Mistral_7B_Instruct_v0_3_GGUF.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Troyanovsky/Local-LLM-Comparison-Colab-UI/blob/main/Mistral_7B_Instruct_v0_3_GGUF.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XNkpBtLvuTOp"
      },
      "source": [
        "## Mistral-7B-Instruct-v0.3-GGUF WebUI\n",
        "\n",
        "1. Run the following cell, takes ~5 min\n",
        "(You may need to confirm to proceed by typing \"Y\")\n",
        "2. Pick the version you need from one of the last two cells and only run that cell.\n",
        "3. Click the gradio link at the bottom\n",
        "4. In Parameter settings - Instruction Template:\n",
        "```\n",
        "<|user|> {prompt}<|end|><|assistant|><|end|>\n",
        "```\n",
        "\n",
        "Original model: https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3\n",
        "\n",
        "Quantized model: https://huggingface.co/MaziyarPanahi/Mistral-7B-Instruct-v0.3-GGUF\n",
        "\n",
        "Want to try other local LLMs? Check out this repo: https://github.com/Troyanovsky/Local-LLM-Comparison-Colab-UI/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "D-MiHp_bveP6",
        "outputId": "c39eec80-959f-42f5-902c-a81b8d3ad7ba"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content\n",
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            "/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc.so.2 is not a symbolic link\n",
            "\n",
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            "\n",
            "Cloning into 'text-generation-webui'...\n",
            "remote: Enumerating objects: 17724, done.\u001b[K\n",
            "remote: Total 17724 (delta 0), reused 0 (delta 0), pack-reused 17724\u001b[K\n",
            "Receiving objects: 100% (17724/17724), 27.48 MiB | 10.69 MiB/s, done.\n",
            "Resolving deltas: 100% (12506/12506), done.\n",
            "Note: switching to '8f12fb028dff4e133460fe10ef49d3f90167b313'.\n",
            "\n",
            "You are in 'detached HEAD' state. You can look around, make experimental\n",
            "changes and commit them, and you can discard any commits you make in this\n",
            "state without impacting any branches by switching back to a branch.\n",
            "\n",
            "If you want to create a new branch to retain commits you create, you may\n",
            "do so (now or later) by using -c with the switch command. Example:\n",
            "\n",
            "  git switch -c <new-branch-name>\n",
            "\n",
            "Or undo this operation with:\n",
            "\n",
            "  git switch -\n",
            "\n",
            "Turn off this advice by setting config variable advice.detachedHead to false\n",
            "\n",
            "/content/text-generation-webui\n",
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            "Building wheels for collected packages: hqq, rouge-score, ffmpy, sqlitedict\n",
            "  Building wheel for hqq (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "  Stored in directory: /root/.cache/pip/wheels/5f/dd/89/461065a73be61a532ff8599a28e9beef17985c9e9c31e541b4\n",
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            "  Created wheel for ffmpy: filename=ffmpy-0.3.2-py3-none-any.whl size=5584 sha256=1d5934a4bc451c68fc95e1d6b893827678a33c94e272ce9a7689a6ba097e0d6b\n",
            "  Stored in directory: /root/.cache/pip/wheels/bd/65/9a/671fc6dcde07d4418df0c592f8df512b26d7a0029c2a23dd81\n",
            "  Building wheel for sqlitedict (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sqlitedict: filename=sqlitedict-2.1.0-py3-none-any.whl size=16862 sha256=8e0bbf32a0d2bf59d27718d57a3735a556963b9b356e528d684c94e2e1e18211\n",
            "  Stored in directory: /root/.cache/pip/wheels/79/d6/e7/304e0e6cb2221022c26d8161f7c23cd4f259a9e41e8bbcfabd\n",
