convert : refactor vocab selection logic (#6355 )

llava : fix MobileVLM (#6364 )
* fix empty bug * Update MobileVLM-README.md added more results on devices * Update MobileVLM-README.md * Update MobileVLM-README.md * Update MobileVLM-README.md * Update MobileVLM-README.md * Update MobileVLM-README.md * Update MobileVLM-README.md * Update examples/llava/MobileVLM-README.md Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update MobileVLM-README.md remove gguf links --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2026-07-14 00:15:54 +02:00 · 2024-03-28 11:44:36 -04:00 · 2024-03-28 16:33:10 +02:00 · 2024-03-28 14:05:54 +02:00 · 2024-03-28 11:27:56 +01:00 · 2024-03-28 09:51:06 +01:00
32 changed files with 4054 additions and 3084 deletions
@@ -24,7 +24,7 @@
    useOpenCL
    useRocm
    useVulkan
-  ],
+  ] && blas.meta.available,
  useCuda ? config.cudaSupport,
  useMetalKit ? stdenv.isAarch64 && stdenv.isDarwin && !useOpenCL,
  useMpi ? false, # Increases the runtime closure size by ~700M
@@ -67,10 +67,15 @@ let
    strings.optionalString (suffices != [ ])
      ", accelerated with ${strings.concatStringsSep ", " suffices}";

+  executableSuffix = effectiveStdenv.hostPlatform.extensions.executable;
+
  # TODO: package the Python in this repository in a Nix-like way.
  # It'd be nice to migrate to buildPythonPackage, as well as ensure this repo
  # is PEP 517-compatible, and ensure the correct .dist-info is generated.
  # https://peps.python.org/pep-0517/
+  #
+  # TODO: Package up each Python script or service appropriately, by making
+  # them into "entrypoints"
  llama-python = python3.withPackages (
    ps: [
      ps.numpy
@@ -159,11 +164,6 @@ effectiveStdenv.mkDerivation (
        --replace '[bundle pathForResource:@"ggml-metal" ofType:@"metal"];' "@\"$out/bin/ggml-metal.metal\";"
      substituteInPlace ./ggml-metal.m \
        --replace '[bundle pathForResource:@"default" ofType:@"metallib"];' "@\"$out/bin/default.metallib\";"
-
-      # TODO: Package up each Python script or service appropriately.
-      # If we were to migrate to buildPythonPackage and prepare the `pyproject.toml`,
-      # we could make those *.py into setuptools' entrypoints
-      substituteInPlace ./*.py --replace "/usr/bin/env python" "${llama-python}/bin/python"
    '';

    # With PR#6015 https://github.com/ggerganov/llama.cpp/pull/6015,
@@ -244,8 +244,8 @@ effectiveStdenv.mkDerivation (
    # TODO(SomeoneSerge): It's better to add proper install targets at the CMake level,
    # if they haven't been added yet.
    postInstall = ''
-      mv $out/bin/main $out/bin/llama
-      mv $out/bin/server $out/bin/llama-server
+      mv $out/bin/main${executableSuffix} $out/bin/llama${executableSuffix}
+      mv $out/bin/server${executableSuffix} $out/bin/llama-server${executableSuffix}
      mkdir -p $out/include
      cp $src/llama.h $out/include/
    '';
@@ -0,0 +1,280 @@
+# Benchmark
+name: Benchmark
+
+on:
+  workflow_dispatch:
+    inputs:
+      gpu-series:
+        description: 'Azure GPU series to run with'
+        required: true
+        type: choice
+        options:
+          - Standard_NC4as_T4_v3
+          - Standard_NC24ads_A100_v4
+          - Standard_NC80adis_H100_v5
+      sha:
+        description: 'Commit SHA1 to build'
+        required: false
+        type: string
+      duration:
+        description: 'Duration of the bench'
+        type: string
+        default: 10m
+
+  push:
+    branches:
+      - master
+    paths: ['.github/workflows/bench.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'examples/server/bench/**.*']
+  pull_request:
+    types: [opened, synchronize, reopened]
+    paths: ['.github/workflows/bench.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'examples/server/bench/**.*']
+  schedule:
+    -  cron: '04 2 * * *'
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: true
+
+jobs:
+  bench-server-baseline:
+    runs-on: Standard_NC4as_T4_v3
+    env:
+      RUNNER_LABEL: Standard_NC4as_T4_v3 # FIXME Do not find a way to not duplicate it
+      N_USERS: 8
+      DURATION: 10m
+    if: ${{ github.event.inputs.gpu-series == 'Standard_NC4as_T4_v3' || github.event.schedule || github.event.pull_request || github.head_ref == 'master' || github.ref_name == 'master' || github.event.push.ref == 'refs/heads/master' }}
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v3
+        with:
+          fetch-depth: 0
+          ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}
+
+      - name: Install python env
+        id: pipenv
+        run: |
+          cd examples/server/bench
+          python3 -m venv venv
+          source venv/bin/activate
+          pip install -r requirements.txt
+
+      - name: Prometheus
+        id: install_prometheus
+        run: |
+          wget --quiet https://github.com/prometheus/prometheus/releases/download/v2.51.0/prometheus-2.51.0.linux-amd64.tar.gz
+          tar xzf prometheus*.tar.gz --strip-components=1
+          ./prometheus --config.file=examples/server/bench/prometheus.yml &
+          while ! nc -z localhost 9090; do
+            sleep 0.1
+          done
+
+      - name: Install k6
+        id: k6_installation
+        run: |
+          cd examples/server/bench
+          wget --quiet https://github.com/grafana/k6/releases/download/v0.49.0/k6-v0.49.0-linux-amd64.tar.gz
+          tar xzf k6*.tar.gz --strip-components=1
+
+      - name: Build
+        id: cmake_build
+        run: |
+          set -eux
+          mkdir build
+          cd build
+          cmake .. \
+              -DLLAMA_NATIVE=OFF \
+              -DLLAMA_BUILD_SERVER=ON \
+              -DLLAMA_CURL=ON \
+              -DLLAMA_CUBLAS=ON \
+              -DCUDAToolkit_ROOT=/usr/local/cuda \
+              -DCMAKE_CUDA_COMPILER=/usr/local/cuda/bin/nvcc \
+              -DCMAKE_CUDA_ARCHITECTURES=75 \
+              -DLLAMA_FATAL_WARNINGS=OFF \
+              -DLLAMA_ALL_WARNINGS=OFF \
+              -DCMAKE_BUILD_TYPE=Release;
+          cmake --build . --config Release -j $(nproc) --target server
+
+      - name: Download the dataset
+        id: download_dataset
+        run: |
+          cd examples/server/bench
+          wget --quiet https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
+
+      - name: Server bench
+        id: server_bench
+        run: |
+          set -eux
+
+          cd examples/server/bench
+          source venv/bin/activate
+          BENCH_K6_BIN_PATH=./k6 python bench.py \
+              --runner-label ${{ env.RUNNER_LABEL }} \
+              --name ${{ github.job }} \
+              --branch ${{ github.head_ref || github.ref_name }} \
+              --commit ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha }} \
+              --scenario script.js \
+              --duration ${{ github.event.inputs.duration || env.DURATION }} \
+              --hf-repo ggml-org/models	 \
+              --hf-file phi-2/ggml-model-q4_0.gguf \
+              --model-path-prefix /models \
+              --parallel ${{ env.N_USERS }} \
+              -ngl 33 \
+              --batch-size 2048 \
+              --ubatch-size	256 \
+              --ctx-size 16384 \
+              --n-prompts 1000 \
+              --max-prompt-tokens 1024 \
+              --max-tokens 2048
+
+          cat results.github.env >> $GITHUB_ENV
+
+          # Remove dataset as we do not want it in the artefact
+          rm ShareGPT_V3_unfiltered_cleaned_split.json
+
+      - uses: actions/upload-artifact@v4
+        with:
+          name: benchmark-results
+          compression-level: 9
+          path: |
+            examples/server/bench/*.jpg
+            examples/server/bench/*.json
+            examples/server/bench/*.log
+
+      - name: Commit status
+        uses: Sibz/github-status-action@v1
+        continue-on-error: true # If not authorized on external repo
+        with:
+          authToken: ${{secrets.GITHUB_TOKEN}}
+          sha: ${{ inputs.sha || github.event.pull_request.head.sha || github.sha }}
+          context: bench-server-baseline
+          description: |
+            ${{ env.BENCH_RESULTS }}
+          state: 'success'
+
+      - name: Upload benchmark images
+        uses: devicons/public-upload-to-imgur@v2.2.2
+        continue-on-error: true # Important as it looks unstable: 503
+        id: imgur_step
+        with:
+          client_id: ${{secrets.IMGUR_CLIENT_ID}}
+          path: |
+            examples/server/bench/prompt_tokens_seconds.jpg
+            examples/server/bench/predicted_tokens_seconds.jpg
+            examples/server/bench/kv_cache_usage_ratio.jpg
+            examples/server/bench/requests_processing.jpg
+
+      - name: Extract mermaid
+        id: set_mermaid
+        run: |
+          set -eux
+
+          cd examples/server/bench
+          PROMPT_TOKENS_SECONDS=$(cat prompt_tokens_seconds.mermaid)
+          echo "PROMPT_TOKENS_SECONDS<<EOF" >> $GITHUB_ENV
+          echo "$PROMPT_TOKENS_SECONDS" >> $GITHUB_ENV
+          echo "EOF" >> $GITHUB_ENV
+
+          PREDICTED_TOKENS_SECONDS=$(cat predicted_tokens_seconds.mermaid)
+          echo "PREDICTED_TOKENS_SECONDS<<EOF" >> $GITHUB_ENV
+          echo "$PREDICTED_TOKENS_SECONDS" >> $GITHUB_ENV
+          echo "EOF" >> $GITHUB_ENV
+
+          KV_CACHE_USAGE_RATIO=$(cat kv_cache_usage_ratio.mermaid)
+          echo "KV_CACHE_USAGE_RATIO<<EOF" >> $GITHUB_ENV
+          echo "$KV_CACHE_USAGE_RATIO" >> $GITHUB_ENV
+          echo "EOF" >> $GITHUB_ENV
+
+          REQUESTS_PROCESSING=$(cat requests_processing.mermaid)
+          echo "REQUESTS_PROCESSING<<EOF" >> $GITHUB_ENV
+          echo "$REQUESTS_PROCESSING" >> $GITHUB_ENV
+          echo "EOF" >> $GITHUB_ENV
+
+      - name: Extract image url
+        id: extract_image_url
+        continue-on-error: true
+        run: |
+          set -eux
+
+          echo "IMAGE_O=${{ fromJSON(steps.imgur_step.outputs.imgur_urls)[0] }}" >> $GITHUB_ENV
+          echo "IMAGE_1=${{ fromJSON(steps.imgur_step.outputs.imgur_urls)[1] }}" >> $GITHUB_ENV
+          echo "IMAGE_2=${{ fromJSON(steps.imgur_step.outputs.imgur_urls)[2] }}" >> $GITHUB_ENV
+          echo "IMAGE_3=${{ fromJSON(steps.imgur_step.outputs.imgur_urls)[3] }}" >> $GITHUB_ENV
+
+      - name: Comment PR
+        uses: mshick/add-pr-comment@v2
+        id: comment_pr
+        if: ${{ github.event.pull_request != '' }}
+        with:
+          message-id: bench-${{ github.job }}-${{ env.RUNNER_LABEL }}
+          message: |
+            📈 **llama.cpp server** for _${{ github.job }}_ on _${{ env.RUNNER_LABEL }}_: **${{ env.BENCH_ITERATIONS}} iterations** 🚀
+
+            - Concurrent users: ${{ env.N_USERS }}, duration: ${{ github.event.inputs.duration || env.DURATION }}
+            - HTTP request          : avg=${{ env.HTTP_REQ_DURATION_AVG }}ms        p(90)=${{ env.HTTP_REQ_DURATION_P_90_ }}ms fails=${{ env.HTTP_REQ_FAILED_PASSES }}, finish reason: stop=${{ env.LLAMACPP_COMPLETIONS_STOP_RATE_PASSES }} truncated=${{ env.LLAMACPP_COMPLETIONS_TRUNCATED_RATE_PASSES }}
+            - Prompt processing (pp): avg=${{ env.LLAMACPP_PROMPT_TOKENS_AVG }}tk/s p(90)=${{ env.LLAMACPP_PROMPT_TOKENS_P_90_ }}tk/s **total=${{ env.LLAMACPP_PROMPT_TOKENS_TOTAL_COUNTER_RATE }}tk/s**
+            - Token generation  (tg): avg=${{ env.LLAMACPP_TOKENS_SECOND_AVG }}tk/s p(90)=${{ env.LLAMACPP_TOKENS_SECOND_P_90_ }}tk/s **total=${{ env.LLAMACPP_COMPLETION_TOKENS_TOTAL_COUNTER_RATE }}tk/s**
+            - ${{ env.BENCH_GRAPH_XLABEL }}
+
+            <details>
+
+            <summary>Time series</summary>
+
+            <p align="center">
+
+            <img width="100%" height="100%" src="${{ env.IMAGE_O }}" alt="prompt_tokens_seconds" />
+
+            <details>
+
+            <summary>More</summary>
+
+            ```mermaid
+            ${{ env.PROMPT_TOKENS_SECONDS }}
+            ```
+
+            </details>
+
+            <img width="100%" height="100%" src="${{ env.IMAGE_1 }}" alt="predicted_tokens_seconds"/>
+
+            <details>
+                <summary>More</summary>
+
+            ```mermaid
+            ${{ env.PREDICTED_TOKENS_SECONDS }}
+            ```
+
+            </details>
+
+            </p>
+
+            <details>
+
+            <summary>Details</summary>
+
+            <p align="center">
+
+            <img width="100%" height="100%" src="${{ env.IMAGE_2 }}" alt="kv_cache_usage_ratio" />
+
+            <details>
+                <summary>More</summary>
+
+            ```mermaid
+            ${{ env.KV_CACHE_USAGE_RATIO }}
+            ```
+
+            </details>
+
+            <img width="100%" height="100%" src="${{ env.IMAGE_3 }}" alt="requests_processing"/>
+
+            <details>
+                <summary>More</summary>
+
+            ```mermaid
+            ${{ env.REQUESTS_PROCESSING }}
+            ```
+
+            </details>
+
+            </p>
+            </details>
+            </details>
@@ -840,7 +840,7 @@ jobs:
        id: pack_artifacts
        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
        run: |
-          7z a llama-${{ steps.tag.outputs.name }}-bin-win-sycl-x64.zip .\build\bin\*
+          7z a llama-${{ steps.tag.outputs.name }}-bin-win-sycl-x64.zip ./build/bin/*

      - name: Upload artifacts
        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
@@ -556,6 +556,7 @@ ifdef LLAMA_CUDA_NO_PEER_COPY
 endif # LLAMA_CUDA_NO_PEER_COPY
 	OBJS        += ggml-cuda.o
 	OBJS        += $(patsubst %.cu,%.o,$(wildcard ggml-cuda/*.cu))
+
 ggml-cuda.o: ggml-cuda.cu ggml-cuda.h ggml.h ggml-backend.h ggml-backend-impl.h ggml-common.h $(wildcard ggml-cuda/*.cuh)
 	$(HIPCC) $(CXXFLAGS) $(HIPFLAGS) -x hip -c -o $@ $<

