examples/llama-cpp-convert-GGUF.ipynb

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      "source": [
        "# 模型文件转GGUF上传huggingface\n",
        "\n",
        "1. 下载模型\n",
        "2. `llama.cpp/convert.py`转化模型权重为GGUF格式\n",
        "3. `llama.cpp/build/bin/quantize`量化模型权重\n",
        "4. 上传模型"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hInIR6k2NDP5",
        "outputId": "b425e3a2-f10a-4970-9cf1-a8408f4ab32c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Cloning into 'llama.cpp'...\n",
            "remote: Enumerating objects: 22769, done.\u001b[K\n",
            "remote: Counting objects: 100% (5/5), done.\u001b[K\n",
            "remote: Compressing objects: 100% (5/5), done.\u001b[K\n",
            "remote: Total 22769 (delta 0), reused 1 (delta 0), pack-reused 22764\u001b[K\n",
            "Receiving objects: 100% (22769/22769), 25.70 MiB | 18.79 MiB/s, done.\n",
            "Resolving deltas: 100% (16095/16095), done.\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "pandas-stubs 2.0.3.230814 requires numpy>=1.25.0; python_version >= \"3.9\", but you have numpy 1.24.4 which is incompatible.\n",
            "tensorflow-metadata 1.14.0 requires protobuf<4.21,>=3.20.3, but you have protobuf 4.25.3 which is incompatible.\n",
            "torchaudio 2.2.1+cu121 requires torch==2.2.1, but you have torch 2.1.2 which is incompatible.\n",
            "torchtext 0.17.1 requires torch==2.2.1, but you have torch 2.1.2 which is incompatible.\n",
            "torchvision 0.17.1+cu121 requires torch==2.2.1, but you have torch 2.1.2 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "!pip install huggingface_hub > /dev/null\n",
        "!git clone https://github.com/ggerganov/llama.cpp.git > /dev/null\n",
        "!pip install -r llama.cpp/requirements.txt > /dev/null"
      ]
    },
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          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
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          },
          "metadata": {},
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        },
        {
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "model_id": "b871982efa474b4ebf88a90336c314ea",
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              "version_minor": 0
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          "metadata": {},
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        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'/content/Llama3-8B-Chinese-Chat-hf'"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Download Model\n",
        "from huggingface_hub import snapshot_download\n",
        "model_id=\"shenzhi-wang/Llama3-8B-Chinese-Chat\"\n",
        "snapshot_download(repo_id=model_id, local_dir=\"Llama3-8B-Chinese-Chat-hf\",\n",
        "                  local_dir_use_symlinks=False, revision=\"main\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "CMixTKb7Nyz6"
      },
      "outputs": [],
      "source": [
        "# convert hf-model to GGUF, fp32 can help model maintain better performance during quantization process\n",
        "# llama3 tokenizer type bpe\n",
        "!python llama.cpp/convert.py Llama3-8B-Chinese-Chat-hf --outtype f16 --outfile \"Llama3-8B-Chinese-Chat-fp16.GGUF\" --vocab-type bpe > /dev/null"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r31KcU3jhWW7",
        "outputId": "bb1dcb52-96bb-4bfa-bc44-6bc2a19e7e48"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Llama3-8B-Chinese-Chat-fp16.GGUF  Llama3-8B-Chinese-Chat-hf  llama.cpp\tsample_data\n"
          ]
        }
      ],
      "source": [
        "!ls /content"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TwKLdnYOS6SW",
        "outputId": "4b18a4d6-342c-4449-ce0d-ec6f5558a053"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content/llama.cpp\n",
            "/content/llama.cpp\n",
            "/content/llama.cpp/build\n",
            "-- The C compiler identification is GNU 11.4.0\n",
            "-- The CXX compiler identification is GNU 11.4.0\n",
            "-- Detecting C compiler ABI info\n",
            "-- Detecting C compiler ABI info - done\n",
            "-- Check for working C compiler: /usr/bin/cc - skipped\n",
            "-- Detecting C compile features\n",
            "-- Detecting C compile features - done\n",
            "-- Detecting CXX compiler ABI info\n",
            "-- Detecting CXX compiler ABI info - done\n",