            "Successfully built hqq rouge-score ffmpy sqlitedict\n",
            "Installing collected packages: sqlitedict, pydub, ninja, gptq-for-llama, ffmpy, zstandard, xxhash, websockets, tomlkit, tcolorpy, smmap, shellingham, setproctitle, sentry-sdk, semantic-version, ruff, rouge, python-multipart, pycountry, pybind11, portalocker, Pillow, pathvalidate, orjson, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, numpy, mbstrdecoder, llvmlite, jsonlines, jinja2, humanfriendly, h11, einops, docker-pycreds, diskcache, dill, cramjam, colorama, aiofiles, uvicorn, typepy, tqdm-multiprocess, tiktoken, starlette, SpeechRecognition, sacrebleu, rouge-score, nvidia-cusparse-cu12, nvidia-cudnn-cu12, numba, multiprocess, llama-cpp-python-cuda-tensorcores, llama-cpp-python-cuda, llama-cpp-python, httpcore, gitdb, gekko, coloredlogs, sse-starlette, nvidia-cusolver-cu12, httpx, gitpython, flask_cloudflared, fastparquet, fastapi, wandb, transformers, openai, gradio-client, datasets, DataProperty, tabledata, gradio, flash-attn, exllamav2, bitsandbytes, autoawq-kernels, accelerate, timm, pytablewriter, peft, optimum, autoawq, aqlm, lm_eval, hqq, auto-gptq\n",
            "  Attempting uninstall: Pillow\n",
            "    Found existing installation: Pillow 9.4.0\n",
            "    Uninstalling Pillow-9.4.0:\n",
            "      Successfully uninstalled Pillow-9.4.0\n",
            "  Attempting uninstall: numpy\n",
            "    Found existing installation: numpy 1.25.2\n",
            "    Uninstalling numpy-1.25.2:\n",
            "      Successfully uninstalled numpy-1.25.2\n",
            "  Attempting uninstall: llvmlite\n",
            "    Found existing installation: llvmlite 0.41.1\n",
            "    Uninstalling llvmlite-0.41.1:\n",
            "      Successfully uninstalled llvmlite-0.41.1\n",
            "  Attempting uninstall: jinja2\n",
            "    Found existing installation: Jinja2 3.1.4\n",
            "    Uninstalling Jinja2-3.1.4:\n",
            "      Successfully uninstalled Jinja2-3.1.4\n",
            "  Attempting uninstall: numba\n",
            "    Found existing installation: numba 0.58.1\n",
            "    Uninstalling numba-0.58.1:\n",
            "      Successfully uninstalled numba-0.58.1\n",
            "  Attempting uninstall: transformers\n",
            "    Found existing installation: transformers 4.41.0\n",
            "    Uninstalling transformers-4.41.0:\n",
            "      Successfully uninstalled transformers-4.41.0\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",
            "imageio 2.31.6 requires pillow<10.1.0,>=8.3.2, but you have pillow 10.3.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed DataProperty-1.0.1 Pillow-10.3.0 SpeechRecognition-3.10.0 accelerate-0.27.2 aiofiles-23.2.1 aqlm-1.1.3 auto-gptq-0.6.0+cu121 autoawq-0.2.3 autoawq-kernels-0.0.6 bitsandbytes-0.43.1 colorama-0.4.6 coloredlogs-15.0.1 cramjam-2.8.3 datasets-2.19.1 dill-0.3.8 diskcache-5.6.3 docker-pycreds-0.4.0 einops-0.8.0 exllamav2-0.0.19+cu121 fastapi-0.110.3 fastparquet-2024.5.0 ffmpy-0.3.2 flash-attn-2.5.6 flask_cloudflared-0.0.14 gekko-1.1.1 gitdb-4.0.11 gitpython-3.1.43 gptq-for-llama-0.1.1+cu121 gradio-4.26.0 gradio-client-0.15.1 h11-0.14.0 hqq-0.1.5 httpcore-1.0.5 httpx-0.27.0 humanfriendly-10.0 jinja2-3.1.2 jsonlines-4.0.0 llama-cpp-python-0.2.65+cpuavx2 llama-cpp-python-cuda-0.2.65+cu121 llama-cpp-python-cuda-tensorcores-0.2.65+cu121 llvmlite-0.42.0 lm_eval-0.3.0 mbstrdecoder-1.1.3 multiprocess-0.70.16 ninja-1.11.1.1 numba-0.59.1 numpy-1.26.4 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.20.5 nvidia-nvjitlink-cu12-12.5.40 nvidia-nvtx-cu12-12.1.105 openai-1.30.1 optimum-1.17.1 orjson-3.10.3 pathvalidate-3.2.0 peft-0.8.2 portalocker-2.8.2 pybind11-2.12.0 pycountry-23.12.11 pydub-0.25.1 pytablewriter-1.2.0 python-multipart-0.0.9 rouge-1.0.1 rouge-score-0.1.2 ruff-0.4.5 sacrebleu-1.5.0 semantic-version-2.10.0 sentry-sdk-2.2.1 setproctitle-1.3.3 shellingham-1.5.4 smmap-5.0.1 sqlitedict-2.1.0 sse-starlette-1.6.5 starlette-0.37.2 tabledata-1.3.3 tcolorpy-0.1.6 tiktoken-0.7.0 timm-1.0.3 tomlkit-0.12.0 tqdm-multiprocess-0.0.11 transformers-4.40.2 typepy-1.3.2 uvicorn-0.29.0 wandb-0.17.0 websockets-11.0.3 xxhash-3.4.1 zstandard-0.22.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "PIL"
