Conversion_to_ggf_.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vTOjceThG66i",
        "outputId": "ceea7c58-7d67-428e-f004-491a454842eb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git\n",
            "  Cloning https://github.com/unslothai/unsloth.git to /tmp/pip-install-y_lzbwok/unsloth_baedc467bbd2408e9a79f5cbd491583d\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/unslothai/unsloth.git /tmp/pip-install-y_lzbwok/unsloth_baedc467bbd2408e9a79f5cbd491583d\n",
            "  Resolved https://github.com/unslothai/unsloth.git to commit 933d9fe2cb2459f949ee2250e90a5b610d277eab\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting tyro (from unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git)\n",
            "  Downloading tyro-0.8.5-py3-none-any.whl (103 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m103.4/103.4 kB\u001b[0m \u001b[31m3.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: transformers>=4.38.2 in /usr/local/lib/python3.10/dist-packages (from unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (4.41.2)\n",
            "Collecting datasets>=2.16.0 (from unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git)\n",
            "  Downloading datasets-2.20.0-py3-none-any.whl (547 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m547.8/547.8 kB\u001b[0m \u001b[31m8.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: sentencepiece in /usr/local/lib/python3.10/dist-packages (from unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (0.1.99)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (4.66.4)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (5.9.5)\n",
            "Requirement already satisfied: wheel>=0.42.0 in /usr/local/lib/python3.10/dist-packages (from unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (0.43.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (1.25.2)\n",
            "Requirement already satisfied: protobuf<4.0.0 in /usr/local/lib/python3.10/dist-packages (from unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (3.20.3)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from datasets>=2.16.0->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (3.15.3)\n",
            "Collecting pyarrow>=15.0.0 (from datasets>=2.16.0->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git)\n",
            "  Downloading pyarrow-16.1.0-cp310-cp310-manylinux_2_28_x86_64.whl (40.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m40.8/40.8 MB\u001b[0m \u001b[31m21.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: pyarrow-hotfix in /usr/local/lib/python3.10/dist-packages (from datasets>=2.16.0->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (0.6)\n",
            "Collecting dill<0.3.9,>=0.3.0 (from datasets>=2.16.0->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git)\n",
            "  Downloading dill-0.3.8-py3-none-any.whl (116 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m16.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets>=2.16.0->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (2.0.3)\n",
            "Collecting requests>=2.32.2 (from datasets>=2.16.0->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git)\n",
            "  Downloading requests-2.32.3-py3-none-any.whl (64 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m64.9/64.9 kB\u001b[0m \u001b[31m9.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting xxhash (from datasets>=2.16.0->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git)\n",
            "  Downloading xxhash-3.4.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (194 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m20.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting multiprocess (from datasets>=2.16.0->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git)\n",
            "  Downloading multiprocess-0.70.16-py310-none-any.whl (134 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m20.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: fsspec[http]<=2024.5.0,>=2023.1.0 in /usr/local/lib/python3.10/dist-packages (from datasets>=2.16.0->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (2023.6.0)\n",
            "Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from datasets>=2.16.0->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (3.9.5)\n",
            "Requirement already satisfied: huggingface-hub>=0.21.2 in /usr/local/lib/python3.10/dist-packages (from datasets>=2.16.0->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (0.23.4)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from datasets>=2.16.0->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (24.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from datasets>=2.16.0->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (6.0.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers>=4.38.2->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (2024.5.15)\n",
            "Requirement already satisfied: tokenizers<0.20,>=0.19 in /usr/local/lib/python3.10/dist-packages (from transformers>=4.38.2->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (0.19.1)\n",
            "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers>=4.38.2->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (0.4.3)\n",
            "Requirement already satisfied: docstring-parser>=0.16 in /usr/local/lib/python3.10/dist-packages (from tyro->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (0.16)\n",
            "Requirement already satisfied: typing-extensions>=4.7.0 in /usr/local/lib/python3.10/dist-packages (from tyro->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (4.12.2)\n",
            "Requirement already satisfied: rich>=11.1.0 in /usr/local/lib/python3.10/dist-packages (from tyro->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (13.7.1)\n",
            "Collecting shtab>=1.5.6 (from tyro->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git)\n",
            "  Downloading shtab-1.7.1-py3-none-any.whl (14 kB)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.16.0->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (1.3.1)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.16.0->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (23.2.0)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.16.0->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (1.4.1)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.16.0->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (6.0.5)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.16.0->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (1.9.4)\n",
            "Requirement already satisfied: async-timeout<5.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.16.0->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (4.0.3)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->datasets>=2.16.0->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->datasets>=2.16.0->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->datasets>=2.16.0->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->datasets>=2.16.0->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (2024.6.2)\n",
            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich>=11.1.0->tyro->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (3.0.0)\n",
            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from rich>=11.1.0->tyro->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (2.16.1)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets>=2.16.0->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets>=2.16.0->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (2023.4)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets>=2.16.0->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (2024.1)\n",
            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich>=11.1.0->tyro->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (0.1.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas->datasets>=2.16.0->unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (1.16.0)\n",
            "Building wheels for collected packages: unsloth\n",
            "  Building wheel for unsloth (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for unsloth: filename=unsloth-2024.6-py3-none-any.whl size=117332 sha256=d74c7774fc123b68af04acaef334ed67e7a4f42c71ffbb55c018ed89a648573f\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-l_25k6tl/wheels/ed/d4/e9/76fb290ee3df0a5fc21ce5c2c788e29e9607a2353d8342fd0d\n",
            "Successfully built unsloth\n",
            "Installing collected packages: xxhash, unsloth, shtab, requests, pyarrow, dill, multiprocess, tyro, datasets\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",
