diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
index b87ea76bc..12481e8be 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -111,6 +111,7 @@ jobs:
 
       - name: Dependencies
         id: depends
+        continue-on-error: true
         run: |
           brew update
 
@@ -129,15 +130,17 @@ jobs:
 
       - name: Dependencies
         id: depends
+        continue-on-error: true
         run: |
           brew update
 
       - name: Build
         id: cmake_build
         run: |
+          sysctl -a
           mkdir build
           cd build
-          cmake -DLLAMA_AVX2=OFF ..
+          cmake -DLLAMA_AVX2=OFF -DLLAMA_FMA=OFF ..
           cmake --build . --config Release
 
       - name: Test
diff --git a/CMakeLists.txt b/CMakeLists.txt
index 34a897327..a2404548f 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -68,8 +68,9 @@ option(LLAMA_ACCELERATE                      "llama: enable Accelerate framework
 option(LLAMA_BLAS                            "llama: use BLAS"                                  OFF)
 set(LLAMA_BLAS_VENDOR "Generic" CACHE STRING "llama: BLAS library vendor")
 option(LLAMA_CUBLAS                          "llama: use cuBLAS"                                OFF)
+option(LLAMA_CUDA_FORCE_DMMV                 "llama: use dmmv instead of mmvq CUDA kernels"     OFF)
 set(LLAMA_CUDA_DMMV_X      "32" CACHE STRING "llama: x stride for dmmv CUDA kernels")
-set(LLAMA_CUDA_DMMV_Y       "1" CACHE STRING "llama: y block size for dmmv CUDA kernels")
+set(LLAMA_CUDA_MMV_Y        "1" CACHE STRING "llama: y block size for mmv CUDA kernels")
 option(LLAMA_CUDA_DMMV_F16                   "llama: use 16 bit floats for dmmv CUDA kernels"   OFF)
 set(LLAMA_CUDA_KQUANTS_ITER "2" CACHE STRING "llama: iters./thread per block for Q2_K/Q6_K")
 option(LLAMA_CLBLAST                         "llama: use CLBlast"                               OFF)
@@ -79,7 +80,7 @@ option(LLAMA_QKK_64                          "llama: use super-block size of 64
 
 option(LLAMA_BUILD_TESTS                "llama: build tests"    ${LLAMA_STANDALONE})
 option(LLAMA_BUILD_EXAMPLES             "llama: build examples" ${LLAMA_STANDALONE})
-option(LLAMA_BUILD_SERVER               "llama: build server example"                           OFF)
+option(LLAMA_BUILD_SERVER               "llama: build server example"                           ON)
 
 #
 # Build info header
@@ -246,8 +247,14 @@ if (LLAMA_CUBLAS)
         set(GGML_SOURCES_CUDA ggml-cuda.cu ggml-cuda.h)
 
         add_compile_definitions(GGML_USE_CUBLAS)
+        if (LLAMA_CUDA_FORCE_DMMV)
+            add_compile_definitions(GGML_CUDA_FORCE_DMMV)
+        endif()
         add_compile_definitions(GGML_CUDA_DMMV_X=${LLAMA_CUDA_DMMV_X})
-        add_compile_definitions(GGML_CUDA_DMMV_Y=${LLAMA_CUDA_DMMV_Y})
+        add_compile_definitions(GGML_CUDA_MMV_Y=${LLAMA_CUDA_MMV_Y})
+        if (DEFINED LLAMA_CUDA_DMMV_Y)
+            add_compile_definitions(GGML_CUDA_MMV_Y=${LLAMA_CUDA_DMMV_Y}) # for backwards compatibility
+        endif()
         if (LLAMA_CUDA_DMMV_F16)
             add_compile_definitions(GGML_CUDA_DMMV_F16)
         endif()
@@ -263,7 +270,7 @@ if (LLAMA_CUBLAS)
         if (LLAMA_CUDA_DMMV_F16)
             set(CMAKE_CUDA_ARCHITECTURES "61") # needed for f16 CUDA intrinsics
         else()
-            set(CMAKE_CUDA_ARCHITECTURES "52") # lowest CUDA 12 standard
+            set(CMAKE_CUDA_ARCHITECTURES "52;61") # lowest CUDA 12 standard + lowest for integer intrinsics
         endif()
     endif()
     message(STATUS "Using CUDA architectures: ${CMAKE_CUDA_ARCHITECTURES}")
diff --git a/Makefile b/Makefile
index 163971525..937b195b8 100644
--- a/Makefile
+++ b/Makefile
@@ -1,11 +1,5 @@
 # Define the default target now so that it is always the first target
-BUILD_TARGETS = main quantize quantize-stats perplexity embedding vdot train-text-from-scratch simple libembdinput.so embd-input-test
-
-ifdef LLAMA_BUILD_SERVER
-	BUILD_TARGETS += server
-	LLAMA_SERVER_VERBOSE ?= 1
-server: private CXXFLAGS += -DSERVER_VERBOSE=$(LLAMA_SERVER_VERBOSE)
-endif
+BUILD_TARGETS = main quantize quantize-stats perplexity embedding vdot train-text-from-scratch simple server libembdinput.so embd-input-test
 
 default: $(BUILD_TARGETS)
 
@@ -61,6 +55,10 @@ else
 	CXXFLAGS += -DNDEBUG
 endif
 
+ifdef LLAMA_SERVER_VERBOSE
+	CXXFLAGS += -DSERVER_VERBOSE=$(LLAMA_SERVER_VERBOSE)
+endif
+
 # warnings
 CFLAGS   += -Wall -Wextra -Wpedantic -Wcast-qual -Wdouble-promotion -Wshadow -Wstrict-prototypes -Wpointer-arith
 CXXFLAGS += -Wall -Wextra -Wpedantic -Wcast-qual -Wno-unused-function -Wno-multichar
@@ -171,16 +169,21 @@ ifdef LLAMA_CUBLAS
 	OBJS      += ggml-cuda.o
 	NVCC      = nvcc
 	NVCCFLAGS = --forward-unknown-to-host-compiler -arch=native
+ifdef LLAMA_CUDA_FORCE_DMMV
+	NVCCFLAGS += -DGGML_CUDA_FORCE_DMMV
+endif # LLAMA_CUDA_FORCE_DMMV
 ifdef LLAMA_CUDA_DMMV_X
 	NVCCFLAGS += -DGGML_CUDA_DMMV_X=$(LLAMA_CUDA_DMMV_X)
 else
 	NVCCFLAGS += -DGGML_CUDA_DMMV_X=32
 endif # LLAMA_CUDA_DMMV_X
-ifdef LLAMA_CUDA_DMMV_Y
-	NVCCFLAGS += -DGGML_CUDA_DMMV_Y=$(LLAMA_CUDA_DMMV_Y)
+ifdef LLAMA_CUDA_MMV_Y
+	NVCCFLAGS += -DGGML_CUDA_MMV_Y=$(LLAMA_CUDA_MMV_Y)
+else ifdef LLAMA_CUDA_DMMV_Y
+	NVCCFLAGS += -DGGML_CUDA_MMV_Y=$(LLAMA_CUDA_DMMV_Y) # for backwards compatibility
 else
-	NVCCFLAGS += -DGGML_CUDA_DMMV_Y=1
-endif # LLAMA_CUDA_DMMV_Y
+	NVCCFLAGS += -DGGML_CUDA_MMV_Y=1
+endif # LLAMA_CUDA_MMV_Y
 ifdef LLAMA_CUDA_DMMV_F16
 	NVCCFLAGS += -DGGML_CUDA_DMMV_F16
 endif # LLAMA_CUDA_DMMV_F16
@@ -277,7 +280,7 @@ libllama.so: llama.o ggml.o $(OBJS)
 	$(CXX) $(CXXFLAGS) -shared -fPIC -o $@ $^ $(LDFLAGS)
 
 clean:
-	rm -vf *.o *.so main quantize quantize-stats perplexity embedding benchmark-matmult save-load-state server vdot train-text-from-scratch embd-input-test build-info.h
+	rm -vf *.o *.so main quantize quantize-stats perplexity embedding benchmark-matmult save-load-state server simple vdot train-text-from-scratch embd-input-test build-info.h
 
 #
 # Examples
diff --git a/README.md b/README.md
index 5f5dee6bf..af0fef66f 100644
--- a/README.md
+++ b/README.md
@@ -11,6 +11,7 @@ Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++
 
 **Hot topics:**
 
+- Simple web chat example: https://github.com/ggerganov/llama.cpp/pull/1998
 - k-quants now support super-block size of 64: https://github.com/ggerganov/llama.cpp/pull/2001
 - New roadmap: https://github.com/users/ggerganov/projects/7
 - Azure CI brainstorming: https://github.com/ggerganov/llama.cpp/discussions/1985
@@ -85,7 +86,7 @@ as the main playground for developing new features for the [ggml](https://github
 - [X] [OpenBuddy 🐶 (Multilingual)](https://github.com/OpenBuddy/OpenBuddy)
 - [X] [Pygmalion 7B / Metharme 7B](#using-pygmalion-7b--metharme-7b)
 - [X] [WizardLM](https://github.com/nlpxucan/WizardLM)
-- [X] [Baichuan-7B](https://huggingface.co/baichuan-inc/baichuan-7B)
+- [X] [Baichuan-7B](https://huggingface.co/baichuan-inc/baichuan-7B) and its derivations (such as [baichuan-7b-sft](https://huggingface.co/hiyouga/baichuan-7b-sft))
 
 **Bindings:**
 
@@ -373,8 +374,9 @@ Building the program with BLAS support may lead to some performance improvements
 
   | Option                  | Legal values           | Default | Description |
   |-------------------------|------------------------|---------|-------------|
+  | LLAMA_CUDA_FORCE_DMMV   | Boolean                |   false | Force the use of dequantization + matrix vector multiplication kernels instead of using kernels that do matrix vector multiplication on quantized data. By default the decision is made based on compute capability (MMVQ for 7.0/Turing/RTX 2000 or higher). Does not affect k-quants. |
   | LLAMA_CUDA_DMMV_X       | Positive integer >= 32 |      32 | Number of values in x direction processed by the CUDA dequantization + matrix vector multiplication kernel per iteration. Increasing this value can improve performance on fast GPUs. Power of 2 heavily recommended. Does not affect k-quants. |
-  | LLAMA_CUDA_DMMV_Y       | Positive integer       |       1 | Block size in y direction for the CUDA dequantization + mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. Does not affect k-quants. |
+  | LLAMA_CUDA_MMV_Y       | Positive integer       |       1 | Block size in y direction for the CUDA mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. Does not affect k-quants. |
   | LLAMA_CUDA_DMMV_F16     | Boolean                |   false | If enabled, use half-precision floating point arithmetic for the CUDA dequantization + mul mat vec kernels. Can improve performance on relatively recent GPUs. |
   | LLAMA_CUDA_KQUANTS_ITER | 1 or 2                 |       2 | Number of values processed per iteration and per CUDA thread for Q2_K and Q6_K quantization formats. Setting this value to 1 can improve performance for slow GPUs. |
 
diff --git a/convert.py b/convert.py
index 142692776..66509b99c 100644
--- a/convert.py
+++ b/convert.py
@@ -154,9 +154,15 @@ class Params:
         # try transformer naming first
         if "model.layers.0.self_attn.q_proj.weight" in model:
             n_layer=next(i for i in itertools.count() if f"model.layers.{i}.self_attn.q_proj.weight" not in model)
+        elif "model.layers.0.self_attn.W_pack.weight" in model:   # next: try baichuan naming
+            n_layer=next(i for i in itertools.count() if f"model.layers.{i}.self_attn.W_pack.weight" not in model)
         else:
             n_layer=next(i for i in itertools.count() if f"layers.{i}.attention.wq.weight" not in model)
 
+        if n_layer < 1:
+            raise Exception("failed to guess 'n_layer'. This model is unknown or unsupported.\n"
+                            "Suggestion: provide 'config.json' of the model in the same directory containing model files.")
+
         n_head=n_embd // 128 # guessed
 
         return Params(
diff --git a/examples/alpaca.sh b/examples/alpaca.sh
index aef207f36..8d2bae691 100755
--- a/examples/alpaca.sh
+++ b/examples/alpaca.sh
@@ -7,7 +7,7 @@
 cd `dirname $0`
 cd ..
 
-./main -m ./models/ggml-alpaca-7b-q4.bin \
+./main -m ./models/alpaca.13b.ggmlv3.q8_0.bin \
        --color \
        -f ./prompts/alpaca.txt \
        --ctx_size 2048 \
diff --git a/examples/embd-input/embd-input-lib.cpp b/examples/embd-input/embd-input-lib.cpp
index 570e273fc..5fa4942be 100644
--- a/examples/embd-input/embd-input-lib.cpp
+++ b/examples/embd-input/embd-input-lib.cpp
@@ -29,7 +29,7 @@ struct MyModel* create_mymodel(int argc, char ** argv) {
 
     fprintf(stderr, "%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT);
 
-    if (params.seed < 0) {
+    if (params.seed == LLAMA_DEFAULT_SEED) {
         params.seed = time(NULL);
     }
     fprintf(stderr, "%s: seed  = %d\n", __func__, params.seed);
diff --git a/examples/embedding/embedding.cpp b/examples/embedding/embedding.cpp
index 2b7eb39c5..03e801c2a 100644
--- a/examples/embedding/embedding.cpp
+++ b/examples/embedding/embedding.cpp
@@ -18,7 +18,7 @@ int main(int argc, char ** argv) {
     params.embedding = true;
 
     if (params.n_ctx > 2048) {
-        fprintf(stderr, "%s: warning: model does not support context sizes greater than 2048 tokens (%d specified);"
+        fprintf(stderr, "%s: warning: model might not support context sizes greater than 2048 tokens (%d specified);"
                 "expect poor results\n", __func__, params.n_ctx);
     }
 
diff --git a/examples/main/main.cpp b/examples/main/main.cpp
index 154a4113a..ef57a8982 100644
--- a/examples/main/main.cpp
+++ b/examples/main/main.cpp
@@ -85,7 +85,7 @@ int main(int argc, char ** argv) {
     }
 
     if (params.n_ctx > 2048) {
-        fprintf(stderr, "%s: warning: model does not support context sizes greater than 2048 tokens (%d specified);"
+        fprintf(stderr, "%s: warning: model might not support context sizes greater than 2048 tokens (%d specified);"
                 "expect poor results\n", __func__, params.n_ctx);
     } else if (params.n_ctx < 8) {
         fprintf(stderr, "%s: warning: minimum context size is 8, using minimum size.\n", __func__);
diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp
index 5c4523795..68f44ba80 100644
--- a/examples/perplexity/perplexity.cpp
+++ b/examples/perplexity/perplexity.cpp
@@ -130,7 +130,7 @@ int main(int argc, char ** argv) {
     params.n_batch = std::min(params.n_batch, params.n_ctx);
 
     if (params.n_ctx > 2048) {
-        fprintf(stderr, "%s: warning: model does not support context sizes greater than 2048 tokens (%d specified);"
+        fprintf(stderr, "%s: warning: model might not support context sizes greater than 2048 tokens (%d specified);"
                 "expect poor results\n", __func__, params.n_ctx);
     }
 
diff --git a/examples/quantize-stats/quantize-stats.cpp b/examples/quantize-stats/quantize-stats.cpp
index 9cea472de..6aa06ec8f 100644
--- a/examples/quantize-stats/quantize-stats.cpp
+++ b/examples/quantize-stats/quantize-stats.cpp
@@ -147,7 +147,7 @@ void test_roundtrip_on_chunk(
         const ggml_tensor * layer,
         int64_t offset,
         int64_t chunk_size,
-        const quantize_fns_t & qfns,
+        const ggml_type_traits_t & qfns,
         bool use_reference,
         float * input_scratch,
         char * quantized_scratch,
@@ -163,11 +163,11 @@ void test_roundtrip_on_chunk(
     }
 
     if (use_reference) {
-        qfns.quantize_row_q_reference(input_scratch, quantized_scratch, chunk_size);
+        qfns.from_float_reference(input_scratch, quantized_scratch, chunk_size);
     } else {
-        qfns.quantize_row_q(input_scratch, quantized_scratch, chunk_size);
+        qfns.from_float(input_scratch, quantized_scratch, chunk_size);
     }
-    qfns.dequantize_row_q(quantized_scratch, output_scratch, chunk_size);
+    qfns.to_float(quantized_scratch, output_scratch, chunk_size);
 
     update_error_stats(chunk_size, input_scratch, output_scratch, stats);
 }
@@ -177,7 +177,7 @@ void test_roundtrip_on_chunk(
 void test_roundtrip_on_layer(
         std::string & name,
         bool print_layer_stats,
-        const quantize_fns_t & qfns,
+        const ggml_type_traits_t & qfns,
         bool use_reference,
         const ggml_tensor * layer,
         std::vector<float> & input_scratch,
@@ -388,8 +388,8 @@ int main(int argc, char ** argv) {
         if (!params.include_types.empty() && std::find(params.include_types.begin(), params.include_types.end(), i) == params.include_types.end()) {
             continue;
         }
-        quantize_fns_t qfns = ggml_internal_get_quantize_fn(i);
-        if (qfns.quantize_row_q && qfns.dequantize_row_q) {
+        ggml_type_traits_t qfns = ggml_internal_get_type_traits(type);
+        if (qfns.from_float && qfns.to_float) {
             if (params.verbose) {
                 printf("testing %s ...\n",  ggml_type_name(type));
             }
diff --git a/examples/server/README.md b/examples/server/README.md
index ba4b2fec9..ad9b6bb08 100644
--- a/examples/server/README.md
+++ b/examples/server/README.md
@@ -1,13 +1,13 @@
 # llama.cpp/example/server
 
-This example demonstrates a simple HTTP API server to interact with llama.cpp.
+This example demonstrates a simple HTTP API server and a simple web front end to interact with llama.cpp.
 
 Command line options:
 
 -   `--threads N`, `-t N`: Set the number of threads to use during computation.
 -   `-m FNAME`, `--model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.bin`).
 -   `-m ALIAS`, `--alias ALIAS`: Set an alias for the model. The alias will be returned in API responses.
--   `-c N`, `--ctx-size N`: Set the size of the prompt context. The default is 512, but LLaMA models were built with a context of 2048, which will provide better results for longer input/inference.
+-   `-c N`, `--ctx-size N`: Set the size of the prompt context. The default is 512, but LLaMA models were built with a context of 2048, which will provide better results for longer input/inference. The size may differ in other models, for example, baichuan models were build with a context of 4096.
 -   `-ngl N`, `--n-gpu-layers N`: When compiled with appropriate support (currently CLBlast or cuBLAS), this option allows offloading some layers to the GPU for computation. Generally results in increased performance.
 -   `-mg i, --main-gpu i`: When using multiple GPUs this option controls which GPU is used for small tensors for which the overhead of splitting the computation across all GPUs is not worthwhile. The GPU in question will use slightly more VRAM to store a scratch buffer for temporary results. By default GPU 0 is used. Requires cuBLAS.
 -   `-ts SPLIT, --tensor-split SPLIT`: When using multiple GPUs this option controls how large tensors should be split across all GPUs. `SPLIT` is a comma-separated list of non-negative values that assigns the proportion of data that each GPU should get in order. For example, "3,2" will assign 60% of the data to GPU 0 and 40% to GPU 1. By default the data is split in proportion to VRAM but this may not be optimal for performance. Requires cuBLAS.
@@ -21,24 +21,22 @@ Command line options:
 -   `-to N`, `--timeout N`: Server read/write timeout in seconds. Default `600`.
 -   `--host`: Set the hostname or ip address to listen. Default `127.0.0.1`.
 -   `--port`: Set the port to listen. Default: `8080`.
+-   `--path`: path from which to serve static files (default examples/server/public)
 -   `--embedding`: Enable embedding extraction, Default: disabled.
 
 ## Build
 
-Build llama.cpp with server from repository root with either make or CMake.
+server is build alongside everything else from the root of the project
 
 - Using `make`:
 
   ```bash
-  LLAMA_BUILD_SERVER=1 make
+  make
   ```
 
 - Using `CMake`:
 
   ```bash
-  mkdir build-server
-  cd build-server
-  cmake -DLLAMA_BUILD_SERVER=ON ..
   cmake --build . --config Release
   ```
 