            "-- Check for working CXX compiler: /usr/bin/c++ - skipped\n",
            "-- Detecting CXX compile features\n",
            "-- Detecting CXX compile features - done\n",
            "-- Found Git: /usr/bin/git (found version \"2.34.1\") \n",
            "-- Performing Test CMAKE_HAVE_LIBC_PTHREAD\n",
            "-- Performing Test CMAKE_HAVE_LIBC_PTHREAD - Success\n",
            "-- Found Threads: TRUE  \n",
            "-- Warning: ccache not found - consider installing it for faster compilation or disable this warning with LLAMA_CCACHE=OFF\n",
            "-- CMAKE_SYSTEM_PROCESSOR: x86_64\n",
            "-- x86 detected\n",
            "-- Configuring done (1.8s)\n",
            "-- Generating done (0.2s)\n",
            "-- Build files have been written to: /content/llama.cpp/build\n"
          ]
        }
      ],
      "source": [
        "# compile llama.cpp\n",
        "%cd llama.cpp\n",
        "!pwd\n",
        "!mkdir build\n",
        "%cd build\n",
        "!cmake ..\n",
        "!cmake --build . --config Release > /dev/null"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kOBDuSpOteEJ",
        "outputId": "42679350-f5ee-4bf6-8c64-e1e4d9495261"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content\n",
            "Llama3-8B-Chinese-Chat-fp16.GGUF  Llama3-8B-Chinese-Chat-hf  llama.cpp\tsample_data  upload.py\n"
          ]
        }
      ],
      "source": [
        "%cd /content\n",
        "!ls"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xcT8-EenwjpJ",
        "outputId": "f78df9b6-c239-41f5-ec62-751170a9c93e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "baby-llama\t\t lookup-stats\t\t      test-llama-grammar\n",
            "batched\t\t\t main\t\t\t      test-model-load-cancel\n",
            "batched-bench\t\t parallel\t\t      test-quantize-fns\n",
            "beam-search\t\t passkey\t\t      test-quantize-perf\n",
            "benchmark\t\t perplexity\t\t      test-rope\n",
            "convert-llama2c-to-ggml  q8dot\t\t\t      test-sampling\n",
            "embedding\t\t quantize\t\t      test-tokenizer-0-falcon\n",
            "eval-callback\t\t quantize-stats\t\t      test-tokenizer-0-llama\n",
            "export-lora\t\t retrieval\t\t      test-tokenizer-1-aquila\n",
            "finetune\t\t save-load-state\t      test-tokenizer-1-baichuan\n",
            "gguf\t\t\t server\t\t\t      test-tokenizer-1-falcon\n",
            "gguf-split\t\t simple\t\t\t      test-tokenizer-1-gpt2\n",
            "gritlm\t\t\t speculative\t\t      test-tokenizer-1-gpt-neox\n",
            "imatrix\t\t\t test-autorelease\t      test-tokenizer-1-llama\n",
            "infill\t\t\t test-backend-ops\t      test-tokenizer-1-mpt\n",
            "llama-bench\t\t test-c\t\t\t      test-tokenizer-1-refact\n",
            "llava-cli\t\t test-chat-template\t      test-tokenizer-1-stablelm-3b-4e1t\n",
            "lookahead\t\t test-grad0\t\t      test-tokenizer-1-starcoder\n",
            "lookup\t\t\t test-grammar-integration     tokenize\n",
            "lookup-create\t\t test-grammar-parser\t      train-text-from-scratch\n",
            "lookup-merge\t\t test-json-schema-to-grammar  vdot\n"
          ]
        }
      ],
      "source": [
        "!ls llama.cpp/build/bin"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zx1Ayhr7wdu6",
        "outputId": "80830b1c-8ec3-491f-fe44-95582b7a1fb0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Writing upload.py\n"
          ]
        }
      ],
      "source": [
        "%%writefile upload.py\n",
        "\n",
        "# 上传模型到huggingface\n",
        "from huggingface_hub import HfApi\n",
        "import os\n",
        "import argparse\n",
        "\n",
        "if __name__ == \"__main__\":\n",
        "  parser = argparse.ArgumentParser()\n",
        "  parser.add_argument(\"--hf_token\")   # huggingface write token\n",
        "  parser.add_argument(\"--model_id\")   # 你要创建的模型ID 一般是 username/modelname\n",
        "  parser.add_argument(\"--local_path\") # 上传的本地文件路径\n",
        "  parser.add_argument(\"--repo_path\")  # repo中的文件路径\n",
        "  args = parser.parse_args()\n",
        "\n",
        "  os.environ[\"HUGGING_FACE_HUB_TOKEN\"] = args.hf_token\n",
        "  api = HfApi()\n",
        "\n",
        "  model_id = args.model_id\n",
        "  api.create_repo(model_id, exist_ok=True, repo_type=\"model\")\n",
        "  api.upload_file(\n",
        "      path_or_fileobj=args.local_path,\n",
