Celeste_12B_V1_6_iMatrix_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/Celeste_12B_V1_6_iMatrix_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": [
        "## Celeste-12B-V1.6_iMatrix_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: ChatML\n",
        "\n",
        "For story writing, use this system prompt:\n",
        "```\n",
        "You are a short story writer. Write a story based on prompt provided by user below.\n",
        "Mode: SFW\n",
        "```\n",
        "\n",
        "For character role play, use this system prompt:\n",
        "```\n",
        "Currently, your role is {{char}}, described in detail below. As {{char}}, continue the narrative exchange with {{user}}.\\n\\n<Guidelines>\\n• Maintain the character persona but allow it to evolve with the story.\\n• Be creative and proactive. Drive the story forward, introducing plotlines and events when relevant.\\n• All types of outputs are encouraged; respond accordingly to the narrative.\\n• Include dialogues, actions, and thoughts in each response.\\n• Utilize all five senses to describe scenarios within {{char}}'s dialogue.\\n• Use emotional symbols such as \\\"!\\\" and \\\"~\\\" in appropriate contexts.\\n• Incorporate onomatopoeia when suitable.\\n• Allow time for {{user}} to respond with their own input, respecting their agency.\\n• Act as secondary characters and NPCs as needed, and remove them when appropriate.\\n• When prompted for an Out of Character [OOC:] reply, answer neutrally and in plaintext, not as {{char}}.\\n</Guidelines>\\n\\n<Forbidden>\\n• Using excessive literary embellishments and purple prose unless dictated by {{char}}'s persona.\\n• Writing for, speaking, thinking, acting, or replying as {{user}} in your response.\\n• Repetitive and monotonous outputs.\\n• Positivity bias in your replies.\\n• Being overly extreme or NSFW when the narrative context is inappropriate.\\n</Forbidden>\\n\\nFollow the instructions in <Guidelines></Guidelines>, avoiding the items listed in <Forbidden></Forbidden>.\n",
        "```\n",
        "\n",
        "Original model: https://huggingface.co/nothingiisreal/Celeste-12B-V1.6\n",
        "\n",
        "Quantized model: https://huggingface.co/MarsupialAI/Celeste-12B-V1.6_iMatrix_GGUF/tree/main\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": "810a4d2d-a30b-4bc1-d793-f79e35e8f4b3"
      },
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "\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",
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            "\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/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, pyarrow, 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, mbstrdecoder, llvmlite, jsonlines, humanfriendly, httptools, h11, gekko, fsspec, einops, docker-pycreds, dnspython, diskcache, dill, cramjam, colorama, aiofiles, watchfiles, 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, 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: tomlkit\n",
            "    Found existing installation: tomlkit 0.13.0\n",
            "    Uninstalling tomlkit-0.13.0:\n",
            "      Successfully uninstalled tomlkit-0.13.0\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: 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: 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: llvmlite\n",
            "    Found existing installation: llvmlite 0.43.0\n",
            "    Uninstalling llvmlite-0.43.0:\n",
            "      Successfully uninstalled llvmlite-0.43.0\n",
            "  Attempting uninstall: fsspec\n",
            "    Found existing installation: fsspec 2024.6.1\n",
            "    Uninstalling fsspec-2024.6.1:\n",
            "      Successfully uninstalled fsspec-2024.6.1\n",
            "  Attempting uninstall: numba\n",
            "    Found existing installation: numba 0.60.0\n",
            "    Uninstalling numba-0.60.0:\n",
            "      Successfully uninstalled numba-0.60.0\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",
            "cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 17.0.0 which is incompatible.\n",
            "gcsfs 2024.6.1 requires fsspec==2024.6.1, but you have fsspec 2024.5.0 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.\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-0.3.3 flash-attn-2.6.1 flask_cloudflared-0.0.14 fsspec-2024.5.0 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 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-1.37.1 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-0.5.5 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"
          ]
        },
        {
          "data": {
            "application/vnd.colab-display-data+json": {
