Meta_Llama_3_1_8B_Instruct_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/Meta_Llama_3_1_8B_Instruct_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": [
        "## Meta-Llama-3.1-8B-Instruct-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: Llama3\n",
        "```\n",
        "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n",
        "\n",
        "{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>\n",
        "\n",
        "{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n",
        "```\n",
        "\n",
        "Original model: https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct\n",
        "\n",
        "Quantized model: https://huggingface.co/lmstudio-community/Meta-Llama-3.1-8B-Instruct-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": "a1334493-eed1-49d6-e2ca-bddb0b8f3899"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content\n",
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            "/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_0.so.3 is not a symbolic link\n",
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            "\n",
            "Cloning into 'text-generation-webui'...\n",
            "remote: Enumerating objects: 18617, done.\u001b[K\n",
            "remote: Counting objects: 100% (253/253), done.\u001b[K\n",
            "remote: Compressing objects: 100% (138/138), done.\u001b[K\n",
            "remote: Total 18617 (delta 169), reused 183 (delta 115), pack-reused 18364\u001b[K\n",
            "Receiving objects: 100% (18617/18617), 27.76 MiB | 20.30 MiB/s, done.\n",
            "Resolving deltas: 100% (13175/13175), done.\n",
            "Note: switching to 'af839d20acee2710022d735d2d1dc9cb48696f73'.\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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            "\u001b[?25hBuilding wheels for collected packages: hqq, rouge-score, ffmpy, sqlitedict\n",
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            "  Stored in directory: /root/.cache/pip/wheels/bd/65/9a/671fc6dcde07d4418df0c592f8df512b26d7a0029c2a23dd81\n",
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            "  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, ffmpy, zstandard, xxhash, websockets, uvloop, tomlkit, tcolorpy, smmap, setproctitle, sentry-sdk, semantic-version, ruff, rouge, requests, python-multipart, python-dotenv, 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, humanfriendly, httptools, h11, einops, docker-pycreds, dnspython, diskcache, dill, cramjam, colorama, aiofiles, watchfiles, uvicorn, typepy, tqdm-multiprocess, tiktoken, starlette, SpeechRecognition, sacrebleu, rouge-score, pyarrow, nvidia-cusparse-cu12, nvidia-cudnn-cu12, numba, multiprocess, llama-cpp-python-cuda-tensorcores, llama-cpp-python-cuda, llama-cpp-python, httpcore, gitdb, gekko, email_validator, coloredlogs, sse-starlette, nvidia-cusolver-cu12, httpx, gitpython, flask_cloudflared, fastparquet, wandb, openai, gradio-client, fastapi-cli, exllamav2, datasets, DataProperty, tabledata, flash-attn, fastapi, bitsandbytes, pytablewriter, peft, optimum, hqq, gradio, aqlm, lm_eval, auto-gptq\n",
            "  Attempting uninstall: requests\n",
            "    Found existing installation: requests 2.31.0\n",
            "    Uninstalling requests-2.31.0:\n",
            "      Successfully uninstalled requests-2.31.0\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: pyarrow\n",
            "    Found existing installation: pyarrow 14.0.2\n",
            "    Uninstalling pyarrow-14.0.2:\n",
            "      Successfully uninstalled pyarrow-14.0.2\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",
            "\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",
            "cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 17.0.0 which is incompatible.\n",
            "google-colab 1.0.0 requires requests==2.31.0, but you have requests 2.32.3 which is incompatible.\n",
            "ibis-framework 8.0.0 requires pyarrow<16,>=2, but you have pyarrow 17.0.0 which is incompatible.\n",