            "\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.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed datasets-2.20.0 dill-0.3.8 multiprocess-0.70.16 pyarrow-16.1.0 requests-2.32.3 shtab-1.7.1 tyro-0.8.5 unsloth-2024.6 xxhash-3.4.1\n",
            "Collecting xformers\n",
            "  Downloading xformers-0.0.26.post1-cp310-cp310-manylinux2014_x86_64.whl (222.7 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m222.7/222.7 MB\u001b[0m \u001b[31m5.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting trl<0.9.0\n",
            "  Downloading trl-0.8.6-py3-none-any.whl (245 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m245.2/245.2 kB\u001b[0m \u001b[31m32.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting peft\n",
            "  Downloading peft-0.11.1-py3-none-any.whl (251 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m251.6/251.6 kB\u001b[0m \u001b[31m33.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting accelerate\n",
            "  Downloading accelerate-0.31.0-py3-none-any.whl (309 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m309.4/309.4 kB\u001b[0m \u001b[31m39.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting bitsandbytes\n",
            "  Downloading bitsandbytes-0.43.1-py3-none-manylinux_2_24_x86_64.whl (119.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m119.8/119.8 MB\u001b[0m \u001b[31m8.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: bitsandbytes, xformers, trl, peft, accelerate\n",
            "Successfully installed accelerate-0.31.0 bitsandbytes-0.43.1 peft-0.11.1 trl-0.8.6 xformers-0.0.26.post1\n"
          ]
        }
      ],
      "source": [
        "# Installs Unsloth, Xformers (Flash Attention) and all other packages!\n",
        "!pip install \"unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git\"\n",
        "!pip install --no-deps xformers \"trl<0.9.0\" peft accelerate bitsandbytes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EczHQjsPHhOm",
        "outputId": "5e2db763-d6b6-4741-b576-db9a9eb15c6e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n"
          ]
        }
      ],
      "source": [
        "from unsloth import FastLanguageModel"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394,
          "referenced_widgets": [
            "5bd94572ec9942a1b5304824152ffe1e",
            "ce6efc24ee134e149b5ba9d5e7f6da8d",
            "f5f5ea7ff25a45c5b897ec16b8e64f35",
            "01d39db694b64ae6b302e106d437869e",
            "161e9ce7e6404168b60c9f2ac9216720",
            "3a261c2ac3e34ce79f848df489c7dcf5",
            "8e835337fda441928e6d82e5c5e6d636",
            "517c757324f9442d8b065dcccd0ea745",
            "e7c4700333a44b77bb34d26704db01b6",
            "dde8956e22904712b707741dc9c4f0e4",
            "a2dd12d89b78403a93c98d6ad593ea18",
            "1c843fd7dc0a4d409486793054b84a92",
            "30ce4896850441b48b53c334fc9842f6",
            "c3d41bdfa4794b258b0c5fb37b729e16",
            "b08b687487a44ed3a68a4503511093a2",
            "a010aed00d864b53a24ff4a6fbbd1a51",
            "552e38070c8946c8a26b54c71c49b6a1",
            "539b957c3baf4c17882d4e76821475bc",
            "7a125a56e21246f3b95843338c4940e3",
            "e09c0b7063d04307a4b6f9b28aa6837e",
            "8d681616d7a44d49a02841b277c99510",
            "57d021ed8da24cd2a8bd018096d39e20",
            "fb220a32bbbd457f80300563490ab852",
            "15bf3d4590444efba34a0fa66fb08456",
            "a3072f76ae5c40de8a1ec37a6c7fa244",
            "911d4b61e771416f8da8c2cf7c2fc006",
            "45d7a2c66f5a4d8ca89994b80b45906a",
            "44a033cf82e54735abbf8152da00eb18",
            "67218b31f35b40bd99dd49a90000523c",
            "acf19e4f681b4569b04600715fe66939",
            "32595dbb6c814f98ab19dfaa543e39cb",
            "d4d9f4a08e3f451a9c82bf93f379aad5",
            "2146ef0871384889aac2099f16fea801",
            "574b4b5fc5fd484a812344679050aca6",
            "d682fc2eac2544a89415f6eddb6fc720",
            "73a31e438d5e46aa904690b4a3a2f164",
            "737392e0ee2442fb9f1c2b3edb008717",
            "8fd94269facf407a8b21e373d6808c57",
            "eef0bcb4e52a4a0f95b30aaf34e16ccd",
            "621f6b9ec9b34c9d9dd4e38a12dbd789",
            "be469d7a77454482b87d8a38b4bd8aa5",
            "df11493595434f95a892b82ae7fc0310",
            "bec9a6baa8a8482d9f368b4dd4dcd54d",
            "31f3f80281f2404580f1bf73cb7aa7ef",
            "c13a730f6ef946f982d18d36774dc307",
            "f0540232e78d4c82b1c73d9b3ab53803",
            "912317468871410dad4a61f70a157840",
            "b139db24666645728ec84a03d16fe8df",
            "e41f64356aa647b08845ed44765d3fa6",
            "9cc778faed39403f8109cbfbf57d9eab",
            "9dbd0882aad74c2e90c3fd5645de9d87",
            "0204b48fbc8e4cc7963c6b40f9a9aead",
            "e3c8727734b94393a2a8b677d259c44d",
            "07fc3456e15a443cbaf0ec30092b413e",
            "10e58236474f451bb4f2c94f39c9ed97",
            "9d4da438959e48babfa3c16d87652ee7",
            "b8d92478a04e4ce195872f6de1618d4f",
            "0f477da56da943dc9a63bb9269b30fa2",
            "cf38a9d55572468d851ccdcf3a596ccf",
            "9ae67e6b90a2410e82fa27a593249628",
            "cf0fb6bc6acb409cbcd1d4419e8799e6",
            "8dc82de26dfc4078ae43cf13ee9fba4e",
            "11fec579fe39409ea4f05e76cc085250",
            "2f8d156247b64cd3ba644a3305a6d8cd",
            "2535aea91b274f07bbb4b6bf4ce4e27b",
            "47e97f71876d44558c17ea96879a6913",
            "37bd782766f54afaaec8d83cb647121d",
            "feac171ff30c4ecdb84bffcedd8c7327",
            "e467ba547f914a368a7a662c59f313c5",
            "60fc1db0944b4d2897c47c830c42b1ad",
            "38202de1459c4e72a6d68a508dfd701e",
            "6e07290041254408b8a135803e3e7aa7",
            "c4ac912053ed4c0e86466dbd8bea84da",
            "2addd5e4788d48909b921b93f1c3828f",
            "33fc12c71bdb465eb8fada84189f88f4",
            "d047abddd9c04478bc8c3dadeeb52367",
            "3f95030236cd4cf593e906683e3e598b",
            "3de4f30bb23240148b31895ebcba8dce",
            "540e616f0d0249819c7cb83fc7b436f8",
            "9fcfa88c0eb04a66af71b21f4e7e873f",
            "875e0842e7854789b72ef584b720a39e",
            "057f98642bc7436296bb87aa0f77957f",
            "8007c549d7eb4d3488aadc6cbcec9741",
            "d07e15ef0d754eddbde18590d44f07c1",
            "97cccec196e74e99ba0ce2f88bf4dc9c",
            "cc48d4fa7cf14e7e9ac4e893bd68f2fe",
            "55e532a8435c41e8ac79199c049ea8f4",
            "7a5d591e9d9448beb473cbdee311af2a"
          ]
        },
        "id": "UHhdVtm1IUpT",
        "outputId": "505c79bd-a8eb-4e1d-8760-c771e8b02ba9"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5bd94572ec9942a1b5304824152ffe1e",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "adapter_config.json:   0%|          | 0.00/741 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1c843fd7dc0a4d409486793054b84a92",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "config.json:   0%|          | 0.00/1.15k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "==((====))==  Unsloth: Fast Llama patching release 2024.6\n",
            "   \\\\   /|    GPU: Tesla T4. Max memory: 14.748 GB. Platform = Linux.\n",
            "O^O/ \\_/ \\    Pytorch: 2.3.0+cu121. CUDA = 7.5. CUDA Toolkit = 12.1.\n",
            "\\        /    Bfloat16 = FALSE. Xformers = 0.0.26.post1. FA = False.\n",
            " \"-____-\"     Free Apache license: http://github.com/unslothai/unsloth\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "fb220a32bbbd457f80300563490ab852",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/5.70G [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "574b4b5fc5fd484a812344679050aca6",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "generation_config.json:   0%|          | 0.00/131 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c13a730f6ef946f982d18d36774dc307",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/51.1k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9d4da438959e48babfa3c16d87652ee7",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/9.09M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "37bd782766f54afaaec8d83cb647121d",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/459 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3de4f30bb23240148b31895ebcba8dce",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "adapter_model.safetensors:   0%|          | 0.00/168M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Unsloth 2024.6 patched 32 layers with 32 QKV layers, 32 O layers and 32 MLP layers.\n"
          ]
        }
      ],
      "source": [
        "model, tokenizer = FastLanguageModel.from_pretrained(\"vinci00/scrapegraphcompanion_finetuning_demo-adapter\", max_seq_length=4096,\n",