@@ -148,6 +148,7 @@ Typically finetunes of the base models below are supported as well.
 - Java: [kherud/java-llama.cpp](https://github.com/kherud/java-llama.cpp)
 - Zig: [deins/llama.cpp.zig](https://github.com/Deins/llama.cpp.zig)
 - Flutter/Dart: [netdur/llama_cpp_dart](https://github.com/netdur/llama_cpp_dart)
+- PHP (API bindings and features built on top of llama.cpp): [distantmagic/resonance](https://github.com/distantmagic/resonance) [(more info)](https://github.com/ggerganov/llama.cpp/pull/6326)

 **UI:**

@@ -1062,8 +1062,8 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
        params.ignore_eos = true;
        return true;
    }
-    if (arg == "--no-penalize-nl") {
-        sparams.penalize_nl = false;
+    if (arg == "--penalize-nl") {
+        sparams.penalize_nl = true;
        return true;
    }
    if (arg == "-l" || arg == "--logit-bias") {
@@ -1373,7 +1373,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
    printf("  -dt N, --defrag-thold N\n");
    printf("                        KV cache defragmentation threshold (default: %.1f, < 0 - disabled)\n", params.defrag_thold);
    printf("  --ignore-eos          ignore end of stream token and continue generating (implies --logit-bias 2-inf)\n");
-    printf("  --no-penalize-nl      do not penalize newline token\n");
+    printf("  --penalize-nl         penalize newline tokens\n");
    printf("  --temp N              temperature (default: %.1f)\n", (double)sparams.temp);
    printf("  --all-logits          return logits for all tokens in the batch (default: disabled)\n");
    printf("  --hellaswag           compute HellaSwag score over random tasks from datafile supplied with -f\n");
@@ -2489,7 +2489,7 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l
    fprintf(stream, "n_predict: %d # default: -1 (unlimited)\n", params.n_predict);
    fprintf(stream, "n_probs: %d # only used by server binary, default: 0\n", sparams.n_probs);
    fprintf(stream, "no_mmap: %s # default: false\n", !params.use_mmap ? "true" : "false");
-    fprintf(stream, "no_penalize_nl: %s # default: false\n", !sparams.penalize_nl ? "true" : "false");
+    fprintf(stream, "penalize_nl: %s # default: false\n", sparams.penalize_nl ? "true" : "false");
    fprintf(stream, "ppl_output_type: %d # default: 0\n", params.ppl_output_type);
    fprintf(stream, "ppl_stride: %d # default: 0\n", params.ppl_stride);
    fprintf(stream, "presence_penalty: %f # default: 0.0\n", sparams.penalty_present);
@@ -23,7 +23,7 @@ if 'NO_LOCAL_GGUF' not in os.environ:
    sys.path.insert(1, str(Path(__file__).parent / 'gguf-py'))
 import gguf

-from convert import HfVocab
+from convert import LlamaHfVocab


 ###### MODEL DEFINITIONS ######
@@ -230,7 +230,7 @@ class Model(ABC):
    def _set_vocab_gpt2(self):
        dir_model = self.dir_model
        hparams = self.hparams
-        tokens: list[bytearray] = []
+        tokens: list[str] = []
        toktypes: list[int] = []

        from transformers import AutoTokenizer
@@ -243,8 +243,7 @@ class Model(ABC):

        for i in range(vocab_size):
            if i not in reverse_vocab:
-                pad_token = f"[PAD{i}]".encode('utf-8')
-                tokens.append(bytearray(pad_token))
+                tokens.append(f"[PAD{i}]")
                toktypes.append(gguf.TokenType.USER_DEFINED)
            elif reverse_vocab[i] in added_vocab:
                tokens.append(reverse_vocab[i])
@@ -266,7 +265,7 @@ class Model(ABC):
    def _set_vocab_qwen(self):
        dir_model = self.dir_model
        hparams = self.hparams
-        tokens: list[bytearray] = []
+        tokens: list[str] = []
        toktypes: list[int] = []

        from transformers import AutoTokenizer
@@ -291,8 +290,7 @@ class Model(ABC):

        for i in range(vocab_size):
            if i not in reverse_vocab:
-                pad_token = f"[PAD{i}]".encode("utf-8")
-                tokens.append(bytearray(pad_token))
+                tokens.append(f"[PAD{i}]")
                toktypes.append(gguf.TokenType.USER_DEFINED)
            elif reverse_vocab[i] in added_vocab:
                tokens.append(reverse_vocab[i])
@@ -372,12 +370,8 @@ class Model(ABC):
        special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
        special_vocab.add_to_gguf(self.gguf_writer)

-    def _set_vocab_hf(self):
-        path = self.dir_model
-        added_tokens_path = self.dir_model
-        vocab = HfVocab(
-            path, added_tokens_path if added_tokens_path.exists() else None
-        )
+    def _set_vocab_llama_hf(self):
+        vocab = LlamaHfVocab(self.dir_model)
        tokens = []
        scores = []
        toktypes = []
@@ -1099,7 +1093,7 @@ class MiniCPMModel(Model):
        self.gguf_writer.add_file_type(self.ftype)

    def set_vocab(self):
-        self._set_vocab_hf()
+        self._set_vocab_llama_hf()

    def _reverse_hf_permute(self, weights: Tensor, n_head: int, n_kv_head: int | None = None) -> Tensor:
        if n_kv_head is not None and n_head != n_kv_head:
@@ -1700,11 +1694,8 @@ class BertModel(Model):
            self.gguf_writer.add_pooling_type(pooling_type)

    def set_vocab(self):
-        path = self.dir_model
-        added_tokens_path = self.dir_model if self.dir_model.exists() else None
-
        # use huggingface vocab to get all tokens
-        vocab = HfVocab(path, added_tokens_path)
+        vocab = LlamaHfVocab(self.dir_model, ignore_nonllama=True)
        tokens, scores, toktypes = zip(*vocab.all_tokens())
        assert len(tokens) == vocab.vocab_size
        self.vocab_size = vocab.vocab_size
@@ -106,12 +106,12 @@ def main():
    tensor_map = gguf.get_tensor_name_map(arch, block_count)
    print(tensor_map)
    for name in tensors.keys():
-        data = tensors[name]
+        data_torch = tensors[name]
        if name.endswith(".self_attention.rotary_emb.inv_freq"):
            continue
-        old_dtype = data.dtype
+        old_dtype = data_torch.dtype
        # TODO: FP16 conversion produces garbage outputs. (Q8_0 does not, so..?)
-        data = data.to(torch.float32).squeeze().numpy()
+        data = data_torch.to(torch.float32).squeeze().numpy()
        new_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias"))
        if new_name is None:
            print("Can not map tensor '" + name + "'")
@@ -16,13 +16,14 @@ import re
 import signal
 import struct
 import sys
+import textwrap
 import time
 import zipfile
-from abc import ABCMeta, abstractmethod
+from abc import ABC, abstractmethod
 from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor
 from dataclasses import dataclass
 from pathlib import Path
-from typing import IO, TYPE_CHECKING, Any, Callable, Iterable, Literal, TypeVar
+from typing import TYPE_CHECKING, Any, Callable, ClassVar, IO, Iterable, Literal, Protocol, TypeVar, runtime_checkable

 import numpy as np
 from sentencepiece import SentencePieceProcessor
@@ -43,6 +44,9 @@ ARCH = gguf.MODEL_ARCH.LLAMA

 DEFAULT_CONCURRENCY = 8

+ADDED_TOKENS_FILE = 'added_tokens.json'
+FAST_TOKENIZER_FILE = 'tokenizer.json'
+
 #
 # data types
 #
@@ -188,8 +192,10 @@ class Params:
            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.")
+            msg = """\
+                failed to guess 'n_layer'. This model is unknown or unsupported.
+                Suggestion: provide 'config.json' of the model in the same directory containing model files."""
+            raise KeyError(textwrap.dedent(msg))

        n_head = n_embd // 128 # guessed
        n_mult = 256           # guessed
@@ -211,7 +217,8 @@ class Params:

    @staticmethod
    def loadHFTransformerJson(model: LazyModel, config_path: Path) -> Params:
-        config = json.load(open(config_path))
+        with open(config_path) as f:
+            config = json.load(f)

        rope_scaling_type = f_rope_scale = n_orig_ctx = rope_finetuned = None
        rope_scaling = config.get("rope_scaling")
@@ -233,8 +240,10 @@ class Params:
        elif "max_position_embeddings" in config:
            n_ctx = config["max_position_embeddings"]
        else:
-            raise Exception("failed to guess 'n_ctx'. This model is unknown or unsupported.\n"
-                            "Suggestion: provide 'config.json' of the model in the same directory containing model files.")
+            msg = """\
+                failed to guess 'n_ctx'. This model is unknown or unsupported.
+                Suggestion: provide 'config.json' of the model in the same directory containing model files."""
+            raise KeyError(textwrap.dedent(msg))

        n_experts      = None
        n_experts_used = None
@@ -265,7 +274,8 @@ class Params:
    # {"dim": 8192, "multiple_of": 4096, "ffn_dim_multiplier": 1.3, "n_heads": 64, "n_kv_heads": 8, "n_layers": 80, "norm_eps": 1e-05, "vocab_size": -1}
    @staticmethod
    def loadOriginalParamsJson(model: LazyModel, config_path: Path) -> Params:
-        config = json.load(open(config_path))
+        with open(config_path) as f:
+            config = json.load(f)

        n_experts      = None
        n_experts_used = None
@@ -331,47 +341,86 @@ class Params:
 # vocab
 #

-class BpeVocab:
+@runtime_checkable
+class BaseVocab(Protocol):
+    tokenizer_model: ClassVar[str]
+    name: ClassVar[str]
+
+
+class NoVocab(BaseVocab):
+    tokenizer_model = "no_vocab"
+    name = "no_vocab"
+
+    def __repr__(self) -> str:
+        return "<NoVocab for a model without integrated vocabulary>"
+
+
+@runtime_checkable
+class Vocab(BaseVocab, Protocol):
+    vocab_size: int
+    added_tokens_dict: dict[str, int]
+    added_tokens_list: list[str]
+    fname_tokenizer: Path
+
+    def __init__(self, base_path: Path): ...
+    def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: ...
+
+
+class BpeVocab(Vocab):
    tokenizer_model = "gpt2"
    name = "bpe"

-    def __init__(self, fname_tokenizer: Path, fname_added_tokens: Path | None) -> None:
-        self.bpe_tokenizer = json.loads(open(str(fname_tokenizer), encoding="utf-8").read())
-        if isinstance(self.bpe_tokenizer.get('model'), dict):
-            self.vocab = self.bpe_tokenizer["model"]["vocab"]
-        else:
-            self.vocab = self.bpe_tokenizer
-        added_tokens: dict[str, int]
-        if fname_added_tokens is not None:
-            # FIXME: Verify that added tokens here _cannot_ overlap with the main vocab.
-            added_tokens = json.load(open(fname_added_tokens, encoding="utf-8"))
-        else:
-            # Fall back to trying to find the added tokens in tokenizer.json
-            tokenizer_json_file = fname_tokenizer.parent / 'tokenizer.json'
-            if not tokenizer_json_file.is_file():
-                added_tokens = {}
-            else:
-                tokenizer_json = json.load(open(tokenizer_json_file, encoding="utf-8"))
-                added_tokens = dict(
-                    (item['content'], item['id'])
-                    for item in tokenizer_json.get('added_tokens', [])
-                    # Added tokens here can be duplicates of the main vocabulary.
-                    if item['content'] not in self.bpe_tokenizer)
+    def __init__(self, base_path: Path):
+        added_tokens: dict[str, int] = {}

-        vocab_size: int = len(self.vocab)
-        expected_ids    = list(range(vocab_size, vocab_size + len(added_tokens)))
-        actual_ids      = sorted(added_tokens.values())
+        if (fname_tokenizer := base_path / 'vocab.json').exists():
+            # "slow" tokenizer
+            with open(fname_tokenizer, encoding="utf-8") as f:
+                self.vocab = json.load(f)
+
+            try:
+                # FIXME: Verify that added tokens here _cannot_ overlap with the main vocab.
+                with open(base_path / ADDED_TOKENS_FILE, encoding="utf-8") as f:
+                    added_tokens = json.load(f)
+            except FileNotFoundError:
+                pass
+        else:
+            # "fast" tokenizer
+            fname_tokenizer = base_path / FAST_TOKENIZER_FILE
+
+            # if this fails, FileNotFoundError propagates to caller
+            with open(fname_tokenizer, encoding="utf-8") as f:
+                tokenizer_json = json.load(f)
+
+            tokenizer_model: dict[str, Any] = tokenizer_json['model']
+            if (
+                tokenizer_model['type'] != 'BPE' or tokenizer_model.get('byte_fallback', False)
+                or tokenizer_json['decoder']['type'] != 'ByteLevel'
+            ):
+                raise FileNotFoundError('Cannot find GPT-2 BPE tokenizer')
+
+            self.vocab = tokenizer_model["vocab"]
+
+            if (added := tokenizer_json.get('added_tokens')) is not None:
+                # Added tokens here can be duplicates of the main vocabulary.
+                added_tokens = {item['content']: item['id']
+                                for item in added
+                                if item['content'] not in self.vocab}
+
+        vocab_size   = len(self.vocab)
+        expected_ids = list(range(vocab_size, vocab_size + len(added_tokens)))
+        actual_ids   = sorted(added_tokens.values())
        if expected_ids != actual_ids:
            expected_end_id = vocab_size + len(actual_ids) - 1
-            raise Exception(f"Expected the {len(actual_ids)} added token ID(s) to be sequential in the range {vocab_size} - {expected_end_id}; got {actual_ids}")
+            raise ValueError(f"Expected the {len(actual_ids)} added token ID(s) to be sequential in the range "
+                             f"{vocab_size} - {expected_end_id}; got {actual_ids}")

        items = sorted(added_tokens.items(), key=lambda text_idx: text_idx[1])
        self.added_tokens_dict    = added_tokens
        self.added_tokens_list    = [text for (text, idx) in items]
-        self.vocab_size_base: int = vocab_size
-        self.vocab_size: int      = self.vocab_size_base + len(self.added_tokens_list)
+        self.vocab_size_base      = vocab_size
+        self.vocab_size           = self.vocab_size_base + len(self.added_tokens_list)
        self.fname_tokenizer      = fname_tokenizer
-        self.fname_added_tokens   = fname_added_tokens

    def bpe_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
        reverse_vocab = {id: encoded_tok for encoded_tok, id in self.vocab.items()}
@@ -392,19 +441,25 @@ class BpeVocab:
        return f"<BpeVocab with {self.vocab_size_base} base tokens and {len(self.added_tokens_list)} added tokens>"


-class SentencePieceVocab:
+class SentencePieceVocab(Vocab):
    tokenizer_model = "llama"
    name = "spm"