        "      path_in_repo=args.repo_path,\n",
        "      repo_id=model_id,\n",
        "  )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0nIQSZH5PFqV",
        "outputId": "07fd5b5c-5ed8-4ed3-fe6b-53fcaf339bfd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "main: build = 2714 (e931888d)\n",
            "main: built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu\n",
            "main: quantizing 'Llama3-8B-Chinese-Chat-fp16.GGUF' to 'Llama3-8B-Chinese-Chat.q2_k.GGUF' as Q2_K\n",
            "llama_model_loader: loaded meta data with 22 key-value pairs and 291 tensors from Llama3-8B-Chinese-Chat-fp16.GGUF (version GGUF V3 (latest))\n",
            "llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.\n",
            "llama_model_loader: - kv   0:                       general.architecture str              = llama\n",
            "llama_model_loader: - kv   1:                               general.name str              = .\n",
            "llama_model_loader: - kv   2:                           llama.vocab_size u32              = 128256\n",
            "llama_model_loader: - kv   3:                       llama.context_length u32              = 8192\n",
            "llama_model_loader: - kv   4:                     llama.embedding_length u32              = 4096\n",
            "llama_model_loader: - kv   5:                          llama.block_count u32              = 32\n",
            "llama_model_loader: - kv   6:                  llama.feed_forward_length u32              = 14336\n",
            "llama_model_loader: - kv   7:                 llama.rope.dimension_count u32              = 128\n",
            "llama_model_loader: - kv   8:                 llama.attention.head_count u32              = 32\n",
            "llama_model_loader: - kv   9:              llama.attention.head_count_kv u32              = 8\n",
            "llama_model_loader: - kv  10:     llama.attention.layer_norm_rms_epsilon f32              = 0.000010\n",
            "llama_model_loader: - kv  11:                       llama.rope.freq_base f32              = 500000.000000\n",
            "llama_model_loader: - kv  12:                          general.file_type u32              = 1\n",
            "llama_model_loader: - kv  13:                       tokenizer.ggml.model str              = gpt2\n",
            "llama_model_loader: - kv  14:                      tokenizer.ggml.tokens arr[str,128256]  = [\"!\", \"\\\"\", \"#\", \"$\", \"%\", \"&\", \"'\", ...\n",
            "llama_model_loader: - kv  15:                      tokenizer.ggml.scores arr[f32,128256]  = [0.000000, 0.000000, 0.000000, 0.0000...\n",
            "llama_model_loader: - kv  16:                  tokenizer.ggml.token_type arr[i32,128256]  = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...\n",
            "llama_model_loader: - kv  17:                      tokenizer.ggml.merges arr[str,280147]  = [\"Ġ Ġ\", \"Ġ ĠĠĠ\", \"ĠĠ ĠĠ\", \"...\n",
            "llama_model_loader: - kv  18:                tokenizer.ggml.bos_token_id u32              = 128000\n",
            "llama_model_loader: - kv  19:                tokenizer.ggml.eos_token_id u32              = 128009\n",
            "llama_model_loader: - kv  20:            tokenizer.ggml.padding_token_id u32              = 128009\n",
            "llama_model_loader: - kv  21:                    tokenizer.chat_template str              = {{ '<|begin_of_text|>' }}{% set syste...\n",
            "llama_model_loader: - type  f32:   65 tensors\n",
            "llama_model_loader: - type  f16:  226 tensors\n",
            "llama_model_quantize_internal: meta size = 8349248 bytes\n",
            "[   1/ 291]                    token_embd.weight - [ 4096, 128256,     1,     1], type =    f16, converting to q2_K .. size =  1002.00 MiB ->   164.39 MiB\n",
            "[   2/ 291]               blk.0.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[   3/ 291]                blk.0.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, converting to q3_K .. size =   112.00 MiB ->    24.06 MiB\n",
            "[   4/ 291]                blk.0.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q2_K .. size =   112.00 MiB ->    18.38 MiB\n",
            "[   5/ 291]                  blk.0.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q2_K .. size =   112.00 MiB ->    18.38 MiB\n",