              "id": "2ccb0407061b4f538e66501c226b34e6",
              "pip_warning": {
                "packages": [
                  "PIL"
                ]
              }
            }
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Requirement already satisfied: annotated-types>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from pydantic>=2.0->gradio==4.26.0) (0.7.0)\n",
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            "Requirement already satisfied: click>=8.0.0 in /usr/local/lib/python3.10/dist-packages (from typer<1.0,>=0.9->typer[all]<1.0,>=0.9; sys_platform != \"emscripten\"->gradio==4.26.0) (8.1.7)\n",
            "Requirement already satisfied: shellingham>=1.3.0 in /usr/local/lib/python3.10/dist-packages (from typer<1.0,>=0.9->typer[all]<1.0,>=0.9; sys_platform != \"emscripten\"->gradio==4.26.0) (1.5.4)\n",
            "Requirement already satisfied: rich>=10.11.0 in /usr/local/lib/python3.10/dist-packages (from typer<1.0,>=0.9->typer[all]<1.0,>=0.9; sys_platform != \"emscripten\"->gradio==4.26.0) (13.7.1)\n",
            "\u001b[33mWARNING: typer 0.12.3 does not provide the extra 'all'\u001b[0m\u001b[33m\n",
            "\u001b[0mRequirement already satisfied: starlette<0.38.0,>=0.37.2 in /usr/local/lib/python3.10/dist-packages (from fastapi->gradio==4.26.0) (0.37.2)\n",
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            "Requirement already satisfied: watchfiles>=0.13 in /usr/local/lib/python3.10/dist-packages (from uvicorn[standard]>=0.12.0->fastapi->gradio==4.26.0) (0.22.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.19.3->gradio==4.26.0) (3.3.2)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.19.3->gradio==4.26.0) (2.0.7)\n",
            "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",
            "6ccb48|\u001b[1;32mOK\u001b[0m  |   168MiB/s|/content/text-generation-webui/models//Celeste-12B-V1.6_Q5km.gguf\n",
            "\n",
            "Status Legend:\n",
            "(OK):download completed.\n",
            "/content/text-generation-webui\n",
            "\u001b[2;36m06:24:36-933566\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Starting Text generation web UI                                            \n",
            "\u001b[2;36m06:24:37-420259\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Loading \u001b[32m\"Celeste-12B-V1.6_Q5km.gguf\"\u001b[0m                                       \n",
            "\u001b[2;36m06:24:37-890289\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m llama.cpp weights detected: \u001b[32m\"models/Celeste-12B-V1.6_Q5km.gguf\"\u001b[0m            \n",
            "llama_model_loader: loaded meta data with 43 key-value pairs and 363 tensors from models/Celeste-12B-V1.6_Q5km.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              = Mistral Nemo Instruct 2407\n",
            "llama_model_loader: - kv   3:                            general.version str              = 2407\n",
            "llama_model_loader: - kv   4:                       general.organization str              = Mistralai\n",
            "llama_model_loader: - kv   5:                           general.finetune str              = Instruct\n",
            "llama_model_loader: - kv   6:                           general.basename str              = Mistral-Nemo\n",
            "llama_model_loader: - kv   7:                         general.size_label str              = 12B\n",
            "llama_model_loader: - kv   8:                            general.license str              = llama3.1\n",
            "llama_model_loader: - kv   9:                               general.tags arr[str,1]       = [\"not-for-all-audiences\"]\n",
            "llama_model_loader: - kv  10:                          general.languages arr[str,1]       = [\"en\"]\n",
            "llama_model_loader: - kv  11:                           general.datasets arr[str,3]       = [\"nothingiisreal/c2-logs-cleaned\", \"k...\n",
            "llama_model_loader: - kv  12:                          llama.block_count u32              = 40\n",
            "llama_model_loader: - kv  13:                       llama.context_length u32              = 1024000\n",
            "llama_model_loader: - kv  14:                     llama.embedding_length u32              = 5120\n",
            "llama_model_loader: - kv  15:                  llama.feed_forward_length u32              = 14336\n",
            "llama_model_loader: - kv  16:                 llama.attention.head_count u32              = 32\n",
            "llama_model_loader: - kv  17:              llama.attention.head_count_kv u32              = 8\n",