-    def __init__(self, fname_tokenizer: Path, fname_added_tokens: Path | None) -> None:
-        self.sentencepiece_tokenizer = SentencePieceProcessor(str(fname_tokenizer))
-        added_tokens: dict[str, int]
-        if fname_added_tokens is not None:
-            added_tokens = json.load(open(fname_added_tokens, encoding="utf-8"))
-        else:
-            added_tokens = {}
+    def __init__(self, base_path: Path):
+        added_tokens: dict[str, int] = {}
+        if (fname_tokenizer := base_path / 'tokenizer.model').exists():
+            # normal location
+            try:
+                with open(base_path / ADDED_TOKENS_FILE, encoding="utf-8") as f:
+                    added_tokens = json.load(f)
+            except FileNotFoundError:
+                pass
+        elif not (fname_tokenizer := base_path.parent / 'tokenizer.model').exists():
+            # not found in alternate location either
+            raise FileNotFoundError('Cannot find tokenizer.model')

-        vocab_size: int = self.sentencepiece_tokenizer.vocab_size()
+        self.sentencepiece_tokenizer = SentencePieceProcessor(str(fname_tokenizer))
+        vocab_size = self.sentencepiece_tokenizer.vocab_size()

        new_tokens       = {id: piece for piece, id in added_tokens.items() if id >= vocab_size}
        expected_new_ids = list(range(vocab_size, vocab_size + len(new_tokens)))
@@ -414,18 +469,17 @@ class SentencePieceVocab:
            raise ValueError(f"Expected new token IDs {expected_new_ids} to be sequential; got {actual_new_ids}")

        # Token pieces that were added to the base vocabulary.
-        self.added_tokens_dict = added_tokens
+        self.added_tokens_dict  = added_tokens
        self.added_tokens_list  = [new_tokens[id] for id in actual_new_ids]
        self.vocab_size_base    = vocab_size
        self.vocab_size         = self.vocab_size_base + len(self.added_tokens_list)
        self.fname_tokenizer    = fname_tokenizer
-        self.fname_added_tokens = fname_added_tokens

    def sentencepiece_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
        tokenizer = self.sentencepiece_tokenizer
        for i in range(tokenizer.vocab_size()):
            piece = tokenizer.id_to_piece(i)
-            text: bytes = piece.encode("utf-8")
+            text         = piece.encode("utf-8")
            score: float = tokenizer.get_score(i)

            toktype = gguf.TokenType.NORMAL
@@ -458,27 +512,42 @@ class SentencePieceVocab:
        return f"<SentencePieceVocab with {self.vocab_size_base} base tokens and {len(self.added_tokens_list)} added tokens>"


-class HfVocab:
+class LlamaHfVocab(Vocab):
    tokenizer_model = "llama"
    name = "hfft"

-    def __init__(self, fname_tokenizer: Path, fname_added_tokens: Path | None = None) -> None:
+    def __init__(self, base_path: Path, ignore_nonllama: bool = False):
+        fname_tokenizer = base_path / FAST_TOKENIZER_FILE
+        # if this fails, FileNotFoundError propagates to caller
+        with open(fname_tokenizer, encoding='utf-8') as f:
+            tokenizer_json = json.load(f)
+
+        # pre-check so we know if we need transformers
+        tokenizer_model: dict[str, Any] = tokenizer_json['model']
+        if ignore_nonllama:
+            pass  # workaround incorrect use of this class for WordPiece
+        elif (
+            tokenizer_model['type'] != 'BPE' or not tokenizer_model.get('byte_fallback', False)
+            or tokenizer_json['decoder']['type'] != 'Sequence'
+        ):
+            raise FileNotFoundError('Cannot find Llama BPE tokenizer')
+
        try:
            from transformers import AutoTokenizer
        except ImportError as e:
            raise ImportError(
-                "To use HfVocab, please install the `transformers` package. "
+                "To use LlamaHfVocab, please install the `transformers` package. "
                "You can install it with `pip install transformers`."
            ) from e

-        print("fname_tokenizer:", fname_tokenizer)
        # Allow the tokenizer to default to slow or fast versions.
        # Explicitly set tokenizer to use local paths.
        self.tokenizer = AutoTokenizer.from_pretrained(
-            fname_tokenizer,
-            cache_dir=fname_tokenizer,
+            base_path,
+            cache_dir=base_path,
            local_files_only=True,
        )
+        assert self.tokenizer.is_fast  # assume tokenizer.json is used

        # Initialize lists and dictionaries for added tokens
        self.added_tokens_list = []
@@ -506,8 +575,7 @@ class HfVocab:
        self.vocab_size_base = self.tokenizer.vocab_size
        self.vocab_size      = self.vocab_size_base + len(self.added_tokens_list)

-        self.fname_tokenizer    = fname_tokenizer
-        self.fname_added_tokens = fname_added_tokens
+        self.fname_tokenizer = fname_tokenizer

    def hf_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
        reverse_vocab = {
@@ -559,18 +627,7 @@ class HfVocab:
        yield from self.added_tokens()

    def __repr__(self) -> str:
-        return f"<HfVocab with {self.vocab_size_base} base tokens and {len(self.added_tokens_list)} added tokens>"
-
-
-class NoVocab:
-    tokenizer_model = "no_vocab"
-    name = "no_vocab"
-
-    def __repr__(self) -> str:
-        return "<NoVocab for a model without integrated vocabulary>"
-
-
-Vocab: TypeAlias = "BpeVocab | SentencePieceVocab | HfVocab | NoVocab"
+        return f"<LlamaHfVocab with {self.vocab_size_base} base tokens and {len(self.added_tokens_list)} added tokens>"


 #
@@ -588,7 +645,7 @@ def permute(weights: NDArray, n_head: int, n_head_kv: int) -> NDArray:
            .reshape(weights.shape))


-class Tensor(metaclass=ABCMeta):
+class Tensor(ABC):
    data_type: DataType

    @abstractmethod
@@ -610,7 +667,7 @@ def bf16_to_fp32(bf16_arr: np.ndarray[Any, np.dtype[np.uint16]]) -> NDArray:


 class UnquantizedTensor(Tensor):
-    def __init__(self, ndarray: NDArray) -> None:
+    def __init__(self, ndarray: NDArray):
        assert isinstance(ndarray, np.ndarray)
        self.ndarray = ndarray
        self.data_type = NUMPY_TYPE_TO_DATA_TYPE[ndarray.dtype]
@@ -689,7 +746,7 @@ class ModelPlus:
    model: LazyModel
    paths: list[Path]  # Where this was read from.
    format: Literal['ggml', 'torch', 'safetensors', 'none']
-    vocab: Vocab | None  # For GGML models (which have vocab built in), the vocab.
+    vocab: BaseVocab | None  # For GGML models (which have vocab built in), the vocab.


 def merge_sharded(models: list[LazyModel]) -> LazyModel:
@@ -698,7 +755,7 @@ def merge_sharded(models: list[LazyModel]) -> LazyModel:
    names = {name: None for model in models for name in model}

    def convert(name: str) -> LazyTensor:
-        lazy_tensors: list[LazyTensor] = [model[name] for model in models]
+        lazy_tensors = [model[name] for model in models]
        if len(lazy_tensors) == 1:
            # only one file; don't go through this procedure since there might
            # be quantized tensors
@@ -719,7 +776,7 @@ def merge_sharded(models: list[LazyModel]) -> LazyModel:

        def load() -> UnquantizedTensor:
            ndarrays = [load_unquantized(tensor) for tensor in lazy_tensors]
-            concatenated: NDArray = np.concatenate(ndarrays, axis=axis)
+            concatenated = np.concatenate(ndarrays, axis=axis)
            return UnquantizedTensor(concatenated)
        description = 'concatenated[[' + '] | ['.join(lt.description for lt in lazy_tensors) + ']]'
        return LazyTensor(load, concatenated_shape, lazy_tensors[0].data_type, description)
@@ -807,10 +864,10 @@ class LazyUnpickler(pickle.Unpickler):

        def load(offset: int, elm_count: int) -> NDArray:
            dtype = data_type.dtype
-            fp = self.zip_file.open(info)
-            fp.seek(offset * dtype.itemsize)
-            size = elm_count * dtype.itemsize
-            data = fp.read(size)
+            with self.zip_file.open(info) as fp:
+                fp.seek(offset * dtype.itemsize)
+                size = elm_count * dtype.itemsize
+                data = fp.read(size)
            assert len(data) == size
            return np.frombuffer(data, dtype)
        description = f'storage data_type={data_type} path-in-zip={filename} path={self.zip_file.filename}'
@@ -831,7 +888,7 @@ class LazyUnpickler(pickle.Unpickler):
    def rebuild_from_type_v2(func, new_type, args, state):
        return func(*args)

-    CLASSES: dict[tuple[str, str], Any] = {
+    CLASSES = {
        # getattr used here as a workaround for mypy not being smart enough to determine
        # the staticmethods have a __func__ attribute.
        ('torch._tensor', '_rebuild_from_type_v2'): getattr(rebuild_from_type_v2, '__func__'),
@@ -890,7 +947,7 @@ def lazy_load_safetensors_file(fp: IO[bytes], path: Path) -> ModelPlus:
 def must_read(fp: IO[bytes], length: int) -> bytes:
    ret = fp.read(length)
    if len(ret) < length:
-        raise Exception("unexpectedly reached end of file")
+        raise EOFError("unexpectedly reached end of file")
    return ret


@@ -948,13 +1005,14 @@ def bounded_parallel_map(func: Callable[[In], Out], iterable: Iterable[In], conc
            yield result


-def check_vocab_size(params: Params, vocab: Vocab, pad_vocab: bool = False) -> None:
+def check_vocab_size(params: Params, vocab: BaseVocab, pad_vocab: bool = False) -> None:
    # Handle special case where the model's vocab size is not set
    if params.n_vocab == -1:
        raise ValueError(
-            f"The model's vocab size is set to -1 in params.json. Please update it manually.{f' Maybe {vocab.vocab_size}?' if hasattr(vocab, 'vocab_size') else ''}"
+            "The model's vocab size is set to -1 in params.json. Please update it manually."
+            + (f" Maybe {vocab.vocab_size}?" if isinstance(vocab, Vocab) else ""),
        )
-    if isinstance(vocab, NoVocab):
+    if not isinstance(vocab, Vocab):
        return  # model has no vocab

    # Check for a vocab size mismatch
@@ -979,11 +1037,11 @@ def check_vocab_size(params: Params, vocab: Vocab, pad_vocab: bool = False) -> N
    if vocab.vocab_size < params.n_vocab:
        msg += " Add the --pad-vocab option and try again."

-    raise Exception(msg)
+    raise ValueError(msg)


 class OutputFile:
-    def __init__(self, fname_out: Path, endianess:gguf.GGUFEndian = gguf.GGUFEndian.LITTLE) -> None:
+    def __init__(self, fname_out: Path, endianess:gguf.GGUFEndian = gguf.GGUFEndian.LITTLE):
        self.gguf = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH], endianess=endianess)

    def add_meta_arch(self, params: Params) -> None:
@@ -1034,8 +1092,6 @@ class OutputFile:
            self.gguf.add_file_type(params.ftype)

    def extract_vocabulary_from_model(self, vocab: Vocab) -> tuple[list[bytes], list[float], list[gguf.TokenType]]:
-        assert not isinstance(vocab, NoVocab)
-
        tokens = []
        scores = []
        toktypes = []
@@ -1135,7 +1191,7 @@ class OutputFile:

    @staticmethod
    def write_all(
-        fname_out: Path, ftype: GGMLFileType, params: Params, model: LazyModel, vocab: Vocab, svocab: gguf.SpecialVocab,
+        fname_out: Path, ftype: GGMLFileType, params: Params, model: LazyModel, vocab: BaseVocab, svocab: gguf.SpecialVocab,
        concurrency: int = DEFAULT_CONCURRENCY, endianess: gguf.GGUFEndian = gguf.GGUFEndian.LITTLE,
        pad_vocab: bool = False,
    ) -> None:
@@ -1145,11 +1201,11 @@ class OutputFile:

        # meta data
        of.add_meta_arch(params)
-        if isinstance(vocab, NoVocab):
-            of.gguf.add_tokenizer_model(vocab.tokenizer_model)
-        else:
+        if isinstance(vocab, Vocab):
            of.add_meta_vocab(vocab)
            of.add_meta_special_vocab(svocab)
+        else:  # NoVocab
+            of.gguf.add_tokenizer_model(vocab.tokenizer_model)

        # tensor info
        for name, lazy_tensor in model.items():
@@ -1176,7 +1232,7 @@ def pick_output_type(model: LazyModel, output_type_str: str | None) -> GGMLFileT

    name_to_type = {name: lazy_tensor.data_type for (name, lazy_tensor) in model.items()}

-    raise Exception(f"Unexpected combination of types: {name_to_type}")
+    raise ValueError(f"Unexpected combination of types: {name_to_type}")


 def convert_to_output_type(model: LazyModel, output_type: GGMLFileType) -> LazyModel:
@@ -1186,7 +1242,7 @@ def convert_to_output_type(model: LazyModel, output_type: GGMLFileType) -> LazyM

 def convert_model_names(model: LazyModel, params: Params, skip_unknown: bool) -> LazyModel:
    tmap = gguf.TensorNameMap(ARCH, params.n_layer)
-    should_skip: set[gguf.MODEL_TENSOR] = set(gguf.MODEL_TENSOR_SKIP.get(ARCH, []))
+    should_skip = set(gguf.MODEL_TENSOR_SKIP.get(ARCH, []))

    tmp = model

@@ -1213,8 +1269,7 @@ def convert_model_names(model: LazyModel, params: Params, skip_unknown: bool) ->
            if skip_unknown:
                print(f"Unexpected tensor name: {name} - skipping")
                continue
-            else:
-                raise Exception(f"Unexpected tensor name: {name}. Use --skip-unknown to ignore it (e.g. LLaVA)")
+            raise ValueError(f"Unexpected tensor name: {name}. Use --skip-unknown to ignore it (e.g. LLaVA)")

        if tensor_type in should_skip:
            print(f"skipping tensor {name_new}")
@@ -1231,7 +1286,7 @@ def nth_multifile_path(path: Path, n: int) -> Path | None:
    the nth path in the model.
    '''
    # Support the following patterns:
-    patterns: list[tuple[str, str]] = [
+    patterns = [
        # - x.00.pth, x.01.pth, etc.
        (r'\.[0-9]{2}\.pth$', f'.{n:02}.pth'),
        # - x-00001-of-00002.bin, x-00002-of-00002.bin, etc.
@@ -1277,9 +1332,9 @@ def load_some_model(path: Path) -> ModelPlus:
            globs = ["consolidated.00.pth", "pytorch_model-00001-of-*.bin", "*.pt", "pytorch_model.bin"]
            files = [file for glob in globs for file in path.glob(glob)]
        if not files:
-            raise Exception(f"Can't find model in directory {path}")
+            raise FileNotFoundError(f"Can't find model in directory {path}")
        if len(files) > 1:
-            raise Exception(f"Found multiple models in {path}, not sure which to pick: {files}")
+            raise ValueError(f"Found multiple models in {path}, not sure which to pick: {files}")
        path = files[0]

    paths = find_multifile_paths(path)
@@ -1293,36 +1348,14 @@ def load_some_model(path: Path) -> ModelPlus:


 class VocabFactory:
-    _FILES = {"spm": "tokenizer.model", "bpe": "vocab.json", "hfft": "tokenizer.json"}
+    _VOCAB_CLASSES: list[type[Vocab]] = [SentencePieceVocab, BpeVocab, LlamaHfVocab]

    def __init__(self, path: Path):
        self.path = path
-        self.file_paths = self._detect_files()
-        print(f"Found vocab files: {self.file_paths}")