        "        dtype=None,\n",
        "        load_in_4bit=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "22095323597f4e738b002ec171684e1f",
            "18d526d19d244e7e9481443da7d1b507",
            "af7c7c09b4e14e77817691e56dbd06a1",
            "921e34c298df419aaef88b4e6bd22d91",
            "a40fede526bf44299292963005ff01f5",
            "5f9392255f6546848b1c054f0ba5b167",
            "86c91cfebf54425798174b579f9f599d",
            "ced21e2a523448059021d7a88a2ce1a5",
            "a393b5d0aa3f43ae82d4c7d0f7bc1a66",
            "09bb6db398904d089393da0c92f83464",
            "a4d29e96dc234e13b3c2cba54ffc49de",
            "db7b862858c24e6c92cdc845e8e21ef9",
            "d4cec1617bbc48ffb3e9f9b24b29e893",
            "6dac9af4bad34e53b5395fcc5dd625b8",
            "e7b403265f7347c2872f4b827f1f7a92",
            "e10364ab6c0c436687fd3ef7624bce9c",
            "96076b40ab0d4430a2b9f33c60f591ed",
            "5f437ce1e879426a91b32bc55db5c1d9",
            "c2a96d96b576402e9b9bba883cac643b",
            "3f17a472484545e8bb8f6e88256e9c28",
            "522e02fd045b4b8cb2d622a01b5e50f8",
            "4938c9d7f6c7473e875b09eb31615c82"
          ]
        },
        "id": "9bwUhAgxIvut",
        "outputId": "fc003d04-0f7a-49be-ef0f-e64c8d162934"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Unsloth: You have 1 CPUs. Using `safe_serialization` is 10x slower.\n",
            "We shall switch to Pytorch saving, which will take 3 minutes and not 30 minutes.\n",
            "To force `safe_serialization`, set it to `None` instead.\n",
            "Unsloth: Kaggle/Colab has limited disk space. We need to delete the downloaded\n",
            "model which will save 4-16GB of disk space, allowing you to save on Kaggle/Colab.\n",
            "Unsloth: Will remove a cached repo with size 5.7G\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Unsloth: Merging 4bit and LoRA weights to 16bit...\n",
            "Unsloth: Will use up to 7.43 out of 12.67 RAM for saving.\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            " 50%|█████     | 16/32 [00:02<00:01,  8.67it/s]We will save to Disk and not RAM now.\n",
            "100%|██████████| 32/32 [01:07<00:00,  2.12s/it]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Unsloth: Saving tokenizer... Done.\n",
            "Unsloth: Saving model... This might take 5 minutes for Llama-7b...\n",
            "Unsloth: Saving vinci00/scrapegraphcompanion_finetuning_demo-GGUF/pytorch_model-00001-of-00004.bin...\n",
            "Unsloth: Saving vinci00/scrapegraphcompanion_finetuning_demo-GGUF/pytorch_model-00002-of-00004.bin...\n",
            "Unsloth: Saving vinci00/scrapegraphcompanion_finetuning_demo-GGUF/pytorch_model-00003-of-00004.bin...\n",
            "Unsloth: Saving vinci00/scrapegraphcompanion_finetuning_demo-GGUF/pytorch_model-00004-of-00004.bin...\n",
            "Done.\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Unsloth: Converting llama model. Can use fast conversion = False.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "==((====))==  Unsloth: Conversion from QLoRA to GGUF information\n",
            "   \\\\   /|    [0] Installing llama.cpp will take 3 minutes.\n",
            "O^O/ \\_/ \\    [1] Converting HF to GUUF 16bits will take 3 minutes.\n",
            "\\        /    [2] Converting GGUF 16bits to ['q4_k_m'] will take 10 minutes each.\n",
            " \"-____-\"     In total, you will have to wait at least 16 minutes.\n",
            "\n",
            "Unsloth: [0] Installing llama.cpp. This will take 3 minutes...\n",
            "Unsloth: [1] Converting model at vinci00/scrapegraphcompanion_finetuning_demo-GGUF into f16 GGUF format.\n",
            "The output location will be ./vinci00/scrapegraphcompanion_finetuning_demo-GGUF/unsloth.F16.gguf\n",
            "This will take 3 minutes...\n",
            "INFO:hf-to-gguf:Loading model: scrapegraphcompanion_finetuning_demo-GGUF\n",
            "INFO:gguf.gguf_writer:gguf: This GGUF file is for Little Endian only\n",
            "INFO:hf-to-gguf:Set model parameters\n",
            "INFO:hf-to-gguf:gguf: context length = 8192\n",
            "INFO:hf-to-gguf:gguf: embedding length = 4096\n",
            "INFO:hf-to-gguf:gguf: feed forward length = 14336\n",
            "INFO:hf-to-gguf:gguf: head count = 32\n",
            "INFO:hf-to-gguf:gguf: key-value head count = 8\n",
            "INFO:hf-to-gguf:gguf: rope theta = 500000.0\n",
            "INFO:hf-to-gguf:gguf: rms norm epsilon = 1e-05\n",
            "INFO:hf-to-gguf:gguf: file type = 1\n",
            "INFO:hf-to-gguf:Set model tokenizer\n",
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
            "INFO:gguf.vocab:Adding 280147 merge(s).\n",
            "INFO:gguf.vocab:Setting special token type bos to 128000\n",
            "INFO:gguf.vocab:Setting special token type eos to 128009\n",
            "INFO:gguf.vocab:Setting special token type pad to 128255\n",
            "INFO:gguf.vocab:Setting chat_template to {% 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 %}{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>\n",
            "\n",
            "' }}{% endif %}\n",
            "INFO:hf-to-gguf:Exporting model...\n",
            "INFO:hf-to-gguf:gguf: loading model weight map from 'pytorch_model.bin.index.json'\n",
            "INFO:hf-to-gguf:gguf: loading model part 'pytorch_model-00001-of-00004.bin'\n",
            "INFO:hf-to-gguf:token_embd.weight,           torch.float16 --> F16, shape = {4096, 128256}\n",
            "INFO:hf-to-gguf:blk.0.attn_q.weight,         torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.0.attn_k.weight,         torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.0.attn_v.weight,         torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.0.attn_output.weight,    torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.0.ffn_gate.weight,       torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.0.ffn_up.weight,         torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.0.ffn_down.weight,       torch.float16 --> F16, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.0.attn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.0.ffn_norm.weight,       torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.1.attn_q.weight,         torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.1.attn_k.weight,         torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.1.attn_v.weight,         torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.1.attn_output.weight,    torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.1.ffn_gate.weight,       torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.1.ffn_up.weight,         torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.1.ffn_down.weight,       torch.float16 --> F16, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.1.attn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.1.ffn_norm.weight,       torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.2.attn_q.weight,         torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.2.attn_k.weight,         torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.2.attn_v.weight,         torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.2.attn_output.weight,    torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.2.ffn_gate.weight,       torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.2.ffn_up.weight,         torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.2.ffn_down.weight,       torch.float16 --> F16, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.2.attn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.2.ffn_norm.weight,       torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.3.attn_q.weight,         torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.3.attn_k.weight,         torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.3.attn_v.weight,         torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.3.attn_output.weight,    torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.3.ffn_gate.weight,       torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.3.ffn_up.weight,         torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.3.ffn_down.weight,       torch.float16 --> F16, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.3.attn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.3.ffn_norm.weight,       torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.4.attn_q.weight,         torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.4.attn_k.weight,         torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.4.attn_v.weight,         torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.4.attn_output.weight,    torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.4.ffn_gate.weight,       torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.4.ffn_up.weight,         torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.4.ffn_down.weight,       torch.float16 --> F16, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.4.attn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.4.ffn_norm.weight,       torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.5.attn_q.weight,         torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.5.attn_k.weight,         torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.5.attn_v.weight,         torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.5.attn_output.weight,    torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.5.ffn_gate.weight,       torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.5.ffn_up.weight,         torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.5.ffn_down.weight,       torch.float16 --> F16, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.5.attn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.5.ffn_norm.weight,       torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.6.attn_q.weight,         torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.6.attn_k.weight,         torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.6.attn_v.weight,         torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.6.attn_output.weight,    torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.6.ffn_gate.weight,       torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.6.ffn_up.weight,         torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.6.ffn_down.weight,       torch.float16 --> F16, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.6.attn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.6.ffn_norm.weight,       torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.7.attn_q.weight,         torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.7.attn_k.weight,         torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.7.attn_v.weight,         torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.7.attn_output.weight,    torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.7.ffn_gate.weight,       torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.7.ffn_up.weight,         torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.7.ffn_down.weight,       torch.float16 --> F16, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.7.attn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.7.ffn_norm.weight,       torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.8.attn_q.weight,         torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.8.attn_k.weight,         torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.8.attn_v.weight,         torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.8.attn_output.weight,    torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.8.ffn_gate.weight,       torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.8.ffn_up.weight,         torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.8.ffn_down.weight,       torch.float16 --> F16, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.8.attn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.8.ffn_norm.weight,       torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:gguf: loading model part 'pytorch_model-00002-of-00004.bin'\n",
            "INFO:hf-to-gguf:blk.9.attn_q.weight,         torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.9.attn_k.weight,         torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.9.attn_v.weight,         torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.9.attn_output.weight,    torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.9.ffn_gate.weight,       torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.9.ffn_up.weight,         torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.9.ffn_down.weight,       torch.float16 --> F16, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.9.attn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.9.ffn_norm.weight,       torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.10.attn_q.weight,        torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.10.attn_k.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.10.attn_v.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.10.attn_output.weight,   torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.10.ffn_gate.weight,      torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.10.ffn_up.weight,        torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.10.ffn_down.weight,      torch.float16 --> F16, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.10.attn_norm.weight,     torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.10.ffn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.11.attn_q.weight,        torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.11.attn_k.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.11.attn_v.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.11.attn_output.weight,   torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.11.ffn_gate.weight,      torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.11.ffn_up.weight,        torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.11.ffn_down.weight,      torch.float16 --> F16, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.11.attn_norm.weight,     torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.11.ffn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.12.attn_q.weight,        torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.12.attn_k.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.12.attn_v.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.12.attn_output.weight,   torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.12.ffn_gate.weight,      torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.12.ffn_up.weight,        torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.12.ffn_down.weight,      torch.float16 --> F16, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.12.attn_norm.weight,     torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.12.ffn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.13.attn_q.weight,        torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.13.attn_k.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.13.attn_v.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.13.attn_output.weight,   torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.13.ffn_gate.weight,      torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.13.ffn_up.weight,        torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.13.ffn_down.weight,      torch.float16 --> F16, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.13.attn_norm.weight,     torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.13.ffn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.14.attn_q.weight,        torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.14.attn_k.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.14.attn_v.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.14.attn_output.weight,   torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.14.ffn_gate.weight,      torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.14.ffn_up.weight,        torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.14.ffn_down.weight,      torch.float16 --> F16, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.14.attn_norm.weight,     torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.14.ffn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.15.attn_q.weight,        torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.15.attn_k.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.15.attn_v.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.15.attn_output.weight,   torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.15.ffn_gate.weight,      torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.15.ffn_up.weight,        torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.15.ffn_down.weight,      torch.float16 --> F16, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.15.attn_norm.weight,     torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.15.ffn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.16.attn_q.weight,        torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.16.attn_k.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.16.attn_v.