@@ -59,7 +57,7 @@ server.exe -m models\7B\ggml-model.bin -c 2048
 ```
 
 The above command will start a server that by default listens on `127.0.0.1:8080`.
-You can consume the endpoints with Postman or NodeJS with axios library.
+You can consume the endpoints with Postman or NodeJS with axios library. You can visit the web front end at the same url.
 
 ## Testing with CURL
 
@@ -190,3 +188,49 @@ Run with bash:
 ```sh
 bash chat.sh
 ```
+
+### API like OAI
+
+API example using Python Flask: [api_like_OAI.py](api_like_OAI.py)
+This example must be used with server.cpp
+
+```sh
+python api_like_OAI.py
+```
+
+After running the API server, you can use it in Python by setting the API base URL.
+```python
+openai.api_base = "http://<Your api-server IP>:port"
+```
+
+Then you can utilize llama.cpp as an OpenAI's **chat.completion** or **text_completion** API
+
+### Extending or building alternative Web Front End
+
+The default location for the static files is `examples/server/public`. You can extend the front end by running the server binary with `--path` set to `./your-directory` and importing `/completion.js` to get access to the llamaComplete() method.
+
+Read the documentation in `/completion.js` to see convenient ways to access llama.
+
+A simple example is below:
+
+```html
+<html>
+  <body>
+    <pre>
+      <script type="module">
+        import { llama } from '/completion.js'
+
+        const prompt = `### Instruction:
+Write dad jokes, each one paragraph.
+You can use html formatting if needed.
+
+### Response:`
+
+        for await (const chunk of llama(prompt)) {
+          document.write(chunk.data.content)
+        }
+      </script>
+    </pre>
+  </body>
+</html>
+```
diff --git a/examples/server/api_like_OAI.py b/examples/server/api_like_OAI.py
new file mode 100755
index 000000000..aa325a03e
--- /dev/null
+++ b/examples/server/api_like_OAI.py
@@ -0,0 +1,219 @@
+import argparse
+from flask import Flask, jsonify, request, Response
+import urllib.parse
+import requests
+import time
+import json
+
+
+app = Flask(__name__)
+
+parser = argparse.ArgumentParser(description="An example of using server.cpp with a similar API to OAI. It must be used together with server.cpp.")
+parser.add_argument("--chat-prompt", type=str, help="the top prompt in chat completions(default: 'A chat between a curious user and an artificial intelligence assistant. The assistant follows the given rules no matter what.\\n')", default='A chat between a curious user and an artificial intelligence assistant. The assistant follows the given rules no matter what.\\n')
+parser.add_argument("--user-name", type=str, help="USER name in chat completions(default: '\\nUSER: ')", default="\\nUSER: ")
+parser.add_argument("--ai-name", type=str, help="ASSISTANT name in chat completions(default: '\\nASSISTANT: ')", default="\\nASSISTANT: ")
+parser.add_argument("--system-name", type=str, help="SYSTEM name in chat completions(default: '\\nASSISTANT's RULE: ')", default="\\nASSISTANT's RULE: ")
+parser.add_argument("--stop", type=str, help="the end of response in chat completions(default: '</s>')", default="</s>")
+parser.add_argument("--llama-api", type=str, help="Set the address of server.cpp in llama.cpp(default: http://127.0.0.1:8080)", default='http://127.0.0.1:8080')
+parser.add_argument("--api-key", type=str, help="Set the api key to allow only few user(default: NULL)", default="")
+parser.add_argument("--host", type=str, help="Set the ip address to listen.(default: 127.0.0.1)", default='127.0.0.1')
+parser.add_argument("--port", type=int, help="Set the port to listen.(default: 8081)", default=8081)
+
+args = parser.parse_args()
+
+def is_present(json, key):
+    try:
+        buf = json[key]
+    except KeyError:
+        return False
+    return True
+
+
+
+#convert chat to prompt
+def convert_chat(messages):
+    prompt = "" + args.chat_prompt.replace("\\n", "\n")
+
+    system_n = args.system_name.replace("\\n", "\n")
+    user_n = args.user_name.replace("\\n", "\n")
+    ai_n = args.ai_name.replace("\\n", "\n")
+    stop = args.stop.replace("\\n", "\n")
+
+
+    for line in messages:
+        if (line["role"] == "system"):
+            prompt += f"{system_n}{line['content']}"
+        if (line["role"] == "user"):
+            prompt += f"{user_n}{line['content']}"
+        if (line["role"] == "assistant"):
+            prompt += f"{ai_n}{line['content']}{stop}"
+    prompt += ai_n.rstrip()
+
+    return prompt
+
+def make_postData(body, chat=False, stream=False):
+    postData = {}
+    if (chat):
+        postData["prompt"] = convert_chat(body["messages"])
+    else:
+        postData["prompt"] = body["prompt"]
+    if(is_present(body, "temperature")): postData["temperature"] = body["temperature"]
+    if(is_present(body, "top_k")): postData["top_k"] = body["top_k"]
+    if(is_present(body, "top_p")): postData["top_p"] = body["top_p"]
+    if(is_present(body, "max_tokens")): postData["n_predict"] = body["max_tokens"]
+    if(is_present(body, "presence_penalty")): postData["presence_penalty"] = body["presence_penalty"]
+    if(is_present(body, "frequency_penalty")): postData["frequency_penalty"] = body["frequency_penalty"]
+    if(is_present(body, "repeat_penalty")): postData["repeat_penalty"] = body["repeat_penalty"]
+    if(is_present(body, "mirostat")): postData["mirostat"] = body["mirostat"]
+    if(is_present(body, "mirostat_tau")): postData["mirostat_tau"] = body["mirostat_tau"]
+    if(is_present(body, "mirostat_eta")): postData["mirostat_eta"] = body["mirostat_eta"]
+    if(is_present(body, "seed")): postData["seed"] = body["seed"]
+    if(is_present(body, "logit_bias")): postData["logit_bias"] = [[int(token), body["logit_bias"][token]] for token in body["logit_bias"].keys()]
+    if (args.stop != ""):
+        postData["stop"] = [args.stop]
+    else:
+        postData["stop"] = []
+    if(is_present(body, "stop")): postData["stop"] += body["stop"]
+    postData["n_keep"] = -1
+    postData["stream"] = stream
+
+    return postData
+
+def make_resData(data, chat=False, promptToken=[]):
+    resData = {
+        "id": "chatcmpl" if (chat) else "cmpl",
+        "object": "chat.completion" if (chat) else "text_completion",
+        "created": int(time.time()),
+        "truncated": data["truncated"],
+        "model": "LLaMA_CPP",
+        "usage": {
+            "prompt_tokens": data["tokens_evaluated"],
+            "completion_tokens": data["tokens_predicted"],
+            "total_tokens": data["tokens_evaluated"] + data["tokens_predicted"]
+        }
+    }
+    if (len(promptToken) != 0):
+        resData["promptToken"] = promptToken
+    if (chat):
+        #only one choice is supported
+        resData["choices"] = [{
+            "index": 0,
+            "message": {
+                "role": "assistant",
+                "content": data["content"],
+            },
+            "finish_reason": "stop" if (data["stopped_eos"] or data["stopped_word"]) else "length"
+        }]
+    else:
+        #only one choice is supported
+        resData["choices"] = [{
+            "text": data["content"],
+            "index": 0,
+            "logprobs": None,
+            "finish_reason": "stop" if (data["stopped_eos"] or data["stopped_word"]) else "length"
+        }]
+    return resData
+
+def make_resData_stream(data, chat=False, time_now = 0, start=False):
+    resData = {
+        "id": "chatcmpl" if (chat) else "cmpl",
+        "object": "chat.completion.chunk" if (chat) else "text_completion.chunk",
+        "created": time_now,
+        "model": "LLaMA_CPP",
+        "choices": [
+            {
+                "finish_reason": None,
+                "index": 0
+            }
+        ]
+    }
+    if (chat):
+        if (start):
+            resData["choices"][0]["delta"] =  {
+                "role": "assistant"
+            }
+        else:
+            resData["choices"][0]["delta"] =  {
+                "content": data["content"]
+            }
+            if (data["stop"]):
+                resData["choices"][0]["finish_reason"] = "stop" if (data["stopped_eos"] or data["stopped_word"]) else "length"
+    else:
+        resData["choices"][0]["text"] = data["content"]
+        if (data["stop"]):
+            resData["choices"][0]["finish_reason"] = "stop" if (data["stopped_eos"] or data["stopped_word"]) else "length"
+
+    return resData
+
+
+@app.route('/chat/completions', methods=['POST'])
+@app.route('/v1/chat/completions', methods=['POST'])
+def chat_completions():
+    if (args.api_key != "" and request.headers["Authorization"].split()[1] != args.api_key):
+        return Response(status=403)
+    body = request.get_json()
+    stream = False
+    tokenize = False
+    if(is_present(body, "stream")): stream = body["stream"]
+    if(is_present(body, "tokenize")): tokenize = body["tokenize"]
+    postData = make_postData(body, chat=True, stream=stream)
+
+    promptToken = []
+    if (tokenize):
+        tokenData = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/tokenize"), data=json.dumps({"content": postData["prompt"]})).json()
+        promptToken = tokenData["tokens"]
+
+    if (not stream):
+        data = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/completion"), data=json.dumps(postData))
+        print(data.json())
+        resData = make_resData(data.json(), chat=True, promptToken=promptToken)
+        return jsonify(resData)
+    else:
+        def generate():
+            data = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/completion"), data=json.dumps(postData), stream=True)
+            time_now = int(time.time())
+            resData = make_resData_stream({}, chat=True, time_now=time_now, start=True)
+            yield 'data: {}\n'.format(json.dumps(resData))
+            for line in data.iter_lines():
+                if line:
+                    decoded_line = line.decode('utf-8')
+                    resData = make_resData_stream(json.loads(decoded_line[6:]), chat=True, time_now=time_now)
+                    yield 'data: {}\n'.format(json.dumps(resData))
+        return Response(generate(), mimetype='text/event-stream')
+
+
+@app.route('/completions', methods=['POST'])
+@app.route('/v1/completions', methods=['POST'])
+def completion():
+    if (args.api_key != "" and request.headers["Authorization"].split()[1] != args.api_key):
+        return Response(status=403)
+    body = request.get_json()
+    stream = False
+    tokenize = False
+    if(is_present(body, "stream")): stream = body["stream"]
+    if(is_present(body, "tokenize")): tokenize = body["tokenize"]
+    postData = make_postData(body, chat=False, stream=stream)
+
+    promptToken = []
+    if (tokenize):
+        tokenData = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/tokenize"), data=json.dumps({"content": postData["prompt"]})).json()
+        promptToken = tokenData["tokens"]
+
+    if (not stream):
+        data = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/completion"), data=json.dumps(postData))
+        print(data.json())
+        resData = make_resData(data.json(), chat=False, promptToken=promptToken)
+        return jsonify(resData)
+    else:
+        def generate():
+            data = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/completion"), data=json.dumps(postData), stream=True)
+            time_now = int(time.time())
+            for line in data.iter_lines():
+                if line:
+                    decoded_line = line.decode('utf-8')
+                    resData = make_resData_stream(json.loads(decoded_line[6:]), chat=False, time_now=time_now)
+                    yield 'data: {}\n'.format(json.dumps(resData))
+        return Response(generate(), mimetype='text/event-stream')
+
+if __name__ == '__main__':
+    app.run(args.host, port=args.port)
diff --git a/examples/server/completion.js.hpp b/examples/server/completion.js.hpp
new file mode 100644
index 000000000..f399fb19a
--- /dev/null
+++ b/examples/server/completion.js.hpp
@@ -0,0 +1,375 @@
+unsigned char completion_js[] = {
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+};
+unsigned int completion_js_len = 4462;
diff --git a/examples/server/deps.sh b/examples/server/deps.sh
new file mode 100755
index 000000000..1e9fe964b
--- /dev/null
+++ b/examples/server/deps.sh
@@ -0,0 +1,18 @@
+#!/bin/bash
+# Download and update deps for binary
+
+# get the directory of this script file
+DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
+PUBLIC=$DIR/public
+
+echo "download js bundle files"
+curl https://npm.reversehttp.com/@preact/signals-core,@preact/signals,htm/preact,preact,preact/hooks > $PUBLIC/index.js
+echo >> $PUBLIC/index.js # add newline
+
+FILES=$(ls $PUBLIC)
+
+for FILE in $FILES; do
+  func=$(echo $FILE | tr '.' '_')
+  echo "generate $FILE.hpp ($func)"
+  xxd -n $func -i $PUBLIC/$FILE > $DIR/$FILE.hpp
+done
diff --git a/examples/server/index.html.hpp b/examples/server/index.html.hpp
new file mode 100644
index 000000000..42707fad9
--- /dev/null
+++ b/examples/server/index.html.hpp
@@ -0,0 +1,899 @@
+unsigned char index_html[] = {
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+};
+unsigned int index_html_len = 10752;
diff --git a/examples/server/index.js.hpp b/examples/server/index.js.hpp
new file mode 100644
index 000000000..a3b5be6d8
--- /dev/null
+++ b/examples/server/index.js.hpp
@@ -0,0 +1,1851 @@
+unsigned char index_js[] = {
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+  0x73, 0x20, 0x73, 0x69, 0x67, 0x6e, 0x61, 0x6c, 0x2c, 0x7a, 0x20, 0x61,
+  0x73, 0x20, 0x74, 0x6f, 0x43, 0x68, 0x69, 0x6c, 0x64, 0x41, 0x72, 0x72,
+  0x61, 0x79, 0x2c, 0x44, 0x74, 0x20, 0x61, 0x73, 0x20, 0x75, 0x73, 0x65,
+  0x43, 0x61, 0x6c, 0x6c, 0x62, 0x61, 0x63, 0x6b, 0x2c, 0x51, 0x74, 0x20,
+  0x61, 0x73, 0x20, 0x75, 0x73, 0x65, 0x43, 0x6f, 0x6d, 0x70, 0x75, 0x74,
+  0x65, 0x64, 0x2c, 0x24, 0x74, 0x20, 0x61, 0x73, 0x20, 0x75, 0x73, 0x65,
+  0x43, 0x6f, 0x6e, 0x74, 0x65, 0x78, 0x74, 0x2c, 0x54, 0x74, 0x20, 0x61,
+  0x73, 0x20, 0x75, 0x73, 0x65, 0x44, 0x65, 0x62, 0x75, 0x67, 0x56, 0x61,
+  0x6c, 0x75, 0x65, 0x2c, 0x45, 0x74, 0x20, 0x61, 0x73, 0x20, 0x75, 0x73,
+  0x65, 0x45, 0x66, 0x66, 0x65, 0x63, 0x74, 0x2c, 0x56, 0x74, 0x20, 0x61,
+  0x73, 0x20, 0x75, 0x73, 0x65, 0x45, 0x72, 0x72, 0x6f, 0x72, 0x42, 0x6f,
+  0x75, 0x6e, 0x64, 0x61, 0x72, 0x79, 0x2c, 0x41, 0x74, 0x20, 0x61, 0x73,
+  0x20, 0x75, 0x73, 0x65, 0x49, 0x64, 0x2c, 0x4e, 0x74, 0x20, 0x61, 0x73,
+  0x20, 0x75, 0x73, 0x65, 0x49, 0x6d, 0x70, 0x65, 0x72, 0x61, 0x74, 0x69,
+  0x76, 0x65, 0x48, 0x61, 0x6e, 0x64, 0x6c, 0x65, 0x2c, 0x55, 0x74, 0x20,
+  0x61, 0x73, 0x20, 0x75, 0x73, 0x65, 0x4c, 0x61, 0x79, 0x6f, 0x75, 0x74,
+  0x45, 0x66, 0x66, 0x65, 0x63, 0x74, 0x2c, 0x50, 0x74, 0x20, 0x61, 0x73,
+  0x20, 0x75, 0x73, 0x65, 0x4d, 0x65, 0x6d, 0x6f, 0x2c, 0x43, 0x74, 0x20,
+  0x61, 0x73, 0x20, 0x75, 0x73, 0x65, 0x52, 0x65, 0x64, 0x75, 0x63, 0x65,
+  0x72, 0x2c, 0x48, 0x74, 0x20, 0x61, 0x73, 0x20, 0x75, 0x73, 0x65, 0x52,
+  0x65, 0x66, 0x2c, 0x4b, 0x74, 0x20, 0x61, 0x73, 0x20, 0x75, 0x73, 0x65,
+  0x53, 0x69, 0x67, 0x6e, 0x61, 0x6c, 0x2c, 0x58, 0x74, 0x20, 0x61, 0x73,
+  0x20, 0x75, 0x73, 0x65, 0x53, 0x69, 0x67, 0x6e, 0x61, 0x6c, 0x45, 0x66,
+  0x66, 0x65, 0x63, 0x74, 0x2c, 0x77, 0x74, 0x20, 0x61, 0x73, 0x20, 0x75,
+  0x73, 0x65, 0x53, 0x74, 0x61, 0x74, 0x65, 0x7d, 0x3b, 0x0a
+};
+unsigned int index_js_len = 22174;
diff --git a/examples/server/public/completion.js b/examples/server/public/completion.js
new file mode 100644
index 000000000..a43d5a7d5
--- /dev/null
+++ b/examples/server/public/completion.js
@@ -0,0 +1,168 @@
+const paramDefaults = {
+  stream: true,
+  n_predict: 500,
+  temperature: 0.2,
+  stop: ["</s>"]
+};
+
+let generation_settings = null;
+
+
+// Completes the prompt as a generator. Recommended for most use cases.
+//
+// Example:
+//
+//    import { llama } from '/completion.js'
+//
+//    const request = llama("Tell me a joke", {n_predict: 800})
+//    for await (const chunk of request) {
+//      document.write(chunk.data.content)
+//    }
+//
+export async function* llama(prompt, params = {}, config = {}) {
+  let controller = config.controller;
+
+  if (!controller) {
+    controller = new AbortController();
+  }
+
+  const completionParams = { ...paramDefaults, ...params, prompt };
+
+  const response = await fetch("/completion", {
+    method: 'POST',
+    body: JSON.stringify(completionParams),
+    headers: {
+      'Connection': 'keep-alive',
+      'Content-Type': 'application/json',
+      'Accept': 'text/event-stream'
+    },
+    signal: controller.signal,
+  });
+
+  const reader = response.body.getReader();
+  const decoder = new TextDecoder();
+
+  let content = "";
+
+  try {
+    let cont = true;
+
+    while (cont) {
+      const result = await reader.read();
+      if (result.done) {
+        break;
+      }
+
+      // sse answers in the form multiple lines of: value\n with data always present as a key. in our case we
+      // mainly care about the data: key here, which we expect as json
+      const text = decoder.decode(result.value);
+
+      // parse all sse events and add them to result
+      const regex = /^(\S+):\s(.*)$/gm;
+      for (const match of text.matchAll(regex)) {
+        result[match[1]] = match[2]
+      }
+
+      // since we know this is llama.cpp, let's just decode the json in data
+      result.data = JSON.parse(result.data);
+      content += result.data.content;
+
+      // yield
+      yield result;
+
+      // if we got a stop token from server, we will break here
+      if (result.data.stop) {
+        if (result.data.generation_settings) {
+          generation_settings = result.data.generation_settings;
+        }
+        break;
+      }
+    }
+  } catch (e) {
+    if (e.name !== 'AbortError') {
+      console.error("llama error: ", e);
+    }
+    throw e;
+  }
+  finally {
+    controller.abort();
+  }
+
+  return content;
+}
+
+// Call llama, return an event target that you can subcribe to
+//
+// Example:
+//
+//    import { llamaEventTarget } from '/completion.js'
+//
+//    const conn = llamaEventTarget(prompt)
+//    conn.addEventListener("message", (chunk) => {
+//      document.write(chunk.detail.content)
+//    })
+//
+export const llamaEventTarget = (prompt, params = {}, config = {}) => {
+  const eventTarget = new EventTarget();
+  (async () => {
+    let content = "";
+    for await (const chunk of llama(prompt, params, config)) {
+      if (chunk.data) {
+        content += chunk.data.content;
+        eventTarget.dispatchEvent(new CustomEvent("message", { detail: chunk.data }));
+      }
+      if (chunk.data.generation_settings) {
+        eventTarget.dispatchEvent(new CustomEvent("generation_settings", { detail: chunk.data.generation_settings }));
+      }
+      if (chunk.data.timings) {
+        eventTarget.dispatchEvent(new CustomEvent("timings", { detail: chunk.data.timings }));
+      }
+    }
+    eventTarget.dispatchEvent(new CustomEvent("done", { detail: { content } }));
+  })();
+  return eventTarget;
+}
+
+// Call llama, return a promise that resolves to the completed text. This does not support streaming
+//
+// Example:
+//
+//     llamaPromise(prompt).then((content) => {
+//       document.write(content)
+//     })
+//
+//     or
+//
+//     const content = await llamaPromise(prompt)
+//     document.write(content)
+//
+export const llamaPromise = (prompt, params = {}, config = {}) => {
+  return new Promise(async (resolve, reject) => {
+    let content = "";
+    try {
+      for await (const chunk of llama(prompt, params, config)) {
+        content += chunk.data.content;
+      }
+      resolve(content);
+    } catch (error) {
+      reject(error);
+    }
+  });
+};
+
+/**
+ * (deprecated)
+ */
+export const llamaComplete = async (params, controller, callback) => {
+  for await (const chunk of llama(params.prompt, params, { controller })) {
+    callback(chunk);
+  }
+}
+
+// Get the model info from the server. This is useful for getting the context window and so on.
+export const llamaModelInfo = async () => {
+  if (!generation_settings) {
+    generation_settings = await fetch("/model.json").then(r => r.json());
+  }
+  return generation_settings;
+}
diff --git a/examples/server/public/index.html b/examples/server/public/index.html
new file mode 100644
index 000000000..8ace0b0af
--- /dev/null
+++ b/examples/server/public/index.html
@@ -0,0 +1,380 @@
+<html>
+
+<head>
+  <meta charset="UTF-8">
+  <meta name="viewport" content="width=device-width, initial-scale=1, maximum-scale=1" />
+  <title>llama.cpp - chat</title>
+
+  <style>
+    body {
+      background-color: #fff;
+      color: #000;
+      font-family: system-ui;
+      font-size: 90%;
+    }
+
+    #container {
+      margin: 0em auto;
+      display: flex;
+      flex-direction: column;
+      justify-content: space-between;
+      height: 100%;
+    }
+
+    main {
+      margin: 3px;
+      display: flex;
+      flex-direction: column;
+      justify-content: space-between;
+      gap: 1em;
+
+      flex-grow: 1;
+      overflow-y: auto;
+
+      border: 1px solid #ccc;
+      border-radius: 5px;
+      padding: 0.5em;
+    }
+
+    body {
+      max-width: 600px;
+      min-width: 300px;
+      line-height: 1.2;
+      margin: 0 auto;
+      padding: 0 0.5em;
+    }
+
+    p {
+      overflow-wrap: break-word;
+      word-wrap: break-word;
+      hyphens: auto;
+      margin-top: 0.5em;
+      margin-bottom: 0.5em;
+    }
+
+    #write form {
+      margin: 1em 0 0 0;
+      display: flex;
+      flex-direction: column;
+      gap: 0.5em;
+      align-items: stretch;
+    }
+
+    .right {
+      display: flex;
+      flex-direction: row;
+      gap: 0.5em;
+      justify-content: flex-end;
+    }
+
+    fieldset {
+      border: none;
+      padding: 0;
+      margin: 0;
+    }
+
+    textarea {
+      padding: 5px;
+      flex-grow: 1;
+      width: 100%;
+    }
+
+    pre code {
+      display: block;
+      background-color: #222;
+      color: #ddd;
+    }
+    code {
+      font-family: monospace;
+      padding: 0.1em 0.3em;
+      border-radius: 3px;
+    }
+
+    fieldset label {
+      margin: 0.5em 0;
+      display: block;
+    }
+
+    header, footer {
+      text-align: center;
+    }
+
+    footer {
+      font-size: 80%;
+      color: #888;
+    }
+  </style>
+
+  <script type="module">
+    import {
+      html, h, signal, effect, computed, render, useSignal, useEffect, useRef
+    } from '/index.js';
+
+    import { llama } from '/completion.js';
+
+    const session = signal({
+      prompt: "This is a conversation between user and llama, a friendly chatbot. respond in simple markdown.",
+      template: "{{prompt}}\n\n{{history}}\n{{char}}:",
+      historyTemplate: "{{name}}: {{message}}",
+      transcript: [],
+      type: "chat",
+      char: "llama",
+      user: "User",
+    })
+
+    const params = signal({
+      n_predict: 400,
+      temperature: 0.7,
+      repeat_last_n: 256,
+      repeat_penalty: 1.18,
+      top_k: 40,
+      top_p: 0.5,
+    })
+
+    const llamaStats = signal(null)
+    const controller = signal(null)
+
+    const generating = computed(() => controller.value == null )
+    const chatStarted = computed(() => session.value.transcript.length > 0)
+
+    const transcriptUpdate = (transcript) => {
+      session.value = {
+        ...session.value,
+        transcript
+      }
+    }
+
+    // simple template replace
+    const template = (str, extraSettings) => {
+      let settings = session.value;
+      if (extraSettings) {
+        settings = { ...settings, ...extraSettings };
+      }
+      return String(str).replaceAll(/\{\{(.*?)\}\}/g, (_, key) => template(settings[key]));
+    }
+
+    // send message to server
+    const chat = async (msg) => {
+      if (controller.value) {
+        console.log('already running...');
+        return;
+      }
+      controller.value = new AbortController();
+
+      transcriptUpdate([...session.value.transcript, ["{{user}}", msg]])
+
+      const prompt = template(session.value.template, {
+        message: msg,
+        history: session.value.transcript.flatMap(([name, message]) => template(session.value.historyTemplate, {name, message})).join("\n"),
+      });
+
+      let currentMessage = '';
+      const history = session.value.transcript
+
+      const llamaParams = {
+        ...params.value,
+        stop: ["</s>", template("{{char}}:"), template("{{user}}:")],
+      }
+
+      for await (const chunk of llama(prompt, llamaParams, { controller: controller.value })) {
+        const data = chunk.data;
+        currentMessage += data.content;
+
+        // remove leading whitespace
+        currentMessage = currentMessage.replace(/^\s+/, "")
+
+        transcriptUpdate([...history, ["{{char}}", currentMessage]])
+
+        if (data.stop) {
+          console.log("Completion finished: '", currentMessage, "', summary: ", data);
+        }
+
+        if (data.timings) {
+          llamaStats.value = data.timings;
+        }
+      }
+
+      controller.value = null;
+    }
+
+    function MessageInput() {
+      const message = useSignal("")
+
+      const stop = (e) => {
+        e.preventDefault();
+        if (controller.value) {
+          controller.value.abort();
+          controller.value = null;
+        }
+      }
+
+      const reset = (e) => {
+        stop(e);
+        transcriptUpdate([]);
+      }
+
+      const submit = (e) => {
+        stop(e);
+        chat(message.value);
+        message.value = "";
+      }
+
+      const enterSubmits = (event) => {
+        if (event.which === 13 && !event.shiftKey) {
+          submit(event);
+        }
+      }
+
+      return html`
+        <form onsubmit=${submit}>
+          <div>
+            <textarea type="text" rows=2 onkeypress=${enterSubmits} value="${message}" oninput=${(e) => message.value = e.target.value} placeholder="Say something..."/>
+          </div>
+          <div class="right">
+            <button type="submit" disabled=${!generating.value} >Send</button>
+            <button onclick=${stop} disabled=${generating}>Stop</button>
+            <button onclick=${reset}>Reset</button>
+          </div>
+        </form>
+      `
+    }
+
+    const ChatLog = (props) => {
+      const messages = session.value.transcript;
+      const container = useRef(null)
+
+      useEffect(() => {
+        // scroll to bottom (if needed)
+        if (container.current && container.current.scrollHeight <= container.current.scrollTop + container.current.offsetHeight + 300) {
+          container.current.scrollTo(0, container.current.scrollHeight)
+        }
+      }, [messages])
+
+      const chatLine = ([user, msg]) => {
+        return html`<p key=${msg}><strong>${template(user)}:</strong> <${Markdownish} text=${template(msg)} /></p>`
+      };
+
+      return html`
+        <section id="chat" ref=${container}>
+          ${messages.flatMap(chatLine)}
+        </section>`;
+    };
+
+    const ConfigForm = (props) => {
+      const updateSession = (el) => session.value = { ...session.value, [el.target.name]: el.target.value }
+      const updateParams = (el) => params.value = { ...params.value, [el.target.name]: el.target.value }
+      const updateParamsFloat = (el) => params.value = { ...params.value, [el.target.name]: parseFloat(el.target.value) }
+
+      return html`
+        <form>
+          <fieldset>
+            <div>
+              <label for="prompt">Prompt</label>
+              <textarea type="text" name="prompt" value="${session.value.prompt}" rows=4 oninput=${updateSession}/>
+            </div>
+
+            <div>
+              <label for="user">User name</label>
+              <input type="text" name="user" value="${session.value.user}" oninput=${updateSession} />
+            </div>
+
+            <div>
+              <label for="bot">Bot name</label>
+              <input type="text" name="char" value="${session.value.char}" oninput=${updateSession} />
+            </div>
+
+            <div>
+              <label for="template">Prompt template</label>
+              <textarea id="template" name="template" value="${session.value.template}" rows=4 oninput=${updateSession}/>
+            </div>
+
+            <div>
+              <label for="template">Chat history template</label>
+              <textarea id="template" name="historyTemplate" value="${session.value.historyTemplate}" rows=1 oninput=${updateSession}/>
+            </div>
+
+            <div>
+              <label for="temperature">Temperature</label>
+              <input type="range" id="temperature" min="0.0" max="1.0" step="0.01" name="temperature" value="${params.value.temperature}" oninput=${updateParamsFloat} />
+              <span>${params.value.temperature}</span>
+            </div>
+
+            <div>
+              <label for="nPredict">Predictions</label>
+              <input type="range" id="nPredict" min="1" max="2048" step="1" name="n_predict" value="${params.value.n_predict}" oninput=${updateParamsFloat} />
+              <span>${params.value.n_predict}</span>
+            </div>
+
+            <div>
+              <label for="repeat_penalty">Penalize repeat sequence</label>
+              <input type="range" id="repeat_penalty" min="0.0" max="2.0" step="0.01" name="repeat_penalty" value="${params.value.repeat_penalty}" oninput=${updateParamsFloat} />
+              <span>${params.value.repeat_penalty}</span>
+            </div>
+
+            <div>
+              <label for="repeat_last_n">Consider N tokens for penalize</label>
+              <input type="range" id="repeat_last_n" min="0.0" max="2048" name="repeat_last_n" value="${params.value.repeat_last_n}" oninput=${updateParamsFloat} />
+              <span>${params.value.repeat_last_n}</span>
+            </div>
+
+          </fieldset>
+        </form>
+      `
+    }
+    // poor mans markdown replacement
+    const Markdownish = (params) => {
+      const md = params.text
+        .replace(/^#{1,6} (.*)$/gim, '<h3>$1</h3>')
+        .replace(/\*\*(.*?)\*\*/g, '<strong>$1</strong>')
+        .replace(/__(.*?)__/g, '<strong>$1</strong>')
+        .replace(/\*(.*?)\*/g, '<em>$1</em>')
+        .replace(/_(.*?)_/g, '<em>$1</em>')
+        .replace(/```.*?\n([\s\S]*?)```/g, '<pre><code>$1</code></pre>')
+        .replace(/`(.*?)`/g, '<code>$1</code>')
+        .replace(/\n/gim, '<br />');
+      return html`<span dangerouslySetInnerHTML=${{ __html: md }} />`;
+    };
+
+    const ModelGenerationInfo = (params) => {
+      if (!llamaStats.value) {
+        return html`<span/>`
+      }
+      return html`
+        <span>
+          ${llamaStats.value.predicted_per_token_ms.toFixed()}ms per token, ${llamaStats.value.predicted_per_second.toFixed(2)} tokens per second
+        </span>
+      `
+    }
+
+    function App(props) {
+
+      return html`
+        <div id="container">
+          <header>
+            <h1>llama.cpp</h1>
+          </header>
+
+          <main id="content">
+            <${chatStarted.value ? ChatLog : ConfigForm} />
+          </main>
+
+          <section id="write">
+            <${MessageInput} />
+          </section>
+
+          <footer>
+            <p><${ModelGenerationInfo} /></p>
+            <p>Powered by <a href="https://github.com/ggerganov/llama.cpp">llama.cpp</a> and <a href="https://ggml.ai">ggml.ai</a>.</p>
+          </footer>
+        </div>
+      `;
+    }
+
+    render(h(App), document.body);
+  </script>
+</head>
+
+<body>
+</body>
+
+</html>
diff --git a/examples/server/public/index.js b/examples/server/public/index.js
new file mode 100644
index 000000000..4fa725a90
--- /dev/null
+++ b/examples/server/public/index.js
@@ -0,0 +1 @@
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diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index e4ddbe986..2cbfc0018 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -2,8 +2,6 @@
 #include "llama.h"
 #include "build-info.h"
 
-// single thread
-#define CPPHTTPLIB_THREAD_POOL_COUNT 1
 #ifndef NDEBUG
 // crash the server in debug mode, otherwise send an http 500 error
 #define CPPHTTPLIB_NO_EXCEPTIONS 1
@@ -12,6 +10,11 @@
 #include "httplib.h"
 #include "json.hpp"
 
+// auto generated files (update with ./deps.sh)
+#include "index.html.hpp"
+#include "index.js.hpp"
+#include "completion.js.hpp"
+
 #ifndef SERVER_VERBOSE
 #define SERVER_VERBOSE 1
 #endif
@@ -19,16 +22,20 @@
 using namespace httplib;
 using json = nlohmann::json;
 
-struct server_params {
+struct server_params
+{
     std::string hostname = "127.0.0.1";
+    std::string public_path = "examples/server/public";
     int32_t port = 8080;
     int32_t read_timeout = 600;
     int32_t write_timeout = 600;
 };
 
 // completion token output with probabilities
-struct completion_token_output {
-    struct token_prob {
+struct completion_token_output
+{
+    struct token_prob
+    {
         llama_token tok;
         float prob;
     };
@@ -37,30 +44,40 @@ struct completion_token_output {
     llama_token tok;
 };
 
-static size_t common_part(const std::vector<llama_token> & a, const std::vector<llama_token> & b) {
+static size_t common_part(const std::vector<llama_token> &a, const std::vector<llama_token> &b)
+{
     size_t i;
-    for (i = 0; i < a.size() && i < b.size() && a[i] == b[i]; i++) {}
+    for (i = 0; i < a.size() && i < b.size() && a[i] == b[i]; i++)
+    {
+    }
     return i;
 }
 
-enum stop_type {
+enum stop_type
+{
     STOP_FULL,
     STOP_PARTIAL,
 };
 
-static bool ends_with(const std::string & str, const std::string & suffix) {
+static bool ends_with(const std::string &str, const std::string &suffix)
+{
     return str.size() >= suffix.size() &&
-        0 == str.compare(str.size() - suffix.size(), suffix.size(), suffix);
+           0 == str.compare(str.size() - suffix.size(), suffix.size(), suffix);
 }
 
-static size_t find_partial_stop_string(const std::string & stop,
-                                       const std::string & text) {
-    if (!text.empty() && !stop.empty()) {
+static size_t find_partial_stop_string(const std::string &stop,
+                                       const std::string &text)
+{
+    if (!text.empty() && !stop.empty())
+    {
         const char text_last_char = text.back();
-        for (int64_t char_index = stop.size() - 1; char_index >= 0; char_index--) {
-            if (stop[char_index] == text_last_char) {
+        for (int64_t char_index = stop.size() - 1; char_index >= 0; char_index--)
+        {
+            if (stop[char_index] == text_last_char)
+            {
                 const std::string current_partial = stop.substr(0, char_index + 1);
-                if (ends_with(text, current_partial)) {
+                if (ends_with(text, current_partial))
+                {
                     return text.size() - char_index - 1;
                 }
             }
@@ -69,26 +86,30 @@ static size_t find_partial_stop_string(const std::string & stop,
     return std::string::npos;
 }
 
-template<class Iter>
-static std::string tokens_to_str(llama_context * ctx, Iter begin, Iter end) {
+template <class Iter>
+static std::string tokens_to_str(llama_context *ctx, Iter begin, Iter end)
+{
     std::string ret;
-    for (; begin != end; ++begin) {
+    for (; begin != end; ++begin)
+    {
         ret += llama_token_to_str(ctx, *begin);
     }
     return ret;
 }
 
-static void server_log(const char * level, const char * function, int line,
-                       const char * message, const nlohmann::ordered_json & extra) {
-    nlohmann::ordered_json log {
-        { "timestamp", time(nullptr) },
-        { "level", level },
-        { "function", function },
-        { "line", line },
-        { "message", message },
+static void server_log(const char *level, const char *function, int line,
+                       const char *message, const nlohmann::ordered_json &extra)
+{
+    nlohmann::ordered_json log{
+        {"timestamp", time(nullptr)},
+        {"level", level},
+        {"function", function},
+        {"line", line},
+        {"message", message},
     };
 
-    if (!extra.empty()) {
+    if (!extra.empty())
+    {
         log.merge_patch(extra);