-    def _detect_files(self) -> dict[str, Path | None]:
-        def locate(file: str) -> Path | None:
-            if (path := self.path / file).exists():
-                return path
-            if (path := self.path.parent / file).exists():
-                return path
-            return None
-
-        return {vt: locate(f) for vt, f in self._FILES.items()}
-
-    def _select_file(self, vocab_types: list[str]) -> tuple[str, Path]:
-        for vtype in vocab_types:
-            try:
-                path = self.file_paths[vtype]
-            except KeyError:
-                raise ValueError(f"Unsupported vocabulary type {vtype}") from None
-            if path is not None:
-                return vtype, path
-        raise FileNotFoundError(f"Could not find any of {[self._FILES[vt] for vt in vocab_types]}")
-
-    def _create_special_vocab(self, vocab: Vocab, model_parent_path: Path) -> gguf.SpecialVocab:
+    def _create_special_vocab(self, vocab: BaseVocab, model_parent_path: Path) -> gguf.SpecialVocab:
        load_merges = vocab.name == "bpe"
-        n_vocab = vocab.vocab_size if hasattr(vocab, "vocab_size") else None
+        n_vocab = vocab.vocab_size if isinstance(vocab, Vocab) else None
        return gguf.SpecialVocab(
            model_parent_path,
            load_merges=load_merges,
@@ -1331,27 +1364,29 @@ class VocabFactory:
        )

    def _create_vocab_by_path(self, vocab_types: list[str]) -> Vocab:
-        vocab_type, path = self._select_file(vocab_types)
-        print(f"Loading vocab file {path!r}, type {vocab_type!r}")
+        vocab_classes: dict[str, type[Vocab]] = {cls.name: cls for cls in self._VOCAB_CLASSES}
+        selected_vocabs: dict[str, type[Vocab]] = {}
+        for vtype in vocab_types:
+            try:
+                selected_vocabs[vtype] = vocab_classes[vtype]
+            except KeyError:
+                raise ValueError(f"Unsupported vocabulary type {vtype}") from None

-        added_tokens_path = path.parent / "added_tokens.json"
-        if vocab_type == "bpe":
-            return BpeVocab(
-                path, added_tokens_path if added_tokens_path.exists() else None
-            )
-        if vocab_type == "spm":
-            return SentencePieceVocab(
-                path, added_tokens_path if added_tokens_path.exists() else None
-            )
-        if vocab_type == "hfft":
-            return HfVocab(
-                path.parent, added_tokens_path if added_tokens_path.exists() else None
-            )
-        raise ValueError(vocab_type)
+        for vtype, cls in selected_vocabs.items():
+            try:
+                vocab = cls(self.path)
+                break
+            except FileNotFoundError:
+                pass  # ignore unavailable tokenizers
+        else:
+            raise FileNotFoundError(f"Could not find a tokenizer matching any of {vocab_types}")

-    def load_vocab(self, vocab_types: list[str], model_parent_path: Path) -> tuple[Vocab, gguf.SpecialVocab]:
-        vocab: Vocab
-        if len(vocab_types) == 1 and "no_vocab" in vocab_types:
+        print(f"Loaded vocab file {vocab.fname_tokenizer!r}, type {vocab.name!r}")
+        return vocab
+
+    def load_vocab(self, vocab_types: list[str] | None, model_parent_path: Path) -> tuple[BaseVocab, gguf.SpecialVocab]:
+        vocab: BaseVocab
+        if vocab_types is None:
            vocab = NoVocab()
        else:
            vocab = self._create_vocab_by_path(vocab_types)
@@ -1408,10 +1443,8 @@ def main(args_in: list[str] | None = None) -> None:
    parser.add_argument("--skip-unknown", action="store_true",    help="skip unknown tensor names instead of failing")

    args = parser.parse_args(args_in)
-    if args.no_vocab:
-        if args.vocab_only:
-            raise ValueError("no need to specify --vocab-only if using --no-vocab")
-        args.vocab_type = "no_vocab"
+    if args.no_vocab and args.vocab_only:
+        raise ValueError("--vocab-only does not make sense with --no-vocab")

    if args.dump_single:
        model_plus = lazy_load_file(args.model)
@@ -1433,10 +1466,12 @@ def main(args_in: list[str] | None = None) -> None:
    params = Params.load(model_plus)
    if params.n_ctx == -1:
        if args.ctx is None:
-            raise Exception("The model doesn't have a context size, and you didn't specify one with --ctx\n"
-                            "Please specify one with --ctx:\n"
-                            " - LLaMA v1: --ctx 2048\n"
-                            " - LLaMA v2: --ctx 4096\n")
+            msg = """\
+                The model doesn't have a context size, and you didn't specify one with --ctx
+                Please specify one with --ctx:
+                 - LLaMA v1: --ctx 2048
+                 - LLaMA v2: --ctx 4096"""
+            parser.error(textwrap.dedent(msg))
        params.n_ctx = args.ctx

    if args.outtype:
@@ -1451,9 +1486,11 @@ def main(args_in: list[str] | None = None) -> None:
    model_parent_path = model_plus.paths[0].parent
    vocab_path = Path(args.vocab_dir or args.model or model_parent_path)
    vocab_factory = VocabFactory(vocab_path)
-    vocab, special_vocab = vocab_factory.load_vocab(args.vocab_type.split(","), model_parent_path)
+    vocab_types = None if args.no_vocab else args.vocab_type.split(",")
+    vocab, special_vocab = vocab_factory.load_vocab(vocab_types, model_parent_path)

    if args.vocab_only:
+        assert isinstance(vocab, Vocab)
        if not args.outfile:
            raise ValueError("need --outfile if using --vocab-only")
        outfile = args.outfile
@@ -880,7 +880,7 @@ int main(int argc, char ** argv) {
    TransformerWeights weights = {};
    {
        LOG("%s: Loading llama2c model from %s\n", __func__, params.fn_llama2c_model);
-        FILE *file = fopen(params.fn_llama2c_model, "r");
+        FILE * file = fopen(params.fn_llama2c_model, "rb");
        if (!file) {
            LOG("%s: Unable to open the checkpoint file %s!\n", __func__, params.fn_llama2c_model);
            return 1;
@@ -178,25 +178,27 @@ int main(int argc, char ** argv) {
    float * out = emb + p * n_embd;
    batch_decode(ctx, batch, out, s, n_embd);

-    // print the first part of the embeddings
+    // print the first part of the embeddings or for a single prompt, the full embedding
    fprintf(stdout, "\n");
    for (int j = 0; j < n_prompts; j++) {
        fprintf(stdout, "embedding %d: ", j);
-        for (int i = 0; i < std::min(16, n_embd); i++) {
+        for (int i = 0; i < (n_prompts > 1 ? std::min(16, n_embd) : n_embd); i++) {
            fprintf(stdout, "%9.6f ", emb[j * n_embd + i]);
        }
        fprintf(stdout, "\n");
    }

    // print cosine similarity matrix
-    fprintf(stdout, "\n");
-    printf("cosine similarity matrix:\n\n");
-    for (int i = 0; i < n_prompts; i++) {
-        for (int j = 0; j < n_prompts; j++) {
-            float sim = llama_embd_similarity_cos(emb + i * n_embd, emb + j * n_embd, n_embd);
-            fprintf(stdout, "%6.2f ", sim);
-        }
+    if (n_prompts > 1) {
        fprintf(stdout, "\n");
+        printf("cosine similarity matrix:\n\n");
+        for (int i = 0; i < n_prompts; i++) {
+            for (int j = 0; j < n_prompts; j++) {
+                float sim = llama_embd_similarity_cos(emb + i * n_embd, emb + j * n_embd, n_embd);
+                fprintf(stdout, "%6.2f ", sim);
+            }
+            fprintf(stdout, "\n");
+        }
    }

    // clean up
@@ -6,7 +6,7 @@ for more information, please go to [Meituan-AutoML/MobileVLM](https://github.com

 The implementation is based on llava, and is compatible with llava and mobileVLM. The usage is basically same as llava.

-Notice: The overall process of model inference for both **MobileVLM** and **MobileVLM_V2** models is the same, but the process of model conversion  is a little different. Therefore, using MobiVLM as an example, the different conversion step will be shown.
+Notice: The overall process of model inference for both **MobileVLM** and **MobileVLM_V2** models is the same, but the process of model conversion is a little different. Therefore, using **MobileVLM-1.7B** as an example, the different conversion step will be shown.

 ## Usage
 Build with cmake or run `make llava-cli` to build it.
@@ -36,7 +36,7 @@ git clone https://huggingface.co/openai/clip-vit-large-patch14-336
 python ./examples/llava/llava-surgery.py -m path/to/MobileVLM-1.7B
 ```

-3. Use `convert-image-encoder-to-gguf.py` with `--projector-type ldp` (for **V2** the arg is `--projector-type ldpv2`) to convert the LLaVA image encoder to GGUF:
+3. Use `convert-image-encoder-to-gguf.py` with `--projector-type ldp` (for **V2** please use `--projector-type ldpv2`) to convert the LLaVA image encoder to GGUF:

 ```sh
 python ./examples/llava/convert-image-encoder-to-gguf \
@@ -78,7 +78,7 @@ cd examples/llava/android/build_64
 ### run on Android
 refer to `android/adb_run.sh`, modify resources' `name` and `path`

-## some result on Android with `Snapdragon 888` chip
+## Some result on Android with `Snapdragon 888` chip
 ### case 1
 **input**
 ```sh
@@ -109,7 +109,6 @@ llama_print_timings:       total time =   34731.93 ms
    --image /data/local/tmp/cat.jpeg \
    -p "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: <image>\nWhat is in the image? ASSISTANT:"
 ```
-
 **output**
 ```sh
 encode_image_with_clip: image encoded in 21149.51 ms by CLIP (  146.87 ms per image patch)
@@ -121,12 +120,82 @@ llama_print_timings:        eval time =    1279.03 ms /    18 runs   (   71.06 m
 llama_print_timings:       total time =   34570.79 ms
 ```

