Smegmma_Deluxe_9B_v1_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/Smegmma_Deluxe_9B_v1_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": [
        "## Smegmma-Deluxe-9B-v1-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",
        "```\n",
        "<bos><start_of_turn>user\n",
        "{prompt}<end_of_turn>\n",
        "<start_of_turn>model\n",
        "<end_of_turn>\n",
        "<start_of_turn>model\n",
        "```\n",
        "\n",
        "Original model: https://huggingface.co/TheDrummer/Smegmma-Deluxe-9B-v1\n",
        "\n",
        "Quantized model: https://huggingface.co/bartowski/Smegmma-Deluxe-9B-v1-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": {
        "id": "D-MiHp_bveP6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "8c7ffb19-e985-4913-ab92-f409eae135ba"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content\n",
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            "\n",
            "Cloning into 'text-generation-webui'...\n",
            "remote: Enumerating objects: 18359, done.\u001b[K\n",
            "remote: Total 18359 (delta 0), reused 0 (delta 0), pack-reused 18359\u001b[K\n",
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            "Note: switching to 'e813b322cfdb16da1eb379b9967d7e0e959cdc7e'.\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",
            "  Building wheel for hqq (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for hqq: filename=hqq-0.1.7.post3-py3-none-any.whl size=59171 sha256=adae12d461eb0b56d4ccb0c84894991a0f96c62c4bd347af4dd6f22fbd69ccac\n",
            "  Stored in directory: /root/.cache/pip/wheels/e6/48/52/64097324429cfc0c8e47c763b060a132484cdb0a99483ecf82\n",
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            "  Stored in directory: /root/.cache/pip/wheels/5f/dd/89/461065a73be61a532ff8599a28e9beef17985c9e9c31e541b4\n",
            "  Building wheel for ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for ffmpy: filename=ffmpy-0.3.2-py3-none-any.whl size=5584 sha256=0e400e71fee9d7579e9446c51682407e4e270acc22ec47db02797cd0b5036ff4\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=28aa6a3c57e7c071c1035bcf8ca5c877bf2e9fbb6e76d1912f62b82212a13dfd\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, ffmpy, zstandard, xxhash, websockets, uvloop, ujson, 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, transformers, openai, gradio-client, fastapi-cli, exllamav2, datasets, DataProperty, tabledata, flash-attn, fastapi, bitsandbytes, autoawq-kernels, accelerate, pytablewriter, peft, optimum, hqq, gradio, autoawq, 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",
            "  Attempting uninstall: transformers\n",
            "    Found existing installation: transformers 4.41.2\n",
            "    Uninstalling transformers-4.41.2:\n",
            "      Successfully uninstalled transformers-4.41.2\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 16.1.0 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 16.1.0 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 accelerate-0.31.0 aiofiles-23.2.1 aqlm-1.1.6 auto-gptq-0.7.1 autoawq-0.2.5 autoawq-kernels-0.0.6 bitsandbytes-0.43.1 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 exllamav2-0.1.6+cu121.torch2.2.2 fastapi-0.111.0 fastapi-cli-0.0.4 fastparquet-2024.5.0 ffmpy-0.3.2 flash-attn-2.5.9.post1 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-0.2.81+cpuavx2 llama-cpp-python-cuda-0.2.81+cu121 llama-cpp-python-cuda-tensorcores-0.2.81+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-1.35.13 optimum-1.17.1 orjson-3.10.6 pathvalidate-3.2.0 peft-0.8.2 portalocker-2.10.0 pyarrow-16.1.0 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-0.5.1 sacrebleu-1.5.0 semantic-version-2.10.0 sentry-sdk-2.9.0 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 transformers-4.42.4 typepy-1.3.2 ujson-5.10.0 uvicorn-0.30.1 uvloop-0.19.0 wandb-0.17.4 watchfiles-0.22.0 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": "538aa75e437c4196bdd2233bac661c73"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "\u001b[0m\n",