                ]
              },
              "id": "182fe0f4ea114080846c86030a496c19"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich<14.0.0,>=10.11.0->typer[all]<1.0,>=0.9->gradio==4.26.0) (0.1.2)\n",
            "Found existing installation: llama_cpp_python 0.2.65+cpuavx2\n",
            "Uninstalling llama_cpp_python-0.2.65+cpuavx2:\n",
            "  Successfully uninstalled llama_cpp_python-0.2.65+cpuavx2\n",
            "Collecting llama-cpp-python\n",
            "  Downloading llama_cpp_python-0.2.75.tar.gz (48.7 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m48.7/48.7 MB\u001b[0m \u001b[31m188.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Installing backend dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: typing-extensions>=4.5.0 in /usr/local/lib/python3.10/dist-packages (from llama-cpp-python) (4.11.0)\n",
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            "Building wheels for collected packages: llama-cpp-python\n",
            "  Building wheel for llama-cpp-python (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for llama-cpp-python: filename=llama_cpp_python-0.2.75-cp310-cp310-linux_x86_64.whl size=76308830 sha256=5118a7741add012f3d5d030afdd175301efb63c6f6b1ed24f766c767873c01ca\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-2ckdbriu/wheels/5e/df/9a/e4bb2e48bfa64fb174f0f786296c8507dbebea2a112f1adf8d\n",
            "Successfully built llama-cpp-python\n",
            "Installing collected packages: llama-cpp-python\n",
            "Successfully installed llama-cpp-python-0.2.75\n",
            "Found existing installation: flash-attn 2.5.6\n",
            "Uninstalling flash-attn-2.5.6:\n",
            "  Successfully uninstalled flash-attn-2.5.6\n",
            "Collecting flash-attn==2.5.6\n",
            "  Downloading flash_attn-2.5.6.tar.gz (2.5 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.5/2.5 MB\u001b[0m \u001b[31m26.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (from flash-attn==2.5.6) (2.3.0+cu121)\n",
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            "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch->flash-attn==2.5.6) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch->flash-attn==2.5.6) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch->flash-attn==2.5.6) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /usr/local/lib/python3.10/dist-packages (from torch->flash-attn==2.5.6) (8.9.2.26)\n",
            "Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /usr/local/lib/python3.10/dist-packages (from torch->flash-attn==2.5.6) (12.1.3.1)\n",
            "Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /usr/local/lib/python3.10/dist-packages (from torch->flash-attn==2.5.6) (11.0.2.54)\n",
            "Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /usr/local/lib/python3.10/dist-packages (from torch->flash-attn==2.5.6) (10.3.2.106)\n",
            "Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /usr/local/lib/python3.10/dist-packages (from torch->flash-attn==2.5.6) (11.4.5.107)\n",
            "Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /usr/local/lib/python3.10/dist-packages (from torch->flash-attn==2.5.6) (12.1.0.106)\n",