            "[   6/ 291]                blk.0.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[   7/ 291]                  blk.0.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q2_K .. size =     8.00 MiB ->     1.31 MiB\n",
            "[   8/ 291]             blk.0.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q3_K .. size =    32.00 MiB ->     6.88 MiB\n",
            "[   9/ 291]                  blk.0.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q2_K .. size =    32.00 MiB ->     5.25 MiB\n",
            "[  10/ 291]                  blk.0.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[  11/ 291]               blk.1.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[  12/ 291]                blk.1.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, converting to q3_K .. size =   112.00 MiB ->    24.06 MiB\n",
            "[  13/ 291]                blk.1.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q2_K .. size =   112.00 MiB ->    18.38 MiB\n",
            "[  14/ 291]                  blk.1.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q2_K .. size =   112.00 MiB ->    18.38 MiB\n",
            "[  15/ 291]                blk.1.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[  16/ 291]                  blk.1.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q2_K .. size =     8.00 MiB ->     1.31 MiB\n",
            "[  17/ 291]             blk.1.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q3_K .. size =    32.00 MiB ->     6.88 MiB\n",
            "[  18/ 291]                  blk.1.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q2_K .. size =    32.00 MiB ->     5.25 MiB\n",
            "[  19/ 291]                  blk.1.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[  20/ 291]               blk.2.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[  21/ 291]                blk.2.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, converting to q3_K .. size =   112.00 MiB ->    24.06 MiB\n",
            "[  22/ 291]                blk.2.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q2_K .. size =   112.00 MiB ->    18.38 MiB\n",
            "[  23/ 291]                  blk.2.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q2_K .. size =   112.00 MiB ->    18.38 MiB\n",
            "[  24/ 291]                blk.2.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[  25/ 291]                  blk.2.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q2_K .. size =     8.00 MiB ->     1.31 MiB\n",
            "[  26/ 291]             blk.2.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q3_K .. size =    32.00 MiB ->     6.88 MiB\n",
            "[  27/ 291]                  blk.2.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q2_K .. size =    32.00 MiB ->     5.25 MiB\n",
            "[  28/ 291]                  blk.2.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[  29/ 291]               blk.3.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[  30/ 291]                blk.3.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, converting to q3_K .. size =   112.00 MiB ->    24.06 MiB\n",
            "[  31/ 291]                blk.3.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q2_K .. size =   112.00 MiB ->    18.38 MiB\n",
            "[  32/ 291]                  blk.3.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q2_K .. "
          ]
        }
      ],
      "source": [
        "# quantize model with different precision, balance quality, prefer q4_k_m\n",
        "!llama.cpp/build/bin/quantize Llama3-8B-Chinese-Chat-fp16.GGUF Llama3-8B-Chinese-Chat.q2_k.GGUF q2_k > /dev/null\n",
        "!python upload.py --hf_token \"huggingface write token\" \\\n",
        "--model_id \"你要创建的模型ID 一般是 username/modelname\" \\\n",
        "--local_path \"上传的本地文件路径\" \\\n",
        "--repo_path \"repo中的文件路径\"\n",
        "# !rm -rf Llama3-8B-Chinese-Chat.q2_k.GGUF"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PHoOf5vkw4Nz"
      },
      "outputs": [],
      "source": [
        "# download \n",
        "# huggingface-cli download --resume-download model_id model_file --local-dir /data/hf --cache-dir /tmp/cache/ --local-dir-use-symlinks False"
      ]
    }
  ],
  "metadata": {
    "colab": {
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      "display_name": "Python 3",
      "name": "python3"
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      "name": "python"
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