            "Download Results:\n",
            "gid   |stat|avg speed  |path/URI\n",
            "======+====+===========+=======================================================\n",
            "9a4362|\u001b[1;32mOK\u001b[0m  |   189MiB/s|/content/text-generation-webui/models//Smegmma-Deluxe-9B-v1-Q5_K_M.gguf\n",
            "\n",
            "Status Legend:\n",
            "(OK):download completed.\n",
            "/content/text-generation-webui\n",
            "\u001b[2;36m09:46:20-439980\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Starting Text generation web UI                                            \n",
            "\u001b[2;36m09:46:20-957451\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Loading \u001b[32m\"Smegmma-Deluxe-9B-v1-Q5_K_M.gguf\"\u001b[0m                                 \n",
            "\u001b[2;36m09:46:21-438155\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m llama.cpp weights detected: \u001b[32m\"models/Smegmma-Deluxe-9B-v1-Q5_K_M.gguf\"\u001b[0m      \n",
            "llama_model_loader: loaded meta data with 33 key-value pairs and 464 tensors from models/Smegmma-Deluxe-9B-v1-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              = gemma2\n",
            "llama_model_loader: - kv   1:                               general.name str              = Smegmma-Deluxe-9B-v1\n",
            "llama_model_loader: - kv   2:                      gemma2.context_length u32              = 8192\n",
            "llama_model_loader: - kv   3:                    gemma2.embedding_length u32              = 3584\n",
            "llama_model_loader: - kv   4:                         gemma2.block_count u32              = 42\n",
            "llama_model_loader: - kv   5:                 gemma2.feed_forward_length u32              = 14336\n",
            "llama_model_loader: - kv   6:                gemma2.attention.head_count u32              = 16\n",
            "llama_model_loader: - kv   7:             gemma2.attention.head_count_kv u32              = 8\n",
            "llama_model_loader: - kv   8:    gemma2.attention.layer_norm_rms_epsilon f32              = 0.000001\n",
            "llama_model_loader: - kv   9:                gemma2.attention.key_length u32              = 256\n",
            "llama_model_loader: - kv  10:              gemma2.attention.value_length u32              = 256\n",
            "llama_model_loader: - kv  11:                          general.file_type u32              = 17\n",
            "llama_model_loader: - kv  12:              gemma2.attn_logit_softcapping f32              = 50.000000\n",
            "llama_model_loader: - kv  13:             gemma2.final_logit_softcapping f32              = 30.000000\n",
            "llama_model_loader: - kv  14:            gemma2.attention.sliding_window u32              = 4096\n",
            "llama_model_loader: - kv  15:                       tokenizer.ggml.model str              = llama\n",
            "llama_model_loader: - kv  16:                         tokenizer.ggml.pre str              = default\n",
            "llama_model_loader: - kv  17:                      tokenizer.ggml.tokens arr[str,256000]  = [\"<pad>\", \"<eos>\", \"<bos>\", \"<unk>\", ...\n",
            "llama_model_loader: - kv  18:                      tokenizer.ggml.scores arr[f32,256000]  = [0.000000, 0.000000, 0.000000, 0.0000...\n",
            "llama_model_loader: - kv  19:                  tokenizer.ggml.token_type arr[i32,256000]  = [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, ...\n",
            "llama_model_loader: - kv  20:                tokenizer.ggml.bos_token_id u32              = 2\n",
            "llama_model_loader: - kv  21:                tokenizer.ggml.eos_token_id u32              = 1\n",
            "llama_model_loader: - kv  22:            tokenizer.ggml.unknown_token_id u32              = 3\n",
            "llama_model_loader: - kv  23:            tokenizer.ggml.padding_token_id u32              = 0\n",