     }
 
@@ -98,32 +119,37 @@ static void server_log(const char * level, const char * function, int line,
 }
 
 // format incomplete utf-8 multibyte character for output
-static std::string tokens_to_output_formatted_string(const llama_context * ctx, const llama_token token) {
+static std::string tokens_to_output_formatted_string(const llama_context *ctx, const llama_token token)
+{
     std::string out = token == -1 ? "" : llama_token_to_str(ctx, token);
     // if first bit is 1, meaning it's a partial character
-    if (out.size() > 0 && (out[0] & 0x80) == 0x80) {
+    if (out.size() > 0 && (out[0] & 0x80) == 0x80)
+    {
         std::stringstream ss;
-        ss<< std::hex << (out[0] & 0xff);
-        std::string res ( ss.str() );
+        ss << std::hex << (out[0] & 0xff);
+        std::string res(ss.str());
         out = "byte: \\x" + res;
     }
     return out;
 }
 
 // convert a vector of completion_token_output to json
-static json probs_vector_to_json(const llama_context * ctx, const std::vector<completion_token_output> probs) {
+static json probs_vector_to_json(const llama_context *ctx, const std::vector<completion_token_output> probs)
+{
     json out = json::array();
-    for (const auto & prob : probs) {
+    for (const auto &prob : probs)
+    {
         json probs_for_token = json::array();
-        for (const auto & p : prob.probs) {
+        for (const auto &p : prob.probs)
+        {
             std::string tok_str = tokens_to_output_formatted_string(ctx, p.tok);
-            probs_for_token.push_back(json {
-                { "tok_str", tok_str },
-                { "prob", p.prob },
+            probs_for_token.push_back(json{
+                {"tok_str", tok_str},
+                {"prob", p.prob},
             });
         }
         std::string tok_str = tokens_to_output_formatted_string(ctx, prob.tok);
-        out.push_back(json {
+        out.push_back(json{
             {"content", tok_str},
             {"probs", probs_for_token},
         });
@@ -134,26 +160,30 @@ static json probs_vector_to_json(const llama_context * ctx, const std::vector<co
 static bool server_verbose = false;
 
 #if SERVER_VERBOSE != 1
-#  define LOG_VERBOSE(MSG, ...)
+#define LOG_VERBOSE(MSG, ...)
 #else
-#  define LOG_VERBOSE(MSG, ...)                                          \
-    do {                                                                 \
-        if (server_verbose) {                                            \
+#define LOG_VERBOSE(MSG, ...)                                            \
+    do                                                                   \
+    {                                                                    \
+        if (server_verbose)                                              \
+        {                                                                \
             server_log("VERBOSE", __func__, __LINE__, MSG, __VA_ARGS__); \
         }                                                                \
-    } while(0)
+    } while (0)
 #endif
 
 #define LOG_ERROR(MSG, ...) server_log("ERROR", __func__, __LINE__, MSG, __VA_ARGS__)
 #define LOG_WARNING(MSG, ...) server_log("WARNING", __func__, __LINE__, MSG, __VA_ARGS__)
 #define LOG_INFO(MSG, ...) server_log("INFO", __func__, __LINE__, MSG, __VA_ARGS__)
 
-struct llama_server_context {
+struct llama_server_context
+{
     bool stream = false;
     bool has_next_token = false;
     std::string generated_text;
     std::vector<completion_token_output> generated_token_probs;
 
+    size_t num_prompt_tokens = 0;
     size_t num_tokens_predicted = 0;
     size_t n_past = 0;
     size_t n_remain = 0;
@@ -161,8 +191,8 @@ struct llama_server_context {
     std::vector<llama_token> embd;
     std::vector<llama_token> last_n_tokens;
 
-    llama_model * model = nullptr;
-    llama_context * ctx = nullptr;
+    llama_model *model = nullptr;
+    llama_context *ctx = nullptr;
     gpt_params params;
 
     bool truncated = false;
@@ -172,19 +202,31 @@ struct llama_server_context {
     std::string stopping_word;
     int32_t multibyte_pending = 0;
 
-    ~llama_server_context() {
-        if (ctx) {
+    std::mutex mutex;
+
+    std::unique_lock<std::mutex> lock()
+    {
+        return std::unique_lock<std::mutex>(mutex);
+    }
+
+    ~llama_server_context()
+    {
+        if (ctx)
+        {
             llama_free(ctx);
             ctx = nullptr;
         }
-        if (model) {
+        if (model)
+        {
             llama_free_model(model);
             model = nullptr;
         }
     }
 
-    void rewind() {
+    void rewind()
+    {
         params.antiprompt.clear();
+        num_prompt_tokens = 0;
         num_tokens_predicted = 0;
         generated_text = "";
         generated_text.reserve(params.n_ctx);
@@ -200,11 +242,13 @@ struct llama_server_context {
         n_past = 0;
     }
 
-    bool loadModel(const gpt_params & params_) {
+    bool loadModel(const gpt_params &params_)
+    {
         params = params_;
         std::tie(model, ctx) = llama_init_from_gpt_params(params);
-        if (model == nullptr) {
-            LOG_ERROR("unable to load model", { { "model", params_.model } });
+        if (model == nullptr)
+        {
+            LOG_ERROR("unable to load model", {{"model", params_.model}});
             return false;
         }
 
@@ -213,34 +257,40 @@ struct llama_server_context {
         return true;
     }
 
-    void loadPrompt() {
+    void loadPrompt()
+    {
         params.prompt.insert(0, 1, ' '); // always add a first space
         std::vector<llama_token> prompt_tokens = ::llama_tokenize(ctx, params.prompt, true);
+        num_prompt_tokens = prompt_tokens.size();
 
-        if (params.n_keep < 0) {
-            params.n_keep = (int)prompt_tokens.size();
+        if (params.n_keep < 0)
+        {
+            params.n_keep = (int)num_prompt_tokens;
         }
         params.n_keep = std::min(params.n_ctx - 4, params.n_keep);
 
         // if input prompt is too big, truncate like normal
-        if (prompt_tokens.size() >= (size_t)params.n_ctx) {
+        if (num_prompt_tokens >= (size_t)params.n_ctx)
+        {
             const int n_left = (params.n_ctx - params.n_keep) / 2;
             std::vector<llama_token> new_tokens(prompt_tokens.begin(), prompt_tokens.begin() + params.n_keep);
-            const int erased_blocks = (prompt_tokens.size() - params.n_keep - n_left - 1) / n_left;
+            const int erased_blocks = (num_prompt_tokens - params.n_keep - n_left - 1) / n_left;
             new_tokens.insert(new_tokens.end(), prompt_tokens.begin() + params.n_keep + erased_blocks * n_left, prompt_tokens.end());
             std::copy(prompt_tokens.end() - params.n_ctx, prompt_tokens.end(), last_n_tokens.begin());
 
             LOG_VERBOSE("input truncated", {
-                { "n_ctx", params.n_ctx },
-                { "n_keep", params.n_keep },
-                { "n_left", n_left },
-                { "new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend()) },
-            });
+                                               {"n_ctx", params.n_ctx},
+                                               {"n_keep", params.n_keep},
+                                               {"n_left", n_left},
+                                               {"new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend())},
+                                           });
 
             truncated = true;
             prompt_tokens = new_tokens;
-        } else {
-            const size_t ps = prompt_tokens.size();
+        }
+        else
+        {
+            const size_t ps = num_prompt_tokens;
             std::fill(last_n_tokens.begin(), last_n_tokens.end() - ps, 0);
             std::copy(prompt_tokens.begin(), prompt_tokens.end(), last_n_tokens.end() - ps);
         }
@@ -248,31 +298,35 @@ struct llama_server_context {
         // compare the evaluated prompt with the new prompt
         n_past = common_part(embd, prompt_tokens);
         embd = prompt_tokens;
-        if (n_past == prompt_tokens.size()) {
+        if (n_past == num_prompt_tokens)
+        {
             // we have to evaluate at least 1 token to generate logits.
             n_past--;
         }
 
         LOG_VERBOSE("prompt ingested", {
-            { "n_past", n_past },
-            { "cached", tokens_to_str(ctx, embd.cbegin(), embd.cbegin() + n_past) },
-            { "to_eval", tokens_to_str(ctx, embd.cbegin() + n_past, embd.cend()) },
-        });
+                                           {"n_past", n_past},
+                                           {"cached", tokens_to_str(ctx, embd.cbegin(), embd.cbegin() + n_past)},
+                                           {"to_eval", tokens_to_str(ctx, embd.cbegin() + n_past, embd.cend())},
+                                       });
 
         has_next_token = true;
     }
 
-    void beginCompletion() {
+    void beginCompletion()
+    {
         // number of tokens to keep when resetting context
         n_remain = params.n_predict;
         llama_set_rng_seed(ctx, params.seed);
     }
 
-    completion_token_output nextToken() {
+    completion_token_output nextToken()
+    {
         completion_token_output result;
         result.tok = -1;
 
-        if (embd.size() >= (size_t)params.n_ctx) {
+        if (embd.size() >= (size_t)params.n_ctx)
+        {
             // Reset context
             const int n_left = (params.n_ctx - params.n_keep) / 2;
 
@@ -282,32 +336,36 @@ struct llama_server_context {
             n_past = params.n_keep;
             truncated = true;
             LOG_VERBOSE("input truncated", {
-                { "n_ctx", params.n_ctx },
-                { "n_keep", params.n_keep },
-                { "n_left", n_left },
-                { "new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend()) },
-            });
+                                               {"n_ctx", params.n_ctx},
+                                               {"n_keep", params.n_keep},
+                                               {"n_left", n_left},
+                                               {"new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend())},
+                                           });
         }
 
-        while (n_past < embd.size()) {
+        while (n_past < embd.size())
+        {
             int n_eval = (int)embd.size() - n_past;
-            if (n_eval > params.n_batch) {
+            if (n_eval > params.n_batch)
+            {
                 n_eval = params.n_batch;
             }
-            if (llama_eval(ctx, &embd[n_past], n_eval, n_past, params.n_threads)) {
+            if (llama_eval(ctx, &embd[n_past], n_eval, n_past, params.n_threads))
+            {
                 LOG_ERROR("failed to eval", {
-                    { "n_eval", n_eval },
-                    { "n_past", n_past },
-                    { "n_threads", params.n_threads },
-                    { "embd", tokens_to_str(ctx, embd.cbegin() + n_past, embd.cend()) },
-                });
+                                                {"n_eval", n_eval},
+                                                {"n_past", n_past},
+                                                {"n_threads", params.n_threads},
+                                                {"embd", tokens_to_str(ctx, embd.cbegin() + n_past, embd.cend())},
+                                            });
                 has_next_token = false;
                 return result;
             }
             n_past += n_eval;
         }
 
-        if (params.n_predict == 0) {
+        if (params.n_predict == 0)
+        {
             has_next_token = false;
             result.tok = llama_token_eos();
             return result;
@@ -330,52 +388,64 @@ struct llama_server_context {
         const int32_t n_probs = params.n_probs;
 
         {
-            auto * logits = llama_get_logits(ctx);
+            auto *logits = llama_get_logits(ctx);
             auto n_vocab = llama_n_vocab(ctx);
 
             // Apply params.logit_bias map
-            for (const auto & it : params.logit_bias) {
+            for (const auto &it : params.logit_bias)
+            {
                 logits[it.first] += it.second;
             }
 
             std::vector<llama_token_data> candidates;
             candidates.reserve(n_vocab);
-            for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
-                candidates.emplace_back(llama_token_data{ token_id, logits[token_id], 0.0f });
+            for (llama_token token_id = 0; token_id < n_vocab; token_id++)
+            {
+                candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
             }
 
-            llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
+            llama_token_data_array candidates_p = {candidates.data(), candidates.size(), false};
 
             // Apply penalties
             float nl_logit = logits[llama_token_nl()];
             auto last_n_repeat = std::min(std::min((int)last_n_tokens.size(), repeat_last_n), params.n_ctx);
             llama_sample_repetition_penalty(ctx, &candidates_p,
-                last_n_tokens.data() + last_n_tokens.size() - last_n_repeat,
-                last_n_repeat, repeat_penalty);
+                                            last_n_tokens.data() + last_n_tokens.size() - last_n_repeat,
+                                            last_n_repeat, repeat_penalty);
             llama_sample_frequency_and_presence_penalties(ctx, &candidates_p,
-                last_n_tokens.data() + last_n_tokens.size() - last_n_repeat,
-                last_n_repeat, alpha_frequency, alpha_presence);
-            if (!penalize_nl) {
+                                                          last_n_tokens.data() + last_n_tokens.size() - last_n_repeat,
+                                                          last_n_repeat, alpha_frequency, alpha_presence);
+            if (!penalize_nl)
+            {
                 logits[llama_token_nl()] = nl_logit;
             }
 
-            if (temp <= 0) {
+            if (temp <= 0)
+            {
                 // Greedy sampling
                 result.tok = llama_sample_token_greedy(ctx, &candidates_p);
-                if (n_probs > 0) {
+                if (n_probs > 0)
+                {
                     llama_sample_softmax(ctx, &candidates_p);
                 }
-            } else {
-                if (mirostat == 1) {
+            }
+            else
+            {
+                if (mirostat == 1)
+                {
                     static float mirostat_mu = 2.0f * mirostat_tau;
                     const int mirostat_m = 100;
                     llama_sample_temperature(ctx, &candidates_p, temp);
                     result.tok = llama_sample_token_mirostat(ctx, &candidates_p, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu);
-                } else if (mirostat == 2) {
+                }
+                else if (mirostat == 2)
+                {
                     static float mirostat_mu = 2.0f * mirostat_tau;
                     llama_sample_temperature(ctx, &candidates_p, temp);
                     result.tok = llama_sample_token_mirostat_v2(ctx, &candidates_p, mirostat_tau, mirostat_eta, &mirostat_mu);
-                } else {
+                }
+                else
+                {
                     // Temperature sampling
                     size_t min_keep = std::max(1, n_probs);
                     llama_sample_top_k(ctx, &candidates_p, top_k, min_keep);
@@ -387,7 +457,8 @@ struct llama_server_context {
                 }
             }
 
-            for (size_t i = 0; i < std::min(candidates_p.size, (size_t) n_probs); ++i) {
+            for (size_t i = 0; i < std::min(candidates_p.size, (size_t)n_probs); ++i)
+            {
                 result.probs.push_back({candidates_p.data[i].id, candidates_p.data[i].p});
             }
             last_n_tokens.erase(last_n_tokens.begin());
@@ -400,8 +471,9 @@ struct llama_server_context {
         // decrement remaining sampling budget
         --n_remain;
 
-        if (!embd.empty() && embd.back() == llama_token_eos()) {
-            //stopping_word = llama_token_to_str(ctx, embd.back());
+        if (!embd.empty() && embd.back() == llama_token_eos())
+        {
+            // stopping_word = llama_token_to_str(ctx, embd.back());
             has_next_token = false;
             stopped_eos = true;
             LOG_VERBOSE("eos token found", {});
@@ -412,22 +484,28 @@ struct llama_server_context {
         return result;
     }
 
-    size_t findStoppingStrings(const std::string & text, const size_t last_token_size,
-                               const stop_type type) {
+    size_t findStoppingStrings(const std::string &text, const size_t last_token_size,
+                               const stop_type type)
+    {
         size_t stop_pos = std::string::npos;
-        for (const std::string & word : params.antiprompt) {
+        for (const std::string &word : params.antiprompt)
+        {
             size_t pos;
-            if (type == STOP_FULL) {
+            if (type == STOP_FULL)
+            {
                 const size_t tmp = word.size() + last_token_size;
                 const size_t from_pos = text.size() > tmp ? text.size() - tmp : 0;
                 pos = text.find(word, from_pos);
             }
-            else {
+            else
+            {
                 pos = find_partial_stop_string(word, text);
             }
             if (pos != std::string::npos &&
-                (stop_pos == std::string::npos || pos < stop_pos)) {
-                if (type == STOP_FULL) {
+                (stop_pos == std::string::npos || pos < stop_pos))
+            {
+                if (type == STOP_FULL)
+                {
                     stopping_word = word;
                     stopped_word = true;
                     has_next_token = false;
@@ -438,74 +516,91 @@ struct llama_server_context {
         return stop_pos;
     }
 
-    completion_token_output doCompletion() {
+    completion_token_output doCompletion()
+    {
         const completion_token_output token_with_probs = nextToken();
 
         const std::string token_text = token_with_probs.tok == -1 ? "" : llama_token_to_str(ctx, token_with_probs.tok);
         generated_text += token_text;
 
-        if (params.n_probs > 0) {
+        if (params.n_probs > 0)
+        {
             generated_token_probs.push_back(token_with_probs);
         }
 
-        if (multibyte_pending > 0) {
+        if (multibyte_pending > 0)
+        {
             multibyte_pending -= token_text.size();
-        } else if (token_text.size() == 1) {
+        }
+        else if (token_text.size() == 1)
+        {
             const char c = token_text[0];
             // 2-byte characters: 110xxxxx 10xxxxxx
-            if ((c & 0xE0) == 0xC0) {
+            if ((c & 0xE0) == 0xC0)
+            {
                 multibyte_pending = 1;
-            // 3-byte characters: 1110xxxx 10xxxxxx 10xxxxxx
-            } else if ((c & 0xF0) == 0xE0) {
+                // 3-byte characters: 1110xxxx 10xxxxxx 10xxxxxx
+            }
+            else if ((c & 0xF0) == 0xE0)
+            {
                 multibyte_pending = 2;
-            // 4-byte characters: 11110xxx 10xxxxxx 10xxxxxx 10xxxxxx
-            } else if ((c & 0xF8) == 0xF0) {
+                // 4-byte characters: 11110xxx 10xxxxxx 10xxxxxx 10xxxxxx
+            }
+            else if ((c & 0xF8) == 0xF0)
+            {
                 multibyte_pending = 3;
-            } else {
+            }
+            else
+            {
                 multibyte_pending = 0;
             }
         }
 
-        if (multibyte_pending > 0 && !has_next_token) {
+        if (multibyte_pending > 0 && !has_next_token)
+        {
             has_next_token = true;
             n_remain++;
         }
 
-        if (!has_next_token && n_remain == 0) {
+        if (!has_next_token && n_remain == 0)
+        {
             stopped_limit = true;
         }
 
         LOG_VERBOSE("next token", {
-            { "token", token_with_probs.tok },
-            { "token_text", tokens_to_output_formatted_string(ctx, token_with_probs.tok) },
-            { "has_next_token", has_next_token },
-            { "n_remain", n_remain },
-            { "num_tokens_predicted", num_tokens_predicted },
-            { "stopped_eos", stopped_eos },
-            { "stopped_word", stopped_word },
-            { "stopped_limit", stopped_limit },
-            { "stopping_word", stopping_word },
-        });
+                                      {"token", token_with_probs.tok},
+                                      {"token_text", tokens_to_output_formatted_string(ctx, token_with_probs.tok)},
+                                      {"has_next_token", has_next_token},
+                                      {"n_remain", n_remain},
+                                      {"num_tokens_predicted", num_tokens_predicted},
+                                      {"stopped_eos", stopped_eos},
+                                      {"stopped_word", stopped_word},
+                                      {"stopped_limit", stopped_limit},
+                                      {"stopping_word", stopping_word},
+                                  });
 
         return token_with_probs;
     }
 
-    std::vector<float> getEmbedding() {
+    std::vector<float> getEmbedding()
+    {
         static const int n_embd = llama_n_embd(ctx);
-        if (!params.embedding) {
+        if (!params.embedding)
+        {
             LOG_WARNING("embedding disabled", {
-                { "params.embedding", params.embedding },
-            });
+                                                  {"params.embedding", params.embedding},
+                                              });
             return std::vector<float>(n_embd, 0.0f);
         }
-        const float * data = llama_get_embeddings(ctx);
+        const float *data = llama_get_embeddings(ctx);
         std::vector<float> embedding(data, data + n_embd);
         return embedding;
     }
 };
 
-static void server_print_usage(const char * argv0, const gpt_params & params,
-                               const server_params & sparams) {
+static void server_print_usage(const char *argv0, const gpt_params &params,
+                               const server_params &sparams)
+{
     fprintf(stderr, "usage: %s [options]\n", argv0);
     fprintf(stderr, "\n");
     fprintf(stderr, "options:\n");
@@ -516,10 +611,12 @@ static void server_print_usage(const char * argv0, const gpt_params & params,
     fprintf(stderr, "  -b N, --batch-size N  batch size for prompt processing (default: %d)\n", params.n_batch);
     fprintf(stderr, "  --memory-f32          use f32 instead of f16 for memory key+value (default: disabled)\n");
     fprintf(stderr, "                        not recommended: doubles context memory required and no measurable increase in quality\n");
-    if (llama_mlock_supported()) {
+    if (llama_mlock_supported())
+    {
         fprintf(stderr, "  --mlock               force system to keep model in RAM rather than swapping or compressing\n");
     }
-    if (llama_mmap_supported()) {
+    if (llama_mmap_supported())
+    {
         fprintf(stderr, "  --no-mmap             do not memory-map model (slower load but may reduce pageouts if not using mlock)\n");
     }
 #ifdef LLAMA_SUPPORTS_GPU_OFFLOAD
@@ -539,77 +636,119 @@ static void server_print_usage(const char * argv0, const gpt_params & params,
     fprintf(stderr, "  --lora-base FNAME     optional model to use as a base for the layers modified by the LoRA adapter\n");
     fprintf(stderr, "  --host                ip address to listen (default  (default: %s)\n", sparams.hostname.c_str());
     fprintf(stderr, "  --port PORT           port to listen (default  (default: %d)\n", sparams.port);
+    fprintf(stderr, "  --path PUBLIC_PATH    path from which to serve static files (default %s)\n", sparams.public_path.c_str());
     fprintf(stderr, "  -to N, --timeout N    server read/write timeout in seconds (default: %d)\n", sparams.read_timeout);
     fprintf(stderr, "  --embedding           enable embedding vector output (default: %s)\n", params.embedding ? "enabled" : "disabled");
     fprintf(stderr, "\n");
 }
 
-static void server_params_parse(int argc, char ** argv, server_params & sparams,
-                                gpt_params & params) {
+static void server_params_parse(int argc, char **argv, server_params &sparams,
+                                gpt_params &params)
+{
     gpt_params default_params;
     server_params default_sparams;
     std::string arg;
     bool invalid_param = false;
 
-    for (int i = 1; i < argc; i++) {
+    for (int i = 1; i < argc; i++)
+    {
         arg = argv[i];
-        if (arg == "--port") {
-            if (++i >= argc) {
+        if (arg == "--port")
+        {
+            if (++i >= argc)
+            {
                 invalid_param = true;
                 break;
             }
             sparams.port = std::stoi(argv[i]);
-        } else if (arg == "--host") {
-            if (++i >= argc) {
+        }
+        else if (arg == "--host")
+        {
+            if (++i >= argc)
+            {
                 invalid_param = true;
                 break;
             }
             sparams.hostname = argv[i];
-        } else if (arg == "--timeout" || arg == "-to") {
-            if (++i >= argc) {
+        }
+        else if (arg == "--path")
+        {
+            if (++i >= argc)
+            {
+                invalid_param = true;
+                break;
+            }
+            sparams.public_path = argv[i];
+        }
+        else if (arg == "--timeout" || arg == "-to")
+        {
+            if (++i >= argc)
+            {
                 invalid_param = true;
                 break;
             }
             sparams.read_timeout = std::stoi(argv[i]);
             sparams.write_timeout = std::stoi(argv[i]);
-        } else if (arg == "-m" || arg == "--model") {
-            if (++i >= argc) {
+        }
+        else if (arg == "-m" || arg == "--model")
+        {
+            if (++i >= argc)
+            {
                 invalid_param = true;
                 break;
             }
             params.model = argv[i];
-        } else if (arg == "-a" || arg == "--alias") {
-            if (++i >= argc) {
+        }
+        else if (arg == "-a" || arg == "--alias")
+        {
+            if (++i >= argc)
+            {
                 invalid_param = true;
                 break;
             }
             params.model_alias = argv[i];
-        } else if (arg == "-h" || arg == "--help") {
+        }
+        else if (arg == "-h" || arg == "--help")
+        {
             server_print_usage(argv[0], default_params, default_sparams);
             exit(0);
-        } else if (arg == "-c" || arg == "--ctx-size" || arg == "--ctx_size") {
-            if (++i >= argc) {
+        }
+        else if (arg == "-c" || arg == "--ctx-size" || arg == "--ctx_size")
+        {
+            if (++i >= argc)
+            {
                 invalid_param = true;
                 break;
             }
             params.n_ctx = std::stoi(argv[i]);
-        } else if (arg == "--memory-f32" || arg == "--memory_f32") {
+        }
+        else if (arg == "--memory-f32" || arg == "--memory_f32")
+        {
             params.memory_f16 = false;
-        } else if (arg == "--threads" || arg == "-t") {
-            if (++i >= argc) {
+        }
+        else if (arg == "--threads" || arg == "-t")
+        {
+            if (++i >= argc)
+            {
                 invalid_param = true;
                 break;
             }
             params.n_threads = std::stoi(argv[i]);
-        } else if (arg == "-b" || arg == "--batch-size") {
-            if (++i >= argc) {
+        }
+        else if (arg == "-b" || arg == "--batch-size")
+        {
+            if (++i >= argc)
+            {
                 invalid_param = true;
                 break;
             }
             params.n_batch = std::stoi(argv[i]);
             params.n_batch = std::min(512, params.n_batch);
-        } else if (arg == "--gpu-layers" || arg == "-ngl" || arg == "--n-gpu-layers") {
-            if (++i >= argc) {
+        }
+        else if (arg == "--gpu-layers" || arg == "-ngl" || arg == "--n-gpu-layers")
+        {
+            if (++i >= argc)
+            {
                 invalid_param = true;
                 break;
             }
@@ -617,11 +756,14 @@ static void server_params_parse(int argc, char ** argv, server_params & sparams,
             params.n_gpu_layers = std::stoi(argv[i]);
 #else
             LOG_WARNING("Not compiled with GPU offload support, --n-gpu-layers option will be ignored. "
-                        "See main README.md for information on enabling GPU BLAS support", { { "n_gpu_layers", params.n_gpu_layers } });
+                        "See main README.md for information on enabling GPU BLAS support",
+                        {{"n_gpu_layers", params.n_gpu_layers}});
 #endif
         }
-        else if (arg == "--tensor-split" || arg == "-ts") {
-            if (++i >= argc) {
+        else if (arg == "--tensor-split" || arg == "-ts")
+        {
+            if (++i >= argc)
+            {
                 invalid_param = true;
                 break;
             }
@@ -629,16 +771,19 @@ static void server_params_parse(int argc, char ** argv, server_params & sparams,
             std::string arg_next = argv[i];
 
             // split string by , and /
-            const std::regex regex{ R"([,/]+)" };
-            std::sregex_token_iterator it{ arg_next.begin(), arg_next.end(), regex, -1 };
-            std::vector<std::string> split_arg{ it, {} };
+            const std::regex regex{R"([,/]+)"};
+            std::sregex_token_iterator it{arg_next.begin(), arg_next.end(), regex, -1};
+            std::vector<std::string> split_arg{it, {}};
             GGML_ASSERT(split_arg.size() <= LLAMA_MAX_DEVICES);
 
-            for (size_t i_device = 0; i_device < LLAMA_MAX_DEVICES; ++i_device) {
-                if (i_device < split_arg.size()) {
+            for (size_t i_device = 0; i_device < LLAMA_MAX_DEVICES; ++i_device)
+            {
+                if (i_device < split_arg.size())
+                {
                     params.tensor_split[i_device] = std::stof(split_arg[i_device]);
                 }
-                else {
+                else
+                {
                     params.tensor_split[i_device] = 0.0f;
                 }
             }
@@ -654,8 +799,10 @@ static void server_params_parse(int argc, char ** argv, server_params & sparams,
             fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. It is not possible to set lower vram usage.\n");
 #endif // GGML_USE_CUBLAS
         }
-        else if (arg == "--main-gpu" || arg == "-mg") {
-            if (++i >= argc) {
+        else if (arg == "--main-gpu" || arg == "-mg")
+        {
+            if (++i >= argc)
+            {
                 invalid_param = true;
                 break;
             }
@@ -664,124 +811,173 @@ static void server_params_parse(int argc, char ** argv, server_params & sparams,
 #else
             LOG_WARNING("llama.cpp was compiled without cuBLAS. It is not possible to set a main GPU.", {});
 #endif
-        } else if (arg == "--lora") {
-            if (++i >= argc) {
+        }
+        else if (arg == "--lora")
+        {
+            if (++i >= argc)
+            {
                 invalid_param = true;
                 break;
             }
             params.lora_adapter = argv[i];
             params.use_mmap = false;
-        } else if (arg == "--lora-base") {
-            if (++i >= argc) {
+        }
+        else if (arg == "--lora-base")
+        {
+            if (++i >= argc)
+            {
                 invalid_param = true;
                 break;
             }
             params.lora_base = argv[i];
-        } else if (arg == "-v" || arg == "--verbose") {
+        }
+        else if (arg == "-v" || arg == "--verbose")
+        {
 #if SERVER_VERBOSE != 1
             LOG_WARNING("server.cpp is not built with verbose logging.", {});
 #else
             server_verbose = true;
 #endif
-        } else if (arg == "--mlock") {
+        }
+        else if (arg == "--mlock")
+        {
             params.use_mlock = true;
-        } else if (arg == "--no-mmap") {
+        }
+        else if (arg == "--no-mmap")
+        {
             params.use_mmap = false;
-        } else if (arg == "--embedding") {
+        }
+        else if (arg == "--embedding")
+        {
             params.embedding = true;
-        } else {
+        }
+        else
+        {
             fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
             server_print_usage(argv[0], default_params, default_sparams);
             exit(1);
         }
     }
 
-    if (invalid_param) {
+    if (invalid_param)
+    {
         fprintf(stderr, "error: invalid parameter for argument: %s\n", arg.c_str());
         server_print_usage(argv[0], default_params, default_sparams);
         exit(1);
     }
 }
 
-static json format_generation_settings(llama_server_context & llama) {
+static json format_generation_settings(llama_server_context &llama)
+{
     const auto eos_bias = llama.params.logit_bias.find(llama_token_eos());
     const bool ignore_eos = eos_bias != llama.params.logit_bias.end() &&
-        eos_bias->second < 0.0f && std::isinf(eos_bias->second);
+                            eos_bias->second < 0.0f && std::isinf(eos_bias->second);
 
-    return json {
-        { "seed", llama.params.seed },
-        { "temp", llama.params.temp },
-        { "top_k", llama.params.top_k },
-        { "top_p", llama.params.top_p },
-        { "tfs_z", llama.params.tfs_z },
-        { "typical_p", llama.params.typical_p },
-        { "repeat_last_n", llama.params.repeat_last_n },
-        { "repeat_penalty", llama.params.repeat_penalty },
-        { "presence_penalty", llama.params.presence_penalty },
-        { "frequency_penalty", llama.params.frequency_penalty },
-        { "mirostat", llama.params.mirostat },
-        { "mirostat_tau", llama.params.mirostat_tau },
-        { "mirostat_eta", llama.params.mirostat_eta },
-        { "penalize_nl", llama.params.penalize_nl },
-        { "stop", llama.params.antiprompt },
-        { "n_predict", llama.params.n_predict },
-        { "n_keep", llama.params.n_keep },
-        { "ignore_eos", ignore_eos },
-        { "stream", llama.stream },
-        { "logit_bias", llama.params.logit_bias },
-        { "n_probs", llama.params.n_probs },
+    return json{
+        {"n_ctx", llama.params.n_ctx},
+        {"model", llama.params.model_alias},
+        {"seed", llama.params.seed},
+        {"temp", llama.params.temp},
+        {"top_k", llama.params.top_k},
+        {"top_p", llama.params.top_p},
+        {"tfs_z", llama.params.tfs_z},
+        {"typical_p", llama.params.typical_p},
+        {"repeat_last_n", llama.params.repeat_last_n},
+        {"repeat_penalty", llama.params.repeat_penalty},
+        {"presence_penalty", llama.params.presence_penalty},
+        {"frequency_penalty", llama.params.frequency_penalty},
+        {"mirostat", llama.params.mirostat},
+        {"mirostat_tau", llama.params.mirostat_tau},
+        {"mirostat_eta", llama.params.mirostat_eta},
+        {"penalize_nl", llama.params.penalize_nl},
+        {"stop", llama.params.antiprompt},
+        {"n_predict", llama.params.n_predict},
+        {"n_keep", llama.params.n_keep},
+        {"ignore_eos", ignore_eos},
+        {"stream", llama.stream},
+        {"logit_bias", llama.params.logit_bias},
+        {"n_probs", llama.params.n_probs},
     };
 }
 
-static json format_embedding_response(llama_server_context & llama) {
-    return json {
-        { "embedding", llama.getEmbedding() },
+static json format_embedding_response(llama_server_context &llama)
+{
+    return json{
+        {"embedding", llama.getEmbedding()},