            "imageio 2.31.6 requires pillow<10.1.0,>=8.3.2, but you have pillow 10.4.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed DataProperty-1.0.1 Pillow-10.4.0 SpeechRecognition-3.10.0 aiofiles-23.2.1 aqlm-1.1.6 auto-gptq-0.7.1 bitsandbytes-0.43.2 colorama-0.4.6 coloredlogs-15.0.1 cramjam-2.8.3 datasets-2.20.0 dill-0.3.8 diskcache-5.6.3 dnspython-2.6.1 docker-pycreds-0.4.0 einops-0.8.0 email_validator-2.2.0 exllamav2-0.1.7+cu121.torch2.2.2 fastapi-0.111.1 fastapi-cli-0.0.4 fastparquet-2024.5.0 ffmpy-0.3.2 flash-attn-2.6.1 flask_cloudflared-0.0.14 gekko-1.2.1 gitdb-4.0.11 gitpython-3.1.43 gradio-4.26.0 gradio-client-0.15.1 h11-0.14.0 hqq-0.1.7.post3 httpcore-1.0.5 httptools-0.6.1 httpx-0.27.0 humanfriendly-10.0 jsonlines-4.0.0 llama-cpp-python-0.2.83+cpuavx2 llama-cpp-python-cuda-0.2.83+cu121 llama-cpp-python-cuda-tensorcores-0.2.83+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-cu12-12.5.82 nvidia-nvtx-cu12-12.1.105 openai-1.37.0 optimum-1.17.1 orjson-3.10.6 pathvalidate-3.2.0 peft-0.8.2 portalocker-2.10.1 pyarrow-17.0.0 pybind11-2.13.1 pycountry-24.6.1 pydub-0.25.1 pytablewriter-1.2.0 python-dotenv-1.0.1 python-multipart-0.0.9 requests-2.32.3 rouge-1.0.1 rouge-score-0.1.2 ruff-0.5.4 sacrebleu-1.5.0 semantic-version-2.10.0 sentry-sdk-2.11.0 setproctitle-1.3.3 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 tomlkit-0.12.0 tqdm-multiprocess-0.0.11 typepy-1.3.2 uvicorn-0.30.3 uvloop-0.19.0 wandb-0.17.5 watchfiles-0.22.0 websockets-11.0.3 xxhash-3.4.1 zstandard-0.23.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "PIL"
                ]
              },
              "id": "df1709bbe90b439d9ed7ace1db96f265"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "\u001b[33mWARNING: typer 0.12.3 does not provide the extra 'all'\u001b[0m\u001b[33m\n",
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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>=10.11.0->typer<1.0,>=0.9->typer[all]<1.0,>=0.9; sys_platform != \"emscripten\"->gradio==4.26.0) (0.1.2)\n",
            "\u001b[0m\n",
            "Download Results:\n",
            "gid   |stat|avg speed  |path/URI\n",
            "======+====+===========+=======================================================\n",
            "29408d|\u001b[1;32mOK\u001b[0m  |   139MiB/s|/content/text-generation-webui/models//Meta-Llama-3.1-8B-Instruct-Q5_K_M.gguf\n",
            "\n",
            "Status Legend:\n",
            "(OK):download completed.\n",
            "/content/text-generation-webui\n",
            "\u001b[2;36m02:47:05-374230\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Starting Text generation web UI                                            \n",
            "\u001b[2;36m02:47:05-771279\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Loading \u001b[32m\"Meta-Llama-3.1-8B-Instruct-Q5_K_M.gguf\"\u001b[0m                           \n",
            "\u001b[2;36m02:47:06-152152\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m llama.cpp weights detected: \u001b[32m\"models/Meta-Llama-3.1-8B-Instruct-Q5_K_M.gguf\"\u001b[0m\n",
            "llama_model_loader: loaded meta data with 33 key-value pairs and 291 tensors from models/Meta-Llama-3.1-8B-Instruct-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.type str              = model\n",
            "llama_model_loader: - kv   2:                               general.name str              = Meta Llama 3.1 8B Instruct\n",
            "llama_model_loader: - kv   3:                           general.finetune str              = Instruct\n",
            "llama_model_loader: - kv   4:                           general.basename str              = Meta-Llama-3.1\n",
            "llama_model_loader: - kv   5:                         general.size_label str              = 8B\n",
            "llama_model_loader: - kv   6:                            general.license str              = llama3.1\n",
            "llama_model_loader: - kv   7:                               general.tags arr[str,6]       = [\"facebook\", \"meta\", \"pytorch\", \"llam...\n",