            "llama_model_loader: - kv  24:               tokenizer.ggml.add_bos_token bool             = true\n",
            "llama_model_loader: - kv  25:               tokenizer.ggml.add_eos_token bool             = false\n",
            "llama_model_loader: - kv  26:                    tokenizer.chat_template str              = {{ bos_token }}{% if messages[0]['rol...\n",
            "llama_model_loader: - kv  27:            tokenizer.ggml.add_space_prefix bool             = false\n",
            "llama_model_loader: - kv  28:               general.quantization_version u32              = 2\n",
            "llama_model_loader: - kv  29:                      quantize.imatrix.file str              = /models/Smegmma-Deluxe-9B-v1-GGUF/Sme...\n",
            "llama_model_loader: - kv  30:                   quantize.imatrix.dataset str              = /training_data/calibration_datav3.txt\n",
            "llama_model_loader: - kv  31:             quantize.imatrix.entries_count i32              = 294\n",
            "llama_model_loader: - kv  32:              quantize.imatrix.chunks_count i32              = 128\n",
            "llama_model_loader: - type  f32:  169 tensors\n",
            "llama_model_loader: - type q5_K:  252 tensors\n",
            "llama_model_loader: - type q6_K:   43 tensors\n",
            "llm_load_vocab: special tokens cache size = 364\n",
            "llm_load_vocab: token to piece cache size = 1.6014 MB\n",
            "llm_load_print_meta: format           = GGUF V3 (latest)\n",
            "llm_load_print_meta: arch             = gemma2\n",
            "llm_load_print_meta: vocab type       = SPM\n",
            "llm_load_print_meta: n_vocab          = 256000\n",
            "llm_load_print_meta: n_merges         = 0\n",
            "llm_load_print_meta: n_ctx_train      = 8192\n",
            "llm_load_print_meta: n_embd           = 3584\n",
            "llm_load_print_meta: n_head           = 16\n",
            "llm_load_print_meta: n_head_kv        = 8\n",
            "llm_load_print_meta: n_layer          = 42\n",
            "llm_load_print_meta: n_rot            = 224\n",
            "llm_load_print_meta: n_swa            = 4096\n",
            "llm_load_print_meta: n_embd_head_k    = 256\n",
            "llm_load_print_meta: n_embd_head_v    = 256\n",
            "llm_load_print_meta: n_gqa            = 2\n",
            "llm_load_print_meta: n_embd_k_gqa     = 2048\n",
            "llm_load_print_meta: n_embd_v_gqa     = 2048\n",
            "llm_load_print_meta: f_norm_eps       = 0.0e+00\n",
            "llm_load_print_meta: f_norm_rms_eps   = 1.0e-06\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        = 2\n",
            "llm_load_print_meta: rope scaling     = linear\n",
            "llm_load_print_meta: freq_base_train  = 10000.0\n",
            "llm_load_print_meta: freq_scale_train = 1\n",
            "llm_load_print_meta: n_ctx_orig_yarn  = 8192\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       = 9B\n",
            "llm_load_print_meta: model ftype      = Q5_K - Medium\n",
            "llm_load_print_meta: model params     = 9.24 B\n",
            "llm_load_print_meta: model size       = 6.19 GiB (5.75 BPW) \n",
            "llm_load_print_meta: general.name     = Smegmma-Deluxe-9B-v1\n",
            "llm_load_print_meta: BOS token        = 2 '<bos>'\n",
            "llm_load_print_meta: EOS token        = 1 '<eos>'\n",
            "llm_load_print_meta: UNK token        = 3 '<unk>'\n",
            "llm_load_print_meta: PAD token        = 0 '<pad>'\n",
            "llm_load_print_meta: LF token         = 227 '<0x0A>'\n",
            "llm_load_print_meta: EOT token        = 107 '<end_of_turn>'\n",
            "llm_load_print_meta: max token length = 93\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 =    0.41 MiB\n",
            "llm_load_tensors: offloading 42 repeating layers to GPU\n",
            "llm_load_tensors: offloading non-repeating layers to GPU\n",
            "llm_load_tensors: offloaded 43/43 layers to GPU\n",
            "llm_load_tensors:        CPU buffer size =   717.77 MiB\n",