            "Requirement already satisfied: nvidia-nccl-cu12==2.20.5 in /usr/local/lib/python3.10/dist-packages (from torch->flash-attn==2.5.6) (2.20.5)\n",
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            "Building wheels for collected packages: flash-attn\n",
            "  Building wheel for flash-attn (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for flash-attn: filename=flash_attn-2.5.6-cp310-cp310-linux_x86_64.whl size=120612040 sha256=6287363ce711b792034cb0b76eb62118d734149984c0b1de37e5eec6784c98e2\n",
            "  Stored in directory: /root/.cache/pip/wheels/a8/1c/88/b959d6818b98a46d61ba231683abb7523b89ac1a7ed1e0c206\n",
            "Successfully built flash-attn\n",
            "Installing collected packages: flash-attn\n",
            "Successfully installed flash-attn-2.5.6\n",
            "\u001b[0m\n",
            "Download Results:\n",
            "gid   |stat|avg speed  |path/URI\n",
            "======+====+===========+=======================================================\n",
            "ccaa07|\u001b[1;32mOK\u001b[0m  |   147MiB/s|/content/text-generation-webui/models//Mistral-7B-Instruct-v0.3.Q5_K_M.gguf\n",
            "\n",
            "Status Legend:\n",
            "(OK):download completed.\n",
            "/content/text-generation-webui\n",
            "\u001b[2;36m06:25:03-928978\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Starting Text generation web UI                                            \n",
            "\u001b[2;36m06:25:04-035713\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Loading \u001b[32m\"Mistral-7B-Instruct-v0.3.Q5_K_M.gguf\"\u001b[0m                             \n",
            "\u001b[2;36m06:25:04-436234\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m llama.cpp weights detected: \u001b[32m\"models/Mistral-7B-Instruct-v0.3.Q5_K_M.gguf\"\u001b[0m  \n",
            "llama_model_loader: loaded meta data with 29 key-value pairs and 291 tensors from models/Mistral-7B-Instruct-v0.3.Q5_K_M.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              = models--mistralai--Mistral-7B-Instruc...\n",
            "llama_model_loader: - kv   2:                          llama.block_count u32              = 32\n",
            "llama_model_loader: - kv   3:                       llama.context_length u32              = 32768\n",
            "llama_model_loader: - kv   4:                     llama.embedding_length u32              = 4096\n",
            "llama_model_loader: - kv   5:                  llama.feed_forward_length u32              = 14336\n",
            "llama_model_loader: - kv   6:                 llama.attention.head_count u32              = 32\n",
            "llama_model_loader: - kv   7:              llama.attention.head_count_kv u32              = 8\n",
            "llama_model_loader: - kv   8:                       llama.rope.freq_base f32              = 1000000.000000\n",
            "llama_model_loader: - kv   9:     llama.attention.layer_norm_rms_epsilon f32              = 0.000010\n",
            "llama_model_loader: - kv  10:                          general.file_type u32              = 17\n",
            "llama_model_loader: - kv  11:                           llama.vocab_size u32              = 32768\n",
            "llama_model_loader: - kv  12:                 llama.rope.dimension_count u32              = 128\n",
            "llama_model_loader: - kv  13:                       tokenizer.ggml.model str              = llama\n",
            "llama_model_loader: - kv  14:                         tokenizer.ggml.pre str              = default\n",
            "llama_model_loader: - kv  15:                      tokenizer.ggml.tokens arr[str,32768]   = [\"<unk>\", \"<s>\", \"</s>\", \"[INST]\", \"[...\n",
            "llama_model_loader: - kv  16:                      tokenizer.ggml.scores arr[f32,32768]   = [0.000000, 0.000000, 0.000000, 0.0000...\n",
