, ggml_nbytes(steer));
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}
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if (mem_per_token == 0) {
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if (mem_per_token == 0) {
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mem_per_token = ggml_used_mem(ctx0)/N;
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mem_per_token = ggml_used_mem(ctx0)/N;
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}
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}
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@ -2184,6 +2228,8 @@ struct llama_context * llama_init_from_file(
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ctx->buf_scratch[0].resize(MEM_REQ_SCRATCH0().at(ctx->model.type));
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ctx->buf_scratch[0].resize(MEM_REQ_SCRATCH0().at(ctx->model.type));
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ctx->buf_scratch[1].resize(MEM_REQ_SCRATCH1().at(ctx->model.type));
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ctx->buf_scratch[1].resize(MEM_REQ_SCRATCH1().at(ctx->model.type));
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ctx->steering_vector.resize(hparams.n_ctx * hparams.n_embd);
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}
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}
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return ctx;
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return ctx;
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3
llama.h
3
llama.h
@ -191,6 +191,9 @@ extern "C" {
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LLAMA_API llama_token llama_token_eos();
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LLAMA_API llama_token llama_token_eos();
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LLAMA_API llama_token llama_token_nl();
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LLAMA_API llama_token llama_token_nl();
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LLAMA_API void llama_set_steering_write(struct llama_context * ctx, int layer, float mul);
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LLAMA_API void llama_set_steering_read(struct llama_context * ctx, int layer, float mul);
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// Sampling functions
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// Sampling functions
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/// @details Repetition penalty described in CTRL academic paper https://arxiv.org/abs/1909.05858, with negative logit fix.
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/// @details Repetition penalty described in CTRL academic paper https://arxiv.org/abs/1909.05858, with negative logit fix.
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