            "llama_model_loader: - kv  18:                       llama.rope.freq_base f32              = 1000000.000000\n",
            "llama_model_loader: - kv  19:     llama.attention.layer_norm_rms_epsilon f32              = 0.000010\n",
            "llama_model_loader: - kv  20:                 llama.attention.key_length u32              = 128\n",
            "llama_model_loader: - kv  21:               llama.attention.value_length u32              = 128\n",
            "llama_model_loader: - kv  22:                          general.file_type u32              = 17\n",
            "llama_model_loader: - kv  23:                           llama.vocab_size u32              = 131072\n",
            "llama_model_loader: - kv  24:                 llama.rope.dimension_count u32              = 128\n",
            "llama_model_loader: - kv  25:            tokenizer.ggml.add_space_prefix bool             = false\n",
            "llama_model_loader: - kv  26:                       tokenizer.ggml.model str              = gpt2\n",
            "llama_model_loader: - kv  27:                         tokenizer.ggml.pre str              = tekken\n",
            "llama_model_loader: - kv  28:                      tokenizer.ggml.tokens arr[str,131072]  = [\"<unk>\", \"<s>\", \"</s>\", \"[INST]\", \"[...\n",
            "llama_model_loader: - kv  29:                  tokenizer.ggml.token_type arr[i32,131072]  = [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, ...\n",
            "Exception ignored on calling ctypes callback function: <function llama_log_callback at 0x7c33849a3760>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/llama_cpp_cuda/_logger.py\", line 30, in llama_log_callback\n",
            "    print(text.decode(\"utf-8\"), end=\"\", flush=True, file=sys.stderr)\n",
            "UnicodeDecodeError: 'utf-8' codec can't decode byte 0xc4 in position 128: invalid continuation byte\n",
            "llama_model_loader: - kv  31:                tokenizer.ggml.bos_token_id u32              = 1\n",
            "llama_model_loader: - kv  32:                tokenizer.ggml.eos_token_id u32              = 2\n",
            "llama_model_loader: - kv  33:            tokenizer.ggml.unknown_token_id u32              = 0\n",
            "llama_model_loader: - kv  34:            tokenizer.ggml.padding_token_id u32              = 10\n",
            "llama_model_loader: - kv  35:               tokenizer.ggml.add_bos_token bool             = true\n",
            "llama_model_loader: - kv  36:               tokenizer.ggml.add_eos_token bool             = false\n",
            "llama_model_loader: - kv  37:                    tokenizer.chat_template str              = {% if not add_generation_prompt is de...\n",
            "llama_model_loader: - kv  38:               general.quantization_version u32              = 2\n",
            "llama_model_loader: - kv  39:                      quantize.imatrix.file str              = Celeste-12B-V1.6-imatrix.dat\n",
            "llama_model_loader: - kv  40:                   quantize.imatrix.dataset str              = groups_merged.txt\n",
            "llama_model_loader: - kv  41:             quantize.imatrix.entries_count i32              = 280\n",
            "llama_model_loader: - kv  42:              quantize.imatrix.chunks_count i32              = 92\n",
            "llama_model_loader: - type  f32:   81 tensors\n",
            "llama_model_loader: - type q5_K:  241 tensors\n",
            "llama_model_loader: - type q6_K:   41 tensors\n",
            "llm_load_vocab: special tokens cache size = 1000\n",
            "llm_load_vocab: token to piece cache size = 0.8498 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          = 131072\n",
            "llm_load_print_meta: n_merges         = 269443\n",
            "llm_load_print_meta: vocab_only       = 0\n",
            "llm_load_print_meta: n_ctx_train      = 1024000\n",
            "llm_load_print_meta: n_embd           = 5120\n",
            "llm_load_print_meta: n_layer          = 40\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  = 1000000.0\n",
            "llm_load_print_meta: freq_scale_train = 1\n",
            "llm_load_print_meta: n_ctx_orig_yarn  = 1024000\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       = 13B\n",
            "llm_load_print_meta: model ftype      = Q5_K - Medium\n",
            "llm_load_print_meta: model params     = 12.25 B\n",
            "llm_load_print_meta: model size       = 8.12 GiB (5.70 BPW) \n",
            "llm_load_print_meta: general.name     = Mistral Nemo Instruct 2407\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: PAD token        = 10 '<pad>'\n",
            "llm_load_print_meta: LF token         = 1196 'Ä'\n",