            "llama_model_loader: - kv  17:                  tokenizer.ggml.token_type arr[i32,32768]   = [2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, ...\n",
            "llama_model_loader: - kv  18:                tokenizer.ggml.bos_token_id u32              = 1\n",
            "llama_model_loader: - kv  19:                tokenizer.ggml.eos_token_id u32              = 2\n",
            "llama_model_loader: - kv  20:            tokenizer.ggml.unknown_token_id u32              = 0\n",
            "llama_model_loader: - kv  21:               tokenizer.ggml.add_bos_token bool             = true\n",
            "llama_model_loader: - kv  22:               tokenizer.ggml.add_eos_token bool             = false\n",
            "llama_model_loader: - kv  23:                    tokenizer.chat_template str              = {{ bos_token }}{% for message in mess...\n",
            "llama_model_loader: - kv  24:               general.quantization_version u32              = 2\n",
            "llama_model_loader: - kv  25:                      quantize.imatrix.file str              = ./imatrix.dat\n",
            "llama_model_loader: - kv  26:                   quantize.imatrix.dataset str              = group_40.txt\n",
            "llama_model_loader: - kv  27:             quantize.imatrix.entries_count i32              = 224\n",
            "llama_model_loader: - kv  28:              quantize.imatrix.chunks_count i32              = 74\n",
            "llama_model_loader: - type  f32:   65 tensors\n",
            "llama_model_loader: - type q5_K:  193 tensors\n",
            "llama_model_loader: - type q6_K:   33 tensors\n",
            "llm_load_vocab: special tokens definition check successful ( 1027/32768 ).\n",
            "llm_load_print_meta: format           = GGUF V3 (latest)\n",
            "llm_load_print_meta: arch             = llama\n",
            "llm_load_print_meta: vocab type       = SPM\n",
            "llm_load_print_meta: n_vocab          = 32768\n",
            "llm_load_print_meta: n_merges         = 0\n",
            "llm_load_print_meta: n_ctx_train      = 32768\n",
            "llm_load_print_meta: n_embd           = 4096\n",
            "llm_load_print_meta: n_head           = 32\n",
            "llm_load_print_meta: n_head_kv        = 8\n",
            "llm_load_print_meta: n_layer          = 32\n",
            "llm_load_print_meta: n_rot            = 128\n",
            "llm_load_print_meta: n_embd_head_k    = 128\n",
            "llm_load_print_meta: n_embd_head_v    = 128\n",
            "llm_load_print_meta: n_gqa            = 4\n",
            "llm_load_print_meta: n_embd_k_gqa     = 1024\n",
            "llm_load_print_meta: n_embd_v_gqa     = 1024\n",
            "llm_load_print_meta: f_norm_eps       = 0.0e+00\n",
            "llm_load_print_meta: f_norm_rms_eps   = 1.0e-05\n",
            "llm_load_print_meta: f_clamp_kqv      = 0.0e+00\n",
            "llm_load_print_meta: f_max_alibi_bias = 0.0e+00\n",
            "llm_load_print_meta: f_logit_scale    = 0.0e+00\n",
            "llm_load_print_meta: n_ff             = 14336\n",
            "llm_load_print_meta: n_expert         = 0\n",
            "llm_load_print_meta: n_expert_used    = 0\n",
            "llm_load_print_meta: causal attn      = 1\n",
            "llm_load_print_meta: pooling type     = 0\n",
            "llm_load_print_meta: rope type        = 0\n",
            "llm_load_print_meta: rope scaling     = linear\n",
            "llm_load_print_meta: freq_base_train  = 1000000.0\n",
            "llm_load_print_meta: freq_scale_train = 1\n",
            "llm_load_print_meta: n_yarn_orig_ctx  = 32768\n",
            "llm_load_print_meta: rope_finetuned   = unknown\n",
            "llm_load_print_meta: ssm_d_conv       = 0\n",