     };
 }
 
-static json format_final_response(llama_server_context & llama, const std::string & content, const std::vector<completion_token_output> & probs) {
+static json format_timings(llama_server_context &llama)
+{
+    const auto timings = llama_get_timings(llama.ctx);
 
-    json res = json {
-        { "content", content },
-        { "stop", true },
-        { "model", llama.params.model_alias },
-        { "tokens_predicted", llama.num_tokens_predicted },
-        { "generation_settings", format_generation_settings(llama) },
-        { "prompt", llama.params.prompt },
-        { "truncated", llama.truncated },
-        { "stopped_eos", llama.stopped_eos },
-        { "stopped_word", llama.stopped_word },
-        { "stopped_limit", llama.stopped_limit },
-        { "stopping_word", llama.stopping_word },
+    assert(timings.n_eval == llama.num_tokens_predicted);
+
+    return json{
+        {"prompt_n", timings.n_eval},
+        {"prompt_ms", timings.t_p_eval_ms},
+        {"prompt_per_token_ms", timings.t_p_eval_ms / timings.n_p_eval},
+        {"prompt_per_second", 1e3 / timings.t_p_eval_ms * timings.n_p_eval},
+
+        {"predicted_n", timings.n_eval},
+        {"predicted_ms", timings.t_eval_ms},
+        {"predicted_per_token_ms", timings.t_eval_ms / timings.n_eval},
+        {"predicted_per_second", 1e3 / timings.t_eval_ms * timings.n_eval},
+    };
+}
+
+static json format_final_response(llama_server_context &llama, const std::string &content, const std::vector<completion_token_output> &probs)
+{
+
+    json res = json{
+        {"content", content},
+        {"stop", true},
+        {"model", llama.params.model_alias},
+        {"tokens_predicted", llama.num_tokens_predicted},
+        {"tokens_evaluated", llama.num_prompt_tokens},
+        {"generation_settings", format_generation_settings(llama)},
+        {"prompt", llama.params.prompt},
+        {"truncated", llama.truncated},
+        {"stopped_eos", llama.stopped_eos},
+        {"stopped_word", llama.stopped_word},
+        {"stopped_limit", llama.stopped_limit},
+        {"stopping_word", llama.stopping_word},
+        {"tokens_cached", llama.n_past},
+        {"tokens_predicted", llama.num_tokens_predicted},
+        {"timings", format_timings(llama)},
     };
 
-    if (llama.params.n_probs > 0) {
+    if (llama.params.n_probs > 0)
+    {
         res["completion_probabilities"] = probs_vector_to_json(llama.ctx, probs);
     }
 
     return res;
 }
 
-static json format_partial_response(llama_server_context & llama, const std::string & content, const std::vector<completion_token_output> & probs) {
-    json res = json {
-        { "content", content },
-        { "stop", false },
+static json format_partial_response(llama_server_context &llama, const std::string &content, const std::vector<completion_token_output> &probs)
+{
+    json res = json{
+        {"content", content},
+        {"stop", false},
     };
 
-    if (llama.params.n_probs > 0) {
+    if (llama.params.n_probs > 0)
+    {
         res["completion_probabilities"] = probs_vector_to_json(llama.ctx, probs);
     }
 
     return res;
 }
 
-static json format_tokenizer_response(const std::vector<llama_token> & tokens) {
-    return json {
-        { "tokens", tokens }
-    };
+static json format_tokenizer_response(const std::vector<llama_token> &tokens)
+{
+    return json{
+        {"tokens", tokens}};
 }
 
-static void parse_options_completion(const json & body, llama_server_context & llama) {
+static void parse_options_completion(const json &body, llama_server_context &llama)
+{
     gpt_params default_params;
 
     llama.stream = body.value("stream", false);
@@ -805,20 +1001,28 @@ static void parse_options_completion(const json & body, llama_server_context & l
     llama.params.n_probs = body.value("n_probs", default_params.n_probs);
 
     llama.params.logit_bias.clear();
-    if (body.value("ignore_eos", false)) {
+    if (body.value("ignore_eos", false))
+    {
         llama.params.logit_bias[llama_token_eos()] = -INFINITY;
     }
 
-    const auto & logit_bias = body.find("logit_bias");
-    if (logit_bias != body.end() && logit_bias->is_array()) {
+    const auto &logit_bias = body.find("logit_bias");
+    if (logit_bias != body.end() && logit_bias->is_array())
+    {
         const int n_vocab = llama_n_vocab(llama.ctx);
-        for (const auto & el : *logit_bias) {
-            if (el.is_array() && el.size() == 2 && el[0].is_number_integer()) {
+        for (const auto &el : *logit_bias)
+        {
+            if (el.is_array() && el.size() == 2 && el[0].is_number_integer())
+            {
                 llama_token tok = el[0].get<llama_token>();
-                if (tok >= 0 && tok < n_vocab) {
-                    if (el[1].is_number()) {
+                if (tok >= 0 && tok < n_vocab)
+                {
+                    if (el[1].is_number())
+                    {
                         llama.params.logit_bias[tok] = el[1].get<float>();
-                    } else if (el[1].is_boolean() && !el[1].get<bool>()) {
+                    }
+                    else if (el[1].is_boolean() && !el[1].get<bool>())
+                    {
                         llama.params.logit_bias[tok] = -INFINITY;
                     }
                 }
@@ -827,10 +1031,13 @@ static void parse_options_completion(const json & body, llama_server_context & l
     }
 
     llama.params.antiprompt.clear();
-    const auto & stop = body.find("stop");
-    if (stop != body.end() && stop->is_array()) {
-        for (const auto & word : *stop) {
-            if (!word.empty()) {
+    const auto &stop = body.find("stop");
+    if (stop != body.end() && stop->is_array())
+    {
+        for (const auto &word : *stop)
+        {
+            if (!word.empty())
+            {
                 llama.params.antiprompt.push_back(word);
             }
         }
@@ -839,18 +1046,25 @@ static void parse_options_completion(const json & body, llama_server_context & l
     LOG_VERBOSE("completion parameters parsed", format_generation_settings(llama));
 }
 
-static void log_server_request(const Request & req, const Response & res) {
+static void log_server_request(const Request &req, const Response &res)
+{
     LOG_INFO("request", {
-        { "remote_addr", req.remote_addr },
-        { "remote_port", req.remote_port },
-        { "status", res.status },
-        { "path", req.path },
-        { "request", req.body },
-        { "response", res.body },
-    });
+                            {"remote_addr", req.remote_addr},
+                            {"remote_port", req.remote_port},
+                            {"status", res.status},
+                            {"method", req.method},
+                            {"path", req.path},
+                            {"params", req.params},
+                        });
+
+    LOG_VERBOSE("request", {
+                               {"request", req.body},
+                               {"response", res.body},
+                           });
 }
 
-int main(int argc, char ** argv) {
+int main(int argc, char **argv)
+{
     // own arguments required by this example
     gpt_params params;
     server_params sparams;
@@ -860,40 +1074,57 @@ int main(int argc, char ** argv) {
 
     server_params_parse(argc, argv, sparams, params);
 
-    if (params.model_alias == "unknown") {
+    if (params.model_alias == "unknown")
+    {
         params.model_alias = params.model;
     }
 
     llama_init_backend(params.numa);
 
-    LOG_INFO("build info", {
-        { "build", BUILD_NUMBER },
-        { "commit", BUILD_COMMIT }
-    });
+    LOG_INFO("build info", {{"build", BUILD_NUMBER},
+                            {"commit", BUILD_COMMIT}});
     LOG_INFO("system info", {
-        { "n_threads", params.n_threads },
-        { "total_threads", std::thread::hardware_concurrency() },
-        { "system_info", llama_print_system_info() },
-    });
+                                {"n_threads", params.n_threads},
+                                {"total_threads", std::thread::hardware_concurrency()},
+                                {"system_info", llama_print_system_info()},
+                            });
 
     // load the model
-    if (!llama.loadModel(params)) {
+    if (!llama.loadModel(params))
+    {
         return 1;
     }
 
     Server svr;
 
-    svr.set_default_headers({
-        { "Access-Control-Allow-Origin", "*" },
-        { "Access-Control-Allow-Headers", "content-type" }
-    });
+    svr.set_default_headers({{"Server", "llama.cpp"},
+                             {"Access-Control-Allow-Origin", "*"},
+                             {"Access-Control-Allow-Headers", "content-type"}});
 
-    svr.Get("/", [](const Request &, Response & res) {
-        res.set_content("<h1>llama.cpp server works</h1>", "text/html");
-    });
+    // this is only called if no index.html is found in the public --path
+    svr.Get("/", [](const Request &, Response &res)
+            {
+        res.set_content(reinterpret_cast<const char*>(&index_html), index_html_len, "text/html");
+        return false; });
+
+    // this is only called if no index.js is found in the public --path
+    svr.Get("/index.js", [](const Request &, Response &res)
+            {
+        res.set_content(reinterpret_cast<const char *>(&index_js), index_js_len, "text/javascript");
+        return false; });
+
+    // this is only called if no index.html is found in the public --path
+    svr.Get("/completion.js", [](const Request &, Response &res)
+            {
+        res.set_content(reinterpret_cast<const char*>(&completion_js), completion_js_len, "application/javascript");
+        return false; });
+
+    svr.Post("/completion", [&llama](const Request &req, Response &res)
+             {
+        auto lock = llama.lock();
 
-    svr.Post("/completion", [&llama](const Request & req, Response & res) {
         llama.rewind();
+
         llama_reset_timings(llama.ctx);
 
         parse_options_completion(json::parse(req.body), llama);
@@ -906,7 +1137,7 @@ int main(int argc, char ** argv) {
 
             while (llama.has_next_token) {
                 const completion_token_output token_with_probs = llama.doCompletion();
-                const std::string token_text = llama_token_to_str(llama.ctx, token_with_probs.tok);
+                const std::string token_text = token_with_probs.tok == -1 ? "" : llama_token_to_str(llama.ctx, token_with_probs.tok);
 
                 stop_pos = llama.findStoppingStrings(llama.generated_text,
                     token_text.size(), STOP_FULL);
@@ -933,7 +1164,7 @@ int main(int argc, char ** argv) {
 
                 while (llama.has_next_token) {
                     const completion_token_output token_with_probs = llama.doCompletion();
-                    const std::string token_text = llama_token_to_str(llama.ctx, token_with_probs.tok);
+                    const std::string token_text = token_with_probs.tok == -1 ? "" : llama_token_to_str(llama.ctx, token_with_probs.tok);
                     if (llama.multibyte_pending > 0) {
                         continue;
                     }
@@ -994,22 +1225,30 @@ int main(int argc, char ** argv) {
                 return true;
             };
             res.set_chunked_content_provider("text/event-stream", chunked_content_provider);
-        }
-    });
+        } });
 
-    svr.Options(R"(/.*)", [](const Request &, Response & res) {
-        return res.set_content("", "application/json");
-    });
+    svr.Get("/model.json", [&llama](const Request &, Response &res)
+            {
+        const json data = format_generation_settings(llama);
+        return res.set_content(data.dump(), "application/json"); });
+
+    svr.Options(R"(/.*)", [](const Request &, Response &res)
+                { return res.set_content("", "application/json"); });
+
+    svr.Post("/tokenize", [&llama](const Request &req, Response &res)
+             {
+        auto lock = llama.lock();
 
-    svr.Post("/tokenize", [&llama](const Request & req, Response & res) {
         const json body = json::parse(req.body);
         const std::string content = body.value("content", "");
         const std::vector<llama_token> tokens = llama_tokenize(llama.ctx, content, false);
         const json data = format_tokenizer_response(tokens);
-        return res.set_content(data.dump(), "application/json");
-    });
+        return res.set_content(data.dump(), "application/json"); });
+
+    svr.Post("/embedding", [&llama](const Request &req, Response &res)
+             {
+        auto lock = llama.lock();
 
-    svr.Post("/embedding", [&llama](const Request & req, Response & res) {
         const json body = json::parse(req.body);
 
         llama.rewind();
@@ -1021,12 +1260,12 @@ int main(int argc, char ** argv) {
         llama.doCompletion();
 
         const json data = format_embedding_response(llama);
-        return res.set_content(data.dump(), "application/json");
-    });
+        return res.set_content(data.dump(), "application/json"); });
 
     svr.set_logger(log_server_request);
 
-    svr.set_exception_handler([](const Request &, Response & res, std::exception_ptr ep) {
+    svr.set_exception_handler([](const Request &, Response &res, std::exception_ptr ep)
+                              {
         const auto * fmt = "500 Internal Server Error\n%s";
         char buf[BUFSIZ];
         try {
@@ -1037,27 +1276,36 @@ int main(int argc, char ** argv) {
             snprintf(buf, sizeof(buf), fmt, "Unknown Exception");
         }
         res.set_content(buf, "text/plain");
-        res.status = 500;
-    });
+        res.status = 500; });
+
+    svr.set_error_handler([](const Request &, Response &res)
+                          {
+        res.set_content("File Not Found", "text/plain");
+        res.status = 404; });
 
     // set timeouts and change hostname and port
     svr.set_read_timeout(sparams.read_timeout);
     svr.set_write_timeout(sparams.write_timeout);
 
-    if (!svr.bind_to_port(sparams.hostname, sparams.port)) {
-        LOG_ERROR("couldn't bind to server socket", {
-            { "hostname", sparams.hostname },
-            { "port", sparams.port },
-        });
+    if (!svr.bind_to_port(sparams.hostname, sparams.port))
+    {
+        fprintf(stderr, "\ncouldn't bind to server socket: hostname=%s port=%d\n\n", sparams.hostname.c_str(), sparams.port);
         return 1;
     }
 
-    LOG_INFO("HTTP server listening", {
-        { "hostname", sparams.hostname },
-        { "port", sparams.port },
-    });
+    // Set the base directory for serving static files
+    svr.set_base_dir(sparams.public_path);
 
-    if (!svr.listen_after_bind()) {
+    // to make it ctrl+clickable:
+    fprintf(stdout, "\nllama server listening at http://%s:%d\n\n", sparams.hostname.c_str(), sparams.port);
+
+    LOG_INFO("HTTP server listening", {
+                                          {"hostname", sparams.hostname},
+                                          {"port", sparams.port},
+                                      });
+
+    if (!svr.listen_after_bind())
+    {
         return 1;
     }
 
diff --git a/ggml-cuda.cu b/ggml-cuda.cu
index 50df20edd..7965ff741 100644
--- a/ggml-cuda.cu
+++ b/ggml-cuda.cu
@@ -70,9 +70,11 @@ typedef void (*ggml_cuda_op_t)(
 
 // QK = number of values after dequantization
 // QR = QK / number of values before dequantization
+// QI = number of 32 bit integers before dequantization
 
 #define QK4_0 32
 #define QR4_0 2
+#define QI4_0 4
 typedef struct {
     half    d;              // delta
     uint8_t qs[QK4_0 / 2];  // nibbles / quants
@@ -81,6 +83,7 @@ static_assert(sizeof(block_q4_0) == sizeof(ggml_fp16_t) + QK4_0 / 2, "wrong q4_0
 
 #define QK4_1 32
 #define QR4_1 2
+#define QI4_1 4
 typedef struct {
     half    d;              // delta
     half    m;              // min
@@ -90,6 +93,7 @@ static_assert(sizeof(block_q4_1) == sizeof(ggml_fp16_t) * 2 + QK4_1 / 2, "wrong
 
 #define QK5_0 32
 #define QR5_0 2
+#define QI5_0 4
 typedef struct {
     half d;                 // delta
     uint8_t qh[4];          // 5-th bit of quants
@@ -99,6 +103,7 @@ static_assert(sizeof(block_q5_0) == sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5
 
 #define QK5_1 32
 #define QR5_1 2
+#define QI5_1 4
 typedef struct {
     half d;                 // delta
     half m;                 // min
@@ -109,12 +114,25 @@ static_assert(sizeof(block_q5_1) == 2 * sizeof(ggml_fp16_t) + sizeof(uint32_t) +
 
 #define QK8_0 32
 #define QR8_0 1
+#define QI8_0 8
 typedef struct {
     half    d;              // delta
     int8_t  qs[QK8_0];      // quants
 } block_q8_0;
 static_assert(sizeof(block_q8_0) == sizeof(ggml_fp16_t) + QK8_0, "wrong q8_0 block size/padding");
 
+#define QK8_1 32
+#define QR8_1 1
+#define QI8_1 8
+typedef struct {
+    half    d;              // delta
+    half    s;              // unquantized sum
+    int8_t  qs[QK8_0];      // quants
+} block_q8_1;
+static_assert(sizeof(block_q8_1) == 2*sizeof(ggml_fp16_t) + QK8_0, "wrong q8_1 block size/padding");
+
+typedef float (*vec_dot_q_cuda_t)(const void * vbq, const block_q8_1 * bq8_1, const int iqs);
+
 //================================= k-quants
 
 #ifdef GGML_QKK_64
@@ -198,14 +216,15 @@ static_assert(sizeof(block_q6_K) == sizeof(ggml_fp16_t) + 13*QK_K/16, "wrong q6_
 #define CUDA_SCALE_BLOCK_SIZE 256
 #define CUDA_ROPE_BLOCK_SIZE 256
 #define CUDA_DIAG_MASK_INF_BLOCK_SIZE 32
+#define CUDA_QUANTIZE_BLOCK_SIZE 256
 #define CUDA_DEQUANTIZE_BLOCK_SIZE 256
 
 // dmmv = dequantize_mul_mat_vec
 #ifndef GGML_CUDA_DMMV_X
 #define GGML_CUDA_DMMV_X 32
 #endif
-#ifndef GGML_CUDA_DMMV_Y
-#define GGML_CUDA_DMMV_Y 1
+#ifndef GGML_CUDA_MMV_Y
+#define GGML_CUDA_MMV_Y 1
 #endif
 
 #ifndef K_QUANTS_PER_ITERATION
@@ -270,7 +289,6 @@ static __global__ void rms_norm_f32(const float * x, float * dst, const int ncol
     }
 
     // sum up partial sums
-    __syncthreads();
 #pragma unroll
     for (int mask = 16; mask > 0; mask >>= 1) {
         tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
@@ -714,7 +732,6 @@ static __global__ void dequantize_mul_mat_vec_q2_k(const void * vx, const float
 #endif
 
     // sum up partial sums and write back result
-    __syncthreads();
 #pragma unroll
     for (int mask = 16; mask > 0; mask >>= 1) {
         tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
@@ -819,7 +836,6 @@ static __global__ void dequantize_mul_mat_vec_q3_k(const void * vx, const float
 #endif
 
     // sum up partial sums and write back result
-    __syncthreads();
 #pragma unroll
     for (int mask = 16; mask > 0; mask >>= 1) {
         tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
@@ -923,7 +939,6 @@ static __global__ void dequantize_mul_mat_vec_q4_k(const void * vx, const float
 #endif
 
     // sum up partial sums and write back result
-    __syncthreads();
 #pragma unroll
     for (int mask = 16; mask > 0; mask >>= 1) {
         tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
@@ -1028,7 +1043,6 @@ static __global__ void dequantize_mul_mat_vec_q5_k(const void * vx, const float
 #endif
 
     // sum up partial sums and write back result
-    __syncthreads();
 #pragma unroll
     for (int mask = 16; mask > 0; mask >>= 1) {
         tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
@@ -1139,7 +1153,6 @@ static __global__ void dequantize_mul_mat_vec_q6_k(const void * vx, const float
 #endif
 
     // sum up partial sums and write back result
-    __syncthreads();
 #pragma unroll
     for (int mask = 16; mask > 0; mask >>= 1) {
         tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
@@ -1158,6 +1171,41 @@ static __device__ void convert_f16(const void * vx, const int ib, const int iqs,
     v.y = x[ib + iqs + 1];
 }
 
+static __global__ void quantize_q8_1(const float * x, void * vy, const int k) {
+    const int i = blockDim.x*blockIdx.x + threadIdx.x;
+
+    if (i >= k) {
+        return;
+    }
+
+    block_q8_1 * y = (block_q8_1 *) vy;
+
+    const int ib = i / QK8_0; // block index
+    const int iqs = i % QK8_0; // quant index
+
+    const float xi = x[i];
+    float amax = fabsf(xi);
+    float sum = xi;
+
+#pragma unroll
+    for (int mask = 16; mask > 0; mask >>= 1) {
+        amax = fmaxf(amax, __shfl_xor_sync(0xffffffff, amax, mask, 32));
+        sum += __shfl_xor_sync(0xffffffff, sum, mask, 32);
+    }
+
+    const float d = amax / 127;
+    const int8_t q = amax == 0.0f ? 0 : roundf(xi / d);
+
+    y[ib].qs[iqs] = q;
+
+    if (iqs > 0) {
+        return;
+    }
+
+    y[ib].d = d;
+    y[ib].s = sum;
+}
+
 template <int qk, int qr, dequantize_kernel_t dequantize_kernel>
 static __global__ void dequantize_block(const void * vx, float * y, const int k) {
     const int i = blockDim.x*blockIdx.x + 2*threadIdx.x;
@@ -1179,6 +1227,182 @@ static __global__ void dequantize_block(const void * vx, float * y, const int k)
     y[iybs + iqs + y_offset] = v.y;
 }
 
+static __device__ __forceinline__ float vec_dot_q4_0_q8_1(const void * vbq, const block_q8_1 * bq8_1, const int iqs) {
+#if __CUDA_ARCH__ >= 600 // lowest compute capability for integer intrinsics
+    const block_q4_0 * bq4_0 = (const block_q4_0 *) vbq;
+
+    int vi;
+    memcpy(&vi,  &bq4_0->qs[sizeof(int) * (iqs + 0)], sizeof(int));
+    const int ui0 = *((int *) &bq8_1->qs[sizeof(int) * (iqs + 0)]);
+    const int ui1 = *((int *) &bq8_1->qs[sizeof(int) * (iqs + QI4_0)]);
+
+    const float d = __half2float(bq4_0->d) * __half2float(bq8_1->d);
+
+    // subtract 8 from each quantized value
+    const int vi0 = __vsub4((vi >> 0) & 0x0F0F0F0F, 0x08080808);
+    const int vi1 = __vsub4((vi >> 4) & 0x0F0F0F0F, 0x08080808);
+
+    // SIMD dot product of quantized values
+    int sumi = __dp4a(vi0, ui0, 0);
+    sumi     = __dp4a(vi1, ui1, sumi);
+
+    return sumi*d;
+#else
+    return 0.0f; // only to satisfy the compiler
+#endif // __CUDA_ARCH__ >= 600
+}
+
+static __device__ __forceinline__ float vec_dot_q4_1_q8_1(const void * vbq, const block_q8_1 * bq8_1, const int iqs) {
+#if __CUDA_ARCH__ >= 600 // lowest compute capability for integer intrinsics
+    const block_q4_1 * bq4_1 = (const block_q4_1 *) vbq;
+
+    const int vi  = *((int *) &bq4_1->qs[sizeof(int) * (iqs + 0)]);
+    const int ui0 = *((int *) &bq8_1->qs[sizeof(int) * (iqs + 0)]);
+    const int ui1 = *((int *) &bq8_1->qs[sizeof(int) * (iqs + QI4_1)]);
+
+    const float d = __half2float(bq4_1->d) * __half2float(bq8_1->d);
+    const float m = bq4_1->m;
+    const float s = bq8_1->s;
+
+    const int vi0 = (vi >> 0) & 0x0F0F0F0F;
+    const int vi1 = (vi >> 4) & 0x0F0F0F0F;
+
+    // SIMD dot product of quantized values
+    int sumi = __dp4a(vi0, ui0, 0);
+    sumi     = __dp4a(vi1, ui1, sumi);
+
+    return sumi*d + m*s / QI4_1; // scale sum by QI4_1 because there are QI4_1 threads working on this block
+#else
+    return 0.0f; // only to satisfy the compiler
+#endif // __CUDA_ARCH__ >= 600
+}
+
+static __device__ __forceinline__ float vec_dot_q5_0_q8_1(const void * vbq, const block_q8_1 * bq8_1, const int iqs) {
+#if __CUDA_ARCH__ >= 600 // lowest compute capability for integer intrinsics
+    const block_q5_0 * bq5_0 = (const block_q5_0 *) vbq;
+
+    int qs;
+    memcpy(&qs, &bq5_0->qs[sizeof(int) * (iqs + 0)], sizeof(int));
+    const int qh0 = bq5_0->qh[iqs/2 + 0] >> 4*(iqs%2);
+    const int qh1 = bq5_0->qh[iqs/2 + 2] >> 4*(iqs%2);
+    const int ui0 = *((int *) &bq8_1->qs[sizeof(int) * (iqs + 0)]);
+    const int ui1 = *((int *) &bq8_1->qs[sizeof(int) * (iqs + QI5_0)]);
+
+    const float d = __half2float(bq5_0->d) * __half2float(bq8_1->d);
+
+    int vi0 = (qs  >>  0) & 0x0F0F0F0F; // lower 4 qs bits, still need qh0 as 5th bits
+    vi0    |= (qh0 <<  4) & 0x00000010; // 1 ->  5
+    vi0    |= (qh0 << 11) & 0x00001000; // 2 -> 13
+    vi0    |= (qh0 << 18) & 0x00100000; // 3 -> 21
+    vi0    |= (qh0 << 25) & 0x10000000; // 4 -> 29
+    vi0     = __vsub4(vi0,  0x10101010); // subtract 16 from quantized values
+    int sumi = __dp4a(vi0, ui0, 0); // SIMD dot product of quantized values
+
+    int vi1 = (qs  >>  4) & 0x0F0F0F0F; // upper 4 qs bits, still need qh1 as 5th bits
+    vi1    |= (qh1 <<  4) & 0x00000010; // 1 ->  5
+    vi1    |= (qh1 << 11) & 0x00001000; // 2 -> 13
+    vi1    |= (qh1 << 18) & 0x00100000; // 3 -> 21
+    vi1    |= (qh1 << 25) & 0x10000000; // 4 -> 29
+    vi1     = __vsub4(vi1,  0x10101010); // subtract 16 from quantized values
+    sumi = __dp4a(vi1, ui1, sumi); // SIMD dot product of quantized values
+
+    return sumi*d;
+#else
+    return 0.0f; // only to satisfy the compiler
+#endif // __CUDA_ARCH__ >= 600
+}
+
+static __device__ __forceinline__ float vec_dot_q5_1_q8_1(const void * vbq, const block_q8_1 * bq8_1, const int iqs) {
+#if __CUDA_ARCH__ >= 600 // lowest compute capability for integer intrinsics
+    const block_q5_1 * bq5_1 = (const block_q5_1 *) vbq;
+
+    const int qs  = *((int *) &bq5_1->qs[sizeof(int) * (iqs + 0)]);
+    const int qh0 = bq5_1->qh[iqs/2 + 0] >> 4*(iqs%2);
+    const int qh1 = bq5_1->qh[iqs/2 + 2] >> 4*(iqs%2);
+    const int ui0 = *((int *) &bq8_1->qs[sizeof(int) * (iqs + 0)]);
+    const int ui1 = *((int *) &bq8_1->qs[sizeof(int) * (iqs + QI5_1)]);
+
+    const float d = __half2float(bq5_1->d) * __half2float(bq8_1->d);
+    const float m = bq5_1->m;
+    const float s = bq8_1->s;
+
+    int vi0 = (qs  >>  0) & 0x0F0F0F0F; // lower 4 qs bits, still need qh0 as 5th bits
+    vi0    |= (qh0 <<  4) & 0x00000010; // 1 ->  5
+    vi0    |= (qh0 << 11) & 0x00001000; // 2 -> 13
+    vi0    |= (qh0 << 18) & 0x00100000; // 3 -> 21
+    vi0    |= (qh0 << 25) & 0x10000000; // 4 -> 29
+    int sumi = __dp4a(vi0, ui0, 0); // SIMD dot product of quantized values
+
+    int vi1 = (qs  >>  4) & 0x0F0F0F0F; // upper 4 qs bits, still need qh1 as 5th bits
+    vi1    |= (qh1 <<  4) & 0x00000010; // 1 ->  5
+    vi1    |= (qh1 << 11) & 0x00001000; // 2 -> 13
+    vi1    |= (qh1 << 18) & 0x00100000; // 3 -> 21
+    vi1    |= (qh1 << 25) & 0x10000000; // 4 -> 29
+    sumi = __dp4a(vi1, ui1, sumi); // SIMD dot product of quantized values
+
+    return sumi*d + m*s / QI5_1; // scale sum by QI5_1 because there are QI5_1 threads working on this block
+#else
+    return 0.0f; // only to satisfy the compiler
+#endif // __CUDA_ARCH__ >= 600
+}
+
+static __device__ __forceinline__ float vec_dot_q8_0_q8_1(const void * vbq, const block_q8_1 * bq8_1, const int iqs) {
+#if __CUDA_ARCH__ >= 600 // lowest compute capability for integer intrinsics
+    const block_q8_0 * bq8_0 = (const block_q8_0 *) vbq;
+
+    int vi;
+    memcpy(&vi,  &bq8_0->qs[sizeof(int) * (iqs + 0)], sizeof(int));
+    const int ui = *((int *) &bq8_1->qs[sizeof(int) * (iqs + 0)]);
+
+    const float d = __half2float(bq8_0->d) * __half2float(bq8_1->d);
+
+    // SIMD dot product of quantized values
+    int sumi = __dp4a(vi, ui, 0);
+
+    return sumi*d;
+#else
+    return 0.0f; // only to satisfy the compiler
+#endif // __CUDA_ARCH__ >= 600
+}
+
+template <int qk, int qi, typename block_q_t, vec_dot_q_cuda_t vec_dot_q_cuda>
+static __global__ void mul_mat_vec_q(const void * vx, const void * vy, float * dst, const int ncols, const int nrows) {
+    const int row = blockIdx.y*blockDim.y + threadIdx.y;
+
+    if (row >= nrows) {
+        return;
+    }
+
+    const int blocks_per_row = ncols / qk;
+    const int blocks_per_warp = WARP_SIZE / qi;
+
+// partial sum for each thread
+    float tmp = 0.0f;
+
+    const block_q_t  * x = (const block_q_t  *) vx;
+    const block_q8_1 * y = (const block_q8_1 *) vy;
+
+    for (int i = 0; i < blocks_per_row; i += blocks_per_warp) {
+        const int ibx = row*blocks_per_row + i + threadIdx.x / qi; // x block index
+
+        const int iby = i + threadIdx.x / qi; // y block index
+
+        const int iqs  = threadIdx.x % qi; // x block quant index when casting the quants to int
+
+        tmp += vec_dot_q_cuda(&x[ibx], &y[iby], iqs);
+    }
+
+    // sum up partial sums and write back result
+#pragma unroll
+    for (int mask = 16; mask > 0; mask >>= 1) {
+        tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
+    }
+
+    if (threadIdx.x == 0) {
+        dst[row] = tmp;
+    }
+}
+
 template <int qk, int qr, dequantize_kernel_t dequantize_kernel>
 static __global__ void dequantize_mul_mat_vec(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows) {
     // qk = quantized weights per x block
@@ -1233,7 +1457,6 @@ static __global__ void dequantize_mul_mat_vec(const void * vx, const dfloat * y,
     }
 
     // sum up partial sums and write back result
-    __syncthreads();
 #pragma unroll
     for (int mask = 16; mask > 0; mask >>= 1) {
         tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
@@ -1284,7 +1507,6 @@ static __global__ void mul_mat_p021_f16_f32(const void * vx, const float * y, fl
     const int idst = channel*nrows_dst + row_dst;
 
     // sum up partial sums and write back result
-    __syncthreads();
 #pragma unroll
     for (int mask = 16; mask > 0; mask >>= 1) {
         tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
@@ -1330,7 +1552,6 @@ static __global__ void mul_mat_vec_nc_f16_f32( // nc == non-contiguous
     }
 
     // sum up partial sums and write back result
-    __syncthreads();
 #pragma unroll
     for (int mask = 16; mask > 0; mask >>= 1) {
         tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
@@ -1440,7 +1661,6 @@ static __global__ void soft_max_f32(const float * x, float * dst, const int ncol
     }
 
     // sum up partial sums
-    __syncthreads();
 #pragma unroll
     for (int mask = 16; mask > 0; mask >>= 1) {
         tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
@@ -1494,6 +1714,11 @@ static void rms_norm_f32_cuda(const float * x, float * dst, const int ncols, con
     rms_norm_f32<<<nrows, block_dims, 0, stream>>>(x, dst, ncols);
 }
 
+static void quantize_row_q8_1_cuda(const float * x, void * vy, const int k, cudaStream_t stream) {
+    const int num_blocks = (k + CUDA_QUANTIZE_BLOCK_SIZE - 1) / CUDA_QUANTIZE_BLOCK_SIZE;
+    quantize_q8_1<<<num_blocks, CUDA_QUANTIZE_BLOCK_SIZE, 0, stream>>>(x, vy, k);
+}
+
 static void dequantize_row_q4_0_cuda(const void * vx, float * y, const int k, cudaStream_t stream) {
     const int num_blocks = (k + CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / CUDA_DEQUANTIZE_BLOCK_SIZE;
     dequantize_block<QK4_0, QR4_0, dequantize_q4_0><<<num_blocks, CUDA_DEQUANTIZE_BLOCK_SIZE, 0, stream>>>(vx, y, k);
@@ -1562,45 +1787,45 @@ static void dequantize_row_q6_K_cuda(const void * vx, float * y, const int k, cu
 
 static void dequantize_mul_mat_vec_q4_0_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
     GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
-    const int block_num_y = (nrows + GGML_CUDA_DMMV_Y - 1) / GGML_CUDA_DMMV_Y;
+    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
     const dim3 block_nums(1, block_num_y, 1);
-    const dim3 block_dims(WARP_SIZE, GGML_CUDA_DMMV_Y, 1);
+    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
     dequantize_mul_mat_vec<QK4_0, QR4_0, dequantize_q4_0>
         <<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
 }
 
 static void dequantize_mul_mat_vec_q4_1_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
     GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
-    const int block_num_y = (nrows + GGML_CUDA_DMMV_Y - 1) / GGML_CUDA_DMMV_Y;
+    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
     const dim3 block_nums(1, block_num_y, 1);
-    const dim3 block_dims(WARP_SIZE, GGML_CUDA_DMMV_Y, 1);
+    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
     dequantize_mul_mat_vec<QK4_1, QR4_1, dequantize_q4_1>
         <<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
 }
 
 static void dequantize_mul_mat_vec_q5_0_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
     GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
-    const int block_num_y = (nrows + GGML_CUDA_DMMV_Y - 1) / GGML_CUDA_DMMV_Y;
+    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
     const dim3 block_nums(1, block_num_y, 1);
-    const dim3 block_dims(WARP_SIZE, GGML_CUDA_DMMV_Y, 1);
+    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
     dequantize_mul_mat_vec<QK5_0, QR5_0, dequantize_q5_0>
         <<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
 }
 
 static void dequantize_mul_mat_vec_q5_1_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
     GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
-    const int block_num_y = (nrows + GGML_CUDA_DMMV_Y - 1) / GGML_CUDA_DMMV_Y;
+    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
     const dim3 block_nums(1, block_num_y, 1);
-    const dim3 block_dims(WARP_SIZE, GGML_CUDA_DMMV_Y, 1);
+    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
     dequantize_mul_mat_vec<QK5_1, QR5_1, dequantize_q5_1>
         <<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
 }
 
 static void dequantize_mul_mat_vec_q8_0_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
     GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
-    const int block_num_y = (nrows + GGML_CUDA_DMMV_Y - 1) / GGML_CUDA_DMMV_Y;
+    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
     const dim3 block_nums(1, block_num_y, 1);
-    const dim3 block_dims(WARP_SIZE, GGML_CUDA_DMMV_Y, 1);
+    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
     dequantize_mul_mat_vec<QK8_0, QR8_0, dequantize_q8_0>
         <<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
 }
@@ -1647,6 +1872,51 @@ static void dequantize_mul_mat_vec_q6_K_cuda(const void * vx, const float * y, f
     dequantize_mul_mat_vec_q6_k<<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
 }
 
+static void mul_mat_vec_q4_0_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
+    GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
+    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
+    const dim3 block_nums(1, block_num_y, 1);
+    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
+    mul_mat_vec_q<QK4_0, QI4_0, block_q4_0, vec_dot_q4_0_q8_1>
+        <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
+}
+
+static void mul_mat_vec_q4_1_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
+    GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
+    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
+    const dim3 block_nums(1, block_num_y, 1);
+    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
+    mul_mat_vec_q<QK4_0, QI4_1, block_q4_1, vec_dot_q4_1_q8_1>
+        <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
+}
+
+static void mul_mat_vec_q5_0_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
+    GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
+    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
+    const dim3 block_nums(1, block_num_y, 1);
+    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
+    mul_mat_vec_q<QK5_0, QI5_0, block_q5_0, vec_dot_q5_0_q8_1>
+        <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
+}
+
+static void mul_mat_vec_q5_1_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
+    GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
+    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
+    const dim3 block_nums(1, block_num_y, 1);
+    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
+    mul_mat_vec_q<QK5_1, QI5_1, block_q5_1, vec_dot_q5_1_q8_1>
+        <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
+}
+
+static void mul_mat_vec_q8_0_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
+    GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
+    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
+    const dim3 block_nums(1, block_num_y, 1);
+    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
+    mul_mat_vec_q<QK8_0, QI8_0, block_q8_0, vec_dot_q8_0_q8_1>
+        <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
+}
+
 static void convert_fp16_to_fp32_cuda(const void * vx, float * y, const int k, cudaStream_t stream) {
     const int num_blocks = (k + CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / CUDA_DEQUANTIZE_BLOCK_SIZE;