            "llm_load_print_meta: max token length = 150\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 =    0.34 MiB\n",
            "llm_load_tensors: offloading 40 repeating layers to GPU\n",
            "llm_load_tensors: offloading non-repeating layers to GPU\n",
            "llm_load_tensors: offloaded 41/41 layers to GPU\n",
            "llm_load_tensors:        CPU buffer size =   440.00 MiB\n",
            "llm_load_tensors:      CUDA0 buffer size =  7875.80 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  = 1000000.0\n",
            "llama_new_context_with_model: freq_scale = 1\n",
            "llama_kv_cache_init:      CUDA0 KV buffer size =  1285.00 MiB\n",
            "llama_new_context_with_model: KV self size  = 1285.00 MiB, K (f16):  642.50 MiB, V (f16):  642.50 MiB\n",
            "llama_new_context_with_model:  CUDA_Host  output buffer size =     0.50 MiB\n",
            "llama_new_context_with_model:      CUDA0 compute buffer size =   566.07 MiB\n",
            "llama_new_context_with_model:  CUDA_Host compute buffer size =    26.07 MiB\n",
            "llama_new_context_with_model: graph nodes  = 1286\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.dataset': 'groups_merged.txt', 'general.quantization_version': '2', 'tokenizer.chat_template': \"{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\\n' + message['content'] + '<|im_end|>' + '\\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\\n' }}{% endif %}\", 'llama.embedding_length': '5120', 'llama.feed_forward_length': '14336', 'general.license': 'llama3.1', 'llama.attention.value_length': '128', 'tokenizer.ggml.add_bos_token': 'true', 'general.size_label': '12B', 'general.type': 'model', 'tokenizer.ggml.add_eos_token': 'false', 'general.organization': 'Mistralai', 'general.version': '2407', 'quantize.imatrix.chunks_count': '92', 'llama.context_length': '1024000', 'general.basename': 'Mistral-Nemo', 'tokenizer.ggml.padding_token_id': '10', 'quantize.imatrix.entries_count': '280', 'llama.attention.head_count_kv': '8', 'general.architecture': 'llama', 'general.name': 'Mistral Nemo Instruct 2407', 'tokenizer.ggml.bos_token_id': '1', 'llama.rope.freq_base': '1000000.000000', 'llama.block_count': '40', 'llama.attention.head_count': '32', 'llama.attention.key_length': '128', 'general.finetune': 'Instruct', 'general.file_type': '17', 'tokenizer.ggml.pre': 'tekken', 'llama.vocab_size': '131072', 'quantize.imatrix.file': 'Celeste-12B-V1.6-imatrix.dat', 'llama.rope.dimension_count': '128', 'tokenizer.ggml.add_space_prefix': 'false', 'tokenizer.ggml.model': 'gpt2', 'llama.attention.layer_norm_rms_epsilon': '0.000010', 'tokenizer.ggml.eos_token_id': '2', 'tokenizer.ggml.unknown_token_id': '0'}\n",
            "Available chat formats from metadata: chat_template.default\n",
            "Using gguf chat template: {% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n",
            "' + message['content'] + '<|im_end|>' + '\n",
            "'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n",
            "' }}{% endif %}\n",
            "Using chat eos_token: </s>\n",
            "Using chat bos_token: <s>\n",
            "\u001b[2;36m06:25:13-350231\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Loaded \u001b[32m\"Celeste-12B-V1.6_Q5km.gguf\"\u001b[0m in \u001b[1;36m35.93\u001b[0m seconds.                      \n",
            "\u001b[2;36m06:25:13-351884\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m LOADER: \u001b[32m\"llama.cpp\"\u001b[0m                                                        \n",
            "\u001b[2;36m06:25:13-353141\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m TRUNCATION LENGTH: \u001b[1;36m8196\u001b[0m                                                    \n",
            "\u001b[2;36m06:25:13-354267\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://63927a060578807686.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",
            "Output generated in 2.43 seconds (9.87 tokens/s, 24 tokens, context 26, seed 731337272)\n",
            "Llama.generate: prefix-match hit\n",
            "Output generated in 1.69 seconds (11.25 tokens/s, 19 tokens, context 78, seed 658047187)\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/MarsupialAI/Celeste-12B-V1.6_iMatrix_GGUF/resolve/main/Celeste-12B-V1.6_Q5km.gguf?download=true -d /content/text-generation-webui/models/ -o Celeste-12B-V1.6_Q5km.gguf\n",
        "\n",
        "%cd /content/text-generation-webui\n",
        "!python server.py --share --n-gpu-layers 100000 --model Celeste-12B-V1.6_Q5km.gguf --n_ctx 8196"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPaIm3mrX4EAsF9SCphxJYj",
      "include_colab_link": true
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}