            "llama_model_loader: - kv   8:                          general.languages arr[str,8]       = [\"en\", \"de\", \"fr\", \"it\", \"pt\", \"hi\", ...\n",
            "llama_model_loader: - kv   9:                          llama.block_count u32              = 32\n",
            "llama_model_loader: - kv  10:                       llama.context_length u32              = 131072\n",
            "llama_model_loader: - kv  11:                     llama.embedding_length u32              = 4096\n",
            "llama_model_loader: - kv  12:                  llama.feed_forward_length u32              = 14336\n",
            "llama_model_loader: - kv  13:                 llama.attention.head_count u32              = 32\n",
            "llama_model_loader: - kv  14:              llama.attention.head_count_kv u32              = 8\n",
            "llama_model_loader: - kv  15:                       llama.rope.freq_base f32              = 500000.000000\n",
            "llama_model_loader: - kv  16:     llama.attention.layer_norm_rms_epsilon f32              = 0.000010\n",
            "llama_model_loader: - kv  17:                          general.file_type u32              = 17\n",
            "llama_model_loader: - kv  18:                           llama.vocab_size u32              = 128256\n",
            "llama_model_loader: - kv  19:                 llama.rope.dimension_count u32              = 128\n",
            "llama_model_loader: - kv  20:                       tokenizer.ggml.model str              = gpt2\n",
            "llama_model_loader: - kv  21:                         tokenizer.ggml.pre str              = llama-bpe\n",
            "llama_model_loader: - kv  22:                      tokenizer.ggml.tokens arr[str,128256]  = [\"!\", \"\\\"\", \"#\", \"$\", \"%\", \"&\", \"'\", ...\n",
            "llama_model_loader: - kv  23:                  tokenizer.ggml.token_type arr[i32,128256]  = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...\n",
            "llama_model_loader: - kv  24:                      tokenizer.ggml.merges arr[str,280147]  = [\"Ġ Ġ\", \"Ġ ĠĠĠ\", \"ĠĠ ĠĠ\", \"...\n",
            "llama_model_loader: - kv  25:                tokenizer.ggml.bos_token_id u32              = 128000\n",
            "llama_model_loader: - kv  26:                tokenizer.ggml.eos_token_id u32              = 128009\n",
            "llama_model_loader: - kv  27:                    tokenizer.chat_template str              = {% set loop_messages = messages %}{% ...\n",
            "llama_model_loader: - kv  28:               general.quantization_version u32              = 2\n",
            "llama_model_loader: - kv  29:                      quantize.imatrix.file str              = /models_out/Meta-Llama-3.1-8B-Instruc...\n",
            "llama_model_loader: - kv  30:                   quantize.imatrix.dataset str              = /training_dir/calibration_datav3.txt\n",
            "llama_model_loader: - kv  31:             quantize.imatrix.entries_count i32              = 224\n",
            "llama_model_loader: - kv  32:              quantize.imatrix.chunks_count i32              = 125\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 cache size = 256\n",
            "llm_load_vocab: token to piece cache size = 0.7999 MB\n",
            "llm_load_print_meta: format           = GGUF V3 (latest)\n",
            "llm_load_print_meta: arch             = llama\n",
            "llm_load_print_meta: vocab type       = BPE\n",
            "llm_load_print_meta: n_vocab          = 128256\n",
            "llm_load_print_meta: n_merges         = 280147\n",
            "llm_load_print_meta: vocab_only       = 0\n",
            "llm_load_print_meta: n_ctx_train      = 131072\n",
            "llm_load_print_meta: n_embd           = 4096\n",
            "llm_load_print_meta: n_layer          = 32\n",
            "llm_load_print_meta: n_head           = 32\n",
            "llm_load_print_meta: n_head_kv        = 8\n",
            "llm_load_print_meta: n_rot            = 128\n",