     dequantize_block<1, 1, convert_f16><<<num_blocks, CUDA_DEQUANTIZE_BLOCK_SIZE, 0, stream>>>(vx, y, k);
@@ -1654,9 +1924,9 @@ static void convert_fp16_to_fp32_cuda(const void * vx, float * y, const int k, c
 
 static void convert_mul_mat_vec_f16_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
     GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
-    const int block_num_y = (nrows + GGML_CUDA_DMMV_Y - 1) / GGML_CUDA_DMMV_Y;
+    const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
     const dim3 block_nums(1, block_num_y, 1);
-    const dim3 block_dims(WARP_SIZE, GGML_CUDA_DMMV_Y, 1);
+    const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
     dequantize_mul_mat_vec<1, 1, convert_f16>
         <<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
 }
@@ -1822,6 +2092,7 @@ static size_t g_scratch_offset = 0;
 
 static int g_device_count = -1;
 static int g_main_device = 0;
+static int g_compute_capabilities[GGML_CUDA_MAX_DEVICES];
 static float g_tensor_split[GGML_CUDA_MAX_DEVICES] = {0};
 
 static cublasHandle_t g_cublas_handles[GGML_CUDA_MAX_DEVICES] = {nullptr};
@@ -1839,9 +2110,12 @@ void ggml_init_cublas() {
         for (int id = 0; id < g_device_count; ++id) {
             cudaDeviceProp prop;
             CUDA_CHECK(cudaGetDeviceProperties(&prop, id));
-            fprintf(stderr, "  Device %d: %s\n", id, prop.name);
+            fprintf(stderr, "  Device %d: %s, compute capability %d.%d\n", id, prop.name, prop.major, prop.minor);
+
             g_tensor_split[id] = total_vram;
             total_vram += prop.totalGlobalMem;
+
+            g_compute_capabilities[id] = 100*prop.major + 10*prop.minor;
         }
         for (int id = 0; id < g_device_count; ++id) {
             g_tensor_split[id] /= total_vram;
@@ -2057,7 +2331,7 @@ inline void ggml_cuda_op_rms_norm(
     (void) i1;
 }
 
-inline void ggml_cuda_op_dequantize_mul_mat_vec(
+inline void ggml_cuda_op_mul_mat_vec(
     const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, char * src0_ddq_i,
     float * src0_ddf_i, float * src1_ddf_i, float * dst_ddf_i, int64_t i02, int64_t i01_low, int64_t i01_high, int i1,
     cudaStream_t & cudaStream_main){
@@ -2069,69 +2343,116 @@ inline void ggml_cuda_op_dequantize_mul_mat_vec(
     const int64_t ne00 = src0->ne[0];
     const int64_t nrows = i01_high - i01_low;
 
-// on some GPUs it is faster to convert src1 to half and to use half precision intrinsics
-#ifdef GGML_CUDA_DMMV_F16
-    size_t ash;
-    dfloat * src1_dfloat = nullptr; // dfloat == half
-
-    bool src1_convert_f16 = src0->type == GGML_TYPE_Q4_0 || src0->type == GGML_TYPE_Q4_1 ||
-        src0->type == GGML_TYPE_Q5_0 || src0->type == GGML_TYPE_Q5_1 ||
-        src0->type == GGML_TYPE_Q8_0 || src0->type == GGML_TYPE_F16;
-
-    if (src1_convert_f16) {
-        src1_dfloat = (half *) ggml_cuda_pool_malloc(ne00*sizeof(half), &ash);
-        ggml_cpy_f32_f16_cuda((char *) src1_ddf_i, (char *) src1_dfloat, ne00,
-                                ne00, 1, sizeof(float), 0, 0,
-                                ne00, 1, sizeof(half),  0, 0, cudaStream_main);
-    }
+#ifdef GGML_CUDA_FORCE_DMMV
+    const bool use_mul_mat_vec_q = false;
 #else
-    dfloat * src1_dfloat = src1_ddf_i; // dfloat == float, no conversion
+    int id;
+    CUDA_CHECK(cudaGetDevice(&id));
+
+    const bool mul_mat_vec_q_implemented = src0->type == GGML_TYPE_Q4_0 ||
+        src0->type == GGML_TYPE_Q4_1 ||
+        src0->type == GGML_TYPE_Q5_0 ||
+        src0->type == GGML_TYPE_Q5_1 ||
+        src0->type == GGML_TYPE_Q8_0;
+
+    // The integer intrinsics used in mul_mat_vec_q are available with compute capability 6.
+    // However, they have bad performance with Pascal cards.
+    // Therefore, in a multi GPU setting decide at runtime which GPUs should use mul_mat_vec_q.
+    const bool use_mul_mat_vec_q = g_compute_capabilities[id] >= 700 && mul_mat_vec_q_implemented;
+#endif
+
+    if (use_mul_mat_vec_q) {
+        size_t as;
+        void * src1_q8_1 = ggml_cuda_pool_malloc(ne00*sizeof(block_q8_1)/QK8_1, &as);
+        quantize_row_q8_1_cuda(src1_ddf_i, src1_q8_1, ne00, cudaStream_main);
+
+        switch (src0->type) {
+            case GGML_TYPE_Q4_0:
+                mul_mat_vec_q4_0_q8_1_cuda(src0_ddq_i, src1_q8_1, dst_ddf_i, ne00, nrows, cudaStream_main);
+                break;
+            case GGML_TYPE_Q4_1:
+                mul_mat_vec_q4_1_q8_1_cuda(src0_ddq_i, src1_q8_1, dst_ddf_i, ne00, nrows, cudaStream_main);
+                break;
+            case GGML_TYPE_Q5_0:
+                mul_mat_vec_q5_0_q8_1_cuda(src0_ddq_i, src1_q8_1, dst_ddf_i, ne00, nrows, cudaStream_main);
+                break;
+            case GGML_TYPE_Q5_1:
+                mul_mat_vec_q5_1_q8_1_cuda(src0_ddq_i, src1_q8_1, dst_ddf_i, ne00, nrows, cudaStream_main);
+                break;
+            case GGML_TYPE_Q8_0:
+                mul_mat_vec_q8_0_q8_1_cuda(src0_ddq_i, src1_q8_1, dst_ddf_i, ne00, nrows, cudaStream_main);
+                break;
+            default:
+                GGML_ASSERT(false);
+                break;
+        }
+
+        ggml_cuda_pool_free(src1_q8_1, as);
+    } else {
+        // on some GPUs it is faster to convert src1 to half and to use half precision intrinsics
+#ifdef GGML_CUDA_DMMV_F16
+        size_t ash;
+        dfloat * src1_dfloat = nullptr; // dfloat == half
+
+        bool src1_convert_f16 = src0->type == GGML_TYPE_Q4_0 || src0->type == GGML_TYPE_Q4_1 ||
+            src0->type == GGML_TYPE_Q5_0 || src0->type == GGML_TYPE_Q5_1 ||
+            src0->type == GGML_TYPE_Q8_0 || src0->type == GGML_TYPE_F16;
+
+        if (src1_convert_f16) {
+            src1_dfloat = (half *) ggml_cuda_pool_malloc(ne00*sizeof(half), &ash);
+            ggml_cpy_f32_f16_cuda((char *) src1_ddf_i, (char *) src1_dfloat, ne00,
+                                    ne00, 1, sizeof(float), 0, 0,
+                                    ne00, 1, sizeof(half),  0, 0, cudaStream_main);
+        }
+#else
+        dfloat * src1_dfloat = src1_ddf_i; // dfloat == float, no conversion
 #endif // GGML_CUDA_DMMV_F16
 
-    switch (src0->type) {
-        case GGML_TYPE_Q4_0:
-            dequantize_mul_mat_vec_q4_0_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main);
-            break;
-        case GGML_TYPE_Q4_1:
-            dequantize_mul_mat_vec_q4_1_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main);
-            break;
-        case GGML_TYPE_Q5_0:
-            dequantize_mul_mat_vec_q5_0_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main);
-            break;
-        case GGML_TYPE_Q5_1:
-            dequantize_mul_mat_vec_q5_1_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main);
-            break;
-        case GGML_TYPE_Q8_0:
-            dequantize_mul_mat_vec_q8_0_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main);
-            break;
-        case GGML_TYPE_Q2_K:
-            dequantize_mul_mat_vec_q2_K_cuda(src0_ddq_i, src1_ddf_i, dst_ddf_i, ne00, nrows, cudaStream_main);
-            break;
-        case GGML_TYPE_Q3_K:
-            dequantize_mul_mat_vec_q3_K_cuda(src0_ddq_i, src1_ddf_i, dst_ddf_i, ne00, nrows, cudaStream_main);
-            break;
-        case GGML_TYPE_Q4_K:
-            dequantize_mul_mat_vec_q4_K_cuda(src0_ddq_i, src1_ddf_i, dst_ddf_i, ne00, nrows, cudaStream_main);
-            break;
-        case GGML_TYPE_Q5_K:
-            dequantize_mul_mat_vec_q5_K_cuda(src0_ddq_i, src1_ddf_i, dst_ddf_i, ne00, nrows, cudaStream_main);
-            break;
-        case GGML_TYPE_Q6_K:
-            dequantize_mul_mat_vec_q6_K_cuda(src0_ddq_i, src1_ddf_i, dst_ddf_i, ne00, nrows, cudaStream_main);
-            break;
-        case GGML_TYPE_F16:
-            convert_mul_mat_vec_f16_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main);
-            break;
-        default:
-            GGML_ASSERT(false);
-            break;
-    }
+        switch (src0->type) {
+            case GGML_TYPE_Q4_0:
+                dequantize_mul_mat_vec_q4_0_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main);
+                break;
+            case GGML_TYPE_Q4_1:
+                dequantize_mul_mat_vec_q4_1_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main);
+                break;
+            case GGML_TYPE_Q5_0:
+                dequantize_mul_mat_vec_q5_0_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main);
+                break;
+            case GGML_TYPE_Q5_1:
+                dequantize_mul_mat_vec_q5_1_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main);
+                break;
+            case GGML_TYPE_Q8_0:
+                dequantize_mul_mat_vec_q8_0_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main);
+                break;
+            case GGML_TYPE_Q2_K:
+                dequantize_mul_mat_vec_q2_K_cuda(src0_ddq_i, src1_ddf_i, dst_ddf_i, ne00, nrows, cudaStream_main);
+                break;
+            case GGML_TYPE_Q3_K:
+                dequantize_mul_mat_vec_q3_K_cuda(src0_ddq_i, src1_ddf_i, dst_ddf_i, ne00, nrows, cudaStream_main);
+                break;
+            case GGML_TYPE_Q4_K:
+                dequantize_mul_mat_vec_q4_K_cuda(src0_ddq_i, src1_ddf_i, dst_ddf_i, ne00, nrows, cudaStream_main);
+                break;
+            case GGML_TYPE_Q5_K:
+                dequantize_mul_mat_vec_q5_K_cuda(src0_ddq_i, src1_ddf_i, dst_ddf_i, ne00, nrows, cudaStream_main);
+                break;
+            case GGML_TYPE_Q6_K:
+                dequantize_mul_mat_vec_q6_K_cuda(src0_ddq_i, src1_ddf_i, dst_ddf_i, ne00, nrows, cudaStream_main);
+                break;
+            case GGML_TYPE_F16:
+                convert_mul_mat_vec_f16_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main);
+                break;
+            default:
+                GGML_ASSERT(false);
+                break;
+        }
 
 #ifdef GGML_CUDA_DMMV_F16
-    if (src1_convert_f16) {
-        ggml_cuda_pool_free(src1_dfloat, ash);
-    }
+        if (src1_convert_f16) {
+            ggml_cuda_pool_free(src1_dfloat, ash);
+        }
 #endif // GGML_CUDA_DMMV_F16
+    }
 
     (void) src1;
     (void) dst;
@@ -2701,8 +3022,8 @@ void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_
     }else if (src0->type == GGML_TYPE_F32) {
         ggml_cuda_op(src0, src1, dst, ggml_cuda_op_mul_mat_cublas, true, false);
     } else if (ggml_is_quantized(src0->type) || src0->type == GGML_TYPE_F16) {
-        if (src1->ne[1] == 1 && src0->ne[0] % GGML_CUDA_DMMV_X == 0 && src0->ne[1] % GGML_CUDA_DMMV_Y == 0) {
-            ggml_cuda_op(src0, src1, dst, ggml_cuda_op_dequantize_mul_mat_vec, false, false);
+        if (src1->ne[1] == 1 && src0->ne[0] % GGML_CUDA_DMMV_X == 0) {
+            ggml_cuda_op(src0, src1, dst, ggml_cuda_op_mul_mat_vec, false, false);
         } else {
             ggml_cuda_op(src0, src1, dst, ggml_cuda_op_mul_mat_cublas, true, false);
         }
@@ -2835,7 +3156,7 @@ void ggml_cuda_transform_tensor(void * data, struct ggml_tensor * tensor) {
 }
 
 void ggml_cuda_free_data(struct ggml_tensor * tensor) {
-    if (tensor->backend != GGML_BACKEND_GPU && tensor->backend != GGML_BACKEND_GPU_SPLIT) {
+    if (!tensor || (tensor->backend != GGML_BACKEND_GPU && tensor->backend != GGML_BACKEND_GPU_SPLIT) ) {
         return;
     }
 
diff --git a/ggml-opencl.cpp b/ggml-opencl.cpp
index fed4ffb0c..fa0bdbefb 100644
--- a/ggml-opencl.cpp
+++ b/ggml-opencl.cpp
@@ -1376,7 +1376,7 @@ static void ggml_cl_mul_f32(const ggml_tensor * src0, const ggml_tensor * src1,
     const int64_t ne00 = src0->ne[0];
     const int64_t ne01 = src0->ne[1];
     const int64_t ne02 = src0->ne[2];
-    const int64_t ne03 = src0->ne[2];
+    const int64_t ne03 = src0->ne[3];
     const int64_t ne0 = ne00 * ne01 * ne02 * ne03;
     const int64_t ne10 = src1->ne[0];
     const int64_t ne11 = src1->ne[1];
diff --git a/ggml.c b/ggml.c
index 71e77d015..99b7b75a8 100644
--- a/ggml.c
+++ b/ggml.c
@@ -224,9 +224,27 @@ inline static void* ggml_aligned_malloc(size_t size) {
 #define GGML_ALIGNED_FREE(ptr)     free(ptr)
 #endif
 
-#define UNUSED(x) (void)(x)
+#define UNUSED GGML_UNUSED
 #define SWAP(x, y, T) do { T SWAP = x; x = y; y = SWAP; } while (0)
 
+//
+// tensor access macros
+//
+
+#define GGML_TENSOR_UNARY_OP_LOCALS \
+    GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne); \
+    GGML_TENSOR_LOCALS(size_t,  nb0, src0, nb); \
+    GGML_TENSOR_LOCALS(int64_t, ne,  dst,  ne); \
+    GGML_TENSOR_LOCALS(size_t,  nb,  dst,  nb);
+
+#define GGML_TENSOR_BINARY_OP_LOCALS \
+    GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne); \
+    GGML_TENSOR_LOCALS(size_t,  nb0, src0, nb); \
+    GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne); \
+    GGML_TENSOR_LOCALS(size_t,  nb1, src1, nb); \
+    GGML_TENSOR_LOCALS(int64_t, ne,  dst,  ne); \
+    GGML_TENSOR_LOCALS(size_t,  nb,  dst,  nb);
+
 #if defined(GGML_USE_ACCELERATE)
 #include <Accelerate/Accelerate.h>
 #if defined(GGML_USE_CLBLAST) // allow usage of CLBlast alongside Accelerate functions
@@ -467,14 +485,14 @@ ggml_fp16_t ggml_fp32_to_fp16(float x) {
     return GGML_FP32_TO_FP16(x);
 }
 
-void ggml_fp16_to_fp32_row(const ggml_fp16_t * x, float * y, size_t n) {
-    for (size_t i = 0; i < n; i++) {
+void ggml_fp16_to_fp32_row(const ggml_fp16_t * x, float * y, int n) {
+    for (int i = 0; i < n; i++) {
         y[i] = GGML_FP16_TO_FP32(x[i]);
     }
 }
 
-void ggml_fp32_to_fp16_row(const float * x, ggml_fp16_t * y, size_t n) {
-    size_t i = 0;
+void ggml_fp32_to_fp16_row(const float * x, ggml_fp16_t * y, int n) {
+    int i = 0;
 #if defined(__F16C__)
     for (; i + 7 < n; i += 8) {
         __m256 x_vec = _mm256_loadu_ps(x + i);
@@ -1613,109 +1631,112 @@ static void dequantize_row_q8_0(const void * restrict vx, float * restrict y, in
     }
 }
 
+static void ggml_vec_dot_f32(const int n, float * restrict s, const float * restrict x, const float * restrict y);
+static void ggml_vec_dot_f16(const int n, float * restrict s, ggml_fp16_t * restrict x, ggml_fp16_t * restrict y);
 static void ggml_vec_dot_q4_0_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy);
 static void ggml_vec_dot_q4_1_q8_1(const int n, float * restrict s, const void * restrict vx, const void * restrict vy);
 static void ggml_vec_dot_q5_0_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy);
 static void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void * restrict vx, const void * restrict vy);
 static void ggml_vec_dot_q8_0_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy);
 
-static const quantize_fns_t quantize_fns[GGML_TYPE_COUNT] = {
+static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
+    [GGML_TYPE_F32] = {
+        .vec_dot                  = (ggml_vec_dot_t) ggml_vec_dot_f32,
+        .vec_dot_type             = GGML_TYPE_F32,
+    },
+    [GGML_TYPE_F16] = {
+        .to_float                 = (ggml_to_float_t) ggml_fp16_to_fp32_row,
+        .from_float               = (ggml_from_float_t) ggml_fp32_to_fp16_row,
+        .from_float_reference     = (ggml_from_float_t) ggml_fp32_to_fp16_row,
+        .vec_dot                  = (ggml_vec_dot_t) ggml_vec_dot_f16,
+        .vec_dot_type             = GGML_TYPE_F16,
+    },
     [GGML_TYPE_Q4_0] = {
-        .dequantize_row_q         = (dequantize_row_q_t) dequantize_row_q4_0,
-        .quantize_row_q           = quantize_row_q4_0,
-        .quantize_row_q_reference = (quantize_row_q_t) quantize_row_q4_0_reference,
-        .quantize_row_q_dot       = quantize_row_q8_0,
-        .vec_dot_q                = ggml_vec_dot_q4_0_q8_0,
+        .to_float                 = (ggml_to_float_t) dequantize_row_q4_0,
+        .from_float               = quantize_row_q4_0,
+        .from_float_reference     = (ggml_from_float_t) quantize_row_q4_0_reference,
+        .vec_dot                  = ggml_vec_dot_q4_0_q8_0,
         .vec_dot_type             = GGML_TYPE_Q8_0,
     },
     [GGML_TYPE_Q4_1] = {
-        .dequantize_row_q         = (dequantize_row_q_t)dequantize_row_q4_1,
-        .quantize_row_q           = quantize_row_q4_1,
-        .quantize_row_q_reference = (quantize_row_q_t) quantize_row_q4_1_reference,
-        .quantize_row_q_dot       = quantize_row_q8_1,
-        .vec_dot_q                = ggml_vec_dot_q4_1_q8_1,
+        .to_float                 = (ggml_to_float_t) dequantize_row_q4_1,
+        .from_float               = quantize_row_q4_1,
+        .from_float_reference     = (ggml_from_float_t) quantize_row_q4_1_reference,
+        .vec_dot                  = ggml_vec_dot_q4_1_q8_1,
         .vec_dot_type             = GGML_TYPE_Q8_1,
     },
     [GGML_TYPE_Q5_0] = {
-        .dequantize_row_q         = (dequantize_row_q_t) dequantize_row_q5_0,
-        .quantize_row_q           = quantize_row_q5_0,
-        .quantize_row_q_reference = (quantize_row_q_t) quantize_row_q5_0_reference,
-        .quantize_row_q_dot       = quantize_row_q8_0,
-        .vec_dot_q                = ggml_vec_dot_q5_0_q8_0,
+        .to_float                 = (ggml_to_float_t) dequantize_row_q5_0,
+        .from_float               = quantize_row_q5_0,
+        .from_float_reference     = (ggml_from_float_t) quantize_row_q5_0_reference,
+        .vec_dot                  = ggml_vec_dot_q5_0_q8_0,
         .vec_dot_type             = GGML_TYPE_Q8_0,
     },
     [GGML_TYPE_Q5_1] = {
-        .dequantize_row_q         = (dequantize_row_q_t) dequantize_row_q5_1,
-        .quantize_row_q           = quantize_row_q5_1,
-        .quantize_row_q_reference = (quantize_row_q_t) quantize_row_q5_1_reference,
-        .quantize_row_q_dot       = quantize_row_q8_1,
-        .vec_dot_q                = ggml_vec_dot_q5_1_q8_1,
+        .to_float                 = (ggml_to_float_t) dequantize_row_q5_1,
+        .from_float               = quantize_row_q5_1,
+        .from_float_reference     = (ggml_from_float_t) quantize_row_q5_1_reference,
+        .vec_dot                  = ggml_vec_dot_q5_1_q8_1,
         .vec_dot_type             = GGML_TYPE_Q8_1,
     },
     [GGML_TYPE_Q8_0] = {
-        .dequantize_row_q         = dequantize_row_q8_0,
-        .quantize_row_q           = quantize_row_q8_0,
-        .quantize_row_q_reference = (quantize_row_q_t) quantize_row_q8_0_reference,
-        .quantize_row_q_dot       = quantize_row_q8_0,
-        .vec_dot_q                = ggml_vec_dot_q8_0_q8_0,
+        .to_float                 = dequantize_row_q8_0,
+        .from_float               = quantize_row_q8_0,
+        .from_float_reference     = (ggml_from_float_t) quantize_row_q8_0_reference,
+        .vec_dot                  = ggml_vec_dot_q8_0_q8_0,
         .vec_dot_type             = GGML_TYPE_Q8_0,
     },
     [GGML_TYPE_Q8_1] = {
-        .dequantize_row_q         = NULL,   // TODO
-        .quantize_row_q           = quantize_row_q8_1,
-        .quantize_row_q_reference = (quantize_row_q_t) quantize_row_q8_1_reference,
-        .quantize_row_q_dot       = quantize_row_q8_1,
-        .vec_dot_q                = NULL,   // TODO
+        .from_float               = quantize_row_q8_1,
+        .from_float_reference     = (ggml_from_float_t) quantize_row_q8_1_reference,
         .vec_dot_type             = GGML_TYPE_Q8_1,
     },
 #ifdef GGML_USE_K_QUANTS
     [GGML_TYPE_Q2_K] = {
-        .dequantize_row_q         = (dequantize_row_q_t) dequantize_row_q2_K,
-        .quantize_row_q           = quantize_row_q2_K,
-        .quantize_row_q_reference = (quantize_row_q_t) quantize_row_q2_K_reference,
-        .quantize_row_q_dot       = quantize_row_q8_K,
-        .vec_dot_q                = ggml_vec_dot_q2_K_q8_K,
+        .to_float                 = (ggml_to_float_t) dequantize_row_q2_K,
+        .from_float               = quantize_row_q2_K,
+        .from_float_reference     = (ggml_from_float_t) quantize_row_q2_K_reference,
+        .vec_dot                  = ggml_vec_dot_q2_K_q8_K,
         .vec_dot_type             = GGML_TYPE_Q8_K,
     },
     [GGML_TYPE_Q3_K] = {
-        .dequantize_row_q         = (dequantize_row_q_t) dequantize_row_q3_K,
-        .quantize_row_q           = quantize_row_q3_K,
-        .quantize_row_q_reference = (quantize_row_q_t) quantize_row_q3_K_reference,
-        .quantize_row_q_dot       = quantize_row_q8_K,
-        .vec_dot_q                = ggml_vec_dot_q3_K_q8_K,
+        .to_float                 = (ggml_to_float_t) dequantize_row_q3_K,
+        .from_float               = quantize_row_q3_K,
+        .from_float_reference     = (ggml_from_float_t) quantize_row_q3_K_reference,
+        .vec_dot                  = ggml_vec_dot_q3_K_q8_K,
         .vec_dot_type             = GGML_TYPE_Q8_K,
     },
     [GGML_TYPE_Q4_K] = {
-        .dequantize_row_q         = (dequantize_row_q_t) dequantize_row_q4_K,
-        .quantize_row_q           = quantize_row_q4_K,
-        .quantize_row_q_reference = (quantize_row_q_t) quantize_row_q4_K_reference,
-        .quantize_row_q_dot       = quantize_row_q8_K,
-        .vec_dot_q                = ggml_vec_dot_q4_K_q8_K,
+        .to_float                 = (ggml_to_float_t) dequantize_row_q4_K,
+        .from_float               = quantize_row_q4_K,
+        .from_float_reference     = (ggml_from_float_t) quantize_row_q4_K_reference,
+        .vec_dot                  = ggml_vec_dot_q4_K_q8_K,
         .vec_dot_type             = GGML_TYPE_Q8_K,
     },
     [GGML_TYPE_Q5_K] = {
-        .dequantize_row_q         = (dequantize_row_q_t) dequantize_row_q5_K,
-        .quantize_row_q           = quantize_row_q5_K,
-        .quantize_row_q_reference = (quantize_row_q_t) quantize_row_q5_K_reference,
-        .quantize_row_q_dot       = quantize_row_q8_K,
-        .vec_dot_q                = ggml_vec_dot_q5_K_q8_K,
+        .to_float                 = (ggml_to_float_t) dequantize_row_q5_K,
+        .from_float               = quantize_row_q5_K,
+        .from_float_reference     = (ggml_from_float_t) quantize_row_q5_K_reference,
+        .vec_dot                  = ggml_vec_dot_q5_K_q8_K,
         .vec_dot_type             = GGML_TYPE_Q8_K,
     },
     [GGML_TYPE_Q6_K] = {
-        .dequantize_row_q         = (dequantize_row_q_t) dequantize_row_q6_K,
-        .quantize_row_q           = quantize_row_q6_K,
-        .quantize_row_q_reference = (quantize_row_q_t) quantize_row_q6_K_reference,
-        .quantize_row_q_dot       = quantize_row_q8_K,
-        .vec_dot_q                = ggml_vec_dot_q6_K_q8_K,
+        .to_float                 = (ggml_to_float_t) dequantize_row_q6_K,
+        .from_float               = quantize_row_q6_K,
+        .from_float_reference     = (ggml_from_float_t) quantize_row_q6_K_reference,
+        .vec_dot                  = ggml_vec_dot_q6_K_q8_K,
         .vec_dot_type             = GGML_TYPE_Q8_K,
     },
+    [GGML_TYPE_Q8_K] = {
+        .from_float               = quantize_row_q8_K,
+    }
 #endif
 };
 
 // For internal test use
-quantize_fns_t ggml_internal_get_quantize_fn(size_t i) {
+ggml_type_traits_t ggml_internal_get_type_traits(enum ggml_type i) {
     GGML_ASSERT(i < GGML_TYPE_COUNT);
-    return quantize_fns[i];
+    return type_traits[i];
 }
 
 
@@ -2261,7 +2282,7 @@ inline static void ggml_vec_neg_f32 (const int n, float * y, const float * x)
 inline static void ggml_vec_mul_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i]  = x[i]*y[i];   }
 inline static void ggml_vec_div_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i]  = x[i]/y[i];   }
 
-inline static void ggml_vec_dot_f32(const int n, float * restrict s, const float * restrict x, const float * restrict y) {
+static void ggml_vec_dot_f32(const int n, float * restrict s, const float * restrict x, const float * restrict y) {
 #ifdef GGML_SIMD
     float sumf = 0.0f;
     const int np = (n & ~(GGML_F32_STEP - 1));
@@ -2298,7 +2319,7 @@ inline static void ggml_vec_dot_f32(const int n, float * restrict s, const float
     *s = sumf;
 }
 
-inline static void ggml_vec_dot_f16(const int n, float * restrict s, ggml_fp16_t * restrict x, ggml_fp16_t * restrict y) {
+static void ggml_vec_dot_f16(const int n, float * restrict s, ggml_fp16_t * restrict x, ggml_fp16_t * restrict y) {
     ggml_float sumf = 0.0;
 
 #if defined(GGML_SIMD)
@@ -3451,6 +3472,8 @@ inline static void ggml_vec_log_f32  (const int n, float * y, const float * x) {
 inline static void ggml_vec_abs_f32  (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = fabsf(x[i]); }
 inline static void ggml_vec_sgn_f32  (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? 1.f : ((x[i] < 0.f) ? -1.f : 0.f); }
 inline static void ggml_vec_step_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? 1.f : 0.f; }
+inline static void ggml_vec_tanh_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = tanhf(x[i]);  }
+inline static void ggml_vec_elu_f32  (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? x[i] : expf(x[i])-1; }
 inline static void ggml_vec_relu_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? x[i] : 0.f; }
 
 static const float GELU_COEF_A    = 0.044715f;
@@ -3602,6 +3625,16 @@ inline static void ggml_vec_norm_inv_f32(const int n, float * s, const float * x
     *s = 1.f/(*s);
 }
 
+inline static void ggml_vec_argmax_f32(const int n, int * s, const float * x) {
+    float max = -INFINITY;
+    int idx = 0;
+    for (int i = 0; i < n; ++i) {
+        max = MAX(max, x[i]);
+        if (max == x[i]) { idx = i; }
+    }
+    *s = idx;
+}
+
 //
 // data types
 //
@@ -3711,12 +3744,15 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
     "SUM",
     "SUM_ROWS",
     "MEAN",
+    "ARGMAX",
     "REPEAT",
     "REPEAT_BACK",
     "ABS",
     "SGN",
     "NEG",
     "STEP",
+    "TANH",
+    "ELU",
     "RELU",
     "GELU",
     "GELU_QUICK",
@@ -3748,9 +3784,8 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
     "ROPE_BACK",
     "ALIBI",
     "CLAMP",
-    "CONV_1D_S1_PH",
-    "CONV_1D_S2_PH",
-    "CONV_2D_SK_P0",
+    "CONV_1D",
+    "CONV_2D",
 
     "FLASH_ATTN",
     "FLASH_FF",
@@ -3769,7 +3804,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
     "CROSS_ENTROPY_LOSS_BACK",
 };
 
-static_assert(GGML_OP_COUNT == 64, "GGML_OP_COUNT != 64");
+static_assert(GGML_OP_COUNT == 66, "GGML_OP_COUNT != 66");
 
 static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
     "none",
@@ -3787,12 +3822,15 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
     "Σx",
     "Σx_k",
     "Σx/n",
+    "argmax(x)",
     "repeat(x)",
     "repeat_back(x)",
     "abs(x)",
     "sgn(x)",
     "-x",
     "step(x)",
+    "tanh(x)",
+    "elu(x)",
     "relu(x)",
     "gelu(x)",
     "gelu_quick(x)",
@@ -3824,9 +3862,8 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
     "rope_back(x)",
     "alibi(x)",
     "clamp(x)",
-    "conv_1d_s1_ph(x)",
-    "conv_1d_s2_ph(x)",
-    "conv_2d_sk_p0(x)",
+    "conv_1d(x)",
+    "conv_2d(x)",
 
     "flash_attn(x)",
     "flash_ff(x)",
@@ -3845,7 +3882,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
     "cross_entropy_loss_back(x,y)",
 };
 
-static_assert(GGML_OP_COUNT == 64, "GGML_OP_COUNT != 64");
+static_assert(GGML_OP_COUNT == 66, "GGML_OP_COUNT != 66");
 
 static_assert(sizeof(struct ggml_object)%GGML_MEM_ALIGN == 0, "ggml_object size must be a multiple of GGML_MEM_ALIGN");
 static_assert(sizeof(struct ggml_tensor)%GGML_MEM_ALIGN == 0, "ggml_tensor size must be a multiple of GGML_MEM_ALIGN");
@@ -3871,9 +3908,8 @@ static void ggml_setup_op_has_task_pass(void) {
         p[GGML_OP_GET_ROWS_BACK          ] = true;
         p[GGML_OP_DIAG_MASK_INF          ] = true;
         p[GGML_OP_DIAG_MASK_ZERO         ] = true;
-        p[GGML_OP_CONV_1D_S1_PH          ] = true;
-        p[GGML_OP_CONV_1D_S2_PH          ] = true;
-        p[GGML_OP_CONV_2D_SK_P0          ] = true;
+        p[GGML_OP_CONV_1D                ] = true;
+        p[GGML_OP_CONV_2D                ] = true;
         p[GGML_OP_FLASH_ATTN_BACK        ] = true;
         p[GGML_OP_CROSS_ENTROPY_LOSS     ] = true;
     }
@@ -5474,6 +5510,30 @@ struct ggml_tensor * ggml_mean(
     return result;
 }
 
+// ggml_argmax
+
+struct ggml_tensor * ggml_argmax(
+        struct ggml_context * ctx,
+        struct ggml_tensor * a) {
+    GGML_ASSERT(ggml_is_matrix(a));
+    bool is_node = false;
+
+    if (a->grad) {
+        GGML_ASSERT(false);
+        is_node = true;
+    }
+
+    int64_t ne[GGML_MAX_DIMS] = { a->ne[1], 1, 1, 1 };
+    struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_I32, a->n_dims, ne);
+
+    result->op   = GGML_OP_ARGMAX;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src0 = a;
+    result->src1 = NULL;
+
+    return result;
+}
+
 // ggml_repeat
 
 struct ggml_tensor * ggml_repeat(
@@ -5667,6 +5727,74 @@ struct ggml_tensor * ggml_step_inplace(
     return ggml_step_impl(ctx, a, true);
 }
 
+// ggml_tanh
+
+struct ggml_tensor * ggml_tanh_impl(
+        struct ggml_context * ctx,
+        struct ggml_tensor * a,
+        bool inplace) {
+    bool is_node = false;
+
+    if (!inplace && (a->grad)) {
+        is_node = true;
+    }
+
+    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
+
+    result->op   = GGML_OP_TANH;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src0 = a;
+    result->src1 = NULL;
+
+    return result;
+}
+
+struct ggml_tensor * ggml_tanh(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a) {
+    return ggml_tanh_impl(ctx, a, false);
+}
+
+struct ggml_tensor * ggml_tanh_inplace(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a) {
+    return ggml_tanh_impl(ctx, a, true);
+}
+
+// ggml_elu
+
+struct ggml_tensor * ggml_elu_impl(
+        struct ggml_context * ctx,
+        struct ggml_tensor * a,
+        bool inplace) {
+    bool is_node = false;
+
+    if (!inplace && (a->grad)) {
+        is_node = true;
+    }
+
+    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
+
+    result->op   = GGML_OP_ELU;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src0 = a;
+    result->src1 = NULL;
+
+    return result;
+}
+
+struct ggml_tensor * ggml_elu(
+    struct ggml_context * ctx,
+    struct ggml_tensor  * a) {
+    return ggml_elu_impl(ctx, a, false);
+}
+
+struct ggml_tensor * ggml_elu_inplace(
+    struct ggml_context * ctx,
+    struct ggml_tensor  * a) {
+    return ggml_elu_impl(ctx, a, true);
+}
+
 // ggml_relu
 
 struct ggml_tensor * ggml_relu_impl(
@@ -6908,6 +7036,8 @@ struct ggml_tensor * ggml_rope_back(
         int                   n_dims,
         int                   mode) {
     GGML_ASSERT(n_past >= 0);
+    GGML_ASSERT((mode & 4) == 0 && "ggml_rope_back() for ChatGLM not implemented yet");
+
     bool is_node = false;
 
     if (a->grad) {
@@ -7008,15 +7138,21 @@ struct ggml_tensor * ggml_clamp(
     return result;
 }
 
-// ggml_conv_1d_s1_ph
+// ggml_conv_1d
 
-struct ggml_tensor * ggml_conv_1d_s1_ph(
+static int64_t ggml_calc_conv_output_size(int64_t ins, int64_t ks, int s, int p, int d) {
+    return (ins + 2 * p - d * (ks - 1) - 1) / s + 1;
+}
+
+GGML_API struct ggml_tensor * ggml_conv_1d(
         struct ggml_context * ctx,
         struct ggml_tensor  * a,
-        struct ggml_tensor  * b) {
+        struct ggml_tensor  * b,
+        int                   s0,
+        int                   p0,
+        int                   d0) {
     GGML_ASSERT(ggml_is_matrix(b));
     GGML_ASSERT(a->ne[1] == b->ne[1]);
-    GGML_ASSERT(a->ne[3] == 1);
     bool is_node = false;
 
     if (a->grad || b->grad) {
@@ -7024,54 +7160,43 @@ struct ggml_tensor * ggml_conv_1d_s1_ph(
         is_node = true;
     }
 
-    const int64_t ne[4] = { b->ne[0], a->ne[2], 1, 1, };
-    struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 2, ne);
+    const int64_t ne[4] = {
+        ggml_calc_conv_output_size(b->ne[0], a->ne[0], s0, p0, d0),
+        a->ne[2], 1, 1,
+    };
+    struct ggml_tensor* result = ggml_new_tensor(ctx, GGML_TYPE_F32, 2, ne);
 
-    result->op   = GGML_OP_CONV_1D_S1_PH;
+    ggml_scratch_save(ctx);
+    struct ggml_tensor* c = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 3);
+    ((int32_t*)c->data)[0] = s0;
+    ((int32_t*)c->data)[1] = p0;
+    ((int32_t*)c->data)[2] = d0;
+    ggml_scratch_load(ctx);
+
+    result->op = GGML_OP_CONV_1D;
     result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
     result->src0 = a;
     result->src1 = b;
+    result->opt[0] = c;
 
     return result;
 }
 
-// ggml_conv_1d_s2_ph
+// ggml_conv_2d
 
-struct ggml_tensor * ggml_conv_1d_s2_ph(
-        struct ggml_context * ctx,
-        struct ggml_tensor  * a,
-        struct ggml_tensor  * b) {
-    GGML_ASSERT(ggml_is_matrix(b));
-    GGML_ASSERT(a->ne[1] == b->ne[1]);
-    GGML_ASSERT(a->ne[3] == 1);
-    bool is_node = false;
+struct ggml_tensor* ggml_conv_2d(
+    struct ggml_context* ctx,
+    struct ggml_tensor * a,
+    struct ggml_tensor * b,
+    int                  s0,
+    int                  s1,
+    int                  p0,
+    int                  p1,
+    int                  d0,
+    int                  d1) {
 
-    if (a->grad || b->grad) {
-        GGML_ASSERT(false); // TODO: implement backward
-        is_node = true;
-    }
-
-    const int64_t ne[4] = { b->ne[0]/2, a->ne[2], 1, 1, };
-    struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 2, ne);
-
-    result->op   = GGML_OP_CONV_1D_S2_PH;
-    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
-    result->src0 = a;
-    result->src1 = b;
-
-    return result;
-}
-
-// ggml_conv_2d_sk_p0
-
-struct ggml_tensor * ggml_conv_2d_sk_p0(
-        struct ggml_context * ctx,
-        struct ggml_tensor  * a,
-        struct ggml_tensor  * b) {
     GGML_ASSERT(b->ne[3] == 1);
     GGML_ASSERT(a->ne[2] == b->ne[2]);
-    GGML_ASSERT(b->ne[0] %  a->ne[0] == 0);
-    GGML_ASSERT(b->ne[1] %  a->ne[1] == 0);
     bool is_node = false;
 
     if (a->grad || b->grad) {
@@ -7079,15 +7204,42 @@ struct ggml_tensor * ggml_conv_2d_sk_p0(
         is_node = true;
     }
 
-    const int64_t ne[4] = { b->ne[0]/a->ne[0], b->ne[1]/a->ne[1], a->ne[3], 1, };
-    struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne);
+    const int64_t ne[4] = {
+        ggml_calc_conv_output_size(b->ne[0], a->ne[0], s0, p0, d0),
+        ggml_calc_conv_output_size(b->ne[1], a->ne[1], s1, p1, d1),
+        a->ne[3], 1,
+    };
+    struct ggml_tensor* result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne);
 
-    result->op   = GGML_OP_CONV_2D_SK_P0;
+    ggml_scratch_save(ctx);
+    struct ggml_tensor* c = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 6);
+    ((int32_t*)c->data)[0] = s0;
+    ((int32_t*)c->data)[1] = s1;
+    ((int32_t*)c->data)[2] = p0;
+    ((int32_t*)c->data)[3] = p1;
+    ((int32_t*)c->data)[4] = d0;
+    ((int32_t*)c->data)[5] = d1;
+    ggml_scratch_load(ctx);
+
+    result->op = GGML_OP_CONV_2D;
     result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
     result->src0 = a;
     result->src1 = b;
+    result->opt[0] = c;
 
     return result;
+
+}
+
+// ggml_conv_1d_ph
+
+struct ggml_tensor* ggml_conv_1d_ph(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        struct ggml_tensor  * b,
+        int                   s,
+        int                   d) {
+    return ggml_conv_1d(ctx, a, b, s, a->ne[0] / 2, d);
 }
 
 // ggml_flash_attn
@@ -7637,25 +7789,7 @@ static void ggml_compute_forward_dup_f16(
         return;
     }
 
-    const int64_t ne00 = src0->ne[0];
-    const int64_t ne01 = src0->ne[1];
-    const int64_t ne02 = src0->ne[2];
-    const int64_t ne03 = src0->ne[3];
-
-    const int64_t ne0 = dst->ne[0];
-    const int64_t ne1 = dst->ne[1];
-    const int64_t ne2 = dst->ne[2];
-    const int64_t ne3 = dst->ne[3];
-
-    const size_t nb00 = src0->nb[0];
-    const size_t nb01 = src0->nb[1];
-    const size_t nb02 = src0->nb[2];
-    const size_t nb03 = src0->nb[3];
-
-    const size_t nb0 = dst->nb[0];
-    const size_t nb1 = dst->nb[1];
-    const size_t nb2 = dst->nb[2];
-    const size_t nb3 = dst->nb[3];
+    GGML_TENSOR_UNARY_OP_LOCALS;
 
     const int ith = params->ith; // thread index
     const int nth = params->nth; // number of threads
@@ -7728,8 +7862,8 @@ static void ggml_compute_forward_dup_f16(
                         id += ne00 * (ne01 - ir1);
                     }
                 }