            "llm_load_tensors:      CUDA0 buffer size =  6333.65 MiB\n",
            "..................................................................................\n",
            "llama_new_context_with_model: n_ctx      = 8192\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  = 10000.0\n",
            "llama_new_context_with_model: freq_scale = 1\n",
            "llama_kv_cache_init:      CUDA0 KV buffer size =  2688.00 MiB\n",
            "llama_new_context_with_model: KV self size  = 2688.00 MiB, K (f16): 1344.00 MiB, V (f16): 1344.00 MiB\n",
            "llama_new_context_with_model:  CUDA_Host  output buffer size =     0.98 MiB\n",
            "llama_new_context_with_model:      CUDA0 compute buffer size =   507.00 MiB\n",
            "llama_new_context_with_model:  CUDA_Host compute buffer size =    39.01 MiB\n",
            "llama_new_context_with_model: graph nodes  = 1690\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.chunks_count': '128', 'gemma2.attn_logit_softcapping': '50.000000', 'gemma2.attention.value_length': '256', 'gemma2.attention.sliding_window': '4096', 'gemma2.attention.head_count': '16', 'gemma2.feed_forward_length': '14336', 'gemma2.block_count': '42', 'tokenizer.ggml.pre': 'default', 'gemma2.embedding_length': '3584', 'general.file_type': '17', 'gemma2.attention.layer_norm_rms_epsilon': '0.000001', 'gemma2.context_length': '8192', 'tokenizer.chat_template': \"{{ bos_token }}{% if messages[0]['role'] == 'system' %}{{ raise_exception('System role not supported') }}{% endif %}{% 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'] == 'assistant') %}{% set role = 'model' %}{% else %}{% set role = message['role'] %}{% endif %}{{ '<start_of_turn>' + role + '\\n' + message['content'] | trim + '<end_of_turn>\\n' }}{% endfor %}{% if add_generation_prompt %}{{'<start_of_turn>model\\n'}}{% endif %}\", 'general.architecture': 'gemma2', 'gemma2.final_logit_softcapping': '30.000000', 'gemma2.attention.head_count_kv': '8', 'tokenizer.ggml.add_eos_token': 'false', 'quantize.imatrix.file': '/models/Smegmma-Deluxe-9B-v1-GGUF/Smegmma-Deluxe-9B-v1.imatrix', 'tokenizer.ggml.add_space_prefix': 'false', 'tokenizer.ggml.model': 'llama', 'general.quantization_version': '2', 'general.name': 'Smegmma-Deluxe-9B-v1', 'tokenizer.ggml.bos_token_id': '2', 'tokenizer.ggml.eos_token_id': '1', 'tokenizer.ggml.unknown_token_id': '3', 'tokenizer.ggml.padding_token_id': '0', 'tokenizer.ggml.add_bos_token': 'true', 'gemma2.attention.key_length': '256', 'quantize.imatrix.dataset': '/training_data/calibration_datav3.txt', 'quantize.imatrix.entries_count': '294'}\n",
            "Available chat formats from metadata: chat_template.default\n",
            "Using gguf chat template: {{ bos_token }}{% if messages[0]['role'] == 'system' %}{{ raise_exception('System role not supported') }}{% endif %}{% 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'] == 'assistant') %}{% set role = 'model' %}{% else %}{% set role = message['role'] %}{% endif %}{{ '<start_of_turn>' + role + '\n",
            "' + message['content'] | trim + '<end_of_turn>\n",
            "' }}{% endfor %}{% if add_generation_prompt %}{{'<start_of_turn>model\n",
            "'}}{% endif %}\n",
            "Using chat eos_token: <eos>\n",
            "Using chat bos_token: <bos>\n",
            "\u001b[2;36m09:46:43-260650\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Loaded \u001b[32m\"Smegmma-Deluxe-9B-v1-Q5_K_M.gguf\"\u001b[0m in \u001b[1;36m22.30\u001b[0m seconds.                \n",
            "\u001b[2;36m09:46:43-262018\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m LOADER: \u001b[32m\"llama.cpp\"\u001b[0m                                                        \n",
            "\u001b[2;36m09:46:43-263067\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m TRUNCATION LENGTH: \u001b[1;36m8192\u001b[0m                                                    \n",