            "llm_load_print_meta: n_swa            = 0\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  = 500000.0\n",
            "llm_load_print_meta: freq_scale_train = 1\n",
            "llm_load_print_meta: n_ctx_orig_yarn  = 131072\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     = 8.03 B\n",
            "llm_load_print_meta: model size       = 5.33 GiB (5.70 BPW) \n",
            "llm_load_print_meta: general.name     = Meta Llama 3.1 8B Instruct\n",
            "llm_load_print_meta: BOS token        = 128000 '<|begin_of_text|>'\n",
            "llm_load_print_meta: EOS token        = 128009 '<|eot_id|>'\n",
            "llm_load_print_meta: LF token         = 128 'Ä'\n",
            "llm_load_print_meta: EOT token        = 128009 '<|eot_id|>'\n",
            "llm_load_print_meta: max token length = 256\n",
            "ggml_cuda_init: GGML_CUDA_FORCE_MMQ:    yes\n",
            "ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: 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 =    0.27 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 =   344.44 MiB\n",
            "llm_load_tensors:      CUDA0 buffer size =  5115.49 MiB\n",
            ".........................................................................................\n",
            "llama_new_context_with_model: n_ctx      = 8224\n",
            "llama_new_context_with_model: n_batch    = 512\n",
            "llama_new_context_with_model: n_ubatch   = 512\n",
            "llama_new_context_with_model: flash_attn = 0\n",
            "llama_new_context_with_model: freq_base  = 500000.0\n",
            "llama_new_context_with_model: freq_scale = 1\n",
            "llama_kv_cache_init:      CUDA0 KV buffer size =  1028.00 MiB\n",
            "llama_new_context_with_model: KV self size  = 1028.00 MiB, K (f16):  514.00 MiB, V (f16):  514.00 MiB\n",
            "llama_new_context_with_model:  CUDA_Host  output buffer size =     0.49 MiB\n",
            "llama_new_context_with_model:      CUDA0 compute buffer size =   562.07 MiB\n",
            "llama_new_context_with_model:  CUDA_Host compute buffer size =    24.07 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 | AVX512_BF16 = 0 | FMA = 1 | NEON = 0 | SVE = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 0 | \n",
            "Model metadata: {'quantize.imatrix.entries_count': '224', 'quantize.imatrix.dataset': '/training_dir/calibration_datav3.txt', 'quantize.imatrix.chunks_count': '125', 'quantize.imatrix.file': '/models_out/Meta-Llama-3.1-8B-Instruct-GGUF/Meta-Llama-3.1-8B-Instruct.imatrix', 'tokenizer.chat_template': \"{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{{ '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\", 'tokenizer.ggml.eos_token_id': '128009', 'general.quantization_version': '2', 'tokenizer.ggml.model': 'gpt2', 'llama.rope.dimension_count': '128', 'llama.vocab_size': '128256', 'general.file_type': '17', 'llama.attention.layer_norm_rms_epsilon': '0.000010', 'llama.rope.freq_base': '500000.000000', 'general.architecture': 'llama', 'general.basename': 'Meta-Llama-3.1', 'tokenizer.ggml.bos_token_id': '128000', 'llama.attention.head_count': '32', 'tokenizer.ggml.pre': 'llama-bpe', 'llama.context_length': '131072', 'general.name': 'Meta Llama 3.1 8B Instruct', 'general.finetune': 'Instruct', 'general.type': 'model', 'general.size_label': '8B', 'general.license': 'llama3.1', 'llama.feed_forward_length': '14336', 'llama.embedding_length': '4096', 'llama.block_count': '32', 'llama.attention.head_count_kv': '8'}\n",
            "Available chat formats from metadata: chat_template.default\n",
            "Using gguf chat template: {% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n",
            "\n",
            "'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{{ '<|start_header_id|>assistant<|end_header_id|>\n",
            "\n",
            "' }}\n",
            "Using chat eos_token: <|eot_id|>\n",
            "Using chat bos_token: <|begin_of_text|>\n",
            "\u001b[2;36m02:47:25-395422\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Loaded \u001b[32m\"Meta-Llama-3.1-8B-Instruct-Q5_K_M.gguf\"\u001b[0m in \u001b[1;36m19.62\u001b[0m seconds.          \n",