+
+## Some result on Android with `Snapdragon 778G` chip
+### MobileVLM-1.7B case
+#### llava-cli release-b2005
+**input**
+```sh
+/data/local/tmp/llava-cli \
+    -m /data/local/tmp/ggml-model-q4_k.gguf \
+    --mmproj /data/local/tmp/mmproj-model-f16.gguf \
+    -t 4 \
+    --image /data/local/tmp/many_llamas.jpeg \
+    -p "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: <image>\nWhat's that? ASSISTANT:"
+```
+**output**
+```sh
+encode_image_with_clip: image encoded in 18728.52 ms by CLIP (  130.06 ms per image patch)
+system_prompt: A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER:
+user_prompt: \nWhat's that? ASSISTANT:
+
+ A group of llamas are standing in a green pasture.
+
+llama_print_timings:        load time =   20357.33 ms
+llama_print_timings:      sample time =       2.96 ms /    14 runs   (    0.21 ms per token,  4734.53 tokens per second)
+llama_print_timings: prompt eval time =    8119.49 ms /   191 tokens (   42.51 ms per token,    23.52 tokens per second)
+llama_print_timings:        eval time =    1005.75 ms /    14 runs   (   71.84 ms per token,    13.92 tokens per second)
+llama_print_timings:       total time =   28038.34 ms /   205 tokens
+```
+#### llava-cli latest-version
+**input**
+
+Just the same as above.
+
+**output**(seems to be much slower)
+```sh
+encode_image_with_clip: image embedding created: 144 tokens
+
+encode_image_with_clip: image encoded in 288268.88 ms by CLIP ( 2001.87 ms per image patch)
+system_prompt: A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER:
+user_prompt: \nWhat's that? ASSISTANT:
+
+ It is a group of sheep standing together in a grass field.
+
+llama_print_timings:        load time =  818120.91 ms
+llama_print_timings:      sample time =       3.44 ms /    14 runs   (    0.25 ms per token,  4067.40 tokens per second)
+llama_print_timings: prompt eval time =  529274.69 ms /   191 tokens ( 2771.07 ms per token,     0.36 tokens per second)
+llama_print_timings:        eval time =   43894.02 ms /    13 runs   ( 3376.46 ms per token,     0.30 tokens per second)
+llama_print_timings:       total time =  865441.76 ms /   204 tokens
+```
+### MobileVLM_V2-1.7B case
+#### llava-cli release-2005b
+**input**
+
+Just the same as above.
+
+**output**
+```sh
+encode_image_with_clip: image encoded in 20609.61 ms by CLIP (  143.12 ms per image patch)
+system_prompt: A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER:
+user_prompt: \nWhat's that? ASSISTANT:
+
+ This image captures a lively scene of 20 llamas in motion on an expansive, grassy field. The llama is scattered across the landscape with some standing and others sitting down as if taking rest or observing their surroundings from different vantage points within this verdant setting.
+
+The background offers glimpses into a picturesque town nestled amidst hills under an overcast sky, adding depth to the scene while also emphasizing that distance between these llama and human-made structures like houses or roads in which they roam freely without any barriers around them. The image is framed by text at both right angles on white backgrounds against a contrasting blue backdrop with green foliage, further drawing attention to the llamas amidst their natural habitat while also inviting viewers into this picturesque landscape within town limits of Alta Llama
+
+llama_print_timings:        load time =   22406.77 ms
+llama_print_timings:      sample time =      49.26 ms /   186 runs   (    0.26 ms per token,  3776.27 tokens per second)
+llama_print_timings: prompt eval time =    9044.54 ms /   191 tokens (   47.35 ms per token,    21.12 tokens per second)
+llama_print_timings:        eval time =   14497.49 ms /   186 runs   (   77.94 ms per token,    12.83 tokens per second)
+llama_print_timings:       total time =   44411.01 ms /   377 tokens
+```
+
 ## Orin compile and run
 ### compile
 ```sh
 make LLAMA_CUDA=1 CUDA_DOCKER_ARCH=sm_87 LLAMA_CUDA_F16=1 -j 32
 ```
-
 ### run on Orin
 ### case 1
 **input**
@@ -175,8 +244,121 @@ llama_print_timings:        eval time =     166.65 ms /    11 runs   (   15.15 m
 llama_print_timings:       total time =    1365.47 ms /   243 tokens
 ```

-## Minor shortcomings
-The `n_patch` of output in `ldp` is 1/4 of the input. In order to implement quickly, we uniformly modified `clip_n_patches` function to a quarter. when counting the time consumption, the calculated time will be 4 times bigger than the real cost.
+## Running on Intel(R) Core(TM) i7-10750H
+### Operating system
+Ubuntu22.04
+### compile
+```sh
+make -j32
+```
+### MobileVLM-1.7B case
+**input**
+```sh
+-m /path/to/ggml-model-q4_k.gguf \
+    --mmproj /path/to/mmproj-model-f16.gguf \
+    --image /path/to/many_llamas.jpeg
+    -p "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: <image>\nWhat's that? ASSISTANT:" \
+```
+**output**
+```sh
+encode_image_with_clip: image embedding created: 144 tokens
+
+encode_image_with_clip: image encoded in  2730.94 ms by CLIP (   18.96 ms per image patch)
+system_prompt: A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER:
+user_prompt: \nWhat's that?ASSISTANT:
+
+ A group of llamas are walking together in a field.
+
+llama_print_timings:        load time =    5506.60 ms
+llama_print_timings:      sample time =       0.44 ms /    13 runs   (    0.03 ms per token, 29545.45 tokens per second)
+llama_print_timings: prompt eval time =    2031.58 ms /   190 tokens (   10.69 ms per token,    93.52 tokens per second)
+llama_print_timings:        eval time =     438.92 ms /    12 runs   (   36.58 ms per token,    27.34 tokens per second)
+llama_print_timings:       total time =    5990.25 ms /   202 tokens
+```
+
+### MobileVLM_V2-1.7B case
+**input**
+
+Just the same as above.
+
+**ouput**
+```sh
+encode_image_with_clip: image embedding created: 144 tokens
+
+encode_image_with_clip: image encoded in  3223.89 ms by CLIP (   22.39 ms per image patch)
+system_prompt: A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER:
+user_prompt: \nWhat's that?ASSISTANT:
+
+ The image captures a tranquil scene in a park, where a group of approximately 20 llamas are gathered. The llamas, a mix of white and black, are standing in a line, their black and white patterns contrasting with the lush green grass of the park. The lamas are arranged in a line, suggesting a social order.
+
+The park itself is lush and green, with trees dotting the landscape in the background. A sign reading "Llamas Tico  Ana" is also visible in the image, possibly indicating the location or the breed of the llamas. The image seems to be taken from a distance, providing a wide view of the scene and the surrounding environment.
+
+The llamas' positions relative to each other, the sign, and the trees create a harmonious composition. The image does not contain any discernible text. The overall scene is one of peace and natural beauty, with the llamas in their natural habitat, surrounded by the vibrant colors and lush greenery of the park.
+
+llama_print_timings:        load time =    6642.61 ms
+llama_print_timings:      sample time =       8.15 ms /   223 runs   (    0.04 ms per token, 27358.61 tokens per second)
+llama_print_timings: prompt eval time =    2475.07 ms /   190 tokens (   13.03 ms per token,    76.77 tokens per second)
+llama_print_timings:        eval time =    8760.60 ms /   222 runs   (   39.46 ms per token,    25.34 tokens per second)
+llama_print_timings:       total time =   15513.95 ms /   412 tokens
+```
+
+## Run on Intel(R) Core(TM) Ultra7 115H
+### operation system
+Windows11
+### comiple
+```sh
+make -j32
+```
+### MobileVLM-1.7B case
+**input**
+```sh
+-m /path/to/ggml-model-q4_k.gguf \
+    --mmproj /path/to/tmp/mmproj-model-f16.gguf \
+    -p "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: <image>\nWhat's that? ASSISTANT:" \
+```
+**output**
+```sh
+encode_image_with_clip: image encoded in  4902.81 ms by CLIP (   34.05 ms per image patch)
+system_prompt: A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER:
+user_prompt: \nWhat's that? ASSISTANT:
+
+ The image features a group of brown and white llamas standing in a grassy field.
+
+llama_print_timings:        load time =    7441.06 ms
+llama_print_timings:      sample time =       0.72 ms /    19 runs   (    0.04 ms per token, 26279.39 tokens per second)
+llama_print_timings: prompt eval time =    2090.71 ms /   191 tokens (   10.95 ms per token,    91.36 tokens per second)
+llama_print_timings:        eval time =     512.35 ms /    18 runs   (   28.46 ms per token,    35.13 tokens per second)
+llama_print_timings:       total time =    7987.23 ms /   209 tokens
+```
+
+### MobileVLM_V2-1.7B case
+**input**
+
+Just the same as above.
+
+**output**
+```sh
+encode_image_with_clip: image encoded in  4682.44 ms by CLIP (   32.52 ms per image patch)
+system_prompt: A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER:
+user_prompt: \nWhat's that? ASSISTANT:
+
+ This image captures a lively scene of a group of 14 llamas in a grassy field. The llamas, with their distinctive black and white coats, are standing and walking in a line, seemingly engaged in a social activity. One
+ of them, possibly the first in the line, has its back turned, perhaps observing something in the distance.
+
+The llama in the front of the line stands out due to its black and white coloring, which is quite unusual for llama patterns. The llama in the front also seems to be more aware of its surroundings, as it faces the camera, giving a sense of engagement with the viewer.
+
+The image is taken from the side of the llama, providing a clear view of the llama in the front and its companions. The lameness in the llama in
+ front is not visible, indicating that it might not be the main focus of the photo.
+
+The background of the image features a grassy field, with a fence and a tree visible in the distance. The tree appears to be bare, suggesting that it might be during a time of year when most trees are dormant or have shed their leaves.
+
+
+llama_print_timings:        load time =    7015.35 ms
+llama_print_timings:      sample time =      10.61 ms /   256 runs   (    0.04 ms per token, 24119.09 tokens per second)
+llama_print_timings: prompt eval time =    2052.45 ms /   191 tokens (   10.75 ms per token,    93.06 tokens per second)
+llama_print_timings:        eval time =    7259.43 ms /   255 runs   (   28.47 ms per token,    35.13 tokens per second)
+llama_print_timings:       total time =   14371.19 ms /   446 tokens
+```

 ## TODO

@@ -191,5 +373,5 @@ The `n_patch` of output in `ldp` is 1/4 of the input. In order to implement quic

 ## contributor
 ```sh
-zhangjidong05, yangyang260, huyiming03, chenxiaotao03
+zhangjidong05, yangyang260, huyiming03, chenxiaotao03, ZiangWu-77
 ```
@@ -835,9 +835,10 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
            mlp_2 = ggml_pool_2d(ctx0, mlp_2, GGML_OP_POOL_AVG, 2, 2, 2, 2, 0, 0);
            // weight ne = [3, 3, 2048, 1]
            struct ggml_tensor * peg_0 = ggml_conv_depthwise_2d(ctx0, model.mm_model_peg_0_w, mlp_2, 1, 1, 1, 1, 1, 1);
-            peg_0 = ggml_add(ctx0, peg_0, mlp_2);
            peg_0 = ggml_cont(ctx0, ggml_permute(ctx0, peg_0, 1, 2, 0, 3));
            peg_0 = ggml_add(ctx0, peg_0, model.mm_model_peg_0_b);
+            mlp_2 = ggml_cont(ctx0, ggml_permute(ctx0, mlp_2, 1, 2, 0, 3));
+            peg_0 = ggml_add(ctx0, peg_0, mlp_2);
            peg_0 = ggml_reshape_3d(ctx0, peg_0, peg_0->ne[0], peg_0->ne[1] * peg_0->ne[2], peg_0->ne[3]);
            embeddings = peg_0;
        }
@@ -1755,7 +1756,7 @@ int clip_n_patches(const struct clip_ctx * ctx) {

    int n_patches = (params.image_size / params.patch_size) * (params.image_size / params.patch_size);

-    if (ctx->proj_type == PROJECTOR_TYPE_LDP) {
+    if (ctx->proj_type == PROJECTOR_TYPE_LDP || ctx->proj_type == PROJECTOR_TYPE_LDPV2) {
        n_patches /= 4;
    }

@@ -296,7 +296,9 @@ These options help improve the performance and memory usage of the LLaMA models.

 ### Batch Size

-   `-b N, --batch-size N`: Set the batch size for prompt processing (default: 512). This large batch size benefits users who have BLAS installed and enabled it during the build. If you don't have BLAS enabled ("BLAS=0"), you can use a smaller number, such as 8, to see the prompt progress as it's evaluated in some situations.
+-   `-b N, --batch-size N`: Set the batch size for prompt processing (default: `2048`). This large batch size benefits users who have BLAS installed and enabled it during the build. If you don't have BLAS enabled ("BLAS=0"), you can use a smaller number, such as 8, to see the prompt progress as it's evaluated in some situations.
+
+- `-ub N`, `--ubatch-size N`: physical maximum batch size. This is for pipeline parallelization. Default: `512`.

 ### Prompt Caching

@@ -0,0 +1,303 @@
+import argparse
+import json
+import os
+import re
+import signal
+import socket
+import subprocess
+import sys
+import threading
+import time
+import traceback
+from contextlib import closing
+from datetime import datetime
+
+import matplotlib
+import matplotlib.dates
+import matplotlib.pyplot as plt
+import requests
+
+
+def main(args_in: list[str] | None = None) -> None:
+    parser = argparse.ArgumentParser(description="Start server benchmark scenario")
+    parser.add_argument("--name", type=str, help="Bench name", required=True)
+    parser.add_argument("--runner-label", type=str, help="Runner label", required=True)
+    parser.add_argument("--branch", type=str, help="Branch name", default="detached")
+    parser.add_argument("--commit", type=str, help="Commit name", default="dirty")
+    parser.add_argument("--host", type=str, help="Server listen host", default="0.0.0.0")
+    parser.add_argument("--port", type=int, help="Server listen host", default="8080")
+    parser.add_argument("--model-path-prefix", type=str, help="Prefix where to store the model files", default="models")
+    parser.add_argument("--n-prompts", type=int,
+                        help="SERVER_BENCH_N_PROMPTS: total prompts to randomly select in the benchmark", required=True)
+    parser.add_argument("--max-prompt-tokens", type=int,
+                        help="SERVER_BENCH_MAX_PROMPT_TOKENS: maximum prompt tokens to filter out in the dataset",
+                        required=True)
+    parser.add_argument("--max-tokens", type=int,
+                        help="SERVER_BENCH_MAX_CONTEXT: maximum context size of the completions request to filter out in the dataset: prompt + predicted tokens",
+                        required=True)
+    parser.add_argument("--hf-repo", type=str, help="Hugging Face model repository", required=True)
+    parser.add_argument("--hf-file", type=str, help="Hugging Face model file", required=True)
+    parser.add_argument("-ngl", "--n-gpu-layers", type=int, help="layers to the GPU for computation", required=True)
+    parser.add_argument("--ctx-size", type=int, help="Set the size of the prompt context", required=True)