-            } else if (ggml_is_quantized(dst->type)) {
-                quantize_row_q_t const quantize_row_q = quantize_fns[dst->type].quantize_row_q;
+            } else if (type_traits[dst->type].from_float) {
+                ggml_from_float_t const quantize_row_q = type_traits[dst->type].from_float;
                 float * src0_f32 = (float *) params->wdata + (ne00 + CACHE_LINE_SIZE_F32) * ith;
 
                 size_t id = 0;
@@ -7926,25 +8060,7 @@ static void ggml_compute_forward_dup_f32(
         return;
     }
 
-    const int64_t ne00 = src0->ne[0];
-    const int64_t ne01 = src0->ne[1];
-    const int64_t ne02 = src0->ne[2];
-    const int64_t ne03 = src0->ne[3];
-
-    const int64_t ne0 = dst->ne[0];
-    const int64_t ne1 = dst->ne[1];
-    const int64_t ne2 = dst->ne[2];
-    const int64_t ne3 = dst->ne[3];
-
-    const size_t nb00 = src0->nb[0];
-    const size_t nb01 = src0->nb[1];
-    const size_t nb02 = src0->nb[2];
-    const size_t nb03 = src0->nb[3];
-
-    const size_t nb0 = dst->nb[0];
-    const size_t nb1 = dst->nb[1];
-    const size_t nb2 = dst->nb[2];
-    const size_t nb3 = dst->nb[3];
+    GGML_TENSOR_UNARY_OP_LOCALS;
 
     const int ith = params->ith; // thread index
     const int nth = params->nth; // number of threads
@@ -7999,26 +8115,8 @@ static void ggml_compute_forward_dup_f32(
                         id += rs * (ne01 - ir1);
                     }
                 }
-            } else if (dst->type == GGML_TYPE_F16) {
-                size_t id = 0;
-                ggml_fp16_t * dst_ptr = (ggml_fp16_t *) dst->data;
-
-                for (int i03 = 0; i03 < ne03; i03++) {
-                    for (int i02 = 0; i02 < ne02; i02++) {
-                        id += ne00 * ir0;
-                        for (int i01 = ir0; i01 < ir1; i01++) {
-                            for (int i00 = 0; i00 < ne00; i00++) {
-                                const float * src0_ptr = (float *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
-
-                                dst_ptr[id] = GGML_FP32_TO_FP16(*src0_ptr);
-                                id++;
-                            }
-                        }
-                        id += ne00 * (ne01 - ir1);
-                    }
-                }
-            } else if (ggml_is_quantized(dst->type)) {
-                quantize_row_q_t const quantize_row_q = quantize_fns[dst->type].quantize_row_q;
+            } else if (type_traits[dst->type].from_float) {
+                ggml_from_float_t const quantize_row_q = type_traits[dst->type].from_float;
 
                 size_t id = 0;
                 size_t rs = nb0 * (ne00 / GGML_BLCK_SIZE[dst->type]);
@@ -8288,24 +8386,8 @@ static void ggml_compute_forward_add_f32(
     const int nth = params->nth;
 
     const int nr  = ggml_nrows(src0);
-    const int64_t ne0 = src0->ne[0];
-    const int64_t ne1 = src0->ne[1];
-    const int64_t ne2 = src0->ne[2];
 
-    const size_t nb00 = src0->nb[0];
-    const size_t nb01 = src0->nb[1];
-    const size_t nb02 = src0->nb[2];
-    const size_t nb03 = src0->nb[3];
-
-    const size_t nb10 = src1->nb[0];
-    const size_t nb11 = src1->nb[1];
-    const size_t nb12 = src1->nb[2];
-    const size_t nb13 = src1->nb[3];
-
-    const size_t nb0 = dst->nb[0];
-    const size_t nb1 = dst->nb[1];
-    const size_t nb2 = dst->nb[2];
-    const size_t nb3 = dst->nb[3];
+    GGML_TENSOR_BINARY_OP_LOCALS;
 
     GGML_ASSERT( nb0 == sizeof(float));
     GGML_ASSERT(nb00 == sizeof(float));
@@ -8374,28 +8456,12 @@ static void ggml_compute_forward_add_f16_f32(
     const int nth = params->nth;
 
     const int nr  = ggml_nrows(src0);
-    const int64_t ne0 = src0->ne[0];
-    const int64_t ne1 = src0->ne[1];
-    const int64_t ne2 = src0->ne[2];
 
-    const size_t nb00 = src0->nb[0];
-    const size_t nb01 = src0->nb[1];
-    const size_t nb02 = src0->nb[2];
-    const size_t nb03 = src0->nb[3];
-
-    const size_t nb10 = src1->nb[0];
-    const size_t nb11 = src1->nb[1];
-    const size_t nb12 = src1->nb[2];
-    const size_t nb13 = src1->nb[3];
-
-    const size_t nb0 = dst->nb[0];
-    const size_t nb1 = dst->nb[1];
-    const size_t nb2 = dst->nb[2];
-    const size_t nb3 = dst->nb[3];
+    GGML_TENSOR_BINARY_OP_LOCALS;
 
     GGML_ASSERT(src0->type == GGML_TYPE_F16);
     GGML_ASSERT(src1->type == GGML_TYPE_F32);
-    GGML_ASSERT(dst->type == GGML_TYPE_F16);
+    GGML_ASSERT(dst->type  == GGML_TYPE_F16);
 
     GGML_ASSERT( nb0 == sizeof(ggml_fp16_t));
     GGML_ASSERT(nb00 == sizeof(ggml_fp16_t));
@@ -8444,24 +8510,8 @@ static void ggml_compute_forward_add_f16_f16(
     const int nth = params->nth;
 
     const int nr  = ggml_nrows(src0);
-    const int64_t ne0 = src0->ne[0];
-    const int64_t ne1 = src0->ne[1];
-    const int64_t ne2 = src0->ne[2];
 
-    const size_t nb00 = src0->nb[0];
-    const size_t nb01 = src0->nb[1];
-    const size_t nb02 = src0->nb[2];
-    const size_t nb03 = src0->nb[3];
-
-    const size_t nb10 = src1->nb[0];
-    const size_t nb11 = src1->nb[1];
-    const size_t nb12 = src1->nb[2];
-    const size_t nb13 = src1->nb[3];
-
-    const size_t nb0 = dst->nb[0];
-    const size_t nb1 = dst->nb[1];
-    const size_t nb2 = dst->nb[2];
-    const size_t nb3 = dst->nb[3];
+    GGML_TENSOR_BINARY_OP_LOCALS;
 
     GGML_ASSERT(src0->type == GGML_TYPE_F16);
     GGML_ASSERT(src1->type == GGML_TYPE_F16);
@@ -8511,32 +8561,15 @@ static void ggml_compute_forward_add_q_f32(
     }
 
     const int nr  = ggml_nrows(src0);
-    const int64_t ne00 = src0->ne[0];
-    const int64_t ne01 = src0->ne[1];
-    const int64_t ne02 = src0->ne[2];
-    //const int64_t ne03 = src0->ne[3];
 
-    const size_t nb00 = src0->nb[0];
-    const size_t nb01 = src0->nb[1];
-    const size_t nb02 = src0->nb[2];
-    const size_t nb03 = src0->nb[3];
-
-    const size_t nb10 = src1->nb[0];
-    const size_t nb11 = src1->nb[1];
-    const size_t nb12 = src1->nb[2];
-    const size_t nb13 = src1->nb[3];
-
-    const size_t nb0  = dst->nb[0];
-    const size_t nb1  = dst->nb[1];
-    const size_t nb2  = dst->nb[2];
-    const size_t nb3  = dst->nb[3];
+    GGML_TENSOR_BINARY_OP_LOCALS;
 
     const int ith = params->ith;
     const int nth = params->nth;
 
     const enum ggml_type type = src0->type;
-    dequantize_row_q_t const dequantize_row_q = quantize_fns[type].dequantize_row_q;
-    quantize_row_q_t const quantize_row_q = quantize_fns[type].quantize_row_q;
+    ggml_to_float_t const dequantize_row_q = type_traits[type].to_float;
+    ggml_from_float_t const quantize_row_q = type_traits[type].from_float;
 
     // we don't support permuted src0 or src1
     GGML_ASSERT(nb00 == GGML_TYPE_SIZE[type]);
@@ -8650,19 +8683,8 @@ static void ggml_compute_forward_add1_f32(
     const int nth = params->nth;
 
     const int nr  = ggml_nrows(src0);
-    const int64_t ne0 = src0->ne[0];
-    const int64_t ne1 = src0->ne[1];
-    const int64_t ne2 = src0->ne[2];
 
-    const size_t nb00 = src0->nb[0];
-    const size_t nb01 = src0->nb[1];
-    const size_t nb02 = src0->nb[2];
-    const size_t nb03 = src0->nb[3];
-
-    const size_t nb0 = dst->nb[0];
-    const size_t nb1 = dst->nb[1];
-    const size_t nb2 = dst->nb[2];
-    const size_t nb3 = dst->nb[3];
+    GGML_TENSOR_UNARY_OP_LOCALS;
 
     GGML_ASSERT( nb0 == sizeof(float));
     GGML_ASSERT(nb00 == sizeof(float));
@@ -8716,23 +8738,12 @@ static void ggml_compute_forward_add1_f16_f32(
     const int nth = params->nth;
 
     const int nr  = ggml_nrows(src0);
-    const int64_t ne0 = src0->ne[0];
-    const int64_t ne1 = src0->ne[1];
-    const int64_t ne2 = src0->ne[2];
 
-    const size_t nb00 = src0->nb[0];
-    const size_t nb01 = src0->nb[1];
-    const size_t nb02 = src0->nb[2];
-    const size_t nb03 = src0->nb[3];
-
-    const size_t nb0 = dst->nb[0];
-    const size_t nb1 = dst->nb[1];
-    const size_t nb2 = dst->nb[2];
-    const size_t nb3 = dst->nb[3];
+    GGML_TENSOR_UNARY_OP_LOCALS;
 
     GGML_ASSERT(src0->type == GGML_TYPE_F16);
     GGML_ASSERT(src1->type == GGML_TYPE_F32);
-    GGML_ASSERT(dst->type == GGML_TYPE_F16);
+    GGML_ASSERT(dst->type  == GGML_TYPE_F16);
 
     GGML_ASSERT( nb0 == sizeof(ggml_fp16_t));
     GGML_ASSERT(nb00 == sizeof(ggml_fp16_t));
@@ -8777,23 +8788,12 @@ static void ggml_compute_forward_add1_f16_f16(
     const int nth = params->nth;
 
     const int nr  = ggml_nrows(src0);
-    const int64_t ne0 = src0->ne[0];
-    const int64_t ne1 = src0->ne[1];
-    const int64_t ne2 = src0->ne[2];
 
-    const size_t nb00 = src0->nb[0];
-    const size_t nb01 = src0->nb[1];
-    const size_t nb02 = src0->nb[2];
-    const size_t nb03 = src0->nb[3];
-
-    const size_t nb0 = dst->nb[0];
-    const size_t nb1 = dst->nb[1];
-    const size_t nb2 = dst->nb[2];
-    const size_t nb3 = dst->nb[3];
+    GGML_TENSOR_UNARY_OP_LOCALS;
 
     GGML_ASSERT(src0->type == GGML_TYPE_F16);
     GGML_ASSERT(src1->type == GGML_TYPE_F16);
-    GGML_ASSERT(dst->type == GGML_TYPE_F16);
+    GGML_ASSERT(dst->type  == GGML_TYPE_F16);
 
     GGML_ASSERT( nb0 == sizeof(ggml_fp16_t));
     GGML_ASSERT(nb00 == sizeof(ggml_fp16_t));
@@ -8838,23 +8838,12 @@ static void ggml_compute_forward_add1_q_f32(
     const int nth = params->nth;
 
     const int nr  = ggml_nrows(src0);
-    const int64_t ne0 = src0->ne[0];
-    const int64_t ne1 = src0->ne[1];
-    const int64_t ne2 = src0->ne[2];
 
-    const size_t nb00 = src0->nb[0];
-    const size_t nb01 = src0->nb[1];
-    const size_t nb02 = src0->nb[2];
-    const size_t nb03 = src0->nb[3];
-
-    const size_t nb0 = dst->nb[0];
-    const size_t nb1 = dst->nb[1];
-    const size_t nb2 = dst->nb[2];
-    const size_t nb3 = dst->nb[3];
+    GGML_TENSOR_UNARY_OP_LOCALS;
 
     const enum ggml_type type = src0->type;
-    dequantize_row_q_t const dequantize_row_q = quantize_fns[type].dequantize_row_q;
-    quantize_row_q_t const quantize_row_q = quantize_fns[type].quantize_row_q;
+    ggml_to_float_t const dequantize_row_q = type_traits[type].to_float;
+    ggml_from_float_t const quantize_row_q = type_traits[type].from_float;
 
     // we don't support permuted src0
     GGML_ASSERT(nb00 == GGML_TYPE_SIZE[type]);
@@ -8982,15 +8971,8 @@ static void ggml_compute_forward_acc_f32(
     const int nr = ggml_nrows(src1);
     const int nc = src1->ne[0];
 
-    const int64_t ne10 = src1->ne[0];
-    const int64_t ne11 = src1->ne[1];
-    const int64_t ne12 = src1->ne[2];
-    const int64_t ne13 = src1->ne[3];
-
-    const size_t nb10 = src1->nb[0];
-    const size_t nb11 = src1->nb[1];
-    const size_t nb12 = src1->nb[2];
-    const size_t nb13 = src1->nb[3];
+    GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne);
+    GGML_TENSOR_LOCALS(size_t,  nb1, src1, nb);
 
     // src0 and dst as viewed during acc
     const size_t nb0 = ggml_element_size(src0);
@@ -9079,24 +9061,8 @@ static void ggml_compute_forward_sub_f32(
     }
 
     const int nr  = ggml_nrows(src0);
-    const int64_t ne0 = src0->ne[0];
-    const int64_t ne1 = src0->ne[1];
-    const int64_t ne2 = src0->ne[2];
 
-    const size_t nb00 = src0->nb[0];
-    const size_t nb01 = src0->nb[1];
-    const size_t nb02 = src0->nb[2];
-    const size_t nb03 = src0->nb[3];
-
-    const size_t nb10 = src1->nb[0];
-    const size_t nb11 = src1->nb[1];
-    const size_t nb12 = src1->nb[2];
-    const size_t nb13 = src1->nb[3];
-
-    const size_t nb0 = dst->nb[0];
-    const size_t nb1 = dst->nb[1];
-    const size_t nb2 = dst->nb[2];
-    const size_t nb3 = dst->nb[3];
+    GGML_TENSOR_BINARY_OP_LOCALS;
 
     GGML_ASSERT( nb0 == sizeof(float));
     GGML_ASSERT(nb00 == sizeof(float));
@@ -9186,29 +9152,7 @@ static void ggml_compute_forward_mul_f32(
 
     const int64_t nr = ggml_nrows(src0);
 
-    const int64_t ne00 = src0->ne[0];
-    const int64_t ne01 = src0->ne[1];
-    const int64_t ne02 = src0->ne[2];
-
-    const int64_t ne10 = src1->ne[0];
-    const int64_t ne11 = src1->ne[1];
-    const int64_t ne12 = src1->ne[2];
-    const int64_t ne13 = src1->ne[3];
-
-    const size_t nb00 = src0->nb[0];
-    const size_t nb01 = src0->nb[1];
-    const size_t nb02 = src0->nb[2];
-    const size_t nb03 = src0->nb[3];
-
-    const size_t nb10 = src1->nb[0];
-    const size_t nb11 = src1->nb[1];
-    const size_t nb12 = src1->nb[2];
-    const size_t nb13 = src1->nb[3];
-
-    const size_t nb0 = dst->nb[0];
-    const size_t nb1 = dst->nb[1];
-    const size_t nb2 = dst->nb[2];
-    const size_t nb3 = dst->nb[3];
+    GGML_TENSOR_BINARY_OP_LOCALS;
 
     GGML_ASSERT( nb0 == sizeof(float));
     GGML_ASSERT(nb00 == sizeof(float));
@@ -9296,24 +9240,8 @@ static void ggml_compute_forward_div_f32(
     }
 
     const int nr  = ggml_nrows(src0);
-    const int64_t ne0 = src0->ne[0];
-    const int64_t ne1 = src0->ne[1];
-    const int64_t ne2 = src0->ne[2];
 
-    const size_t nb00 = src0->nb[0];
-    const size_t nb01 = src0->nb[1];
-    const size_t nb02 = src0->nb[2];
-    const size_t nb03 = src0->nb[3];
-
-    const size_t nb10 = src1->nb[0];
-    const size_t nb11 = src1->nb[1];
-    const size_t nb12 = src1->nb[2];
-    const size_t nb13 = src1->nb[3];
-
-    const size_t nb0 = dst->nb[0];
-    const size_t nb1 = dst->nb[1];
-    const size_t nb2 = dst->nb[2];
-    const size_t nb3 = dst->nb[3];
+    GGML_TENSOR_BINARY_OP_LOCALS;
 
     GGML_ASSERT( nb0 == sizeof(float));
     GGML_ASSERT(nb00 == sizeof(float));
@@ -9520,14 +9448,8 @@ static void ggml_compute_forward_sum_f32(
     assert(ggml_is_scalar(dst));
     assert(src0->nb[0] == sizeof(float));
 
-    const int64_t ne00 = src0->ne[0];
-    const int64_t ne01 = src0->ne[1];
-    const int64_t ne02 = src0->ne[2];
-    const int64_t ne03 = src0->ne[3];
-
-    const size_t nb01 = src0->nb[1];
-    const size_t nb02 = src0->nb[2];
-    const size_t nb03 = src0->nb[3];
+    GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne);
+    GGML_TENSOR_LOCALS(size_t,  nb0, src0, nb);
 
     ggml_float sum     = 0;
     ggml_float row_sum = 0;
@@ -9576,29 +9498,13 @@ static void ggml_compute_forward_sum_rows_f32(
     GGML_ASSERT(src0->nb[0] == sizeof(float));
     GGML_ASSERT(dst->nb[0] == sizeof(float));
 
-    const int64_t ne00 = src0->ne[0];
-    const int64_t ne01 = src0->ne[1];
-    const int64_t ne02 = src0->ne[2];
-    const int64_t ne03 = src0->ne[3];
-
-    const int64_t ne0 = dst->ne[0];
-    const int64_t ne1 = dst->ne[1];
-    const int64_t ne2 = dst->ne[2];
-    const int64_t ne3 = dst->ne[3];
+    GGML_TENSOR_UNARY_OP_LOCALS;
 
     GGML_ASSERT(ne0 == 1);
     GGML_ASSERT(ne1 == ne01);
     GGML_ASSERT(ne2 == ne02);
     GGML_ASSERT(ne3 == ne03);
 
-    const size_t nb01 = src0->nb[1];
-    const size_t nb02 = src0->nb[2];
-    const size_t nb03 = src0->nb[3];
-
-    const size_t nb1 = dst->nb[1];
-    const size_t nb2 = dst->nb[2];
-    const size_t nb3 = dst->nb[3];
-
     for (int64_t i3 = 0; i3 < ne03; i3++) {
         for (int64_t i2 = 0; i2 < ne02; i2++) {
             for (int64_t i1 = 0; i1 < ne01; i1++) {
@@ -9642,19 +9548,7 @@ static void ggml_compute_forward_mean_f32(
 
     assert(src0->nb[0] == sizeof(float));
 
-    const int64_t ne00 = src0->ne[0];
-    const int64_t ne01 = src0->ne[1];
-    const int64_t ne02 = src0->ne[2];
-    const int64_t ne03 = src0->ne[3];
-
-    const size_t nb01 = src0->nb[1];
-    const size_t nb02 = src0->nb[2];
-    const size_t nb03 = src0->nb[3];
-
-    const int64_t ne0 = dst->ne[0];
-    const int64_t ne1 = dst->ne[1];
-    const int64_t ne2 = dst->ne[2];
-    const int64_t ne3 = dst->ne[3];
+    GGML_TENSOR_UNARY_OP_LOCALS;
 
     assert(ne0 == 1);
     assert(ne1 == ne01);
@@ -9666,10 +9560,6 @@ static void ggml_compute_forward_mean_f32(
     UNUSED(ne2);
     UNUSED(ne3);
 
-    const size_t nb1 = dst->nb[1];
-    const size_t nb2 = dst->nb[2];
-    const size_t nb3 = dst->nb[3];
-
     for (int64_t i03 = 0; i03 < ne03; i03++) {
         for (int64_t i02 = 0; i02 < ne02; i02++) {
             for (int64_t i01 = 0; i01 < ne01; i01++) {
@@ -9699,6 +9589,52 @@ static void ggml_compute_forward_mean(
     }
 }
 
+// ggml_compute_forward_argmax
+
+static void ggml_compute_forward_argmax_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    assert(params->ith == 0);
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    assert(src0->nb[0] == sizeof(float));
+    assert(dst->nb[0] == sizeof(float));
+
+    const int64_t ne00 = src0->ne[0];
+    const int64_t ne01 = src0->ne[1];
+
+    const size_t nb01 = src0->nb[1];
+    const size_t nb0 = dst->nb[0];
+
+    for (int64_t i1 = 0; i1 < ne01; i1++) {
+        float * src = (float *) ((char *) src0->data + i1*nb01);
+        int32_t * dst_ = (int32_t *) ((char *)  dst->data + i1*nb0);
+        int v = 0;
+        ggml_vec_argmax_f32(ne00, &v, src);
+        dst_[0] = v;
+    }
+}
+
+static void ggml_compute_forward_argmax(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_argmax_f32(params, src0, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
 // ggml_compute_forward_repeat
 
 static void ggml_compute_forward_repeat_f32(
@@ -9712,25 +9648,7 @@ static void ggml_compute_forward_repeat_f32(
         return;
     }
 
-    const int64_t ne0  = dst->ne[0];
-    const int64_t ne1  = dst->ne[1];
-    const int64_t ne2  = dst->ne[2];
-    const int64_t ne3  = dst->ne[3];
-
-    const int64_t ne00 = src0->ne[0];
-    const int64_t ne01 = src0->ne[1];
-    const int64_t ne02 = src0->ne[2];
-    const int64_t ne03 = src0->ne[3];
-
-    const size_t nb0  = dst->nb[0];
-    const size_t nb1  = dst->nb[1];
-    const size_t nb2  = dst->nb[2];
-    const size_t nb3  = dst->nb[3];
-
-    const size_t nb00 = src0->nb[0];
-    const size_t nb01 = src0->nb[1];
-    const size_t nb02 = src0->nb[2];
-    const size_t nb03 = src0->nb[3];
+    GGML_TENSOR_UNARY_OP_LOCALS;
 
     // guaranteed to be an integer due to the check in ggml_can_repeat
     const int nr0 = (int)(ne0/ne00);
@@ -9791,25 +9709,7 @@ static void ggml_compute_forward_repeat_back_f32(
         return;
     }
 
-    const int64_t ne0  = dst->ne[0];
-    const int64_t ne1  = dst->ne[1];
-    const int64_t ne2  = dst->ne[2];
-    const int64_t ne3  = dst->ne[3];
-
-    const int64_t ne00 = src0->ne[0];
-    const int64_t ne01 = src0->ne[1];
-    const int64_t ne02 = src0->ne[2];
-    const int64_t ne03 = src0->ne[3];
-
-    const size_t nb0  = dst->nb[0];
-    const size_t nb1  = dst->nb[1];
-    const size_t nb2  = dst->nb[2];
-    const size_t nb3  = dst->nb[3];
-
-    const size_t nb00 = src0->nb[0];
-    const size_t nb01 = src0->nb[1];
-    const size_t nb02 = src0->nb[2];
-    const size_t nb03 = src0->nb[3];
+    GGML_TENSOR_UNARY_OP_LOCALS;
 
     // guaranteed to be an integer due to the check in ggml_can_repeat
     const int nr0 = (int)(ne00/ne0);
@@ -10039,6 +9939,90 @@ static void ggml_compute_forward_step(
     }
 }
 
+// ggml_compute_forward_tanh
+
+static void ggml_compute_forward_tanh_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    assert(params->ith == 0);
+    assert(ggml_are_same_shape(src0, dst));
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    const int n  = ggml_nrows(src0);
+    const int nc = src0->ne[0];
+
+    assert(dst->nb[0]  == sizeof(float));
+    assert(src0->nb[0] == sizeof(float));
+
+    for (int i = 0; i < n; i++) {
+        ggml_vec_tanh_f32(nc,
+                (float *) ((char *) dst->data  + i*( dst->nb[1])),
+                (float *) ((char *) src0->data + i*(src0->nb[1])));
+    }
+}
+
+static void ggml_compute_forward_tanh(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_tanh_f32(params, src0, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+// ggml_compute_forward_elu
+
+static void ggml_compute_forward_elu_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    assert(params->ith == 0);
+    assert(ggml_are_same_shape(src0, dst));
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    const int n  = ggml_nrows(src0);
+    const int nc = src0->ne[0];
+
+    assert(dst->nb[0]  == sizeof(float));
+    assert(src0->nb[0] == sizeof(float));
+
+    for (int i = 0; i < n; i++) {
+        ggml_vec_elu_f32(nc,
+                (float *) ((char *) dst->data  + i*( dst->nb[1])),
+                (float *) ((char *) src0->data + i*(src0->nb[1])));
+    }
+}
+
+static void ggml_compute_forward_elu(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        struct ggml_tensor * dst) {
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_elu_f32(params, src0, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
 // ggml_compute_forward_relu
 
 static void ggml_compute_forward_relu_f32(
@@ -10340,18 +10324,7 @@ static void ggml_compute_forward_norm_f32(
     const int ith = params->ith;
     const int nth = params->nth;
 
-    const int64_t ne00 = src0->ne[0];
-    const int64_t ne01 = src0->ne[1];
-    const int64_t ne02 = src0->ne[2];
-    const int64_t ne03 = src0->ne[3];
-
-    const size_t nb01 = src0->nb[1];
-    const size_t nb02 = src0->nb[2];
-    const size_t nb03 = src0->nb[3];
-
-    const size_t nb1 = dst->nb[1];
-    const size_t nb2 = dst->nb[2];
-    const size_t nb3 = dst->nb[3];
+    GGML_TENSOR_UNARY_OP_LOCALS;
 
     const float eps = 1e-5f; // TODO: make this a parameter
 
@@ -10417,18 +10390,7 @@ static void ggml_compute_forward_rms_norm_f32(
     const int ith = params->ith;
     const int nth = params->nth;
 
-    const int64_t ne00 = src0->ne[0];
-    const int64_t ne01 = src0->ne[1];
-    const int64_t ne02 = src0->ne[2];
-    const int64_t ne03 = src0->ne[3];
-
-    const size_t nb01 = src0->nb[1];
-    const size_t nb02 = src0->nb[2];
-    const size_t nb03 = src0->nb[3];
-
-    const size_t nb1 = dst->nb[1];
-    const size_t nb2 = dst->nb[2];
-    const size_t nb3 = dst->nb[3];
+    GGML_TENSOR_UNARY_OP_LOCALS;
 
     const float eps = 1e-6f; // TODO: make this a parameter
 
@@ -10493,22 +10455,7 @@ static void ggml_compute_forward_rms_norm_back_f32(
     const int ith = params->ith;
     const int nth = params->nth;
 
-    const int64_t ne00 = src0->ne[0];
-    const int64_t ne01 = src0->ne[1];
-    const int64_t ne02 = src0->ne[2];
-    const int64_t ne03 = src0->ne[3];
-
-    const size_t nb01 = src0->nb[1];
-    const size_t nb02 = src0->nb[2];
-    const size_t nb03 = src0->nb[3];
-
-    const size_t nb11 = src1->nb[1];
-    const size_t nb12 = src1->nb[2];
-    const size_t nb13 = src1->nb[3];
-
-    const size_t nb1 = dst->nb[1];
-    const size_t nb2 = dst->nb[2];
-    const size_t nb3 = dst->nb[3];
+    GGML_TENSOR_BINARY_OP_LOCALS;
 
     const float eps = 1e-6f; // TODO: make this a parameter
 
@@ -10696,7 +10643,7 @@ static bool ggml_compute_forward_mul_mat_use_blas(
 }
 #endif
 
-static void ggml_compute_forward_mul_mat_f32(
+static void ggml_compute_forward_mul_mat(
         const struct ggml_compute_params * params,
         const struct ggml_tensor * src0,
         const struct ggml_tensor * src1,
@@ -10704,406 +10651,7 @@ static void ggml_compute_forward_mul_mat_f32(
     int64_t t0 = ggml_perf_time_us();
     UNUSED(t0);
 
-    const int64_t ne00 = src0->ne[0];
-    const int64_t ne01 = src0->ne[1];
-    const int64_t ne02 = src0->ne[2];
-    const int64_t ne03 = src0->ne[3];
-
-#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
-    const int64_t ne10 = src1->ne[0];
-#endif
-    const int64_t ne11 = src1->ne[1];
-#ifndef NDEBUG
-    const int64_t ne12 = src1->ne[2];
-    const int64_t ne13 = src1->ne[3];
-
-    const int64_t ne0  = dst->ne[0];
-    const int64_t ne1  = dst->ne[1];
-    const int64_t ne2  = dst->ne[2];
-    const int64_t ne3  = dst->ne[3];
-
-    const int nb00 = src0->nb[0];
-#endif
-    const int nb01 = src0->nb[1];
-    const int nb02 = src0->nb[2];
-    const int nb03 = src0->nb[3];
-
-#ifndef NDEBUG
-    const int nb10 = src1->nb[0];
-#endif
-    const int nb11 = src1->nb[1];
-    const int nb12 = src1->nb[2];
-    const int nb13 = src1->nb[3];
-
-    const int nb0  = dst->nb[0];
-    const int nb1  = dst->nb[1];
-    const int nb2  = dst->nb[2];
-    const int nb3  = dst->nb[3];
-
-    const int ith = params->ith;
-    const int nth = params->nth;
-
-    assert(ne02 == ne12);
-    assert(ne03 == ne13);
-    assert(ne2  == ne12);
-    assert(ne3  == ne13);
-
-    // we don't support permuted src0 or src1
-    assert(nb00 == sizeof(float));
-    assert(nb10 == sizeof(float));
-
-    // dst cannot be transposed or permuted
-    assert(nb0 == sizeof(float));
-    assert(nb0 <= nb1);
-    assert(nb1 <= nb2);
-    assert(nb2 <= nb3);
-
-    assert(ne0 == ne01);
-    assert(ne1 == ne11);
-    assert(ne2 == ne02);
-    assert(ne3 == ne03);
-
-    // nb01 >= nb00 - src0 is not transposed
-    //   compute by src0 rows
-
-#if defined(GGML_USE_CLBLAST)
-    if (ggml_cl_can_mul_mat(src0, src1, dst)) {
-        if (params->ith == 0 && params->type == GGML_TASK_COMPUTE) {
-            ggml_cl_mul_mat(src0, src1, dst, params->wdata, params->wsize);
-        }
-        return;
-    }
-#endif
-
-#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
-    if (ggml_compute_forward_mul_mat_use_blas(src0, src1, dst)) {
-        if (params->ith != 0) {
-            return;
-        }
-
-        if (params->type == GGML_TASK_INIT) {
-            return;
-        }
-
-        if (params->type == GGML_TASK_FINALIZE) {
-            return;
-        }
-
-        for (int64_t i03 = 0; i03 < ne03; i03++) {
-            for (int64_t i02 = 0; i02 < ne02; i02++) {
-                const float * x = (float *) ((char *) src0->data + i02*nb02 + i03*nb03);
-                const float * y = (float *) ((char *) src1->data + i02*nb12 + i03*nb13);
-                float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3);
-
-                cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans,
-                        ne11, ne01, ne10,
-                        1.0f,    y, ne10,
-                                 x, ne00,
-                        0.0f,    d, ne01);
-            }
-        }
-        //printf("CBLAS F32 = %f ms, %d x %d x %d x %d\n", (ggml_perf_time_us() - t0)/1000.0, ne0, ne1, ne2, ne3);
-
-        return;
-    }
-#endif
-
-    if (params->type == GGML_TASK_INIT) {
-        return;
-    }
-
-    if (params->type == GGML_TASK_FINALIZE) {
-        return;
-    }
-
-    // parallelize by src0 rows using ggml_vec_dot_f32
-
-    // total rows in src0
-    const int nr = ne01*ne02*ne03;
-
-    // rows per thread
-    const int dr = (nr + nth - 1)/nth;
-
-    // row range for this thread
-    const int ir0 = dr*ith;
-    const int ir1 = MIN(ir0 + dr, nr);
-
-    for (int ir = ir0; ir < ir1; ++ir) {
-        // src0 indices
-        const int i03 = ir/(ne02*ne01);
-        const int i02 = (ir - i03*ne02*ne01)/ne01;
-        const int i01 = (ir - i03*ne02*ne01 - i02*ne01);
-
-        for (int64_t ic = 0; ic < ne11; ++ic) {
-            // src1 indices
-            const int i13 = i03;
-            const int i12 = i02;
-            const int i11 = ic;
-
-            // dst indices
-            const int i0 = i01;
-            const int i1 = i11;
-            const int i2 = i02;
-            const int i3 = i03;
-
-            ggml_vec_dot_f32(ne00,
-                    (float *) ((char *)  dst->data + (i0*nb0 + i1*nb1 + i2*nb2 + i3*nb3)),
-                    (float *) ((char *) src0->data + (i01*nb01 + i02*nb02 + i03*nb03)),
-                    (float *) ((char *) src1->data + (i11*nb11 + i12*nb12 + i13*nb13)));
-        }
-    }
-
-    //int64_t t1 = ggml_perf_time_us();
-    //static int64_t acc = 0;
-    //acc += t1 - t0;
-    //if (t1 - t0 > 10) {
-    //    printf("\n");
-    //    printf("ne00 = %5d, ne01 = %5d, ne02 = %5d, ne03 = %5d\n", ne00, ne01, ne02, ne03);
-    //    printf("nb00 = %5d, nb01 = %5d, nb02 = %5d, nb03 = %5d\n", nb00, nb01, nb02, nb03);
-    //    printf("ne10 = %5d, ne11 = %5d, ne12 = %5d, ne13 = %5d\n", ne10, ne11, ne12, ne13);
-    //    printf("nb10 = %5d, nb11 = %5d, nb12 = %5d, nb13 = %5d\n", nb10, nb11, nb12, nb13);
-
-    //    printf("XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX task %d/%d: %d us, acc = %d\n", ith, nth, (int) (t1 - t0), (int) acc);
-    //}
-}
-
-static void ggml_compute_forward_mul_mat_f16_f32(
-        const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
-              struct ggml_tensor * dst) {
-    int64_t t0 = ggml_perf_time_us();
-    UNUSED(t0);
-
-    const int64_t ne00 = src0->ne[0];
-    const int64_t ne01 = src0->ne[1];
-    const int64_t ne02 = src0->ne[2];
-    const int64_t ne03 = src0->ne[3];
-
-    const int64_t ne10 = src1->ne[0];
-    const int64_t ne11 = src1->ne[1];
-    const int64_t ne12 = src1->ne[2];
-    const int64_t ne13 = src1->ne[3];
-
-    const int64_t ne0  = dst->ne[0];
-    const int64_t ne1  = dst->ne[1];
-    const int64_t ne2  = dst->ne[2];
-    const int64_t ne3  = dst->ne[3];
-    //const int64_t ne   = ne0*ne1*ne2*ne3;
-
-    const int nb00 = src0->nb[0];
-    const int nb01 = src0->nb[1];
-    const int nb02 = src0->nb[2];
-    const int nb03 = src0->nb[3];
-
-    const int nb10 = src1->nb[0];
-    const int nb11 = src1->nb[1];
-    const int nb12 = src1->nb[2];
-    const int nb13 = src1->nb[3];
-
-    const int nb0  = dst->nb[0];
-    const int nb1  = dst->nb[1];
-    const int nb2  = dst->nb[2];
-    const int nb3  = dst->nb[3];
-
-    const int ith = params->ith;
-    const int nth = params->nth;
-
-    GGML_ASSERT(ne02 == ne12);
-    GGML_ASSERT(ne03 == ne13);
-    GGML_ASSERT(ne2  == ne12);
-    GGML_ASSERT(ne3  == ne13);
-
-    // TODO: we don't support permuted src0
-    GGML_ASSERT(nb00 == sizeof(ggml_fp16_t));
-
-    // dst cannot be transposed or permuted
-    GGML_ASSERT(nb0 == sizeof(float));
-    GGML_ASSERT(nb0 <= nb1);
-    GGML_ASSERT(nb1 <= nb2);
-    GGML_ASSERT(nb2 <= nb3);
-
-    GGML_ASSERT(ne0 == ne01);
-    GGML_ASSERT(ne1 == ne11);
-    GGML_ASSERT(ne2 == ne02);
-    GGML_ASSERT(ne3 == ne03);
-
-    // nb01 >= nb00 - src0 is not transposed
-    //   compute by src0 rows
-
-#if defined(GGML_USE_CLBLAST)
-    if (ggml_cl_can_mul_mat(src0, src1, dst)) {
-        if (params->ith == 0 && params->type == GGML_TASK_COMPUTE) {
-            ggml_cl_mul_mat(src0, src1, dst, params->wdata, params->wsize);
-        }
-        return;
-    }
-#endif
-
-#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
-    if (ggml_compute_forward_mul_mat_use_blas(src0, src1, dst)) {
-        GGML_ASSERT(nb10 == sizeof(float));
-
-        if (params->ith != 0) {
-            return;
-        }
-
-        if (params->type == GGML_TASK_INIT) {
-            return;
-        }
-
-        if (params->type == GGML_TASK_FINALIZE) {
-            return;
-        }
-
-        for (int64_t i03 = 0; i03 < ne03; i03++) {
-            for (int64_t i02 = 0; i02 < ne02; i02++) {
-                float * const wdata = params->wdata;
-                {
-                    size_t id = 0;
-                    for (int64_t i01 = 0; i01 < ne01; ++i01) {
-                        for (int64_t i00 = 0; i00 < ne00; ++i00) {
-                            wdata[id++] = GGML_FP16_TO_FP32(*(ggml_fp16_t *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00));
-                        }
-                    }
-
-                    assert(id*sizeof(float) <= params->wsize);
-                }
-
-                const float * x = wdata;
-                const float * y = (float *) ((char *) src1->data + i02*nb12 + i03*nb13);
-
-                float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3);
-
-                // zT = y * xT
-                cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans,
-                        ne11, ne01, ne10,
-                        1.0f,    y, ne10,
-                                 x, ne00,
-                        0.0f,    d, ne01);
-            }
-        }
-
-        /*printf("CBLAS F16 = %f ms, %d x %d x %d x %d\n", (ggml_perf_time_us() - t0)/1000.0, ne0, ne1, ne2, ne3);*/
-
-        return;
-    }
-#endif
-
-    if (params->type == GGML_TASK_INIT) {
-        ggml_fp16_t * const wdata = params->wdata;
-
-        size_t id = 0;
-        for (int64_t i13 = 0; i13 < ne13; ++i13) {
-            for (int64_t i12 = 0; i12 < ne12; ++i12) {
-                for (int64_t i11 = 0; i11 < ne11; ++i11) {
-                    for (int64_t i10 = 0; i10 < ne10; ++i10) {
-                        wdata[id++] = GGML_FP32_TO_FP16(*(float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11 + i10*nb10));
-                    }
-                }
-            }
-        }
-
-        GGML_ASSERT(id*sizeof(ggml_fp16_t) <= params->wsize);
-
-        return;
-    }
-
-    if (params->type == GGML_TASK_FINALIZE) {
-        return;
-    }
-
-    // fp16 -> half the size, so divide by 2
-    // TODO: do not support transposed src1
-    assert(nb10/2 == sizeof(ggml_fp16_t));
-
-    // parallelize by src0 rows using ggml_vec_dot_f16
-
-    // total rows in src0
-    const int nr = ne01*ne02*ne03;
-
-    // rows per thread
-    const int dr = (nr + nth - 1)/nth;
-
-    // row range for this thread
-    const int ir0 = dr*ith;
-    const int ir1 = MIN(ir0 + dr, nr);
-
-    ggml_fp16_t * wdata = params->wdata;
-
-    for (int ir = ir0; ir < ir1; ++ir) {
-        // src0 indices
-        const int i03 = ir/(ne02*ne01);
-        const int i02 = (ir - i03*ne02*ne01)/ne01;
-        const int i01 = (ir - i03*ne02*ne01 - i02*ne01);
-
-        const int i13 = i03;
-        const int i12 = i02;
-
-        const int i0 = i01;
-        const int i2 = i02;
-        const int i3 = i03;
-
-        ggml_fp16_t * src0_row = (ggml_fp16_t *) ((char *) src0->data + (i01*nb01 + i02*nb02 + i03*nb03));
-        ggml_fp16_t * src1_col =                                wdata + (       0 + i12*ne11 + i13*ne12*ne11)*ne00;
-
-        float * dst_col = (float *) ((char *) dst->data + (i0*nb0 + 0*nb1 + i2*nb2 + i3*nb3));
-
-        for (int64_t ic = 0; ic < ne11; ++ic) {
-            ggml_vec_dot_f16(ne00, &dst_col[ic*ne0], src0_row, src1_col + ic*ne00);
-        }
-    }
-
-    //int64_t t1 = ggml_time_us();
-    //static int64_t acc = 0;
-    //acc += t1 - t0;
-    //if (t1 - t0 > 10) {
-    //    printf("\n");
-    //    printf("ne00 = %5d, ne01 = %5d, ne02 = %5d, ne03 = %5d\n", ne00, ne01, ne02, ne03);
-    //    printf("nb00 = %5d, nb01 = %5d, nb02 = %5d, nb03 = %5d\n", nb00, nb01, nb02, nb03);
-    //    printf("ne10 = %5d, ne11 = %5d, ne12 = %5d, ne13 = %5d\n", ne10, ne11, ne12, ne13);
-
-    //    printf("XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX task %d/%d: %d us, acc = %d\n", ith, nth, (int) (t1 - t0), (int) acc);
-    //}
-}
-
-static void ggml_compute_forward_mul_mat_q_f32(
-        const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
-              struct ggml_tensor * dst) {
-    int64_t t0 = ggml_perf_time_us();
-    UNUSED(t0);
-
-    const int64_t ne00 = src0->ne[0];
-    const int64_t ne01 = src0->ne[1];
-    const int64_t ne02 = src0->ne[2];
-    const int64_t ne03 = src0->ne[3];
-
-    const int64_t ne10 = src1->ne[0];
-    const int64_t ne11 = src1->ne[1];
-    const int64_t ne12 = src1->ne[2];
-    const int64_t ne13 = src1->ne[3];
-
-    const int64_t ne0  = dst->ne[0];
-    const int64_t ne1  = dst->ne[1];
-    const int64_t ne2  = dst->ne[2];
-    const int64_t ne3  = dst->ne[3];
-
-    const int nb00 = src0->nb[0];
-    const int nb01 = src0->nb[1];
-    const int nb02 = src0->nb[2];
-    const int nb03 = src0->nb[3];
-
-    const int nb10 = src1->nb[0];
-    const int nb11 = src1->nb[1];
-    const int nb12 = src1->nb[2];
-    const int nb13 = src1->nb[3];
-
-    const int nb0  = dst->nb[0];
-    const int nb1  = dst->nb[1];
-    const int nb2  = dst->nb[2];
-    const int nb3  = dst->nb[3];
+    GGML_TENSOR_BINARY_OP_LOCALS;
 
     const int ith = params->ith;
     const int nth = params->nth;
@@ -11114,12 +10662,13 @@ static void ggml_compute_forward_mul_mat_q_f32(
     GGML_ASSERT(ne3  == ne13);
 
     const enum ggml_type type = src0->type;
-    quantize_row_q_t const quantize_row_q_dot = quantize_fns[type].quantize_row_q_dot;
-    vec_dot_q_t      const vec_dot_q          = quantize_fns[type].vec_dot_q;
-    enum ggml_type   const vec_dot_type       = quantize_fns[type].vec_dot_type;
+
+    ggml_vec_dot_t    const vec_dot               = type_traits[type].vec_dot;