            "llm_load_print_meta: ssm_d_inner      = 0\n",
            "llm_load_print_meta: ssm_d_state      = 0\n",
            "llm_load_print_meta: ssm_dt_rank      = 0\n",
            "llm_load_print_meta: model type       = 8B\n",
            "llm_load_print_meta: model ftype      = Q5_K - Medium\n",
            "llm_load_print_meta: model params     = 7.25 B\n",
            "llm_load_print_meta: model size       = 4.78 GiB (5.67 BPW) \n",
            "llm_load_print_meta: general.name     = models--mistralai--Mistral-7B-Instruct-v0.3\n",
            "llm_load_print_meta: BOS token        = 1 '<s>'\n",
            "llm_load_print_meta: EOS token        = 2 '</s>'\n",
            "llm_load_print_meta: UNK token        = 0 '<unk>'\n",
            "llm_load_print_meta: LF token         = 781 '<0x0A>'\n",
            "ggml_cuda_init: GGML_CUDA_FORCE_MMQ:   yes\n",
            "ggml_cuda_init: CUDA_USE_TENSOR_CORES: no\n",
            "ggml_cuda_init: found 1 CUDA devices:\n",
            "  Device 0: Tesla T4, compute capability 7.5, VMM: yes\n",
            "llm_load_tensors: ggml ctx size =    0.30 MiB\n",
            "llm_load_tensors: offloading 32 repeating layers to GPU\n",
            "llm_load_tensors: offloading non-repeating layers to GPU\n",
            "llm_load_tensors: offloaded 33/33 layers to GPU\n",
            "llm_load_tensors:        CPU buffer size =    88.00 MiB\n",
            "llm_load_tensors:      CUDA0 buffer size =  4809.52 MiB\n",
            "...................................................................................................\n",
            "llama_new_context_with_model: n_ctx      = 32768\n",
            "llama_new_context_with_model: n_batch    = 512\n",
            "llama_new_context_with_model: n_ubatch   = 512\n",
            "llama_new_context_with_model: freq_base  = 1000000.0\n",
            "llama_new_context_with_model: freq_scale = 1\n",
            "llama_kv_cache_init:      CUDA0 KV buffer size =  4096.00 MiB\n",
            "llama_new_context_with_model: KV self size  = 4096.00 MiB, K (f16): 2048.00 MiB, V (f16): 2048.00 MiB\n",
            "llama_new_context_with_model:  CUDA_Host  output buffer size =     0.12 MiB\n",
            "llama_new_context_with_model:      CUDA0 compute buffer size =  2144.00 MiB\n",
            "llama_new_context_with_model:  CUDA_Host compute buffer size =    72.01 MiB\n",
            "llama_new_context_with_model: graph nodes  = 1030\n",
            "llama_new_context_with_model: graph splits = 2\n",
            "AVX = 1 | AVX_VNNI = 0 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 1 | NEON = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | LAMMAFILE = 1 | \n",
            "Model metadata: {'quantize.imatrix.entries_count': '224', 'quantize.imatrix.dataset': 'group_40.txt', 'quantize.imatrix.chunks_count': '74', 'quantize.imatrix.file': './imatrix.dat', 'tokenizer.chat_template': \"{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token}}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}\", 'tokenizer.ggml.add_eos_token': 'false', 'tokenizer.ggml.unknown_token_id': '0', 'tokenizer.ggml.eos_token_id': '2', 'general.quantization_version': '2', 'tokenizer.ggml.model': 'llama', 'general.architecture': 'llama', 'llama.rope.freq_base': '1000000.000000', 'tokenizer.ggml.pre': 'default', 'llama.context_length': '32768', 'general.name': 'models--mistralai--Mistral-7B-Instruct-v0.3', 'tokenizer.ggml.add_bos_token': 'true', 'llama.embedding_length': '4096', 'llama.feed_forward_length': '14336', 'llama.attention.layer_norm_rms_epsilon': '0.000010', 'tokenizer.ggml.bos_token_id': '1', 'llama.attention.head_count': '32', 'llama.block_count': '32', 'llama.attention.head_count_kv': '8', 'general.file_type': '17', 'llama.vocab_size': '32768', 'llama.rope.dimension_count': '128'}\n",