+    parser.add_argument("--parallel", type=int, help="Set the number of slots for process requests", required=True)
+    parser.add_argument("--batch-size", type=int, help="Set the batch size for prompt processing", required=True)
+    parser.add_argument("--ubatch-size", type=int, help="physical maximum batch size", required=True)
+    parser.add_argument("--scenario", type=str, help="Scenario to run", required=True)
+    parser.add_argument("--duration", type=str, help="Bench scenario", required=True)
+
+    args = parser.parse_args(args_in)
+
+    start_time = time.time()
+
+    # Start the server and performance scenario
+    try:
+        server_process = start_server(args)
+    except Exception:
+        print("bench: server start error :")
+        traceback.print_exc(file=sys.stdout)
+        sys.exit(1)
+
+    # start the benchmark
+    try:
+        start_benchmark(args)
+
+        iterations = 0
+        with open("results.github.env", 'w') as github_env:
+            # parse output
+            with open('k6-results.json', 'r') as bench_results:
+                # Load JSON data from file
+                data = json.load(bench_results)
+                for metric_name in data['metrics']:
+                    for metric_metric in data['metrics'][metric_name]:
+                        value = data['metrics'][metric_name][metric_metric]
+                        if isinstance(value, float) or isinstance(value, int):
+                            value = round(value, 2)
+                            data['metrics'][metric_name][metric_metric]=value
+                            github_env.write(
+                                f"{escape_metric_name(metric_name)}_{escape_metric_name(metric_metric)}={value}\n")
+                token_seconds = data['metrics']['llamacpp_tokens_second']['avg']
+                iterations = data['root_group']['checks']['success completion']['passes']
+
+    except Exception:
+        print("bench: error :")
+        traceback.print_exc(file=sys.stdout)
+
+    # Stop the server
+    if server_process:
+        try:
+            print(f"bench: shutting down server pid={server_process.pid} ...")
+            if os.name == 'nt':
+                interrupt = signal.CTRL_C_EVENT
+            else:
+                interrupt = signal.SIGINT
+            server_process.send_signal(interrupt)
+            server_process.wait(0.5)
+
+        except subprocess.TimeoutExpired:
+            print(f"server still alive after 500ms, force-killing pid={server_process.pid} ...")
+            server_process.kill()  # SIGKILL
+            server_process.wait()
+
+        while is_server_listening(args.host, args.port):
+            time.sleep(0.1)
+
+    title = (f"llama.cpp {args.name} on {args.runner_label}\n "
+             f"duration={args.duration} {iterations} iterations")
+    xlabel = (f"{args.hf_repo}/{args.hf_file}\n"
+              f"parallel={args.parallel} ctx-size={args.ctx_size} ngl={args.n_gpu_layers} batch-size={args.batch_size} ubatch-size={args.ubatch_size} pp={args.max_prompt_tokens} pp+tg={args.max_tokens}\n"
+              f"branch={args.branch} commit={args.commit}")
+
+    # Prometheus
+    end_time = time.time()
+    if is_server_listening("0.0.0.0", 9090):
+        metrics = ['prompt_tokens_seconds', 'predicted_tokens_seconds',
+                   'kv_cache_usage_ratio', 'requests_processing', 'requests_deferred']
+
+        for metric in metrics:
+            resp = requests.get(f"http://localhost:9090/api/v1/query_range",
+                                params={'query': 'llamacpp:' + metric, 'start': start_time, 'end': end_time, 'step': 2})
+
+            with open(f"{metric}.json", 'w') as metric_json:
+                metric_json.write(resp.text)
+
+            if resp.status_code != 200:
+                print(f"bench: unable to extract prometheus metric {metric}: {resp.text}")
+            else:
+                metric_data = resp.json()
+                values = metric_data['data']['result'][0]['values']
+                timestamps, metric_values = zip(*values)
+                metric_values = [float(value) for value in metric_values]
+                timestamps_dt = [datetime.fromtimestamp(int(ts)) for ts in timestamps]
+                plt.figure(figsize=(16, 10), dpi=80)
+                plt.plot(timestamps_dt, metric_values, label=metric)
+                plt.xticks(rotation=0, fontsize=14, horizontalalignment='center', alpha=.7)
+                plt.yticks(fontsize=12, alpha=.7)
+
+                ylabel = f"llamacpp:{metric}"
+                plt.title(title,
+                          fontsize=14, wrap=True)
+                plt.grid(axis='both', alpha=.3)
+                plt.ylabel(ylabel, fontsize=22)
+                plt.xlabel(xlabel, fontsize=14, wrap=True)
+                plt.gca().xaxis.set_major_locator(matplotlib.dates.MinuteLocator())
+                plt.gca().xaxis.set_major_formatter(matplotlib.dates.DateFormatter("%Y-%m-%d %H:%M:%S"))
+                plt.gcf().autofmt_xdate()
+
+                # Remove borders
+                plt.gca().spines["top"].set_alpha(0.0)
+                plt.gca().spines["bottom"].set_alpha(0.3)
+                plt.gca().spines["right"].set_alpha(0.0)
+                plt.gca().spines["left"].set_alpha(0.3)
+
+                # Save the plot as a jpg image
+                plt.savefig(f'{metric}.jpg', dpi=60)
+                plt.close()
+
+                # Mermaid format in case images upload failed
+                with (open(f"{metric}.mermaid", 'w') as mermaid_f):
+                    mermaid = (
+                    f"""---
+config:
+    xyChart:
+        titleFontSize: 12
+        width: 900
+        height: 600
+    themeVariables:
+        xyChart:
+            titleColor: "#000000"
+---
+xychart-beta
+    title "{title}"
+    y-axis "llamacpp:{metric}"
+    x-axis "llamacpp:{metric}" {int(min(timestamps))} --> {int(max(timestamps))}
+    line [{', '.join([str(round(float(value), 2)) for value in metric_values])}]
+                    """)
+                    mermaid_f.write(mermaid)
+
+    # 140 chars max for commit status description
+    bench_results = {
+        "req": {
+            "p90": data['metrics']["http_req_duration"]["p(90)"],
+            "avg": data['metrics']["http_req_duration"]["avg"],
+        },
+        "pp": {
+            "p90": data['metrics']["llamacpp_prompt_tokens"]["p(90)"],
+            "avg": data['metrics']["llamacpp_prompt_tokens"]["avg"],
+        },
+        "tg": {
+            "p90": data['metrics']["llamacpp_tokens_second"]["p(90)"],
+            "avg": data['metrics']["llamacpp_tokens_second"]["avg"],
+        },
+    }
+    with open("results.github.env", 'a') as github_env:
+        github_env.write(f"BENCH_RESULTS={json.dumps(bench_results, indent=None, separators=(',', ':') )}\n")
+        github_env.write(f"BENCH_ITERATIONS={iterations}\n")
+
+        title = title.replace('\n', ' ')
+        xlabel = xlabel.replace('\n', ' ')
+        github_env.write(f"BENCH_GRAPH_TITLE={title}\n")
+        github_env.write(f"BENCH_GRAPH_XLABEL={xlabel}\n")
+
+
+def start_benchmark(args):
+    k6_path = 'k6'
+    if 'BENCH_K6_BIN_PATH' in os.environ:
+        k6_path = os.environ['BENCH_K6_BIN_PATH']
+    k6_args = [
+        'run', args.scenario,
+        '--no-color',
+    ]
+    k6_args.extend(['--duration', args.duration])
+    k6_args.extend(['--iterations', args.n_prompts])
+    k6_args.extend(['--vus', args.parallel])
+    k6_args.extend(['--summary-export', 'k6-results.json'])
+    args = f"SERVER_BENCH_N_PROMPTS={args.n_prompts} SERVER_BENCH_MAX_PROMPT_TOKENS={args.max_prompt_tokens} SERVER_BENCH_MAX_CONTEXT={args.max_tokens} "
+    args = args + ' '.join([str(arg) for arg in [k6_path, *k6_args]])
+    print(f"bench: starting k6 with: {args}")
+    k6_completed = subprocess.run(args, shell=True, stdout=sys.stdout, stderr=sys.stderr)
+    if k6_completed.returncode != 0:
+        raise Exception("bench: unable to run k6")
+
+
+def start_server(args):
+    server_process = start_server_background(args)
+
+    attempts = 0
+    max_attempts = 20
+    if 'GITHUB_ACTIONS' in os.environ:
+        max_attempts *= 2
+
+    while not is_server_listening(args.host, args.port):
+        attempts += 1
+        if attempts > max_attempts:
+            assert False, "server not started"
+        print(f"bench:     waiting for server to start ...")
+        time.sleep(0.5)
+
+    print("bench: server started.")
+    return server_process
+
+
+def start_server_background(args):
+    # Start the server
+    server_path = '../../../build/bin/server'
+    if 'LLAMA_SERVER_BIN_PATH' in os.environ:
+        server_path = os.environ['LLAMA_SERVER_BIN_PATH']
+    server_args = [
+        '--host', args.host,
+        '--port', args.port,
+    ]
+    model_file = args.model_path_prefix + os.path.sep + args.hf_file
+    model_dir  = os.path.dirname(model_file)
+    if not os.path.exists(model_dir):
+        os.makedirs(model_dir)
+    server_args.extend(['--model', model_file])
+    server_args.extend(['--hf-repo', args.hf_repo])
+    server_args.extend(['--hf-file', args.hf_file])
+    server_args.extend(['--n-gpu-layers', args.n_gpu_layers])
+    server_args.extend(['--ctx-size', args.ctx_size])
+    server_args.extend(['--parallel', args.parallel])
+    server_args.extend(['--batch-size', args.batch_size])
+    server_args.extend(['--ubatch-size', args.ubatch_size])
+    server_args.extend(['--n-predict', args.max_tokens * 2])
+    server_args.extend(['--defrag-thold', "0.1"])
+    server_args.append('--cont-batching')
+    server_args.append('--metrics')
+    server_args.extend(['--log-format', "text"])
+    args = [str(arg) for arg in [server_path, *server_args]]
+    print(f"bench: starting server with: {' '.join(args)}")
+    pkwargs = {
+        'stdout': subprocess.PIPE,
+        'stderr': subprocess.PIPE
+    }
+    server_process = subprocess.Popen(
+        args,
+        **pkwargs)
+
+    def server_log(in_stream, out_stream):
+        for line in iter(in_stream.readline, b''):
+            print(line.decode('utf-8'), end='', file=out_stream)
+
+    thread_stdout = threading.Thread(target=server_log, args=(server_process.stdout, sys.stdout))
+    thread_stdout.start()
+    thread_stderr = threading.Thread(target=server_log, args=(server_process.stderr, sys.stderr))
+    thread_stderr.start()
+
+    return server_process
+
+
+def is_server_listening(server_fqdn, server_port):
+    with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as sock:
+        result = sock.connect_ex((server_fqdn, server_port))
+        _is_server_listening = result == 0
+        if _is_server_listening:
+            print(f"server is listening on {server_fqdn}:{server_port}...")
+        return _is_server_listening
+
+
+def escape_metric_name(metric_name):
+    return re.sub('[^A-Z0-9]', '_', metric_name.upper())
+
+
+if __name__ == '__main__':
+    main()
@@ -0,0 +1,9 @@
+global:
+  scrape_interval:     10s
+  external_labels:
+    llamacpp: 'server'
+
+scrape_configs:
+  - job_name: 'llama.cpp server'
+    static_configs:
+      - targets: ['localhost:8080']
@@ -0,0 +1,2 @@
+matplotlib
+requests
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+  0x6e, 0x67, 0x73, 0x3b, 0x0a, 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x72,
+  0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x67, 0x65, 0x6e, 0x65, 0x72, 0x61,
+  0x74, 0x69, 0x6f, 0x6e, 0x5f, 0x73, 0x65, 0x74, 0x74, 0x69, 0x6e, 0x67,
+  0x73, 0x3b, 0x0a, 0x7d, 0x0a
 };
-size_t completion_js_len = 5796;
+unsigned int completion_js_len = 5909;
@@ -1928,4 +1928,4 @@ unsigned char index_js[] = {
  0x45, 0x66, 0x66, 0x65, 0x63, 0x74, 0x2c, 0x50, 0x74, 0x20, 0x61, 0x73,
  0x20, 0x75, 0x73, 0x65, 0x53, 0x74, 0x61, 0x74, 0x65, 0x7d, 0x3b, 0x0a
 };
-size_t index_js_len = 23136;
+unsigned int index_js_len = 23136;
@@ -21,6 +21,7 @@ let generation_settings = null;
 //
 export async function* llama(prompt, params = {}, config = {}) {
  let controller = config.controller;
+  const api_url = config.api_url || "";

  if (!controller) {
    controller = new AbortController();
@@ -28,7 +29,7 @@ export async function* llama(prompt, params = {}, config = {}) {

  const completionParams = { ...paramDefaults, ...params, prompt };

-  const response = await fetch("/completion", {
+  const response = await fetch(`${api_url}/completion`, {
    method: 'POST',
    body: JSON.stringify(completionParams),
    headers: {
@@ -193,9 +194,10 @@ export const llamaComplete = async (params, controller, callback) => {
 }

 // Get the model info from the server. This is useful for getting the context window and so on.
-export const llamaModelInfo = async () => {
+export const llamaModelInfo = async (config = {}) => {
  if (!generation_settings) {
-    const props = await fetch("/props").then(r => r.json());
+    const api_url = config.api_url || "";
+    const props = await fetch(`${api_url}/props`).then(r => r.json());
    generation_settings = props.default_generation_settings;
  }
  return generation_settings;
@@ -199,10 +199,10 @@
  <script type="module">
    import {
      html, h, signal, effect, computed, render, useSignal, useEffect, useRef, Component
-    } from '/index.js';
+    } from './index.js';

-    import { llama } from '/completion.js';
-    import { SchemaConverter } from '/json-schema-to-grammar.mjs';
+    import { llama } from './completion.js';
+    import { SchemaConverter } from './json-schema-to-grammar.mjs';
    let selected_image = false;
    var slot_id = -1;

@@ -405,7 +405,7 @@
        throw new Error("already running");
      }
      controller.value = new AbortController();
-      for await (const chunk of llama(prompt, llamaParams, { controller: controller.value })) {
+      for await (const chunk of llama(prompt, llamaParams, { controller: controller.value, api_url: document.baseURI.replace(/\/+$/, '') })) {
        const data = chunk.data;

        if (data.stop) {
@@ -3566,6 +3566,7 @@ int main(int argc, char ** argv) {
    sigemptyset (&sigint_action.sa_mask);
    sigint_action.sa_flags = 0;
    sigaction(SIGINT, &sigint_action, NULL);
+    sigaction(SIGTERM, &sigint_action, NULL);
 #elif defined (_WIN32)
    auto console_ctrl_handler = +[](DWORD ctrl_type) -> BOOL {
        return (ctrl_type == CTRL_C_EVENT) ? (signal_handler(SIGINT), true) : false;
@@ -1114,7 +1114,10 @@ def start_server_background(context):
        server_args.append('--verbose')
    if 'SERVER_LOG_FORMAT_JSON' not in os.environ:
        server_args.extend(['--log-format', "text"])
-    print(f"starting server with: {context.server_path} {server_args}")
+
+    args = [str(arg) for arg in [context.server_path, *server_args]]
+    print(f"bench: starting server with: {' '.join(args)}")
+
    flags = 0
    if 'nt' == os.name:
        flags |= subprocess.DETACHED_PROCESS