+    enum ggml_type    const vec_dot_type          = type_traits[type].vec_dot_type;
+    ggml_from_float_t const from_float_to_vec_dot = type_traits[vec_dot_type].from_float;
 
     // we don't support permuted src0 or src1
-    GGML_ASSERT(nb00 == (int) GGML_TYPE_SIZE[type]);
+    GGML_ASSERT(nb00 == GGML_TYPE_SIZE[type]);
     GGML_ASSERT(nb10 == sizeof(float));
 
     // dst cannot be transposed or permuted
@@ -11159,27 +10708,27 @@ static void ggml_compute_forward_mul_mat_q_f32(
             return;
         }
 
-        float * const wdata = params->wdata;
-        dequantize_row_q_t const dequantize_row_q = quantize_fns[type].dequantize_row_q;
-
         for (int64_t i03 = 0; i03 < ne03; i03++) {
             for (int64_t i02 = 0; i02 < ne02; i02++) {
+                const void * x = (char *) src0->data + i03*nb03 + i02*nb02;
                 const float * y = (float *) ((char *) src1->data + i02*nb12 + i03*nb13);
 
                 float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3);
 
-                {
+                if (type != GGML_TYPE_F32) {
+                    float * const wdata = params->wdata;
+                    ggml_to_float_t const to_float = type_traits[type].to_float;
+
                     size_t id = 0;
                     for (int64_t i01 = 0; i01 < ne01; ++i01) {
-                        dequantize_row_q((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01, wdata + id, ne00);
+                        to_float((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01, wdata + id, ne00);
                         id += ne00;
                     }
 
                     assert(id*sizeof(float) <= params->wsize);
+                    x = wdata;
                 }
 
-                const float * x = wdata;
-
                 cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans,
                         ne11, ne01, ne10,
                         1.0f,    y, ne10,
@@ -11195,14 +10744,16 @@ static void ggml_compute_forward_mul_mat_q_f32(
 #endif
 
     if (params->type == GGML_TASK_INIT) {
-        char * wdata = params->wdata;
-        const size_t row_size = ne10*GGML_TYPE_SIZE[vec_dot_type]/GGML_BLCK_SIZE[vec_dot_type];
+        if (src1->type != vec_dot_type) {
+            char * wdata = params->wdata;
+            const size_t row_size = ne10*GGML_TYPE_SIZE[vec_dot_type]/GGML_BLCK_SIZE[vec_dot_type];
 
-        for (int64_t i13 = 0; i13 < ne13; ++i13) {
-            for (int64_t i12 = 0; i12 < ne12; ++i12) {
-                for (int64_t i11 = 0; i11 < ne11; ++i11) {
-                    quantize_row_q_dot((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11), (void *) wdata, ne10);
-                    wdata += row_size;
+            for (int64_t i13 = 0; i13 < ne13; ++i13) {
+                for (int64_t i12 = 0; i12 < ne12; ++i12) {
+                    for (int64_t i11 = 0; i11 < ne11; ++i11) {
+                        from_float_to_vec_dot((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11), (void *) wdata, ne10);
+                        wdata += row_size;
+                    }
                 }
             }
         }
@@ -11226,7 +10777,7 @@ static void ggml_compute_forward_mul_mat_q_f32(
     const int ir0 = dr*ith;
     const int ir1 = MIN(ir0 + dr, nr);
 
-    void * wdata = params->wdata;
+    void * wdata = (src1->type == vec_dot_type) ? src1->data : params->wdata;
     const size_t row_size = ne00*GGML_TYPE_SIZE[vec_dot_type]/GGML_BLCK_SIZE[vec_dot_type];
 
     for (int ir = ir0; ir < ir1; ++ir) {
@@ -11250,7 +10801,7 @@ static void ggml_compute_forward_mul_mat_q_f32(
         assert(ne00 % 32 == 0);
 
         for (int64_t ic = 0; ic < ne11; ++ic) {
-            vec_dot_q(ne00, &dst_col[ic*ne0], src0_row, (void *) (src1_col + ic*row_size));
+            vec_dot(ne00, &dst_col[ic*ne0], src0_row, (void *) (src1_col + ic*row_size));
         }
     }
 
@@ -11267,40 +10818,6 @@ static void ggml_compute_forward_mul_mat_q_f32(
     //}
 }
 
-static void ggml_compute_forward_mul_mat(
-        const struct ggml_compute_params * params,
-        const struct ggml_tensor * src0,
-        const struct ggml_tensor * src1,
-        struct ggml_tensor * dst) {
-    switch (src0->type) {
-        case GGML_TYPE_Q4_0:
-        case GGML_TYPE_Q4_1:
-        case GGML_TYPE_Q5_0:
-        case GGML_TYPE_Q5_1:
-        case GGML_TYPE_Q8_0:
-        case GGML_TYPE_Q8_1:
-        case GGML_TYPE_Q2_K:
-        case GGML_TYPE_Q3_K:
-        case GGML_TYPE_Q4_K:
-        case GGML_TYPE_Q5_K:
-        case GGML_TYPE_Q6_K:
-            {
-                ggml_compute_forward_mul_mat_q_f32(params, src0, src1, dst);
-            } break;
-        case GGML_TYPE_F16:
-            {
-                ggml_compute_forward_mul_mat_f16_f32(params, src0, src1, dst);
-            } break;
-        case GGML_TYPE_F32:
-            {
-                ggml_compute_forward_mul_mat_f32(params, src0, src1, dst);
-            } break;
-        default:
-            {
-                GGML_ASSERT(false);
-            } break;
-    }
-}
 
 // ggml_compute_forward_out_prod
 
@@ -11313,35 +10830,7 @@ static void ggml_compute_forward_out_prod_f32(
     int64_t t0 = ggml_perf_time_us();
     UNUSED(t0);
 
-    const int64_t ne00 = src0->ne[0];
-    const int64_t ne01 = src0->ne[1];
-    const int64_t ne02 = src0->ne[2];
-    const int64_t ne03 = src0->ne[3];
-
-    const int64_t ne10 = src1->ne[0];
-    //const int64_t ne11 = src1->ne[1];
-    const int64_t ne12 = src1->ne[2];
-    const int64_t ne13 = src1->ne[3];
-
-    const int64_t ne0  = dst->ne[0];
-    const int64_t ne1  = dst->ne[1];
-    const int64_t ne2  = dst->ne[2];
-    const int64_t ne3  = dst->ne[3];
-
-    const int nb00 = src0->nb[0];
-    const int nb01 = src0->nb[1];
-    const int nb02 = src0->nb[2];
-    const int nb03 = src0->nb[3];
-
-    const int nb10 = src1->nb[0];
-    const int nb11 = src1->nb[1];
-    const int nb12 = src1->nb[2];
-    const int nb13 = src1->nb[3];
-
-    const int nb0  = dst->nb[0];
-    const int nb1  = dst->nb[1];
-    const int nb2  = dst->nb[2];
-    const int nb3  = dst->nb[3];
+    GGML_TENSOR_BINARY_OP_LOCALS;
 
     const int ith = params->ith;
     const int nth = params->nth;
@@ -11576,15 +11065,8 @@ static void ggml_compute_forward_set_f32(
     const int nr = ggml_nrows(src1);
     const int nc = src1->ne[0];
 
-    const int64_t ne10 = src1->ne[0];
-    const int64_t ne11 = src1->ne[1];
-    const int64_t ne12 = src1->ne[2];
-    const int64_t ne13 = src1->ne[3];
-
-    const size_t nb10 = src1->nb[0];
-    const size_t nb11 = src1->nb[1];
-    const size_t nb12 = src1->nb[2];
-    const size_t nb13 = src1->nb[3];
+    GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne);
+    GGML_TENSOR_LOCALS(size_t,  nb1, src1, nb);
 
     // src0 and dst as viewed during set
     const size_t nb0 = ggml_element_size(src0);
@@ -11725,7 +11207,7 @@ static void ggml_compute_forward_get_rows_q(
     const int nc = src0->ne[0];
     const int nr = ggml_nelements(src1);
     const enum ggml_type type = src0->type;
-    dequantize_row_q_t const dequantize_row_q = quantize_fns[type].dequantize_row_q;
+    ggml_to_float_t const dequantize_row_q = type_traits[type].to_float;
 
     assert( dst->ne[0] == nc);
     assert( dst->ne[1] == nr);
@@ -11975,29 +11457,14 @@ static void ggml_compute_forward_diag_f32(
 
     // TODO: handle transposed/permuted matrices
 
-    const int ne00 = src0->ne[0];
-    const int ne01 = src0->ne[1];
-    const int ne02 = src0->ne[2];
-    const int ne03 = src0->ne[3];
-    const int ne0 = dst->ne[0];
-    const int ne1 = dst->ne[1];
-    const int ne2 = dst->ne[2];
-    const int ne3 = dst->ne[3];
+    GGML_TENSOR_UNARY_OP_LOCALS;
+
     GGML_ASSERT(ne00 == ne0);
     GGML_ASSERT(ne00 == ne1);
     GGML_ASSERT(ne01 == 1);
     GGML_ASSERT(ne02 == ne2);
     GGML_ASSERT(ne03 == ne3);
 
-    const int nb00 = src0->nb[0];
-    //const int nb01 = src0->nb[1];
-    const int nb02 = src0->nb[2];
-    const int nb03 = src0->nb[3];
-    const int nb0 = dst->nb[0];
-    const int nb1 = dst->nb[1];
-    const int nb2 = dst->nb[2];
-    const int nb3 = dst->nb[3];
-
     GGML_ASSERT(nb00 == sizeof(float));
     GGML_ASSERT(nb0  == sizeof(float));
 
@@ -12574,20 +12041,7 @@ static void ggml_compute_forward_rope_f32(
 
     assert(n_past >= 0);
 
-    const size_t nb00 = src0->nb[0];
-    const size_t nb01 = src0->nb[1];
-    const size_t nb02 = src0->nb[2];
-    const size_t nb03 = src0->nb[3];
-
-    const int64_t ne0 = dst->ne[0];
-    const int64_t ne1 = dst->ne[1];
-    const int64_t ne2 = dst->ne[2];
-    const int64_t ne3 = dst->ne[3];
-
-    const size_t nb0 = dst->nb[0];
-    const size_t nb1 = dst->nb[1];
-    const size_t nb2 = dst->nb[2];
-    const size_t nb3 = dst->nb[3];
+    GGML_TENSOR_UNARY_OP_LOCALS;
 
     //printf("ne0: %d, ne1: %d, ne2: %d, ne3: %d\n", ne0, ne1, ne2, ne3);
     //printf("n_past = %d, ne2 = %d\n", n_past, ne2);
@@ -12714,20 +12168,7 @@ static void ggml_compute_forward_rope_f16(
 
     assert(n_past >= 0);
 
-    const size_t nb00 = src0->nb[0];
-    const size_t nb01 = src0->nb[1];
-    const size_t nb02 = src0->nb[2];
-    const size_t nb03 = src0->nb[3];
-
-    const int64_t ne0 = dst->ne[0];
-    const int64_t ne1 = dst->ne[1];
-    const int64_t ne2 = dst->ne[2];
-    const int64_t ne3 = dst->ne[3];
-
-    const size_t nb0 = dst->nb[0];
-    const size_t nb1 = dst->nb[1];
-    const size_t nb2 = dst->nb[2];
-    const size_t nb3 = dst->nb[3];
+    GGML_TENSOR_UNARY_OP_LOCALS;
 
     //printf("ne0: %d, ne1: %d, ne2: %d, ne3: %d\n", ne0, ne1, ne2, ne3);
     //printf("n_past = %d, ne2 = %d\n", n_past, ne2);
@@ -12880,21 +12321,7 @@ static void ggml_compute_forward_rope_back_f32(
 
     assert(n_past >= 0);
 
-    const size_t nb00 = src0->nb[0];
-    const size_t nb01 = src0->nb[1];
-    const size_t nb02 = src0->nb[2];
-    const size_t nb03 = src0->nb[3];
-
-    const int64_t ne0 = dst->ne[0];
-    const int64_t ne1 = dst->ne[1];
-    const int64_t ne2 = dst->ne[2];
-    const int64_t ne3 = dst->ne[3];
-
-    const size_t nb0 = dst->nb[0];
-    const size_t nb1 = dst->nb[1];
-    const size_t nb2 = dst->nb[2];
-    const size_t nb3 = dst->nb[3];
-
+    GGML_TENSOR_UNARY_OP_LOCALS;
 
     //printf("ne0: %d, ne1: %d, ne2: %d, ne3: %d\n", ne0, ne1, ne2, ne3);
     //printf("n_past = %d, ne2 = %d\n", n_past, ne2);
@@ -12993,21 +12420,7 @@ static void ggml_compute_forward_rope_back_f16(
 
     assert(n_past >= 0);
 
-    const size_t nb00 = src0->nb[0];
-    const size_t nb01 = src0->nb[1];
-    const size_t nb02 = src0->nb[2];
-    const size_t nb03 = src0->nb[3];
-
-    const int64_t ne0 = dst->ne[0];
-    const int64_t ne1 = dst->ne[1];
-    const int64_t ne2 = dst->ne[2];
-    const int64_t ne3 = dst->ne[3];
-
-    const size_t nb0 = dst->nb[0];
-    const size_t nb1 = dst->nb[1];
-    const size_t nb2 = dst->nb[2];
-    const size_t nb3 = dst->nb[3];
-
+    GGML_TENSOR_UNARY_OP_LOCALS;
 
     //printf("ne0: %d, ne1: %d, ne2: %d, ne3: %d\n", ne0, ne1, ne2, ne3);
     //printf("n_past = %d, ne2 = %d\n", n_past, ne2);
@@ -13105,7 +12518,7 @@ static void ggml_compute_forward_rope_back(
     }
 }
 
-// ggml_compute_forward_conv_1d_s1_ph
+// ggml_compute_forward_conv_1d
 
 static void ggml_compute_forward_conv_1d_s1_ph_f16_f32(
         const struct ggml_compute_params * params,
@@ -13119,36 +12532,7 @@ static void ggml_compute_forward_conv_1d_s1_ph_f16_f32(
     int64_t t0 = ggml_perf_time_us();
     UNUSED(t0);
 
-    const int64_t ne00 = src0->ne[0];
-    const int64_t ne01 = src0->ne[1];
-    const int64_t ne02 = src0->ne[2];
-    //const int64_t ne03 = src0->ne[3];
-
-    const int64_t ne10 = src1->ne[0];
-    const int64_t ne11 = src1->ne[1];
-    //const int64_t ne12 = src1->ne[2];
-    //const int64_t ne13 = src1->ne[3];
-
-    //const int64_t ne0  = dst->ne[0];
-    //const int64_t ne1  = dst->ne[1];
-    //const int64_t ne2  = dst->ne[2];
-    //const int64_t ne3  = dst->ne[3];
-    //const int64_t ne   = ne0*ne1*ne2*ne3;
-
-    const int nb00 = src0->nb[0];
-    const int nb01 = src0->nb[1];
-    const int nb02 = src0->nb[2];
-    //const int nb03 = src0->nb[3];
-
-    const int nb10 = src1->nb[0];
-    const int nb11 = src1->nb[1];
-    //const int nb12 = src1->nb[2];
-    //const int nb13 = src1->nb[3];
-
-    //const int nb0  = dst->nb[0];
-    const int nb1  = dst->nb[1];
-    //const int nb2  = dst->nb[2];
-    //const int nb3  = dst->nb[3];
+    GGML_TENSOR_BINARY_OP_LOCALS;
 
     const int ith = params->ith;
     const int nth = params->nth;
@@ -13239,36 +12623,7 @@ static void ggml_compute_forward_conv_1d_s1_ph_f32(
     int64_t t0 = ggml_perf_time_us();
     UNUSED(t0);
 
-    const int64_t ne00 = src0->ne[0];
-    const int64_t ne01 = src0->ne[1];
-    const int64_t ne02 = src0->ne[2];
-    //const int64_t ne03 = src0->ne[3];
-
-    const int64_t ne10 = src1->ne[0];
-    const int64_t ne11 = src1->ne[1];
-    //const int64_t ne12 = src1->ne[2];
-    //const int64_t ne13 = src1->ne[3];
-
-    //const int64_t ne0  = dst->ne[0];
-    //const int64_t ne1  = dst->ne[1];
-    //const int64_t ne2  = dst->ne[2];
-    //const int64_t ne3  = dst->ne[3];
-    //const int64_t ne   = ne0*ne1*ne2*ne3;
-
-    const int nb00 = src0->nb[0];
-    const int nb01 = src0->nb[1];
-    const int nb02 = src0->nb[2];
-    //const int nb03 = src0->nb[3];
-
-    const int nb10 = src1->nb[0];
-    const int nb11 = src1->nb[1];
-    //const int nb12 = src1->nb[2];
-    //const int nb13 = src1->nb[3];
-
-    //const int nb0  = dst->nb[0];
-    const int nb1  = dst->nb[1];
-    //const int nb2  = dst->nb[2];
-    //const int nb3  = dst->nb[3];
+    GGML_TENSOR_BINARY_OP_LOCALS;
 
     const int ith = params->ith;
     const int nth = params->nth;
@@ -13368,8 +12723,6 @@ static void ggml_compute_forward_conv_1d_s1_ph(
     }
 }
 
-// ggml_compute_forward_conv_1d_s2_ph
-
 static void ggml_compute_forward_conv_1d_s2_ph_f16_f32(
         const struct ggml_compute_params * params,
         const struct ggml_tensor * src0,
@@ -13382,36 +12735,7 @@ static void ggml_compute_forward_conv_1d_s2_ph_f16_f32(
     int64_t t0 = ggml_perf_time_us();
     UNUSED(t0);
 
-    const int64_t ne00 = src0->ne[0];
-    const int64_t ne01 = src0->ne[1];
-    const int64_t ne02 = src0->ne[2];
-    //const int64_t ne03 = src0->ne[3];
-
-    const int64_t ne10 = src1->ne[0];
-    const int64_t ne11 = src1->ne[1];
-    //const int64_t ne12 = src1->ne[2];
-    //const int64_t ne13 = src1->ne[3];
-
-    //const int64_t ne0  = dst->ne[0];
-    //const int64_t ne1  = dst->ne[1];
-    //const int64_t ne2  = dst->ne[2];
-    //const int64_t ne3  = dst->ne[3];
-    //const int64_t ne   = ne0*ne1*ne2*ne3;
-
-    const int nb00 = src0->nb[0];
-    const int nb01 = src0->nb[1];
-    const int nb02 = src0->nb[2];
-    //const int nb03 = src0->nb[3];
-
-    const int nb10 = src1->nb[0];
-    const int nb11 = src1->nb[1];
-    //const int nb12 = src1->nb[2];
-    //const int nb13 = src1->nb[3];
-
-    //const int nb0  = dst->nb[0];
-    const int nb1  = dst->nb[1];
-    //const int nb2  = dst->nb[2];
-    //const int nb3  = dst->nb[3];
+    GGML_TENSOR_BINARY_OP_LOCALS;
 
     const int ith = params->ith;
     const int nth = params->nth;
@@ -13502,36 +12826,7 @@ static void ggml_compute_forward_conv_1d_s2_ph_f32(
     int64_t t0 = ggml_perf_time_us();
     UNUSED(t0);
 
-    const int64_t ne00 = src0->ne[0];
-    const int64_t ne01 = src0->ne[1];
-    const int64_t ne02 = src0->ne[2];
-    //const int64_t ne03 = src0->ne[3];
-
-    const int64_t ne10 = src1->ne[0];
-    const int64_t ne11 = src1->ne[1];
-    //const int64_t ne12 = src1->ne[2];
-    //const int64_t ne13 = src1->ne[3];
-
-    //const int64_t ne0  = dst->ne[0];
-    //const int64_t ne1  = dst->ne[1];
-    //const int64_t ne2  = dst->ne[2];
-    //const int64_t ne3  = dst->ne[3];
-    //const int64_t ne   = ne0*ne1*ne2*ne3;
-
-    const int nb00 = src0->nb[0];
-    const int nb01 = src0->nb[1];
-    const int nb02 = src0->nb[2];
-    //const int nb03 = src0->nb[3];
-
-    const int nb10 = src1->nb[0];
-    const int nb11 = src1->nb[1];
-    //const int nb12 = src1->nb[2];
-    //const int nb13 = src1->nb[3];
-
-    //const int nb0  = dst->nb[0];
-    const int nb1  = dst->nb[1];
-    //const int nb2  = dst->nb[2];
-    //const int nb3  = dst->nb[3];
+    GGML_TENSOR_BINARY_OP_LOCALS;
 
     const int ith = params->ith;
     const int nth = params->nth;
@@ -13631,6 +12926,28 @@ static void ggml_compute_forward_conv_1d_s2_ph(
     }
 }
 
+// ggml_compute_forward_conv_1d
+
+static void ggml_compute_forward_conv_1d(
+    const struct ggml_compute_params * params,
+    const struct ggml_tensor * src0,
+    const struct ggml_tensor * src1,
+    const struct ggml_tensor * opt0,
+    struct ggml_tensor * dst) {
+    const int32_t s0 = ((const int32_t*)(opt0->data))[0];
+    const int32_t p0 = ((const int32_t*)(opt0->data))[1];
+    const int32_t d0 = ((const int32_t*)(opt0->data))[2];
+    GGML_ASSERT(d0 == 1); // dilation not supported
+    GGML_ASSERT(p0 == src0->ne[0]/2); // only half padding supported
+    if (s0 == 1) {
+        ggml_compute_forward_conv_1d_s1_ph(params, src0, src1, dst);
+    } else if (s0 == 2) {
+        ggml_compute_forward_conv_1d_s2_ph(params, src0, src1, dst);
+    } else {
+        GGML_ASSERT(false); // only stride 1 and 2 supported
+    };
+}
+
 // ggml_compute_forward_conv_2d_sk_p0
 
 static void ggml_compute_forward_conv_2d_sk_p0_f16_f32(
@@ -13645,36 +12962,7 @@ static void ggml_compute_forward_conv_2d_sk_p0_f16_f32(
     int64_t t0 = ggml_perf_time_us();
     UNUSED(t0);
 
-    const int ne00 = src0->ne[0];
-    const int ne01 = src0->ne[1];
-    const int ne02 = src0->ne[2];
-    //const int ne03 = src0->ne[3];
-
-    const int ne10 = src1->ne[0];
-    //const int ne11 = src1->ne[1];
-    const int ne12 = src1->ne[2];
-    //const int ne13 = src1->ne[3];
-
-    const int ne0  = dst->ne[0];
-    const int ne1  = dst->ne[1];
-    const int ne2  = dst->ne[2];
-    //const int ne3  = dst->ne[3];
-    //const int ne   = ne0*ne1*ne2*ne3;
-
-    const int nb00 = src0->nb[0];
-    //const int nb01 = src0->nb[1];
-    //const int nb02 = src0->nb[2];
-    const int nb03 = src0->nb[3];
-
-    const int nb10 = src1->nb[0];
-    //const int nb11 = src1->nb[1];
-    const int nb12 = src1->nb[2];
-    //const int nb13 = src1->nb[3];
-
-    //const int nb0  = dst->nb[0];
-    //const int nb1  = dst->nb[1];
-    const int nb2  = dst->nb[2];
-    //const int nb3  = dst->nb[3];
+    GGML_TENSOR_BINARY_OP_LOCALS;
 
     const int ith = params->ith;
     const int nth = params->nth;
@@ -13767,6 +13055,34 @@ static void ggml_compute_forward_conv_2d_sk_p0(
     }
 }
 
+// ggml_compute_forward_conv_2d
+
+static void ggml_compute_forward_conv_2d(
+    const struct ggml_compute_params* params,
+    const struct ggml_tensor* src0,
+    const struct ggml_tensor* src1,
+    const struct ggml_tensor* opt0,
+    struct ggml_tensor* dst) {
+    const int32_t s0 = ((const int32_t*)(opt0->data))[0];
+    const int32_t s1 = ((const int32_t*)(opt0->data))[1];
+    const int32_t p0 = ((const int32_t*)(opt0->data))[2];
+    const int32_t p1 = ((const int32_t*)(opt0->data))[3];
+    const int32_t d0 = ((const int32_t*)(opt0->data))[4];
+    const int32_t d1 = ((const int32_t*)(opt0->data))[5];
+    GGML_ASSERT(d0 == 1); // dilation not supported
+    GGML_ASSERT(d1 == 1);
+    GGML_ASSERT(p0 == 0); // padding not supported
+    GGML_ASSERT(p1 == 0);
+
+    if (s0 == src0->ne[0] && s1 == src0->ne[1]) {
+        ggml_compute_forward_conv_2d_sk_p0(params, src0, src1, dst);
+    }
+    else {
+        GGML_ASSERT(false); // only stride equal to kernel size is supported
+    };
+}
+
+
 // ggml_compute_forward_flash_attn
 
 static void ggml_compute_forward_flash_attn_f32(
@@ -13779,45 +13095,14 @@ static void ggml_compute_forward_flash_attn_f32(
     int64_t t0 = ggml_perf_time_us();
     UNUSED(t0);
 
-    const int64_t neq0 = q->ne[0];
-    const int64_t neq1 = q->ne[1];
-    const int64_t neq2 = q->ne[2];
-    const int64_t neq3 = q->ne[3];
-
-    const int64_t nek0 = k->ne[0];
-    const int64_t nek1 = k->ne[1];
-    //const int64_t nek2 = k->ne[2];
-    //const int64_t nek3 = k->ne[3];
-
-    //const int64_t nev0 = v->ne[0];
-    const int64_t nev1 = v->ne[1];
-    //const int64_t nev2 = v->ne[2];
-    //const int64_t nev3 = v->ne[3];
-
-    const int64_t ne0  = dst->ne[0];
-    const int64_t ne1  = dst->ne[1];
-    //const int64_t ne2  = dst->ne[2];
-    //const int64_t ne3  = dst->ne[3];
-
-    const int nbk0 = k->nb[0];
-    const int nbk1 = k->nb[1];
-    const int nbk2 = k->nb[2];
-    const int nbk3 = k->nb[3];
-
-    const int nbq0 = q->nb[0];
-    const int nbq1 = q->nb[1];
-    const int nbq2 = q->nb[2];
-    const int nbq3 = q->nb[3];
-
-    const int nbv0 = v->nb[0];
-    const int nbv1 = v->nb[1];
-    const int nbv2 = v->nb[2];
-    const int nbv3 = v->nb[3];
-
-    const int nb0  = dst->nb[0];
-    const int nb1  = dst->nb[1];
-    const int nb2  = dst->nb[2];
-    const int nb3  = dst->nb[3];
+    GGML_TENSOR_LOCALS(int64_t, neq, q,   ne);
+    GGML_TENSOR_LOCALS(size_t,  nbq, q,   nb);
+    GGML_TENSOR_LOCALS(int64_t, nek, k,   ne);
+    GGML_TENSOR_LOCALS(size_t,  nbk, k,   nb);
+    GGML_TENSOR_LOCALS(int64_t, nev, v,   ne);
+    GGML_TENSOR_LOCALS(size_t,  nbv, v,   nb);
+    GGML_TENSOR_LOCALS(int64_t, ne,  dst, ne);
+    GGML_TENSOR_LOCALS(size_t,  nb,  dst, nb);
 
     const int ith = params->ith;
     const int nth = params->nth;
@@ -13988,45 +13273,14 @@ static void ggml_compute_forward_flash_attn_f16(
     int64_t t0 = ggml_perf_time_us();
     UNUSED(t0);
 
-    const int64_t neq0 = q->ne[0];
-    const int64_t neq1 = q->ne[1];
-    const int64_t neq2 = q->ne[2];
-    const int64_t neq3 = q->ne[3];
-
-    const int64_t nek0 = k->ne[0];
-    const int64_t nek1 = k->ne[1];
-    //const int64_t nek2 = k->ne[2];
-    //const int64_t nek3 = k->ne[3];
-
-    //const int64_t nev0 = v->ne[0];
-    const int64_t nev1 = v->ne[1];
-    //const int64_t nev2 = v->ne[2];
-    //const int64_t nev3 = v->ne[3];
-
-    const int64_t ne0  = dst->ne[0];
-    const int64_t ne1  = dst->ne[1];
-    //const int64_t ne2  = dst->ne[2];
-    //const int64_t ne3  = dst->ne[3];
-
-    const int nbk0 = k->nb[0];
-    const int nbk1 = k->nb[1];
-    const int nbk2 = k->nb[2];
-    const int nbk3 = k->nb[3];
-
-    const int nbq0 = q->nb[0];
-    const int nbq1 = q->nb[1];
-    const int nbq2 = q->nb[2];
-    const int nbq3 = q->nb[3];
-
-    const int nbv0 = v->nb[0];
-    const int nbv1 = v->nb[1];
-    const int nbv2 = v->nb[2];
-    const int nbv3 = v->nb[3];
-
-    const int nb0  = dst->nb[0];
-    const int nb1  = dst->nb[1];
-    const int nb2  = dst->nb[2];
-    const int nb3  = dst->nb[3];
+    GGML_TENSOR_LOCALS(int64_t, neq, q,   ne);
+    GGML_TENSOR_LOCALS(size_t,  nbq, q,   nb);
+    GGML_TENSOR_LOCALS(int64_t, nek, k,   ne);
+    GGML_TENSOR_LOCALS(size_t,  nbk, k,   nb);
+    GGML_TENSOR_LOCALS(int64_t, nev, v,   ne);
+    GGML_TENSOR_LOCALS(size_t,  nbv, v,   nb);
+    GGML_TENSOR_LOCALS(int64_t, ne,  dst, ne);
+    GGML_TENSOR_LOCALS(size_t,  nb,  dst, nb);
 
     const int ith = params->ith;
     const int nth = params->nth;
@@ -14260,65 +13514,18 @@ static void ggml_compute_forward_flash_ff_f16(
     int64_t t0 = ggml_perf_time_us();
     UNUSED(t0);
 
-    const int64_t nea0 = a->ne[0];
-    const int64_t nea1 = a->ne[1];
-    const int64_t nea2 = a->ne[2];
-    const int64_t nea3 = a->ne[3];
-
-    const int64_t neb00 = b0->ne[0];
-    const int64_t neb01 = b0->ne[1];
-    //const int64_t neb02 = b0->ne[2];
-    //const int64_t neb03 = b0->ne[3];
-
-    const int64_t neb10 = b1->ne[0];
-    const int64_t neb11 = b1->ne[1];
-    //const int64_t neb12 = b1->ne[2];
-    //const int64_t neb13 = b1->ne[3];
-
-    const int64_t nec00 = c0->ne[0];
-    const int64_t nec01 = c0->ne[1];
-    //const int64_t nec02 = c0->ne[2];
-    //const int64_t nec03 = c0->ne[3];
-
-    const int64_t nec10 = c1->ne[0];
-    const int64_t nec11 = c1->ne[1];
-    //const int64_t nec12 = c1->ne[2];
-    //const int64_t nec13 = c1->ne[3];
-
-    const int64_t ne0 = dst->ne[0];
-    const int64_t ne1 = dst->ne[1];
-    const int64_t ne2 = dst->ne[2];
-    //const int64_t ne3 = dst->ne[3];
-
-    const int nba0 = a->nb[0];
-    const int nba1 = a->nb[1];
-    const int nba2 = a->nb[2];
-    const int nba3 = a->nb[3];
-
-    const int nbb00 = b0->nb[0];
-    const int nbb01 = b0->nb[1];
-    const int nbb02 = b0->nb[2];
-    const int nbb03 = b0->nb[3];
-
-    const int nbb10 = b1->nb[0];
-    //const int nbb11 = b1->nb[1];
-    //const int nbb12 = b1->nb[2];
-    //const int nbb13 = b1->nb[3];
-
-    const int nbc00 = c0->nb[0];
-    const int nbc01 = c0->nb[1];
-    const int nbc02 = c0->nb[2];
-    const int nbc03 = c0->nb[3];
-
-    const int nbc10 = c1->nb[0];
-    //const int nbc11 = c1->nb[1];
-    //const int nbc12 = c1->nb[2];
-    //const int nbc13 = c1->nb[3];
-
-    const int nb0 = dst->nb[0];
-    const int nb1 = dst->nb[1];
-    const int nb2 = dst->nb[2];
-    const int nb3 = dst->nb[3];
+    GGML_TENSOR_LOCALS(int64_t, nea,  a,   ne);
+    GGML_TENSOR_LOCALS(size_t,  nba,  a,   nb);
+    GGML_TENSOR_LOCALS(int64_t, neb0, b0,  ne);
+    GGML_TENSOR_LOCALS(size_t,  nbb0, b0,  nb);
+    GGML_TENSOR_LOCALS(int64_t, neb1, b1,  ne);
+    GGML_TENSOR_LOCALS(size_t,  nbb1, b1,  nb);
+    GGML_TENSOR_LOCALS(int64_t, nec0, c0,  ne);
+    GGML_TENSOR_LOCALS(size_t,  nbc0, c0,  nb);
+    GGML_TENSOR_LOCALS(int64_t, nec1, c1,  ne);
+    GGML_TENSOR_LOCALS(size_t,  nbc1, c1,  nb);
+    GGML_TENSOR_LOCALS(int64_t, ne,   dst, ne);
+    GGML_TENSOR_LOCALS(size_t,  nb,   dst, nb);
 
     const int ith = params->ith;
     const int nth = params->nth;
@@ -14466,55 +13673,16 @@ static void ggml_compute_forward_flash_attn_back_f32(
     int64_t t0 = ggml_perf_time_us();
     UNUSED(t0);
 
-    const int64_t neq0 = q->ne[0];
-    const int64_t neq1 = q->ne[1];
-    const int64_t neq2 = q->ne[2];
-    const int64_t neq3 = q->ne[3];
-
-    const int64_t nek0 = k->ne[0];
-    const int64_t nek1 = k->ne[1];
-    //const int64_t nek2 = k->ne[2];
-    //const int64_t nek3 = k->ne[3];
-
-    const int64_t nev0 = v->ne[0];
-    const int64_t nev1 = v->ne[1];
-    //const int64_t nev2 = v->ne[2];
-    //const int64_t nev3 = v->ne[3];
-
-    const int64_t ned0 = d->ne[0];
-    const int64_t ned1 = d->ne[1];
-    //const int64_t ned2 = d->ne[2];
-    //const int64_t ned3 = d->ne[3];
-
-    const int64_t ne0  = dst->ne[0];
-    const int64_t ne1  = dst->ne[1];
-    const int64_t ne2  = dst->ne[2];
-    const int64_t ne3  = dst->ne[3];
-
-    const int nbk0 = k->nb[0];
-    const int nbk1 = k->nb[1];
-    const int nbk2 = k->nb[2];
-    const int nbk3 = k->nb[3];
-
-    const int nbq0 = q->nb[0];
-    const int nbq1 = q->nb[1];
-    const int nbq2 = q->nb[2];
-    const int nbq3 = q->nb[3];
-
-    const int nbv0 = v->nb[0];
-    const int nbv1 = v->nb[1];
-    const int nbv2 = v->nb[2];
-    const int nbv3 = v->nb[3];
-
-    const int nbd0 = d->nb[0];
-    const int nbd1 = d->nb[1];
-    const int nbd2 = d->nb[2];
-    const int nbd3 = d->nb[3];
-
-    const int nb0  = dst->nb[0];
-    const int nb1  = dst->nb[1];
-    const int nb2  = dst->nb[2];
-    const int nb3  = dst->nb[3];
+    GGML_TENSOR_LOCALS(int64_t, neq, q,   ne);
+    GGML_TENSOR_LOCALS(size_t,  nbq, q,   nb);
+    GGML_TENSOR_LOCALS(int64_t, nek, k,   ne);
+    GGML_TENSOR_LOCALS(size_t,  nbk, k,   nb);
+    GGML_TENSOR_LOCALS(int64_t, nev, v,   ne);
+    GGML_TENSOR_LOCALS(size_t,  nbv, v,   nb);
+    GGML_TENSOR_LOCALS(int64_t, ned, d,   ne);
+    GGML_TENSOR_LOCALS(size_t,  nbd, d,   nb);
+    GGML_TENSOR_LOCALS(int64_t, ne,  dst, ne);
+    GGML_TENSOR_LOCALS(size_t,  nb,  dst, nb);
 
     const int ith = params->ith;
     const int nth = params->nth;
@@ -14872,15 +14040,8 @@ static void ggml_compute_forward_win_part_f32(
         return;
     }
 
-    const int64_t ne00 = src0->ne[0]; UNUSED(ne00);
-    const int64_t ne01 = src0->ne[1];
-    const int64_t ne02 = src0->ne[2];
-    const int64_t ne03 = src0->ne[3]; UNUSED(ne03);
-
-    const int64_t ne0 = dst->ne[0];
-    const int64_t ne1 = dst->ne[1];
-    const int64_t ne2 = dst->ne[2];
-    const int64_t ne3 = dst->ne[3]; UNUSED(ne3);
+    GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne);
+    GGML_TENSOR_LOCALS(int64_t, ne,  dst,  ne);
 
     const int32_t nep0 = ((const int32_t *)(opt0->data))[0];
     const int32_t nep1 = ((const int32_t *)(opt0->data))[1];
@@ -14943,14 +14104,8 @@ static void ggml_compute_forward_win_unpart_f32(
         return;
     }
 
-    const int64_t ne00 = src0->ne[0];
-    const int64_t ne01 = src0->ne[1];
-    const int64_t ne02 = src0->ne[2];
-    //const int64_t ne03 = src0->ne[3];
-
-    const int64_t ne0 = dst->ne[0];
-    const int64_t ne1 = dst->ne[1];
-    const int64_t ne2 = dst->ne[2];
+    GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne);
+    GGML_TENSOR_LOCALS(int64_t, ne,  dst,  ne);
 
     const int32_t w = ((const int32_t *)(opt0->data))[0];
 
@@ -15556,6 +14711,10 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
             {
                 ggml_compute_forward_mean(params, tensor->src0, tensor);
             } break;
+        case GGML_OP_ARGMAX:
+            {
+                ggml_compute_forward_argmax(params, tensor->src0, tensor);
+            } break;
         case GGML_OP_REPEAT:
             {
                 ggml_compute_forward_repeat(params, tensor->src0, tensor);
@@ -15580,6 +14739,14 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
             {
                 ggml_compute_forward_step(params, tensor->src0, tensor);
             } break;
+        case GGML_OP_TANH:
+            {
+                ggml_compute_forward_tanh(params, tensor->src0, tensor);
+            } break;
+        case GGML_OP_ELU:
+            {
+                ggml_compute_forward_elu(params, tensor->src0, tensor);
+            } break;
         case GGML_OP_RELU:
             {
                 ggml_compute_forward_relu(params, tensor->src0, tensor);
@@ -15696,17 +14863,13 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
             {
                 ggml_compute_forward_clamp(params, tensor->src0, tensor->src1, tensor);
             } break;
-        case GGML_OP_CONV_1D_S1_PH:
+        case GGML_OP_CONV_1D:
             {
-                ggml_compute_forward_conv_1d_s1_ph(params, tensor->src0, tensor->src1, tensor);
+                ggml_compute_forward_conv_1d(params, tensor->src0, tensor->src1, tensor->opt[0], tensor);
             } break;
-        case GGML_OP_CONV_1D_S2_PH:
+        case GGML_OP_CONV_2D:
             {
-                ggml_compute_forward_conv_1d_s2_ph(params, tensor->src0, tensor->src1, tensor);
-            } break;
-        case GGML_OP_CONV_2D_SK_P0:
-            {
-                ggml_compute_forward_conv_2d_sk_p0(params, tensor->src0, tensor->src1, tensor);
+                ggml_compute_forward_conv_2d(params, tensor->src0, tensor->src1, tensor->opt[0], tensor);
             } break;
         case GGML_OP_FLASH_ATTN:
             {
@@ -15963,6 +15126,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
                 }
             } break;
         case GGML_OP_MEAN:
+        case GGML_OP_ARGMAX:
             {
                 GGML_ASSERT(false); // TODO: implement
             } break;
@@ -16016,6 +15180,14 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
                     // noop
                 }
             } break;
+        case GGML_OP_TANH:
+            {
+                GGML_ASSERT(false); // TODO: not implemented
+            } break;
+        case GGML_OP_ELU:
+            {
+                GGML_ASSERT(false); // TODO: not implemented
+            } break;
         case GGML_OP_RELU:
             {
                 if (src0->grad) {
@@ -16035,14 +15207,6 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
             {
                 GGML_ASSERT(false); // TODO: not implemented
             } break;
-        case GGML_OP_ALIBI:
-            {
-                GGML_ASSERT(false); // TODO: not implemented
-            } break;
-        case GGML_OP_CLAMP:
-            {
-                GGML_ASSERT(false); // TODO: not implemented
-            } break;
         case GGML_OP_SILU:
             {
                 // necessary for llama
@@ -16359,7 +15523,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
                 // necessary for llama
                 if (src0->grad) {
                     assert(src1->type == GGML_TYPE_I32);
-                    assert(ggml_nelements(src1) == 3);
+                    assert(ggml_nelements(src1) == 4);
                     const int n_past = ((int32_t *) src1->data)[0];
                     const int n_dims = ((int32_t *) src1->data)[1];
                     const int mode   = ((int32_t *) src1->data)[2];
@@ -16399,15 +15563,19 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
                     // noop
                 }
             } break;
-        case GGML_OP_CONV_1D_S1_PH:
+        case GGML_OP_ALIBI:
             {
                 GGML_ASSERT(false); // TODO: not implemented
             } break;
-        case GGML_OP_CONV_1D_S2_PH:
+        case GGML_OP_CLAMP:
             {
                 GGML_ASSERT(false); // TODO: not implemented
             } break;
-        case GGML_OP_CONV_2D_SK_P0:
+        case GGML_OP_CONV_1D:
+            {
+                GGML_ASSERT(false); // TODO: not implemented
+            } break;
+        case GGML_OP_CONV_2D:
             {
                 GGML_ASSERT(false); // TODO: not implemented
             } break;
@@ -17064,12 +16232,15 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph)
                 case GGML_OP_SUM:
                 case GGML_OP_SUM_ROWS:
                 case GGML_OP_MEAN:
+                case GGML_OP_ARGMAX:
                 case GGML_OP_REPEAT:
                 case GGML_OP_REPEAT_BACK:
                 case GGML_OP_ABS:
                 case GGML_OP_SGN:
                 case GGML_OP_NEG:
                 case GGML_OP_STEP:
+                case GGML_OP_TANH:
+                case GGML_OP_ELU:
                 case GGML_OP_RELU:
                     {
                         node->n_tasks = 1;
@@ -17098,6 +16269,7 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph)
                         //printf("nr0 = %8d, nr1 = %8d, nr0*nr1 = %8d, n_tasks = %d\n", nr0, nr1, nr0*nr1, node->n_tasks);
 
                         size_t cur = 0;
+                        const enum ggml_type vec_dot_type = type_traits[node->src0->type].vec_dot_type;
 
 #if defined(GGML_USE_CUBLAS)
                         if (ggml_cuda_can_mul_mat(node->src0, node->src1, node)) {
@@ -17113,39 +16285,20 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph)
                         }
                         else
 #endif
-                        if (node->src0->type == GGML_TYPE_F16 && node->src1->type == GGML_TYPE_F32) {
 #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
-                            if (ggml_compute_forward_mul_mat_use_blas(node->src0, node->src1, node)) {
-                                node->n_tasks = 1; // TODO: this actually is doing nothing
-                                                   //       the threads are still spinning
+                        if (ggml_compute_forward_mul_mat_use_blas(node->src0, node->src1, node)) {
+                            node->n_tasks = 1; // TODO: this actually is doing nothing
+                                               //       the threads are still spinning
+                            if (node->src0->type != GGML_TYPE_F32) {
                                 // here we need memory just for single 2D matrix from src0
                                 cur = GGML_TYPE_SIZE[GGML_TYPE_F32]*(node->src0->ne[0]*node->src0->ne[1]);
-                            } else {
-                                cur = GGML_TYPE_SIZE[GGML_TYPE_F16]*ggml_nelements(node->src1);
                             }
-#else