            "\u001b[2;36m09:46:43-264013\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://2b26ab25a793185716.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:1054: RuntimeWarning: Detected duplicate leading \"<bos>\" in prompt, this will likely reduce response quality, consider removing it...\n",
            "  warnings.warn(\n",
            "\n",
            "llama_print_timings:        load time =     542.10 ms\n",
            "llama_print_timings:      sample time =    4029.18 ms /   177 runs   (   22.76 ms per token,    43.93 tokens per second)\n",
            "llama_print_timings: prompt eval time =     541.26 ms /    26 tokens (   20.82 ms per token,    48.04 tokens per second)\n",
            "llama_print_timings:        eval time =    6115.58 ms /   176 runs   (   34.75 ms per token,    28.78 tokens per second)\n",
            "llama_print_timings:       total time =   14824.98 ms /   202 tokens\n",
            "Output generated in 15.42 seconds (11.42 tokens/s, 176 tokens, context 44, seed 1656536235)\n",
            "Llama.generate: prefix-match hit\n",
            "\n",
            "llama_print_timings:        load time =     542.10 ms\n",
            "llama_print_timings:      sample time =   15866.60 ms /   593 runs   (   26.76 ms per token,    37.37 tokens per second)\n",
            "llama_print_timings: prompt eval time =     119.84 ms /    28 tokens (    4.28 ms per token,   233.65 tokens per second)\n",
            "llama_print_timings:        eval time =   21412.06 ms /   592 runs   (   36.17 ms per token,    27.65 tokens per second)\n",
            "llama_print_timings:       total time =   52445.16 ms /   620 tokens\n",
            "Output generated in 53.03 seconds (11.16 tokens/s, 592 tokens, context 270, seed 363928619)\n",
            "Llama.generate: prefix-match hit\n",
            "\n",
            "llama_print_timings:        load time =     542.10 ms\n",
            "llama_print_timings:      sample time =    9102.25 ms /   409 runs   (   22.25 ms per token,    44.93 tokens per second)\n",
            "llama_print_timings: prompt eval time =     140.77 ms /     5 tokens (   28.15 ms per token,    35.52 tokens per second)\n",
            "llama_print_timings:        eval time =   14983.03 ms /   408 runs   (   36.72 ms per token,    27.23 tokens per second)\n",
            "llama_print_timings:       total time =   33816.39 ms /   413 tokens\n",
            "Output generated in 34.42 seconds (11.85 tokens/s, 408 tokens, context 512, seed 594895041)\n",
            "\u001b[2;36m09:50:44-706908\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Deleted \u001b[32m\"logs/instruct/20240712-09-47-26.json\"\u001b[0m.                            \n",
            "Llama.generate: prefix-match hit\n",
            "\n",
            "llama_print_timings:        load time =     542.10 ms\n",
            "llama_print_timings:      sample time =    4278.22 ms /   154 runs   (   27.78 ms per token,    36.00 tokens per second)\n",
            "llama_print_timings: prompt eval time =     130.20 ms /    33 tokens (    3.95 ms per token,   253.45 tokens per second)\n",
            "llama_print_timings:        eval time =    5413.47 ms /   153 runs   (   35.38 ms per token,    28.26 tokens per second)\n",
            "llama_print_timings:       total time =   13221.26 ms /   186 tokens\n",
            "Output generated in 13.95 seconds (10.97 tokens/s, 153 tokens, context 56, seed 1221404474)\n"
          ]
        }
      ],
      "source": [
        "%cd /content\n",
        "!apt-get -y install -qq aria2\n",
        "\n",
        "!git clone -b V20240711 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/bartowski/Smegmma-Deluxe-9B-v1-GGUF/resolve/main/Smegmma-Deluxe-9B-v1-Q5_K_M.gguf?download=true -d /content/text-generation-webui/models/ -o Smegmma-Deluxe-9B-v1-Q5_K_M.gguf\n",
        "\n",
        "%cd /content/text-generation-webui\n",
        "!python server.py --share --n-gpu-layers 100000 --model Smegmma-Deluxe-9B-v1-Q5_K_M.gguf"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPUBQOwokSK+JTL3s6/g9dD",
      "include_colab_link": true
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}