@@ -1130,16 +1133,14 @@ def start_server_background(context):
        [str(arg) for arg in [context.server_path, *server_args]],
        **pkwargs)

-    def log_stdout(process):
-        for line in iter(process.stdout.readline, b''):
-            print(line.decode('utf-8'), end='')
-    thread_stdout = threading.Thread(target=log_stdout, args=(context.server_process,))
+    def server_log(in_stream, out_stream):
+        for line in iter(in_stream.readline, b''):
+            print(line.decode('utf-8'), end='', file=out_stream)
+
+    thread_stdout = threading.Thread(target=server_log, args=(context.server_process.stdout, sys.stdout))
    thread_stdout.start()

-    def log_stderr(process):
-        for line in iter(process.stderr.readline, b''):
-            print(line.decode('utf-8'), end='', file=sys.stderr)
-    thread_stderr = threading.Thread(target=log_stderr, args=(context.server_process,))
+    thread_stderr = threading.Thread(target=server_log, args=(context.server_process.stderr, sys.stderr))
    thread_stderr.start()

    print(f"server pid={context.server_process.pid}, behave pid={os.getpid()}")
@@ -145,6 +145,7 @@
            # the same path you would with an overlay.
            legacyPackages = {
              llamaPackages = pkgs.callPackage .devops/nix/scope.nix { inherit llamaVersion; };
+              llamaPackagesWindows = pkgs.pkgsCross.mingwW64.callPackage .devops/nix/scope.nix { inherit llamaVersion; };
              llamaPackagesCuda = pkgsCuda.callPackage .devops/nix/scope.nix { inherit llamaVersion; };
              llamaPackagesRocm = pkgsRocm.callPackage .devops/nix/scope.nix { inherit llamaVersion; };
            };
@@ -155,6 +156,7 @@
              {
                default = config.legacyPackages.llamaPackages.llama-cpp;
                vulkan = config.packages.default.override { useVulkan = true; };
+                windows = config.legacyPackages.llamaPackagesWindows.llama-cpp;
              }
              // lib.optionalAttrs pkgs.stdenv.isLinux {
                opencl = config.packages.default.override { useOpenCL = true; };
@@ -168,9 +170,14 @@
              };

            # Packages exposed in `.#checks` will be built by the CI and by
-            # `nix flake check`. Currently we expose all packages, but we could
-            # make more granular choices
-            checks = config.packages;
+            # `nix flake check`.
+            #
+            # We could test all outputs e.g. as `checks = confg.packages`.
+            #
+            # TODO: Build more once https://github.com/ggerganov/llama.cpp/issues/6346 has been addressed
+            checks = {
+              inherit (config.packages) default vulkan;
+            };
          };
      };
 }
@@ -377,13 +377,20 @@ typedef struct {
 } block_iq1_s;
 static_assert(sizeof(block_iq1_s) == sizeof(ggml_half) + QK_K/8 + QK_K/16, "wrong iq1_s block size/padding");

-// 1.8125 bpw
+// 1.75 bpw
 typedef struct {
    uint8_t  qs[QK_K/8];      // grid index, low 8 bits
    uint8_t  qh[QK_K/16];     // grid index, high 3 bits + grid shift bit (for two groups of 8)
-    uint8_t  scales[QK_K/32]; // 4-bit block scales
+#if QK_K == 64
+    ggml_half d;
+#endif
+    uint8_t  scales[QK_K/32]; // 3-bit block scales (4-bit if QK_K == 64)
 } block_iq1_m;
+#if QK_K == 64
+static_assert(sizeof(block_iq1_m) == QK_K/8 + QK_K/16 + QK_K/32 + sizeof(ggml_half), "wrong iq1_m block size/padding");
+#else
 static_assert(sizeof(block_iq1_m) == QK_K/8 + QK_K/16 + QK_K/32, "wrong iq1_m block size/padding");
+#endif

 // Used by IQ1_M quants
 typedef union {
@@ -4497,7 +4497,9 @@ void kernel_mul_mv_iq1_m_f32_impl(

    device const float * y4 = y + 32 * ix;

+#if QK_K != 64
    iq1m_scale_t scale;
+#endif

    for (int ib32 = ix; ib32 < nb32; ib32 += 32) {

@@ -4519,7 +4521,9 @@ void kernel_mul_mv_iq1_m_f32_impl(

        for (int row = 0; row < N_DST; row++) {

+#if QK_K != 64
            scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
+#endif

            constant uint8_t * grid1 = (constant uint8_t *)(iq1s_grid_gpu + (qs[0] | ((qh[0] << 8) & 0x700)));
            constant uint8_t * grid2 = (constant uint8_t *)(iq1s_grid_gpu + (qs[1] | ((qh[0] << 4) & 0x700)));
@@ -4535,8 +4539,14 @@ void kernel_mul_mv_iq1_m_f32_impl(
            }
            const float delta1 = sumy[0] * (qh[0] & 0x08 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA) + sumy[1] * (qh[0] & 0x80 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA);
            const float delta2 = sumy[2] * (qh[1] & 0x08 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA) + sumy[3] * (qh[1] & 0x80 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA);
+#if QK_K == 64
+            const float d = (float) *((device const half *)(sc - 1));
+            sumf[row] += d * ((sum[0] + delta1) * (2*((sc[0] >> (8*(ib%2)+0)) & 0xf) + 1) +
+                              (sum[1] + delta2) * (2*((sc[0] >> (8*(ib%2)+4)) & 0xf) + 1));
+#else
            sumf[row] += (float)scale.f16 * ((sum[0] + delta1) * (2*((sc[ib/2] >> (6*(ib%2)+0)) & 7) + 1) +
                                             (sum[1] + delta2) * (2*((sc[ib/2] >> (6*(ib%2)+3)) & 7) + 1));
+#endif

            sc += nb*sizeof(block_iq1_m)/2;
            qs += nb*sizeof(block_iq1_m);
@@ -5277,13 +5287,21 @@ void dequantize_iq1_m(device const block_iq1_m * xb, short il, thread type4x4 &
    // il is 0...15 for QK_K = 256 => index of block of 32 is il/2
    const int ib32 = il/2;
    il = il%2;
-    iq1m_scale_t scale;
    device const uint16_t * sc = (device const uint16_t *)xb->scales;
+#if QK_K == 64
+    const float d = xb->d;
+#else
+    iq1m_scale_t scale;
    scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
    const float d = scale.f16;
+#endif
    device const uint8_t * qs = xb->qs + 4*ib32 + 2*il;
    device const uint8_t * qh = xb->qh + 2*ib32 + il;
+#if QK_K == 64
+    const float dl  = d * (2*((sc[ib32/2] >> (8*(ib32%2)+4*il)) & 0xf) + 1);
+#else
    const float dl  = d * (2*((sc[ib32/2] >> (6*(ib32%2)+3*il)) & 7) + 1);
+#endif
    const float ml1 = dl * (qh[0] & 0x08 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA);
    const float ml2 = dl * (qh[0] & 0x80 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA);
    constant uint8_t * grid1 = (constant uint8_t *)(iq1s_grid_gpu + (qs[0] | ((qh[0] << 8) & 0x700)));
@@ -3481,19 +3481,30 @@ void dequantize_row_iq1_m(const block_iq1_m * restrict x, float * restrict y, in
    float delta[4];
    uint16_t idx[4];

+#if QK_K != 64
    iq1m_scale_t scale;
+#endif

    for (int i = 0; i < nb; i++) {

        const uint16_t * sc = (const uint16_t *)x[i].scales;
+#if QK_K == 64
+        const float d = GGML_FP16_TO_FP32(x[i].d);
+#else
        scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
        const float d = GGML_FP16_TO_FP32(scale.f16);
+#endif
        const uint8_t * qs = x[i].qs;
        const uint8_t * qh = x[i].qh;

        for (int ib = 0; ib < QK_K/32; ++ib) {
+#if QK_K == 64
+            const float dl1 = d * (2*((sc[ib/2] >> (8*(ib%2)+0)) & 0xf) + 1);
+            const float dl2 = d * (2*((sc[ib/2] >> (8*(ib%2)+4)) & 0xf) + 1);
+#else
            const float dl1 = d * (2*((sc[ib/2] >> (6*(ib%2)+0)) & 0x7) + 1);
            const float dl2 = d * (2*((sc[ib/2] >> (6*(ib%2)+3)) & 0x7) + 1);
+#endif
            idx[0] = qs[0] | ((qh[0] << 8) & 0x700);
            idx[1] = qs[1] | ((qh[0] << 4) & 0x700);
            idx[2] = qs[2] | ((qh[1] << 8) & 0x700);
@@ -9756,11 +9767,17 @@ void ggml_vec_dot_iq1_m_q8_K  (int n, float * restrict s, size_t bs, const void

    const int nb = n / QK_K;

+#if QK_K != 64
    iq1m_scale_t scale;
+#endif

 #if defined __ARM_NEON

+#if QK_K == 64
+    const int32x4_t mask  = vdupq_n_s32(0xf);
+#else
    const int32x4_t mask  = vdupq_n_s32(0x7);
+#endif
    const int32x4_t mone  = vdupq_n_s32(1);
    const int32x4_t mzero = vdupq_n_s32(0);

@@ -9784,7 +9801,9 @@ void ggml_vec_dot_iq1_m_q8_K  (int n, float * restrict s, size_t bs, const void
        const uint8_t  * qh = x[i].qh;
        const uint16_t * sc = (const uint16_t *)x[i].scales;

+#if QK_K != 64
        scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
+#endif

        int32x4_t sumi1 = mzero;
        int32x4_t sumi2 = mzero;
@@ -9813,7 +9832,11 @@ void ggml_vec_dot_iq1_m_q8_K  (int n, float * restrict s, size_t bs, const void
            const int32x4_t p4 = vpaddq_s32(ggml_vdotq_s32(mzero, deltas.val[aux8[2]], q8b.val[2]), ggml_vdotq_s32(mzero, deltas.val[aux8[3]], q8b.val[3]));
            const int32x4_t p34 = vpaddq_s32(p3, p4);

+#if QK_K == 64
+            int32x4_t scales_4 = ggml_vld1q_u32(sc[0] >> 0, sc[0] >> 4, sc[0] >> 8, sc[0] >> 12);
+#else
            int32x4_t scales_4 = ggml_vld1q_u32(sc[ib/2] >> 0, sc[ib/2] >> 3, sc[ib/2] >> 6, sc[ib/2] >> 9);
+#endif
            scales_4 = vaddq_s32(vshlq_n_s32(vandq_s32(scales_4, mask), 1), mone);

            sumi1 = vmlaq_s32(sumi1, scales_4, p12);
@@ -9823,14 +9846,22 @@ void ggml_vec_dot_iq1_m_q8_K  (int n, float * restrict s, size_t bs, const void

        }

+#if QK_K == 64
+        sumf += y[i].d * GGML_FP16_TO_FP32(x[i].d) * (vaddvq_s32(sumi1) + IQ1M_DELTA * vaddvq_s32(sumi2));
+#else
        sumf += y[i].d * GGML_FP16_TO_FP32(scale.f16) * (vaddvq_s32(sumi1) + IQ1M_DELTA * vaddvq_s32(sumi2));
+#endif
    }

    *s = sumf;

 #elif defined __AVX2__

+#if QK_K == 64
+    const __m256i mask = _mm256_set1_epi16(0xf);
+#else
    const __m256i mask = _mm256_set1_epi16(0x7);
+#endif
    const __m256i mone = _mm256_set1_epi16(1);

    __m256 accum1 = _mm256_setzero_ps();
@@ -9842,7 +9873,9 @@ void ggml_vec_dot_iq1_m_q8_K  (int n, float * restrict s, size_t bs, const void
        const uint8_t  * qh = x[i].qh;
        const uint16_t * sc = (const uint16_t *)x[i].scales;

+#if QK_K != 64
        scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
+#endif

        __m256i sumi1 = _mm256_setzero_si256();
        __m256i sumi2 = _mm256_setzero_si256();
@@ -9872,8 +9905,13 @@ void ggml_vec_dot_iq1_m_q8_K  (int n, float * restrict s, size_t bs, const void

            const __m256i dot3 = mul_add_epi8(delta1, q8b_1);
            const __m256i dot4 = mul_add_epi8(delta2, q8b_2);
+#if QK_K == 64
+            __m256i scale1 = MM256_SET_M128I(_mm_set1_epi16(sc[0] >>  4), _mm_set1_epi16(sc[0] >> 0));
+            __m256i scale2 = MM256_SET_M128I(_mm_set1_epi16(sc[0] >> 12), _mm_set1_epi16(sc[0] >> 8));
+#else
            __m256i scale1 = MM256_SET_M128I(_mm_set1_epi16(sc[ib/2] >> 3), _mm_set1_epi16(sc[ib/2] >> 0));
            __m256i scale2 = MM256_SET_M128I(_mm_set1_epi16(sc[ib/2] >> 9), _mm_set1_epi16(sc[ib/2] >> 6));
+#endif
            scale1 = _mm256_add_epi16(_mm256_slli_epi16(_mm256_and_si256(scale1, mask), 1), mone);
            scale2 = _mm256_add_epi16(_mm256_slli_epi16(_mm256_and_si256(scale2, mask), 1), mone);
            const __m256i p1 = _mm256_madd_epi16(dot1, scale1);
@@ -9887,7 +9925,11 @@ void ggml_vec_dot_iq1_m_q8_K  (int n, float * restrict s, size_t bs, const void
            qs += 8; qh += 4;
        }

+#if QK_K == 64
+        const __m256 d = _mm256_set1_ps(y[i].d * GGML_FP16_TO_FP32(x[i].d));
+#else
        const __m256 d = _mm256_set1_ps(y[i].d * GGML_FP16_TO_FP32(scale.f16));
+#endif
        accum1 = _mm256_fmadd_ps(d, _mm256_cvtepi32_ps(sumi1), accum1);
        accum2 = _mm256_fmadd_ps(d, _mm256_cvtepi32_ps(sumi2), accum2);

@@ -9907,7 +9949,9 @@ void ggml_vec_dot_iq1_m_q8_K  (int n, float * restrict s, size_t bs, const void
        const uint8_t  * qh = x[i].qh;
        const uint16_t * sc = (const uint16_t *)x[i].scales;

+#if QK_K != 64
        scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
+#endif

        int sumi1 = 0, sumi2 = 0;
        for (int ib = 0; ib < QK_K/32; ++ib) {
@@ -9927,15 +9971,24 @@ void ggml_vec_dot_iq1_m_q8_K  (int n, float * restrict s, size_t bs, const void
                sum1[l/2] += lsum1;
                sum2[l/2] += lsum2*delta[l];
            }
+#if QK_K == 64
+            const int ls1 = 2*((sc[0] >> (8*(ib%2)+0)) & 0xf) + 1;
+            const int ls2 = 2*((sc[0] >> (8*(ib%2)+4)) & 0xf) + 1;
+#else
            const int ls1 = 2*((sc[ib/2] >> (6*(ib%2)+0)) & 0x7) + 1;
            const int ls2 = 2*((sc[ib/2] >> (6*(ib%2)+3)) & 0x7) + 1;
+#endif
            sumi1 += sum1[0] * ls1 + sum1[1] * ls2;
            sumi2 += sum2[0] * ls1 + sum2[1] * ls2;
            qs += 4;
            qh += 2;
        }

+#if QK_K == 64
+        sumf += GGML_FP16_TO_FP32(x[i].d) * y[i].d * (sumi1 + IQ1M_DELTA * sumi2);
+#else
        sumf += GGML_FP16_TO_FP32(scale.f16) * y[i].d * (sumi1 + IQ1M_DELTA * sumi2);
+#endif
    }

    *s = sumf;
@@ -11986,7 +12039,9 @@ static void quantize_row_iq1_m_impl(const float * restrict x, void * restrict vy

    for (int ibl = 0; ibl < nbl; ++ibl) {

-        //y[ibl].d = GGML_FP32_TO_FP16(0.f);
+#if QK_K == 64
+        y[ibl].d = GGML_FP32_TO_FP16(0.f);
+#endif
        memset(y[ibl].qs, 0, QK_K/8);
        memset(y[ibl].qh, 0, QK_K/16);
        memset(y[ibl].scales, 0, QK_K/32);
@@ -12161,13 +12216,22 @@ static void quantize_row_iq1_m_impl(const float * restrict x, void * restrict vy
        }

        uint16_t * sc = (uint16_t *)y[ibl].scales;
+#if QK_K == 64
+        float d = max_scale/31;
+#else
        float d = max_scale/15;
+#endif
        float id = 1/d;
        float sumqx_f = 0, sumq2_f = 0;
        for (int ib = 0; ib < QK_K/block_size; ++ib) {
            int l = nearest_int(0.5f*(id*scales[ib+0]-1));
+#if QK_K == 64
+            l = MAX(0, MIN(15, l));
+            sc[ib/4] |= (l << 4*(ib%4));
+#else
            l = MAX(0, MIN(7, l));
            sc[ib/4] |= (l << 3*(ib%4));
+#endif
            y[ibl].qh[ib] |= masks[shifts[ib]];
            const float * xb = xbl + block_size*ib;
            if (quant_weights) {
@@ -12190,10 +12254,14 @@ static void quantize_row_iq1_m_impl(const float * restrict x, void * restrict vy
        }
        if (sumq2_f > 0) d = sumqx_f/sumq2_f;
        s.f16 = GGML_FP32_TO_FP16(d*1.1125f); // 1.1125f is another fudge factor. Don't ask me why it is needed.
+#if QK_K == 64
+        y[ibl].d = s.f16;
+#else
        sc[0] |= ((s.u16 & 0x000f) << 12);
        sc[1] |= ((s.u16 & 0x00f0) <<  8);
        sc[2] |= ((s.u16 & 0x0f00) <<  4);
        sc[3] |= ((s.u16 & 0xf000) <<  0);
+#endif
    }
 }

@@ -2968,7 +2968,7 @@ namespace dpct
 #include "ggml-common.h"

 static int g_ggml_sycl_debug=0;
-#define GGML_SYCL_DEBUG(...) do{if(g_ggml_sycl_debug) printf(__VA_ARGS__);}while(0)
+#define GGML_SYCL_DEBUG(...) do{if(g_ggml_sycl_debug) fprintf(stderr, __VA_ARGS__);}while(0)

 #define CHECK_TRY_ERROR(expr)                                                  \
  [&]() {                                                                      \
@@ -12868,6 +12868,7 @@ void print_device_detail(int id, sycl::device &device, std::string device_type)
 }

 void ggml_backend_sycl_print_sycl_devices() {