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.16.attn_output.weight,   torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.16.ffn_gate.weight,      torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.16.ffn_up.weight,        torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.16.ffn_down.weight,      torch.float16 --> F16, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.16.attn_norm.weight,     torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.16.ffn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.17.attn_q.weight,        torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.17.attn_k.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.17.attn_v.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.17.attn_output.weight,   torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.17.ffn_gate.weight,      torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.17.ffn_up.weight,        torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.17.ffn_down.weight,      torch.float16 --> F16, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.17.attn_norm.weight,     torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.17.ffn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.18.attn_q.weight,        torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.18.attn_k.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.18.attn_v.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.18.attn_output.weight,   torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.18.ffn_gate.weight,      torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.18.ffn_up.weight,        torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.18.ffn_down.weight,      torch.float16 --> F16, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.18.attn_norm.weight,     torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.18.ffn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.19.attn_q.weight,        torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.19.attn_k.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.19.attn_v.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.19.attn_output.weight,   torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.19.ffn_gate.weight,      torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.19.ffn_up.weight,        torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.19.ffn_down.weight,      torch.float16 --> F16, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.19.attn_norm.weight,     torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.19.ffn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.20.attn_q.weight,        torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.20.attn_k.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.20.attn_v.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.20.attn_output.weight,   torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.20.ffn_gate.weight,      torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:gguf: loading model part 'pytorch_model-00003-of-00004.bin'\n",
            "INFO:hf-to-gguf:blk.20.ffn_up.weight,        torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.20.ffn_down.weight,      torch.float16 --> F16, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.20.attn_norm.weight,     torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.20.ffn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.21.attn_q.weight,        torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.21.attn_k.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.21.attn_v.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.21.attn_output.weight,   torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.21.ffn_gate.weight,      torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.21.ffn_up.weight,        torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.21.ffn_down.weight,      torch.float16 --> F16, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.21.attn_norm.weight,     torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.21.ffn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.22.attn_q.weight,        torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.22.attn_k.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.22.attn_v.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.22.attn_output.weight,   torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.22.ffn_gate.weight,      torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.22.ffn_up.weight,        torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.22.ffn_down.weight,      torch.float16 --> F16, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.22.attn_norm.weight,     torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.22.ffn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.23.attn_q.weight,        torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.23.attn_k.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.23.attn_v.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.23.attn_output.weight,   torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.23.ffn_gate.weight,      torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.23.ffn_up.weight,        torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.23.ffn_down.weight,      torch.float16 --> F16, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.23.attn_norm.weight,     torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.23.ffn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.24.attn_q.weight,        torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.24.attn_k.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.24.attn_v.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.24.attn_output.weight,   torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.24.ffn_gate.weight,      torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.24.ffn_up.weight,        torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.24.ffn_down.weight,      torch.float16 --> F16, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.24.attn_norm.weight,     torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.24.ffn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.25.attn_q.weight,        torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.25.attn_k.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.25.attn_v.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.25.attn_output.weight,   torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.25.ffn_gate.weight,      torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.25.ffn_up.weight,        torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.25.ffn_down.weight,      torch.float16 --> F16, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.25.attn_norm.weight,     torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.25.ffn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.26.attn_q.weight,        torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.26.attn_k.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.26.attn_v.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.26.attn_output.weight,   torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.26.ffn_gate.weight,      torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.26.ffn_up.weight,        torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.26.ffn_down.weight,      torch.float16 --> F16, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.26.attn_norm.weight,     torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.26.ffn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.27.attn_q.weight,        torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.27.attn_k.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.27.attn_v.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.27.attn_output.weight,   torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.27.ffn_gate.weight,      torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.27.ffn_up.weight,        torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.27.ffn_down.weight,      torch.float16 --> F16, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.27.attn_norm.weight,     torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.27.ffn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.28.attn_q.weight,        torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.28.attn_k.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.28.attn_v.