            "Guessed chat format: mistral-instruct\n",
            "\u001b[2;36m06:25:14-579683\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m LOADER: \u001b[32m\"llama.cpp\"\u001b[0m                                                        \n",
            "\u001b[2;36m06:25:14-581717\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m TRUNCATION LENGTH: \u001b[1;36m32768\u001b[0m                                                   \n",
            "\u001b[2;36m06:25:14-583401\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m INSTRUCTION TEMPLATE: \u001b[32m\"Custom \u001b[0m\u001b[32m(\u001b[0m\u001b[32mobtained from model metadata\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\"\u001b[0m              \n",
            "\u001b[2;36m06:25:14-585179\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Loaded the model in \u001b[1;36m10.55\u001b[0m seconds.                                         \n",
            "\n",
            "Running on local URL:  http://127.0.0.1:7860\n",
            "\n",
            "Running on public URL: https://d028a6c3daaed6f3c7.gradio.live\n",
            "\n",
            "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n",
            "\n",
            "llama_print_timings:        load time =     621.13 ms\n",
            "llama_print_timings:      sample time =     481.39 ms /   212 runs   (    2.27 ms per token,   440.40 tokens per second)\n",
            "llama_print_timings: prompt eval time =     617.96 ms /    24 tokens (   25.75 ms per token,    38.84 tokens per second)\n",
            "llama_print_timings:        eval time =    6274.34 ms /   211 runs   (   29.74 ms per token,    33.63 tokens per second)\n",
            "llama_print_timings:       total time =    9460.77 ms /   235 tokens\n",
            "Output generated in 10.18 seconds (20.73 tokens/s, 211 tokens, context 28, seed 1073906816)\n",
            "Llama.generate: prefix-match hit\n",
            "\n",
            "llama_print_timings:        load time =     621.13 ms\n",
            "llama_print_timings:      sample time =     322.01 ms /   140 runs   (    2.30 ms per token,   434.77 tokens per second)\n",
            "llama_print_timings: prompt eval time =     778.60 ms /   229 tokens (    3.40 ms per token,   294.12 tokens per second)\n",
            "llama_print_timings:        eval time =    4367.64 ms /   139 runs   (   31.42 ms per token,    31.82 tokens per second)\n",
            "llama_print_timings:       total time =    6521.59 ms /   368 tokens\n"
          ]
        }
      ],
      "source": [
        "%cd /content\n",
        "!apt-get -y install -qq aria2\n",
        "\n",
        "!git clone -b V20240506 https://github.com/Troyanovsky/text-generation-webui\n",
        "%cd /content/text-generation-webui\n",
        "!pip install -r requirements.txt\n",
        "!pip install -U gradio==4.26.0\n",
        "\n",
        "!pip uninstall -y llama-cpp-python -y\n",
        "!CMAKE_ARGS=\"-DLLAMA_CUDA=on\" FORCE_CMAKE=1 pip install llama-cpp-python --no-cache-dir\n",
        "\n",
        "!pip uninstall flash-attn -y\n",
        "!pip install --no-build-isolation flash-attn==2.5.6\n",
        "\n",
        "!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/MaziyarPanahi/Mistral-7B-Instruct-v0.3-GGUF/resolve/main/Mistral-7B-Instruct-v0.3.Q5_K_M.gguf?download=true -d /content/text-generation-webui/models/ -o Mistral-7B-Instruct-v0.3.Q5_K_M.gguf\n",
        "\n",
        "%cd /content/text-generation-webui\n",
        "!python server.py --share --n-gpu-layers 100000 --model Mistral-7B-Instruct-v0.3.Q5_K_M.gguf"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPD952BWnwB4BDZcBUdcWxq",
      "include_colab_link": true
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}