+    GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_print_sycl_devices\n");
    int device_count = dpct::dev_mgr::instance().device_count();
    std::map<std::string, size_t> DeviceNums;
    fprintf(stderr, "found %d SYCL devices:\n", device_count);
@@ -12925,7 +12926,9 @@ static void ggml_init_sycl() try {
    static bool initialized = false;

    if (!initialized) {
+        fprintf(stderr, "[SYCL] call ggml_init_sycl\n");
        g_ggml_sycl_debug = get_sycl_env("GGML_SYCL_DEBUG", 0);
+
        fprintf(stderr, "%s: GGML_SYCL_DEBUG: %d\n", __func__, g_ggml_sycl_debug);

 #if defined(GGML_SYCL_F16)
@@ -14986,6 +14989,9 @@ static void ggml_sycl_mul_mat_batched_sycl(const ggml_tensor *src0,
    SYCL_CHECK(ggml_sycl_set_device(g_main_device));
    dpct::queue_ptr main_stream = g_syclStreams[g_main_device][0];

+    bool no_mixed_dtypes = main_stream->get_backend() == sycl::backend::ext_oneapi_cuda ||
+                           main_stream->get_backend() == sycl::backend::ext_oneapi_hip;
+
    SYCL_CHECK(
        CHECK_TRY_ERROR(g_sycl_handles[g_main_device] = main_stream));

@@ -15016,24 +15022,38 @@ static void ggml_sycl_mul_mat_batched_sycl(const ggml_tensor *src0,

    dpct::library_data_t cu_compute_type = dpct::library_data_t::real_float;
    dpct::library_data_t cu_data_type = dpct::library_data_t::real_float;
+    if (no_mixed_dtypes) {
+        cu_compute_type = dpct::library_data_t::real_half;
+        cu_data_type = dpct::library_data_t::real_half;
+    }

    // dst strides
    size_t nbd2 = dst->nb[2];
    size_t nbd3 = dst->nb[3];

+    const float alpha_f32 = 1.0f;
+    const float beta_f32 = 0.0f;
+
    const sycl::half alpha_f16 = 1.0f;
    const sycl::half beta_f16 = 0.0f;

-    const float alpha_f32 = 1.0f;
-    const float beta_f32  = 0.0f;
-
    const void * alpha = &alpha_f32;
    const void * beta  = &beta_f32;
+    if (no_mixed_dtypes) {
+        alpha = &alpha_f16;
+        beta  = &beta_f16;
+    }

    // TODO: Renable (dst->op_params[0] =! GGML_PREC_DEFAULT) pathway
-    // oneMKL open source supports half, half, float, float: datatypes
+    // when oneMKL open source supports half, half, float, float: datatypes

    dst_t = (char *) dst_ddf;
+    if (no_mixed_dtypes) {
+        dst_t = (char *) dst_f16.alloc(ne_dst);
+
+        nbd2 /= sizeof(float) / sizeof(sycl::half);
+        nbd3 /= sizeof(float) / sizeof(sycl::half);
+    }

    GGML_ASSERT(ne12 % ne02 == 0);
    GGML_ASSERT(ne13 % ne03 == 0);
@@ -15119,6 +15139,10 @@ static void ggml_sycl_mul_mat_batched_sycl(const ggml_tensor *src0,
    }
 #endif

+    if (no_mixed_dtypes) {
+        const to_fp32_sycl_t to_fp32_sycl = ggml_get_to_fp32_sycl(GGML_TYPE_F16);
+        to_fp32_sycl(dst_f16.get(), dst_ddf, ne_dst, main_stream);
+    }
 }
 catch (sycl::exception const &exc) {
  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
@@ -16018,6 +16042,7 @@ bool ggml_sycl_compute_forward(struct ggml_compute_params * params, struct ggml_
 }

 GGML_API GGML_CALL void   ggml_sycl_get_gpu_list(int *id_list, int max_len) try {
+    GGML_SYCL_DEBUG("[SYCL] call ggml_sycl_get_gpu_list\n");
    for(int i=0;i<max_len;i++) id_list[i] = -1;

    if (!g_sycl_gpu_mgr) {
@@ -16052,6 +16077,7 @@ catch (sycl::exception const &exc) {

 GGML_API GGML_CALL void ggml_sycl_get_device_description(int device, char *description,
                                      size_t description_size) try {
+    GGML_SYCL_DEBUG("[SYCL] call ggml_sycl_get_device_description\n");
    dpct::device_info prop;
    int device_id = g_sycl_gpu_mgr->gpus[device];
    SYCL_CHECK(CHECK_TRY_ERROR(dpct::get_device_info(
@@ -16066,6 +16092,7 @@ catch (sycl::exception const &exc) {

 GGML_CALL void ggml_backend_sycl_get_device_memory(int device, size_t *free,
                                                   size_t *total) try {
+    GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_get_device_memory\n");
    ggml_sycl_set_device(device);

    /*
@@ -16417,7 +16444,8 @@ static ggml_backend_buffer_type_i ggml_backend_sycl_buffer_type_interface = {
 };

 ggml_backend_buffer_type_t ggml_backend_sycl_buffer_type(int device_index) {
-    ggml_init_sycl();
+    GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_buffer_type\n");
+
    if (device_index>=g_device_count or device_index<0) {
        printf("ggml_backend_sycl_buffer_type error: device_index:%d is out of range [0, %d], miss to call ggml_backend_sycl_set_single_device()\n",
            device_index, g_device_count-1);
@@ -16787,6 +16815,7 @@ static ggml_backend_buffer_type_i ggml_backend_sycl_split_buffer_type_interface
 };

 GGML_CALL ggml_backend_buffer_type_t ggml_backend_sycl_split_buffer_type(const float * tensor_split) {
+    GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_split_buffer_type\n");
    ggml_init_sycl();
    // FIXME: this is not thread safe
    static std::map<std::array<float, GGML_SYCL_MAX_DEVICES>, struct ggml_backend_buffer_type> buft_map;
@@ -16859,6 +16888,7 @@ static ggml_backend_buffer_t ggml_backend_sycl_host_buffer_type_alloc_buffer(ggm
 }

 ggml_backend_buffer_type_t ggml_backend_sycl_host_buffer_type() {
+    GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_host_buffer_type\n");
    static struct ggml_backend_buffer_type ggml_backend_sycl_buffer_type_host = {
        /* .iface    = */ {
            /* .get_name         = */ ggml_backend_sycl_host_buffer_type_name,
@@ -17155,6 +17185,7 @@ static ggml_guid_t ggml_backend_sycl_guid() {
 }

 GGML_CALL ggml_backend_t ggml_backend_sycl_init(int device) {
+    GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_init\n");
    ggml_init_sycl();

    check_allow_gpu_index(device);
@@ -17181,6 +17212,7 @@ bool ggml_backend_is_sycl(ggml_backend_t backend) {
 }

 GGML_CALL int ggml_backend_sycl_get_device_count() {
+    GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_get_device_count\n");
    if (!g_sycl_gpu_mgr) g_sycl_gpu_mgr = new sycl_gpu_mgr();
    return g_sycl_gpu_mgr->get_gpu_count();
 }
@@ -17193,16 +17225,21 @@ GGML_CALL static ggml_backend_t ggml_backend_reg_sycl_init(const char * params,
 }

 GGML_API GGML_CALL int ggml_backend_sycl_get_device_index(int device_id) {
+    GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_get_device_index\n");
    return g_sycl_gpu_mgr->get_index(device_id);
 }

 GGML_API GGML_CALL int ggml_backend_sycl_get_device_id(int device_index) {
+    GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_get_device_id\n");
    return g_sycl_gpu_mgr->gpus[device_index];
 }

 GGML_API GGML_CALL void ggml_backend_sycl_set_single_device_mode(int main_gpu_id) {
-    GGML_ASSERT(main_gpu_id<g_all_sycl_device_count);
+    ggml_init_sycl();
+    GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_set_single_device_mode\n");
    fprintf(stderr, "ggml_backend_sycl_set_single_device: use single device: [%d]\n", main_gpu_id);
+    GGML_ASSERT(main_gpu_id<g_all_sycl_device_count);
+
    if (g_sycl_gpu_mgr) {
        delete g_sycl_gpu_mgr;
    }
@@ -17213,6 +17250,9 @@ GGML_API GGML_CALL void ggml_backend_sycl_set_single_device_mode(int main_gpu_id
 }

 GGML_API GGML_CALL void ggml_backend_sycl_set_mul_device_mode() {
+    ggml_init_sycl();
+    GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_set_mul_device_mode\n");
+
    if (g_ggml_sycl_backend_gpu_mode == SYCL_MUL_GPU_MODE) {
        return;
    }
@@ -2938,7 +2938,7 @@ static struct ggml_tensor * ggml_new_tensor_impl(
        data_size *= ne[i];
    }

-    GGML_ASSERT(view_src == NULL || data_size + view_offs <= ggml_nbytes(view_src));
+    GGML_ASSERT(view_src == NULL || data_size == 0 || data_size + view_offs <= ggml_nbytes(view_src));

    void * data = view_src != NULL ? view_src->data : NULL;
    if (data != NULL) {
@@ -9152,8 +9152,9 @@ struct llm_build_context {
            if (il == n_layer - 1) {
                // skip computing output for unused tokens
                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
-                cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);
-                inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
+                cur     = ggml_get_rows(ctx0,     cur, inp_out_ids);
+                inpL    = ggml_get_rows(ctx0,    inpL, inp_out_ids);
+                ffn_inp = ggml_get_rows(ctx0, ffn_inp, inp_out_ids);
            }

            struct ggml_tensor * attn_out = cur;
@@ -60,9 +60,9 @@ extern "C" {

    enum llama_vocab_type {
        LLAMA_VOCAB_TYPE_NONE = 0, // For models without vocab
-        LLAMA_VOCAB_TYPE_SPM  = 1, // SentencePiece
-        LLAMA_VOCAB_TYPE_BPE  = 2, // Byte Pair Encoding
-        LLAMA_VOCAB_TYPE_WPM  = 3, // WordPiece
+        LLAMA_VOCAB_TYPE_SPM  = 1, // LLaMA tokenizer based on byte-level BPE with byte fallback
+        LLAMA_VOCAB_TYPE_BPE  = 2, // GPT-2 tokenizer based on byte-level BPE
+        LLAMA_VOCAB_TYPE_WPM  = 3, // BERT tokenizer based on WordPiece
    };

    // note: these values should be synchronized with ggml_rope
Author	SHA1	Message	Date
Jared Van Bortel	be55134a53	convert : refactor vocab selection logic (#6355 )	2024-03-28 11:44:36 -04:00
Ziang Wu	66ba560256	llava : fix MobileVLM (#6364 ) * fix empty bug * Update MobileVLM-README.md added more results on devices * Update MobileVLM-README.md * Update MobileVLM-README.md * Update MobileVLM-README.md * Update MobileVLM-README.md * Update MobileVLM-README.md * Update MobileVLM-README.md * Update examples/llava/MobileVLM-README.md Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update MobileVLM-README.md remove gguf links --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>	2024-03-28 16:33:10 +02:00
compilade	0308f5e3d7	llama : fix command-r inference when omitting outputs (#6367 )	2024-03-28 14:05:54 +02:00
Pierrick Hymbert	28cb9a09c4	ci: bench: fix master not schedule, fix commit status failed on external repo (#6365 )	2024-03-28 11:27:56 +01:00
Ting Sun	cfc4d75df6	doc: fix outdated default value of batch size (#6336 ) * doc: fix outdated default value of batch size * doc: add doc for ubatch-size	2024-03-28 09:51:06 +01:00
Eric Zhang	6902cb7f2e	server : stop gracefully on SIGTERM (#6348 )	2024-03-28 09:50:48 +01:00
hutli	d2d8f38996	nix: removed unnessesary indentation	2024-03-28 07:48:27 +00:00
hutli	d39b308eaf	nix: moved blas availability check to package inputs so it is still overridable	2024-03-28 07:48:27 +00:00
hutli	c873976649	using blas.meta.available to check host platform	2024-03-28 07:48:27 +00:00
hutli	dbb03e2b9c	only using explicit blas if hostPlatform is allowed	2024-03-28 07:48:27 +00:00
Someone Serge	e9f17dc3bf	nix: .#windows: proper cross-compilation set-up Take all dependencies from the cross stage, rather tha only stdenv	2024-03-28 07:48:27 +00:00
Someone Serge	22a462cc1f	nix: package: don't introduce the dependency on python - The generic /usr/bin/env shebangs are good enough - Python deps are provisioned in the devShells - We need to be able to leave python out at least on windows (currently breaks eval)	2024-03-28 07:48:27 +00:00
hutli	f6a0f5c642	nix: .#widnows: init initial nix build for windows using zig mingwW64 build removes nix zig windows build removes nix zig windows build removed unnessesary glibc.static removed unnessesary import of pkgs in nix fixed missing trailing newline on non-windows nix builds overriding stdenv when building for crosscompiling to windows in nix better variables when crosscompiling windows in nix cross compile windows on macos removed trailing whitespace remove unnessesary overwrite of "CMAKE_SYSTEM_NAME" in nix windows build nix: keep file extension when copying result files during cross compile for windows nix: better checking for file extensions when using MinGW nix: using hostPlatform instead of targetPlatform when cross compiling for Windows using hostPlatform.extensions.executable to extract executable format	2024-03-28 07:48:27 +00:00
Ziang Wu	d0e2f6416b	doc: fix typo in MobileVLM-README.md (#6181 )	2024-03-28 13:03:30 +09:00
Neo Zhang Jianyu	25f4a613c4	[SYCL] fix set main gpu crash (#6339 )	2024-03-28 08:55:24 +08:00
Pierrick Hymbert	a016026a3a	server: continuous performance monitoring and PR comment (#6283 ) * server: bench: init * server: bench: reduce list of GPU nodes * server: bench: fix graph, fix output artifact * ci: bench: add mermaid in case of image cannot be uploaded * ci: bench: more resilient, more metrics * ci: bench: trigger build * ci: bench: fix duration * ci: bench: fix typo * ci: bench: fix mermaid values, markdown generated * typo on the step name Co-authored-by: Xuan Son Nguyen <thichthat@gmail.com> * ci: bench: trailing spaces * ci: bench: move images in a details section * ci: bench: reduce bullet point size --------- Co-authored-by: Xuan Son Nguyen <thichthat@gmail.com>	2024-03-27 20:26:49 +01:00
Someone Serge	53c7ec53d5	nix: ci: dont test cuda and rocm (for now) Until https://github.com/ggerganov/llama.cpp/issues/6346 is resolved	2024-03-27 19:18:55 +00:00
slaren	e5b89a441a	ggml : fix bounds checking of zero size views (#6347 )	2024-03-27 15:07:50 +01:00
Georgi Gerganov	3a0345970e	make : whitespace	2024-03-27 15:02:49 +02:00
howlger	1e13987fba	embedding : show full embedding for single prompt (#6342 ) * embedding : show full embedding for single prompt To support the use case of creating an embedding for a given prompt, the entire embedding and not just the first part needed to be printed. Also, show cosine similarity matrix only if there is more than one prompt, as the cosine similarity matrix for a single prompt is always `1.00`. * Update examples/embedding/embedding.cpp --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>	2024-03-27 13:15:44 +02:00
AidanBeltonS	e82f9e2b83	[SYCL] Fix batched impl for NVidia GPU (#6164 ) * Fix batched impl * Maintain previous behaviour for igpu * retrigger CI --------- Co-authored-by: Abhilash Majumder <30946547+abhilash1910@users.noreply.github.com>	2024-03-27 13:46:40 +05:30
Kawrakow	cbc8343619	Make IQ1_M work for QK_K = 64 (#6327 ) * iq1_m: make it work for QK_K = 64 (WIP) * iq1_m: make it work for QK_K = 64 (scalar and AVX2) * iq1_m: QK_K = 64 seems to work on Metal and ARM_NEON --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>	2024-03-27 08:44:27 +01:00
Sigbjørn Skjæret	e562b9714b	common : change --no-penalize-nl to --penalize-nl (#6334 ) * Change --no-penalize-nl to --penalize-nl * Update documentation too	2024-03-27 09:23:10 +02:00
Georgi Gerganov	2ab4f00d25	llama2c : open file as binary (#6332 )	2024-03-27 09:16:02 +02:00
Mateusz Charytoniuk	1740d6dd4e	readme : add php api bindings (#6326 ) * add php bindings to readme * readme : add link to PR --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>	2024-03-27 09:08:59 +02:00
Eric Zhang	0642b22cd1	server: public: use relative routes for static files (#6325 ) server: public: support custom `api_url`, default to relative base path	2024-03-27 06:55:29 +01:00
Neo Zhang Jianyu	a4f569e8a3	[SYCL] fix no file in win rel (#6314 )	2024-03-27 09:47:06 +08:00