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.28.attn_output.weight,   torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.28.ffn_gate.weight,      torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.28.ffn_up.weight,        torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.28.ffn_down.weight,      torch.float16 --> F16, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.28.attn_norm.weight,     torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.28.ffn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.29.attn_q.weight,        torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.29.attn_k.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.29.attn_v.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.29.attn_output.weight,   torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.29.ffn_gate.weight,      torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.29.ffn_up.weight,        torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.29.ffn_down.weight,      torch.float16 --> F16, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.29.attn_norm.weight,     torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.29.ffn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.30.attn_q.weight,        torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.30.attn_k.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.30.attn_v.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.30.attn_output.weight,   torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.30.ffn_gate.weight,      torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.30.ffn_up.weight,        torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.30.ffn_down.weight,      torch.float16 --> F16, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.30.attn_norm.weight,     torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.30.ffn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.31.attn_q.weight,        torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.31.attn_k.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.31.attn_v.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.31.attn_output.weight,   torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.31.ffn_gate.weight,      torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.31.ffn_up.weight,        torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:gguf: loading model part 'pytorch_model-00004-of-00004.bin'\n",
            "INFO:hf-to-gguf:blk.31.ffn_down.weight,      torch.float16 --> F16, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.31.attn_norm.weight,     torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.31.ffn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:output_norm.weight,          torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:output.weight,               torch.float16 --> F16, shape = {4096, 128256}\n",
            "INFO:gguf.gguf_writer:Writing the following files:\n",
            "INFO:gguf.gguf_writer:vinci00/scrapegraphcompanion_finetuning_demo-GGUF/unsloth.F16.gguf: n_tensors = 291, total_size = 16.1G\n",
            "Writing: 100%|██████████| 16.1G/16.1G [03:12<00:00, 83.5Mbyte/s]\n",
            "INFO:hf-to-gguf:Model successfully exported.\n",
            "Unsloth: Conversion completed! Output location: ./vinci00/scrapegraphcompanion_finetuning_demo-GGUF/unsloth.F16.gguf\n",
            "Unsloth: [2] Converting GGUF 16bit into q4_k_m. This will take 20 minutes...\n",
            "main: build = 3235 (88540445)\n",
            "main: built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu\n",
            "main: quantizing './vinci00/scrapegraphcompanion_finetuning_demo-GGUF/unsloth.F16.gguf' to './vinci00/scrapegraphcompanion_finetuning_demo-GGUF/unsloth.Q4_K_M.gguf' as Q4_K_M using 4 threads\n",
            "llama_model_loader: loaded meta data with 23 key-value pairs and 291 tensors from ./vinci00/scrapegraphcompanion_finetuning_demo-GGUF/unsloth.F16.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              = scrapegraphcompanion_finetuning_demo-...\n",
            "llama_model_loader: - kv   2:                          llama.block_count u32              = 32\n",
            "llama_model_loader: - kv   3:                       llama.context_length u32              = 8192\n",
            "llama_model_loader: - kv   4:                     llama.embedding_length u32              = 4096\n",
            "llama_model_loader: - kv   5:                  llama.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              = 500000.000000\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              = 1\n",
            "llama_model_loader: - kv  11:                           llama.vocab_size u32              = 128256\n",
            "llama_model_loader: - kv  12:                 llama.rope.dimension_count u32              = 128\n",
            "llama_model_loader: - kv  13:                       tokenizer.ggml.model str              = gpt2\n",
            "llama_model_loader: - kv  14:                         tokenizer.ggml.pre str              = llama-bpe\n",
            "llama_model_loader: - kv  15:                      tokenizer.ggml.tokens arr[str,128256]  = [\"!\", \"\\\"\", \"#\", \"$\", \"%\", \"&\", \"'\", ...\n",
            "llama_model_loader: - kv  16:                  tokenizer.ggml.token_type arr[i32,128256]  = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...\n",
            "llama_model_loader: - kv  17:                      tokenizer.ggml.merges arr[str,280147]  = [\"Ġ Ġ\", \"Ġ ĠĠĠ\", \"ĠĠ ĠĠ\", \"...\n",
            "llama_model_loader: - kv  18:                tokenizer.ggml.bos_token_id u32              = 128000\n",
            "llama_model_loader: - kv  19:                tokenizer.ggml.eos_token_id u32              = 128009\n",
            "llama_model_loader: - kv  20:            tokenizer.ggml.padding_token_id u32              = 128255\n",
            "llama_model_loader: - kv  21:                    tokenizer.chat_template str              = {% set loop_messages = messages %}{% ...\n",
            "llama_model_loader: - kv  22:               general.quantization_version u32              = 2\n",
            "llama_model_loader: - type  f32:   65 tensors\n",
            "llama_model_loader: - type  f16:  226 tensors\n",
            "[   1/ 291]                    token_embd.weight - [ 4096, 128256,     1,     1], type =    f16, converting to q4_K .. size =  1002.00 MiB ->   281.81 MiB\n",
            "[   2/ 291]                  blk.0.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[   3/ 291]                  blk.0.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[   4/ 291]                  blk.0.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[   5/ 291]             blk.0.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[   6/ 291]                blk.0.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[   7/ 291]                  blk.0.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[   8/ 291]                blk.0.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, converting to q6_K .. size =   112.00 MiB ->    45.94 MiB\n",
            "[   9/ 291]               blk.0.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[  10/ 291]                blk.0.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[  11/ 291]                  blk.1.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[  12/ 291]                  blk.1.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[  13/ 291]                  blk.1.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[  14/ 291]             blk.1.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[  15/ 291]                blk.1.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[  16/ 291]                  blk.1.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[  17/ 291]                blk.1.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, converting to q6_K .. size =   112.00 MiB ->    45.94 MiB\n",
            "[  18/ 291]               blk.1.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[  19/ 291]                blk.1.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[  20/ 291]                  blk.2.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[  21/ 291]                  blk.2.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[  22/ 291]                  blk.2.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[  23/ 291]             blk.2.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[  24/ 291]                blk.2.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[  25/ 291]                  blk.2.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[  26/ 291]                blk.2.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, converting to q6_K .. size =   112.00 MiB ->    45.94 MiB\n",