            "\u001b[2;36m02:47:25-397293\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m LOADER: \u001b[32m\"llama.cpp\"\u001b[0m                                                        \n",
            "\u001b[2;36m02:47:25-398599\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m TRUNCATION LENGTH: \u001b[1;36m8196\u001b[0m                                                    \n",
            "\u001b[2;36m02:47:25-399810\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",
            "\n",
            "Running on local URL:  http://127.0.0.1:7860\n",
            "\n",
            "Running on public URL: https://74bf0a20316077b897.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",
            "/usr/local/lib/python3.10/dist-packages/llama_cpp_cuda/llama.py:1129: RuntimeWarning: Detected duplicate leading \"<|begin_of_text|>\" in prompt, this will likely reduce response quality, consider removing it...\n",
            "  warnings.warn(\n",
            "\n",
            "llama_print_timings:        load time =     498.28 ms\n",
            "llama_print_timings:      sample time =    1098.97 ms /   128 runs   (    8.59 ms per token,   116.47 tokens per second)\n",
            "llama_print_timings: prompt eval time =     496.65 ms /    27 tokens (   18.39 ms per token,    54.36 tokens per second)\n",
            "llama_print_timings:        eval time =    3480.64 ms /   127 runs   (   27.41 ms per token,    36.49 tokens per second)\n",
            "llama_print_timings:       total time =    6653.28 ms /   154 tokens\n",
            "Output generated in 7.47 seconds (17.01 tokens/s, 127 tokens, context 58, seed 695741280)\n",
            "Llama.generate: prefix-match hit\n",
            "\n",
            "llama_print_timings:        load time =     498.28 ms\n",
            "llama_print_timings:      sample time =     195.79 ms /    19 runs   (   10.30 ms per token,    97.04 tokens per second)\n",
            "llama_print_timings: prompt eval time =      94.17 ms /    30 tokens (    3.14 ms per token,   318.56 tokens per second)\n",
            "llama_print_timings:        eval time =     521.84 ms /    18 runs   (   28.99 ms per token,    34.49 tokens per second)\n",
            "llama_print_timings:       total time =    1219.75 ms /    48 tokens\n",
            "Output generated in 2.00 seconds (9.02 tokens/s, 18 tokens, context 245, seed 1328714782)\n",
            "Llama.generate: prefix-match hit\n",
            "\n",
            "llama_print_timings:        load time =     498.28 ms\n",
            "llama_print_timings:      sample time =     138.64 ms /    18 runs   (    7.70 ms per token,   129.83 tokens per second)\n",
            "llama_print_timings: prompt eval time =      91.36 ms /    25 tokens (    3.65 ms per token,   273.66 tokens per second)\n",
            "llama_print_timings:        eval time =     500.06 ms /    17 runs   (   29.42 ms per token,    34.00 tokens per second)\n",
            "llama_print_timings:       total time =     925.03 ms /    42 tokens\n",
            "Output generated in 1.51 seconds (11.29 tokens/s, 17 tokens, context 318, seed 406111054)\n"
          ]
        }
      ],
      "source": [
        "%cd /content\n",
        "!apt-get -y install -qq aria2\n",
        "\n",
        "!git clone -b V20240724 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",
        "!aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lmstudio-community/Meta-Llama-3.1-8B-Instruct-GGUF/resolve/main/Meta-Llama-3.1-8B-Instruct-Q5_K_M.gguf?download=true -d /content/text-generation-webui/models/ -o Meta-Llama-3.1-8B-Instruct-Q5_K_M.gguf\n",
        "\n",
        "%cd /content/text-generation-webui\n",
        "!python server.py --share --n-gpu-layers 100000 --model Meta-Llama-3.1-8B-Instruct-Q5_K_M.gguf --n_ctx 8196"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOyuJ/sYRJBK5LD/5lUVAuP",
      "include_colab_link": true
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}