-                            cur = GGML_TYPE_SIZE[GGML_TYPE_F16]*ggml_nelements(node->src1);
+                        } else
 #endif
-                        } else if (node->src0->type == GGML_TYPE_F32 && node->src1->type == GGML_TYPE_F32) {
-                            cur = 0;
-#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
-                            if (ggml_compute_forward_mul_mat_use_blas(node->src0, node->src1, node)) {
-                                node->n_tasks = 1;
-                            }
-#endif
-                        } else if (ggml_is_quantized(node->src0->type) && node->src1->type == GGML_TYPE_F32) {
-#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
-                            if (ggml_compute_forward_mul_mat_use_blas(node->src0, node->src1, node)) {
-                                node->n_tasks = 1;
-                                cur = GGML_TYPE_SIZE[GGML_TYPE_F32]*(node->src0->ne[0]*node->src0->ne[1]);
-                            } else
-#endif
-                            {
-                                const enum ggml_type type_q = quantize_fns[node->src0->type].vec_dot_type;
-                                cur = GGML_TYPE_SIZE[type_q]*ggml_nelements(node->src1)/GGML_BLCK_SIZE[type_q];
-                            }
+                        if (node->src1->type != vec_dot_type) {
+                            cur = GGML_TYPE_SIZE[vec_dot_type]*ggml_nelements(node->src1)/GGML_BLCK_SIZE[vec_dot_type];
                         } else {
-                            GGML_ASSERT(false);
+                            cur = 0;
                         }
 
                         work_size = MAX(work_size, cur);
@@ -17185,8 +16338,7 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph)
                     {
                         node->n_tasks = 1; //TODO
                     } break;
-                case GGML_OP_CONV_1D_S1_PH:
-                case GGML_OP_CONV_1D_S2_PH:
+                case GGML_OP_CONV_1D:
                     {
                         node->n_tasks = n_threads;
 
@@ -17215,7 +16367,7 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph)
 
                         work_size = MAX(work_size, cur);
                     } break;
-                case GGML_OP_CONV_2D_SK_P0:
+                case GGML_OP_CONV_2D:
                     {
                         node->n_tasks = n_threads;
 
@@ -17577,13 +16729,6 @@ void ggml_graph_export(const struct ggml_cgraph * cgraph, const char * fname) {
                     fwrite(&nb, sizeof(uint64_t), 1, fout);
                 }
 
-                // store the pointer address
-                {
-                    const uint64_t ptr = (uint64_t) tensor->data;
-
-                    fwrite(&ptr, sizeof(uint64_t), 1, fout);
-                }
-
                 fwrite(tensor->name, sizeof(char), GGML_MAX_NAME, fout);
 
                 // dump the data
@@ -17617,13 +16762,6 @@ void ggml_graph_export(const struct ggml_cgraph * cgraph, const char * fname) {
                     fwrite(&nb, sizeof(uint64_t), 1, fout);
                 }
 
-                // store the pointer address
-                {
-                    const uint64_t ptr = (uint64_t) tensor->data;
-
-                    fwrite(&ptr, sizeof(uint64_t), 1, fout);
-                }
-
                 fwrite(tensor->name, sizeof(char), GGML_MAX_NAME, fout);
 
                 // output the op arguments
@@ -17808,8 +16946,6 @@ struct ggml_cgraph ggml_graph_import(const char * fname, struct ggml_context **
 
                 tensor->op = (enum ggml_op) op;
 
-                uint64_t ptr_cur = *(const uint64_t *) ptr; ptr += sizeof(ptr_cur);
-
                 memcpy(tensor->name, ptr, GGML_MAX_NAME); ptr += GGML_MAX_NAME;
 
                 tensor->data = (void *) ptr;
@@ -17855,8 +16991,6 @@ struct ggml_cgraph ggml_graph_import(const char * fname, struct ggml_context **
                     nb[j] = nb_cur;
                 }
 
-                uint64_t ptr_cur = *(const uint64_t *) ptr; ptr += sizeof(ptr_cur); // TODO: not yet used
-
                 const char * ptr_name = ptr; ptr += GGML_MAX_NAME;
 
                 const int32_t * ptr_arg_idx = (const int32_t *) ptr; ptr += (2 + GGML_MAX_OPT)*sizeof(int32_t);
diff --git a/ggml.h b/ggml.h
index de7bd2640..f204d1342 100644
--- a/ggml.h
+++ b/ggml.h
@@ -201,6 +201,8 @@
 #define GGML_MAX_NAME          48
 #define GGML_DEFAULT_N_THREADS 4
 
+#define GGML_UNUSED(x) (void)(x)
+
 #define GGML_ASSERT(x) \
     do { \
         if (!(x)) { \
@@ -209,6 +211,30 @@
         } \
     } while (0)
 
+// used to copy the number of elements and stride in bytes of tensors into local variables.
+// main purpose is to reduce code duplication and improve readability.
+//
+// example:
+//
+//    GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne);
+//    GGML_TENSOR_LOCALS(size_t,  nb1, src1, nb);
+//
+#define GGML_TENSOR_LOCALS_1(type, prefix, pointer, array) \
+    const type prefix##0 = (pointer)->array[0]; \
+    GGML_UNUSED(prefix##0);
+#define GGML_TENSOR_LOCALS_2(type, prefix, pointer, array) \
+    GGML_TENSOR_LOCALS_1    (type, prefix, pointer, array) \
+    const type prefix##1 = (pointer)->array[1]; \
+    GGML_UNUSED(prefix##1);
+#define GGML_TENSOR_LOCALS_3(type, prefix, pointer, array) \
+    GGML_TENSOR_LOCALS_2    (type, prefix, pointer, array) \
+    const type prefix##2 = (pointer)->array[2]; \
+    GGML_UNUSED(prefix##2);
+#define GGML_TENSOR_LOCALS(type, prefix, pointer, array) \
+    GGML_TENSOR_LOCALS_3  (type, prefix, pointer, array) \
+    const type prefix##3 = (pointer)->array[3]; \
+    GGML_UNUSED(prefix##3);
+
 #ifdef  __cplusplus
 extern "C" {
 #endif
@@ -224,8 +250,8 @@ extern "C" {
     GGML_API float       ggml_fp16_to_fp32(ggml_fp16_t x);
     GGML_API ggml_fp16_t ggml_fp32_to_fp16(float x);
 
-    GGML_API void ggml_fp16_to_fp32_row(const ggml_fp16_t * x, float * y, size_t n);
-    GGML_API void ggml_fp32_to_fp16_row(const float * x, ggml_fp16_t * y, size_t n);
+    GGML_API void ggml_fp16_to_fp32_row(const ggml_fp16_t * x, float * y, int n);
+    GGML_API void ggml_fp32_to_fp16_row(const float * x, ggml_fp16_t * y, int n);
 
     struct ggml_object;
     struct ggml_context;
@@ -295,12 +321,15 @@ extern "C" {
         GGML_OP_SUM,
         GGML_OP_SUM_ROWS,
         GGML_OP_MEAN,
+        GGML_OP_ARGMAX,
         GGML_OP_REPEAT,
         GGML_OP_REPEAT_BACK,
         GGML_OP_ABS,
         GGML_OP_SGN,
         GGML_OP_NEG,
         GGML_OP_STEP,
+        GGML_OP_TANH,
+        GGML_OP_ELU,
         GGML_OP_RELU,
         GGML_OP_GELU,
         GGML_OP_GELU_QUICK,
@@ -332,9 +361,8 @@ extern "C" {
         GGML_OP_ROPE_BACK,
         GGML_OP_ALIBI,
         GGML_OP_CLAMP,
-        GGML_OP_CONV_1D_S1_PH,
-        GGML_OP_CONV_1D_S2_PH,
-        GGML_OP_CONV_2D_SK_P0,
+        GGML_OP_CONV_1D,
+        GGML_OP_CONV_2D,
 
         GGML_OP_FLASH_ATTN,
         GGML_OP_FLASH_FF,
@@ -703,6 +731,11 @@ extern "C" {
             struct ggml_context * ctx,
             struct ggml_tensor  * a);
 
+    // argmax along rows
+    GGML_API struct ggml_tensor * ggml_argmax(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
     // if a is the same shape as b, and a is not parameter, return a
     // otherwise, return a new tensor: repeat(a) to fit in b
     GGML_API struct ggml_tensor * ggml_repeat(
@@ -747,6 +780,22 @@ extern "C" {
             struct ggml_context * ctx,
             struct ggml_tensor  * a);
 
+    GGML_API struct ggml_tensor * ggml_tanh(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_tanh_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_elu(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_elu_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
     GGML_API struct ggml_tensor * ggml_relu(
             struct ggml_context * ctx,
             struct ggml_tensor  * a);
@@ -1097,58 +1146,33 @@ extern "C" {
             float                 min,
             float                 max);
 
-    // TODO: implement general-purpose convolutions
-    // GGML_API struct ggml_tensor * ggml_conv_1d(
-    //        struct ggml_context * ctx,
-    //        struct ggml_tensor  * a,
-    //        struct ggml_tensor  * b,
-    //        int                   s0
-    //        int                   p0,
-    //        int                   d0);
-    //
-    // GGML_API struct ggml_tensor * ggml_conv_2d(
-    //        struct ggml_context * ctx,
-    //        struct ggml_tensor  * a,
-    //        struct ggml_tensor  * b,
-    //        int                   s0,
-    //        int                   s1,
-    //        int                   p0,
-    //        int                   p1,
-    //        int                   d0,
-    //        int                   d1);
-
-    // padding = half
-    // TODO: we don't support extra parameters for now
-    //       that's why we are hard-coding the stride, padding, and dilation
-    //       not great ..
-    // example:
-    // a:      3   80  768    1
-    // b:   3000   80    1    1
-    // res: 3000  768    1    1
-    // used in whisper
-    GGML_API struct ggml_tensor * ggml_conv_1d_s1_ph(
+    GGML_API struct ggml_tensor * ggml_conv_1d(
             struct ggml_context * ctx,
             struct ggml_tensor  * a,
-            struct ggml_tensor  * b);
+            struct ggml_tensor  * b,
+            int                   s0,  // stride
+            int                   p0,  // padding
+            int                   d0); // dilation
 
-    // used in whisper
-    GGML_API struct ggml_tensor * ggml_conv_1d_s2_ph(
+    GGML_API struct ggml_tensor * ggml_conv_2d(
             struct ggml_context * ctx,
             struct ggml_tensor  * a,
-            struct ggml_tensor  * b);
+            struct ggml_tensor  * b,
+            int                   s0,
+            int                   s1,
+            int                   p0,
+            int                   p1,
+            int                   d0,
+            int                   d1);
 
-    // kernel size is a->ne[0] x a->ne[1]
-    // stride is equal to kernel size
-    // padding is zero
-    // example:
-    // a:     16   16    3  768
-    // b:   1024 1024    3    1
-    // res:   64   64  768    1
-    // used in sam
-    GGML_API struct ggml_tensor * ggml_conv_2d_sk_p0(
+    // conv_1d with padding = half
+    // alias for ggml_conv_1d(a, b, s, a->ne[0]/2, d)
+    GGML_API struct ggml_tensor* ggml_conv_1d_ph(
             struct ggml_context * ctx,
             struct ggml_tensor  * a,
-            struct ggml_tensor  * b);
+            struct ggml_tensor  * b,
+            int                   s,
+            int                   d);
 
     GGML_API struct ggml_tensor * ggml_flash_attn(
             struct ggml_context * ctx,
@@ -1504,25 +1528,24 @@ extern "C" {
     //
 
 #ifdef  __cplusplus
-    // restrict not standard in C++
+// restrict not standard in C++
 #define GGML_RESTRICT
 #else
 #define GGML_RESTRICT restrict
 #endif
-    typedef void (*dequantize_row_q_t)(const void * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
-    typedef void (*quantize_row_q_t)  (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
-    typedef void (*vec_dot_q_t)       (const int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT x, const void * GGML_RESTRICT y);
+    typedef void (*ggml_to_float_t)  (const void  * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
+    typedef void (*ggml_from_float_t)(const float * GGML_RESTRICT x, void  * GGML_RESTRICT y, int k);
+    typedef void (*ggml_vec_dot_t)   (const int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT x, const void * GGML_RESTRICT y);
 
     typedef struct {
-        dequantize_row_q_t dequantize_row_q;
-        quantize_row_q_t   quantize_row_q;
-        quantize_row_q_t   quantize_row_q_reference;
-        quantize_row_q_t   quantize_row_q_dot;
-        vec_dot_q_t        vec_dot_q;
-        enum ggml_type     vec_dot_type;
-    } quantize_fns_t;
+        ggml_to_float_t   to_float;
+        ggml_from_float_t from_float;
+        ggml_from_float_t from_float_reference;
+        ggml_vec_dot_t    vec_dot;
+        enum ggml_type    vec_dot_type;
+    } ggml_type_traits_t;
 
-    quantize_fns_t ggml_internal_get_quantize_fn(size_t i);
+    ggml_type_traits_t ggml_internal_get_type_traits(enum ggml_type i);
 
 #ifdef  __cplusplus
 }
diff --git a/llama.cpp b/llama.cpp
index af22bf64b..99abde348 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -198,8 +198,8 @@ struct llama_layer {
 };
 
 struct llama_kv_cache {
-    struct ggml_tensor * k;
-    struct ggml_tensor * v;
+    struct ggml_tensor * k = NULL;
+    struct ggml_tensor * v = NULL;
 
     struct ggml_context * ctx = NULL;
 
@@ -488,9 +488,7 @@ struct llama_file_loader {
             std::string word = file.read_string(len);
 
             float score = 0.0f;
-            if (file_version >= LLAMA_FILE_VERSION_GGMF_V1) {
-                file.read_raw(&score, sizeof(score));
-            }
+            file.read_raw(&score, sizeof(score));
 
             vocab.token_to_id[word] = i;
 
@@ -1174,6 +1172,7 @@ static void llama_model_load_internal(
             }
         }
 #endif // GGML_USE_CUBLAS
+
 #if defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST)
         const int n_gpu = std::min(n_gpu_layers, int(hparams.n_layer));
 
@@ -1182,6 +1181,10 @@ static void llama_model_load_internal(
             fprintf(stderr, "%s: offloading non-repeating layers to GPU\n", __func__);
         }
         size_t vram_kv_cache = 0;
+
+#ifdef GGML_USE_CUBLAS
+        const int max_backend_supported_layers = hparams.n_layer + 3;
+        const int max_offloadable_layers = low_vram ? hparams.n_layer + 1 : hparams.n_layer + 3;
         if (n_gpu_layers > (int) hparams.n_layer + 1) {
             if (low_vram) {
                 fprintf(stderr, "%s: cannot offload v cache to GPU due to low VRAM option\n", __func__);
@@ -1198,14 +1201,18 @@ static void llama_model_load_internal(
                 vram_kv_cache += MEM_REQ_KV_SELF().at(model.type) / 2;
             }
         }
-        const int max_offloadable_layers = low_vram ? hparams.n_layer + 1 : hparams.n_layer + 3;
+#elif defined(GGML_USE_CLBLAST)
+        const int max_backend_supported_layers = hparams.n_layer + 1;
+        const int max_offloadable_layers = hparams.n_layer + 1;
+#endif // GGML_USE_CUBLAS
+
         fprintf(stderr, "%s: offloaded %d/%d layers to GPU\n",
-                __func__, std::min(n_gpu_layers, max_offloadable_layers), hparams.n_layer + 3);
+                __func__, std::min(n_gpu_layers, max_offloadable_layers), max_backend_supported_layers);
         fprintf(stderr, "%s: total VRAM used: %zu MB\n",
                 __func__, (vram_weights + vram_scratch + vram_kv_cache + MB - 1) / MB); // round up
 #else
         (void) n_gpu_layers;
-#endif
+#endif // defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST)
     }
 
     // populate `tensors_by_name`
@@ -1938,10 +1945,10 @@ void llama_sample_top_p(struct llama_context * ctx, llama_token_data_array * can
         return;
     }
 
-    const int64_t t_start_sample_us = ggml_time_us();
-
     llama_sample_softmax(ctx, candidates);
 
+    const int64_t t_start_sample_us = ggml_time_us();
+
     // Compute the cumulative probabilities
     float cum_sum = 0.0f;
     size_t last_idx = candidates->size;
@@ -1970,9 +1977,8 @@ void llama_sample_tail_free(struct llama_context * ctx, llama_token_data_array *
         return;
     }
 
-    const int64_t t_start_sample_us = ggml_time_us();
-
     llama_sample_softmax(nullptr, candidates);
+    const int64_t t_start_sample_us = ggml_time_us();
 
     // Compute the first and second derivatives
     std::vector<float> first_derivatives(candidates->size - 1);
@@ -2024,11 +2030,11 @@ void llama_sample_typical(struct llama_context * ctx, llama_token_data_array * c
         return;
     }
 
-    const int64_t t_start_sample_us = ggml_time_us();
-
     // Compute the softmax of logits and calculate entropy
     llama_sample_softmax(nullptr, candidates);
 
+    const int64_t t_start_sample_us = ggml_time_us();
+
     float entropy = 0.0f;
     for (size_t i = 0; i < candidates->size; ++i) {
         entropy += -candidates->data[i].p * logf(candidates->data[i].p);
@@ -2197,13 +2203,11 @@ llama_token llama_sample_token_mirostat(struct llama_context * ctx, llama_token_
 
     if (ctx) {
         ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
-        ctx->n_sample++;
     }
     return X;
 }
 
 llama_token llama_sample_token_mirostat_v2(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, float * mu) {
-    assert(ctx);
     int64_t t_start_sample_us;
     t_start_sample_us = ggml_time_us();
 
@@ -2218,13 +2222,14 @@ llama_token llama_sample_token_mirostat_v2(struct llama_context * ctx, llama_tok
         candidates->size = 1;
     }
 
+    if (ctx) {
+        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
+    }
+
     // Normalize the probabilities of the remaining words
     llama_sample_softmax(ctx, candidates);
 
     // Sample the next word X from the remaining words
-    if (ctx) {
-        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
-    }
     llama_token X = llama_sample_token(ctx, candidates);
     t_start_sample_us = ggml_time_us();
 
@@ -2292,10 +2297,10 @@ static void llama_convert_tensor_internal(const llama_load_tensor & tensor, llam
     }
     float * f32_output = (float *) output.addr;
 
-    quantize_fns_t qtype;
+    ggml_type_traits_t qtype;
     if (ggml_is_quantized(tensor.type)) {
-        qtype = ggml_internal_get_quantize_fn(tensor.type);
-        if (qtype.dequantize_row_q == NULL) {
+        qtype = ggml_internal_get_type_traits(tensor.type);
+        if (qtype.to_float == NULL) {
             throw std::runtime_error(format("type %s unsupported for integer quantization: no dequantization available", ggml_type_name(tensor.type)));
         }
     } else if (tensor.type != GGML_TYPE_F16) {
@@ -2306,7 +2311,7 @@ static void llama_convert_tensor_internal(const llama_load_tensor & tensor, llam
         if (tensor.type == GGML_TYPE_F16) {
             ggml_fp16_to_fp32_row((ggml_fp16_t *)tensor.data, f32_output, nelements);
         } else if (ggml_is_quantized(tensor.type)) {
-            qtype.dequantize_row_q(tensor.data, f32_output, nelements);
+            qtype.to_float(tensor.data, f32_output, nelements);
         } else {
             LLAMA_ASSERT(false); // unreachable
         }
@@ -2331,7 +2336,7 @@ static void llama_convert_tensor_internal(const llama_load_tensor & tensor, llam
             if (typ == GGML_TYPE_F16) {
                 ggml_fp16_to_fp32_row((ggml_fp16_t *)inbuf, outbuf, nels);
             } else {
-                qtype.dequantize_row_q(inbuf, outbuf, nels);
+                qtype.to_float(inbuf, outbuf, nels);
             }
         };
         workers.push_back(std::thread(compute, tensor.type, tensor.data + in_buff_offs, f32_output + out_buff_offs, thr_elems));
@@ -3548,23 +3553,35 @@ llama_token llama_token_nl() {
     return 13;
 }
 
+struct llama_timings llama_get_timings(struct llama_context * ctx) {
+    struct llama_timings result = {
+        /*.t_start_ms  =*/ 1e-3 * ctx->t_start_us,
+        /*.t_end_ms    =*/ 1.00 * ggml_time_ms(),
+        /*.t_load_ms   =*/ 1e-3 * ctx->t_load_us,
+        /*.t_sample_ms =*/ 1e-3 * ctx->t_sample_us,
+        /*.t_p_eval_ms =*/ 1e-3 * ctx->t_p_eval_us,
+        /*.t_eval_ms   =*/ 1e-3 * ctx->t_eval_us,
+
+        /*.n_sample =*/ std::max(1, ctx->n_sample),
+        /*.n_p_eval =*/ std::max(1, ctx->n_p_eval),
+        /*.n_eval   =*/ std::max(1, ctx->n_eval),
+    };
+
+    return result;
+}
 
 void llama_print_timings(struct llama_context * ctx) {
-    const int64_t t_end_us = ggml_time_us();
-
-    const int32_t n_sample = std::max(1, ctx->n_sample);
-    const int32_t n_eval   = std::max(1, ctx->n_eval);
-    const int32_t n_p_eval = std::max(1, ctx->n_p_eval);
+    const llama_timings timings = llama_get_timings(ctx);
 
     fprintf(stderr, "\n");
-    fprintf(stderr, "%s:        load time = %8.2f ms\n", __func__, ctx->t_load_us / 1000.0);
+    fprintf(stderr, "%s:        load time = %8.2f ms\n", __func__, timings.t_load_ms);
     fprintf(stderr, "%s:      sample time = %8.2f ms / %5d runs   (%8.2f ms per token, %8.2f tokens per second)\n",
-            __func__, 1e-3 * ctx->t_sample_us, n_sample, 1e-3 * ctx->t_sample_us / n_sample, 1e6 / ctx->t_sample_us * n_sample);
+            __func__, timings.t_sample_ms, timings.n_sample, timings.t_sample_ms / timings.n_sample, 1e3 / timings.t_sample_ms * timings.n_sample);
     fprintf(stderr, "%s: prompt eval time = %8.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)\n",
-            __func__, 1e-3 * ctx->t_p_eval_us, n_p_eval, 1e-3 * ctx->t_p_eval_us / n_p_eval, 1e6 / ctx->t_p_eval_us * n_p_eval);
+            __func__, timings.t_p_eval_ms, timings.n_p_eval, timings.t_p_eval_ms / timings.n_p_eval, 1e3 / timings.t_p_eval_ms * timings.n_p_eval);
     fprintf(stderr, "%s:        eval time = %8.2f ms / %5d runs   (%8.2f ms per token, %8.2f tokens per second)\n",
-            __func__, 1e-3 * ctx->t_eval_us,   n_eval,   1e-3 * ctx->t_eval_us   / n_eval,   1e6 / ctx->t_eval_us   * n_eval);
-    fprintf(stderr, "%s:       total time = %8.2f ms\n", __func__, (t_end_us - ctx->t_start_us)/1000.0);
+            __func__, timings.t_eval_ms, timings.n_eval, timings.t_eval_ms / timings.n_eval, 1e3 / timings.t_eval_ms * timings.n_eval);
+    fprintf(stderr, "%s:       total time = %8.2f ms\n", __func__, (timings.t_end_ms - timings.t_start_ms));
 }
 
 void llama_reset_timings(struct llama_context * ctx) {
diff --git a/llama.h b/llama.h
index 1920584c5..14bc432c7 100644
--- a/llama.h
+++ b/llama.h
@@ -134,6 +134,20 @@ extern "C" {
         bool quantize_output_tensor; // quantize output.weight
     } llama_model_quantize_params;
 
+    // performance timing information
+    struct llama_timings {
+        double t_start_ms;
+        double t_end_ms;
+        double t_load_ms;
+        double t_sample_ms;
+        double t_p_eval_ms;
+        double t_eval_ms;
+
+        int32_t n_sample;
+        int32_t n_p_eval;
+        int32_t n_eval;
+    };
+
     LLAMA_API struct llama_context_params llama_context_default_params();
     LLAMA_API struct llama_model_quantize_params llama_model_quantize_default_params();
 
@@ -335,6 +349,7 @@ extern "C" {
     LLAMA_API llama_token llama_sample_token(struct llama_context * ctx, llama_token_data_array * candidates);
 
     // Performance information
+    LLAMA_API struct llama_timings llama_get_timings(struct llama_context * ctx);
     LLAMA_API void llama_print_timings(struct llama_context * ctx);
     LLAMA_API void llama_reset_timings(struct llama_context * ctx);
 
diff --git a/pocs/vdot/q8dot.cpp b/pocs/vdot/q8dot.cpp
index 5748c8ac2..4e0e02357 100644
--- a/pocs/vdot/q8dot.cpp
+++ b/pocs/vdot/q8dot.cpp
@@ -136,7 +136,7 @@ int main(int argc, char** argv) {
 
     auto ggml_type = type == 0 ? GGML_TYPE_Q4_0 : GGML_TYPE_Q4_1;
 
-    auto funcs = ggml_internal_get_quantize_fn(ggml_type);
+    auto funcs = ggml_internal_get_type_traits(ggml_type);
 
     Stat simple, ggml;
 
@@ -156,8 +156,8 @@ int main(int argc, char** argv) {
 
         t1 = std::chrono::high_resolution_clock::now();
         float fs;
-        if (type == 0) funcs.vec_dot_q(kVecSize * QK4_1, &fs, x40.data(), y.data());
-        else funcs.vec_dot_q(kVecSize * QK4_1, &fs, x41.data(), y.data());
+        if (type == 0) funcs.vec_dot(kVecSize * QK4_1, &fs, x40.data(), y.data());
+        else funcs.vec_dot(kVecSize * QK4_1, &fs, x41.data(), y.data());
         t2 = std::chrono::high_resolution_clock::now();
         t = 1e-3*std::chrono::duration_cast<std::chrono::nanoseconds>(t2-t1).count();
         if (iloop > 3) ggml.addResult(fs, t);
diff --git a/pocs/vdot/vdot.cpp b/pocs/vdot/vdot.cpp
index 7b18090d6..48758cda8 100644
--- a/pocs/vdot/vdot.cpp
+++ b/pocs/vdot/vdot.cpp
@@ -235,7 +235,7 @@ int main(int argc, char** argv) {
     int n4 = useQ4_1 ? kVecSize / QK4_1 : kVecSize / QK4_0; n4 = 64*((n4 + 63)/64);
     int n8 = kVecSize / QK8_0; n8 = 64*((n8 + 63)/64);
 
-    auto funcs = useQ4_1 ? ggml_internal_get_quantize_fn(GGML_TYPE_Q4_1) : ggml_internal_get_quantize_fn(GGML_TYPE_Q4_0);
+    auto funcs = useQ4_1 ? ggml_internal_get_type_traits(GGML_TYPE_Q4_1) : ggml_internal_get_type_traits(GGML_TYPE_Q4_0);
 
     std::vector<block_q4_0> q40;
     std::vector<block_q4_1> q41;
@@ -261,9 +261,9 @@ int main(int argc, char** argv) {
         // Note, we do not include this in the timing as in practical application
         // we already have the quantized model weights.
         if (useQ4_1) {
-            funcs.quantize_row_q(x1.data(), q41.data(), kVecSize);
+            funcs.from_float(x1.data(), q41.data(), kVecSize);
         } else {
-            funcs.quantize_row_q(x1.data(), q40.data(), kVecSize);
+            funcs.from_float(x1.data(), q40.data(), kVecSize);
         }
 
         // Now measure time the dot product needs using the "scalar" version above
@@ -282,9 +282,10 @@ int main(int argc, char** argv) {
             dot_q4_q8(kVecSize, &result, q40.data(), q8.data());
         }
         else {
-            funcs.quantize_row_q_dot(y1.data(), q8.data(), kVecSize);
-            if (useQ4_1) funcs.vec_dot_q(kVecSize, &result, q41.data(), q8.data());
-            else funcs.vec_dot_q(kVecSize, &result, q40.data(), q8.data());
+            auto vdot = ggml_internal_get_type_traits(funcs.vec_dot_type);
+            vdot.from_float(y1.data(), q8.data(), kVecSize);
+            if (useQ4_1) funcs.vec_dot(kVecSize, &result, q41.data(), q8.data());
+            else funcs.vec_dot(kVecSize, &result, q40.data(), q8.data());
         }
         sumq += result;
         t2 = std::chrono::high_resolution_clock::now();
diff --git a/scripts/sync-ggml.sh b/scripts/sync-ggml.sh
index e6e39ff8f..02ea6ec15 100755
--- a/scripts/sync-ggml.sh
+++ b/scripts/sync-ggml.sh
@@ -1,6 +1,14 @@
 #!/bin/bash
 
-cp -rpv ../ggml/src/ggml.c          ./ggml.c
-cp -rpv ../ggml/src/ggml-cuda.cu    ./ggml-cuda.cu
-cp -rpv ../ggml/src/ggml-cuda.h     ./ggml-cuda.h
-cp -rpv ../ggml/include/ggml/ggml.h ./ggml.h
+cp -rpv ../ggml/src/ggml.c           ./ggml.c
+cp -rpv ../ggml/src/ggml-cuda.h      ./ggml-cuda.h
+cp -rpv ../ggml/src/ggml-cuda.cu     ./ggml-cuda.cu
+cp -rpv ../ggml/src/ggml-opencl.h    ./ggml-opencl.h
+cp -rpv ../ggml/src/ggml-opencl.cpp  ./ggml-opencl.cpp
+cp -rpv ../ggml/src/ggml-metal.h     ./ggml-metal.h
+cp -rpv ../ggml/src/ggml-metal.m     ./ggml-metal.m
+cp -rpv ../ggml/src/ggml-metal.metal ./ggml-metal.metal
+cp -rpv ../ggml/include/ggml/ggml.h  ./ggml.h
+
+cp -rpv ../ggml/tests/test-opt.c    ./tests/test-opt.c
+cp -rpv ../ggml/tests/test-grad0.c  ./tests/test-grad0.c
diff --git a/tests/test-grad0.c b/tests/test-grad0.c
index b5a499c1d..a3e25214b 100644
--- a/tests/test-grad0.c
+++ b/tests/test-grad0.c
@@ -1154,7 +1154,7 @@ int main(int argc, const char ** argv) {
                             continue;
                         }
 
-                        struct ggml_tensor * f = ggml_sum(ctx0, ggml_rope(ctx0, x[0], n_past, n_rot, mode));
+                        struct ggml_tensor * f = ggml_sum(ctx0, ggml_rope(ctx0, x[0], n_past, n_rot, mode, 0));
 
                         GGML_PRINT_DEBUG("rope: n_past: %d n_rot: %d mode: %d\n", n_past, n_rot, mode);
                         check_gradient("rope", ctx0, x, f, ndims, nargs, 1e-2f, 1e-3f, INFINITY);
diff --git a/tests/test-quantize-fns.cpp b/tests/test-quantize-fns.cpp
index c40f1b29c..8d3c162d2 100644
--- a/tests/test-quantize-fns.cpp
+++ b/tests/test-quantize-fns.cpp
@@ -40,26 +40,26 @@ float array_rmse(const float * a1, const float * a2, size_t n) {
 }
 
 // Total quantization error on test data
-float total_quantization_error(quantize_fns_t & qfns, size_t test_size, const float * test_data) {
+float total_quantization_error(ggml_type_traits_t & qfns, size_t test_size, const float * test_data) {
     std::vector<uint8_t> tmp_q(2*test_size);
     std::vector<float> tmp_out(test_size);
 
-    qfns.quantize_row_q(test_data, tmp_q.data(), test_size);
-    qfns.dequantize_row_q(tmp_q.data(), tmp_out.data(), test_size);
+    qfns.from_float(test_data, tmp_q.data(), test_size);
+    qfns.to_float(tmp_q.data(), tmp_out.data(), test_size);
     return array_rmse(test_data, tmp_out.data(), test_size);
 }
 
 // Total quantization error on test data
-float reference_quantization_error(quantize_fns_t & qfns, size_t test_size, const float * test_data) {
+float reference_quantization_error(ggml_type_traits_t & qfns, size_t test_size, const float * test_data) {
     std::vector<uint8_t> tmp_q(2*test_size);
     std::vector<float> tmp_out(test_size);
     std::vector<float> tmp_out_ref(test_size);
 
-    qfns.quantize_row_q(test_data, tmp_q.data(), test_size);
-    qfns.dequantize_row_q(tmp_q.data(), tmp_out.data(), test_size);
+    qfns.from_float(test_data, tmp_q.data(), test_size);
+    qfns.to_float(tmp_q.data(), tmp_out.data(), test_size);
 
-    qfns.quantize_row_q_reference(test_data, tmp_q.data(), test_size);
-    qfns.dequantize_row_q(tmp_q.data(), tmp_out_ref.data(), test_size);
+    qfns.from_float_reference(test_data, tmp_q.data(), test_size);
+    qfns.to_float(tmp_q.data(), tmp_out_ref.data(), test_size);
 
     return array_rmse(tmp_out.data(), tmp_out_ref.data(), test_size);
 }
@@ -73,15 +73,17 @@ float dot_product(const float * a1, const float * a2, size_t test_size) {
 }
 
 // Total dot product error
-float dot_product_error(quantize_fns_t & qfns, size_t test_size, const float * test_data1, const float *test_data2) {
+float dot_product_error(ggml_type_traits_t & qfns, size_t test_size, const float * test_data1, const float *test_data2) {
     std::vector<uint8_t> tmp_q1(2*test_size);
     std::vector<uint8_t> tmp_q2(2*test_size);
 
-    qfns.quantize_row_q    (test_data1, tmp_q1.data(), test_size);
-    qfns.quantize_row_q_dot(test_data2, tmp_q2.data(), test_size);
+    auto vdot = ggml_internal_get_type_traits(qfns.vec_dot_type);
+
+    qfns.from_float(test_data1, tmp_q1.data(), test_size);
+    vdot.from_float(test_data2, tmp_q2.data(), test_size);
 
     float result = INFINITY;
-    qfns.vec_dot_q(test_size, &result, tmp_q1.data(), tmp_q2.data());
+    qfns.vec_dot(test_size, &result, tmp_q1.data(), tmp_q2.data());
 
     const float dot_ref = dot_product(test_data1, test_data2, test_size);
 
@@ -123,9 +125,9 @@ int main(int argc, char * argv[]) {
 
     for (int i = 0; i < GGML_TYPE_COUNT; i++) {
         ggml_type type = (ggml_type) i;
-        quantize_fns_t qfns = ggml_internal_get_quantize_fn(i);
+        ggml_type_traits_t qfns = ggml_internal_get_type_traits(type);
 
-        if (qfns.quantize_row_q && qfns.dequantize_row_q) {
+        if (qfns.from_float && qfns.to_float) {
             const float total_error = total_quantization_error(qfns, test_size, test_data.data());
             const float max_quantization_error =
                 type == GGML_TYPE_Q2_K ? MAX_QUANTIZATION_TOTAL_ERROR_2BITS :
diff --git a/tests/test-quantize-perf.cpp b/tests/test-quantize-perf.cpp
index c0e361e92..0bb9537f6 100644
--- a/tests/test-quantize-perf.cpp
+++ b/tests/test-quantize-perf.cpp
@@ -123,9 +123,9 @@ void usage(char * argv[]) {
     printf("  --type TYPE           set test type as");
     for (int i = 0; i < GGML_TYPE_COUNT; i++) {
         ggml_type type = (ggml_type) i;
-        quantize_fns_t qfns = ggml_internal_get_quantize_fn(type);
+        ggml_type_traits_t qfns = ggml_internal_get_type_traits(type);
         if (ggml_type_name(type) != NULL) {
-            if (qfns.quantize_row_q && qfns.dequantize_row_q) {
+            if (qfns.from_float && qfns.to_float) {
                 printf(" %s", ggml_type_name(type));
             }
         }
@@ -271,12 +271,12 @@ int main(int argc, char * argv[]) {
 
     for (int i = 0; i < GGML_TYPE_COUNT; i++) {
         ggml_type type = (ggml_type) i;
-        quantize_fns_t qfns = ggml_internal_get_quantize_fn(i);
+        ggml_type_traits_t qfns = ggml_internal_get_type_traits(type);
         if (!params.include_types.empty() && ggml_type_name(type) && std::find(params.include_types.begin(), params.include_types.end(), ggml_type_name(type)) == params.include_types.end()) {
             continue;
         }
 
-        if (qfns.quantize_row_q && qfns.dequantize_row_q) {
+        if (qfns.from_float && qfns.to_float) {
             printf("%s\n", ggml_type_name(type));
 
             if (params.op_quantize_row_q_reference) {
@@ -284,7 +284,7 @@ int main(int argc, char * argv[]) {
                 for (size_t size : params.test_sizes) {
                     printf("    %zu values (%.2f MB)\n", size, 4*size/(float)(1024*1024));
                     auto quantize_fn = [&](void ) {
-                        qfns.quantize_row_q_reference(test_data1, test_q1, size);
+                        qfns.from_float_reference(test_data1, test_q1, size);
                         return test_q1[0];
                     };
                     size_t quantized_size = size / ggml_blck_size(type) * ggml_type_size(type);
@@ -298,7 +298,7 @@ int main(int argc, char * argv[]) {
                 for (size_t size : params.test_sizes) {
                     printf("    %zu values (%.2f MB)\n", size, 4*size/(float)(1024*1024));
                     auto quantize_fn = [&](void ) {
-                        qfns.quantize_row_q(test_data1, test_q1, size);
+                        qfns.from_float(test_data1, test_q1, size);
                         return test_q1[0];
                     };
                     size_t quantized_size = size / ggml_blck_size(type) * ggml_type_size(type);
@@ -309,11 +309,11 @@ int main(int argc, char * argv[]) {
 
             if (params.op_dequantize_row_q) {
                 printf("  dequantize_row_q\n");
-                qfns.quantize_row_q(test_data1, test_q1, largest);
+                qfns.from_float(test_data1, test_q1, largest);
                 for (size_t size : params.test_sizes) {
                     printf("    %zu values (%.2f MB)\n", size, 4*size/(float)(1024*1024));
                     auto quantize_fn = [&](void ) {
-                        qfns.dequantize_row_q(test_q1, test_out, size);
+                        qfns.to_float(test_q1, test_out, size);
                         return test_out[0];
                     };
                     size_t quantized_size = size / ggml_blck_size(type) * ggml_type_size(type);
@@ -327,7 +327,8 @@ int main(int argc, char * argv[]) {
                 for (size_t size : params.test_sizes) {
                     printf("    %zu values (%.2f MB)\n", size, 4*size/(float)(1024*1024));
                     auto quantize_fn = [&](void ) {
-                        qfns.quantize_row_q_dot(test_data1, test_q1, size);
+                        auto vdot = ggml_internal_get_type_traits(qfns.vec_dot_type);
+                        vdot.from_float(test_data1, test_q1, size);
                         return test_q1[0];
                     };
                     size_t quantized_size = size / ggml_blck_size(type) * ggml_type_size(type);
@@ -338,13 +339,13 @@ int main(int argc, char * argv[]) {
 
             if (params.op_vec_dot_q) {
                 printf("  vec_dot_q\n");
-                qfns.quantize_row_q(test_data1, test_q1, largest);
-                qfns.quantize_row_q(test_data2, test_q2, largest);
+                qfns.from_float(test_data1, test_q1, largest);
+                qfns.from_float(test_data2, test_q2, largest);
                 for (size_t size : params.test_sizes) {
                     printf("    %zu values (%.2f MB)\n", size, 4*size/(float)(1024*1024));
                     auto quantize_fn = [&](void ) {
                         float result;
-                        qfns.vec_dot_q(size, &result, test_q1, test_q2);
+                        qfns.vec_dot(size, &result, test_q1, test_q2);
                         return result;
                     };
                     size_t quantized_size = size / ggml_blck_size(type) * ggml_type_size(type);