            "[  27/ 291]               blk.2.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[  28/ 291]                blk.2.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[  29/ 291]                  blk.3.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[  30/ 291]                  blk.3.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[  31/ 291]                  blk.3.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[  32/ 291]             blk.3.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[  33/ 291]                blk.3.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[  34/ 291]                  blk.3.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[  35/ 291]                blk.3.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, converting to q6_K .. size =   112.00 MiB ->    45.94 MiB\n",
            "[  36/ 291]               blk.3.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[  37/ 291]                blk.3.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[  38/ 291]                  blk.4.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[  39/ 291]                  blk.4.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[  40/ 291]                  blk.4.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[  41/ 291]             blk.4.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[  42/ 291]                blk.4.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[  43/ 291]                  blk.4.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[  44/ 291]                blk.4.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[  45/ 291]               blk.4.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[  46/ 291]                blk.4.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[  47/ 291]                  blk.5.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[  48/ 291]                  blk.5.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[  49/ 291]                  blk.5.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[  50/ 291]             blk.5.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[  51/ 291]                blk.5.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[  52/ 291]                  blk.5.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[  53/ 291]                blk.5.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[  54/ 291]               blk.5.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[  55/ 291]                blk.5.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[  56/ 291]                  blk.6.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[  57/ 291]                  blk.6.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[  58/ 291]                  blk.6.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[  59/ 291]             blk.6.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[  60/ 291]                blk.6.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[  61/ 291]                  blk.6.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[  62/ 291]                blk.6.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, converting to q6_K .. size =   112.00 MiB ->    45.94 MiB\n",
            "[  63/ 291]               blk.6.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[  64/ 291]                blk.6.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[  65/ 291]                  blk.7.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[  66/ 291]                  blk.7.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[  67/ 291]                  blk.7.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[  68/ 291]             blk.7.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[  69/ 291]                blk.7.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[  70/ 291]                  blk.7.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[  71/ 291]                blk.7.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[  72/ 291]               blk.7.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[  73/ 291]                blk.7.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[  74/ 291]                  blk.8.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[  75/ 291]                  blk.8.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[  76/ 291]                  blk.8.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[  77/ 291]             blk.8.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[  78/ 291]                blk.8.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[  79/ 291]                  blk.8.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[  80/ 291]                blk.8.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[  81/ 291]               blk.8.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[  82/ 291]                blk.8.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[  83/ 291]                  blk.9.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[  84/ 291]                  blk.9.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[  85/ 291]                  blk.9.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[  86/ 291]             blk.9.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[  87/ 291]                blk.9.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[  88/ 291]                  blk.9.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[  89/ 291]                blk.9.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, converting to q6_K .. size =   112.00 MiB ->    45.94 MiB\n",
            "[  90/ 291]               blk.9.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[  91/ 291]                blk.9.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[  92/ 291]                 blk.10.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[  93/ 291]                 blk.10.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[  94/ 291]                 blk.10.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[  95/ 291]            blk.10.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[  96/ 291]               blk.10.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[  97/ 291]                 blk.10.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[  98/ 291]               blk.10.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[  99/ 291]              blk.10.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 100/ 291]               blk.10.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 101/ 291]                 blk.11.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[ 102/ 291]                 blk.11.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[ 103/ 291]                 blk.11.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[ 104/ 291]            blk.11.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[ 105/ 291]               blk.11.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[ 106/ 291]                 blk.11.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[ 107/ 291]               blk.11.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[ 108/ 291]              blk.11.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 109/ 291]               blk.11.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 110/ 291]                 blk.12.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[ 111/ 291]                 blk.12.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[ 112/ 291]                 blk.12.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[ 113/ 291]            blk.12.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[ 114/ 291]               blk.12.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[ 115/ 291]                 blk.12.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[ 116/ 291]               blk.12.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, converting to q6_K .. size =   112.00 MiB ->    45.94 MiB\n",
            "[ 117/ 291]              blk.12.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 118/ 291]               blk.12.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 119/ 291]                 blk.13.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[ 120/ 291]                 blk.13.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[ 121/ 291]                 blk.13.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[ 122/ 291]            blk.13.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[ 123/ 291]               blk.13.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[ 124/ 291]                 blk.13.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[ 125/ 291]               blk.13.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[ 126/ 291]              blk.13.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 127/ 291]               blk.13.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 128/ 291]                 blk.14.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[ 129/ 291]                 blk.14.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[ 130/ 291]                 blk.14.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[ 131/ 291]            blk.14.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[ 132/ 291]               blk.14.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[ 133/ 291]                 blk.14.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[ 134/ 291]               blk.14.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[ 135/ 291]              blk.14.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 136/ 291]               blk.14.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 137/ 291]                 blk.15.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[ 138/ 291]                 blk.15.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[ 139/ 291]                 blk.15.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[ 140/ 291]            blk.15.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[ 141/ 291]               blk.15.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[ 142/ 291]                 blk.15.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[ 143/ 291]               blk.15.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, converting to q6_K .. size =   112.00 MiB ->    45.94 MiB\n",
            "[ 144/ 291]              blk.15.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 145/ 291]               blk.15.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 146/ 291]                 blk.16.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[ 147/ 291]                 blk.16.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[ 148/ 291]                 blk.16.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[ 149/ 291]            blk.16.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[ 150/ 291]               blk.16.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[ 151/ 291]                 blk.16.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[ 152/ 291]               blk.16.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[ 153/ 291]              blk.16.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 154/ 291]               blk.16.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 155/ 291]                 blk.17.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[ 156/ 291]                 blk.17.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[ 157/ 291]                 blk.17.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[ 158/ 291]            blk.17.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[ 159/ 291]               blk.17.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[ 160/ 291]                 blk.17.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[ 161/ 291]               blk.17.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[ 162/ 291]              blk.17.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 163/ 291]               blk.17.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 164/ 291]                 blk.18.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[ 165/ 291]                 blk.18.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[ 166/ 291]                 blk.18.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[ 167/ 291]            blk.18.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[ 168/ 291]               blk.18.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[ 169/ 291]                 blk.18.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[ 170/ 291]               blk.18.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, converting to q6_K .. size =   112.00 MiB ->    45.94 MiB\n",
            "[ 171/ 291]              blk.18.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 172/ 291]               blk.18.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 173/ 291]                 blk.19.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[ 174/ 291]                 blk.19.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[ 175/ 291]                 blk.19.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[ 176/ 291]            blk.19.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[ 177/ 291]               blk.19.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[ 178/ 291]                 blk.19.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[ 179/ 291]               blk.19.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[ 180/ 291]              blk.19.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 181/ 291]               blk.19.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 182/ 291]                 blk.20.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[ 183/ 291]                 blk.20.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[ 184/ 291]                 blk.20.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[ 185/ 291]            blk.20.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[ 186/ 291]               blk.20.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[ 187/ 291]                 blk.20.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[ 188/ 291]               blk.20.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[ 189/ 291]              blk.20.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 190/ 291]               blk.20.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 191/ 291]                 blk.21.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[ 192/ 291]                 blk.21.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[ 193/ 291]                 blk.21.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[ 194/ 291]            blk.21.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[ 195/ 291]               blk.21.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[ 196/ 291]                 blk.21.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[ 197/ 291]               blk.21.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, converting to q6_K .. size =   112.00 MiB ->    45.94 MiB\n",
            "[ 198/ 291]              blk.21.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 199/ 291]               blk.21.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 200/ 291]                 blk.22.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[ 201/ 291]                 blk.22.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[ 202/ 291]                 blk.22.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[ 203/ 291]            blk.22.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[ 204/ 291]               blk.22.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[ 205/ 291]                 blk.22.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[ 206/ 291]               blk.22.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[ 207/ 291]              blk.22.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 208/ 291]               blk.22.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 209/ 291]                 blk.23.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[ 210/ 291]                 blk.23.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[ 211/ 291]                 blk.23.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[ 212/ 291]            blk.23.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[ 213/ 291]               blk.23.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[ 214/ 291]                 blk.23.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[ 215/ 291]               blk.23.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[ 216/ 291]              blk.23.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 217/ 291]               blk.23.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 218/ 291]                 blk.24.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[ 219/ 291]                 blk.24.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[ 220/ 291]                 blk.24.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[ 221/ 291]            blk.24.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[ 222/ 291]               blk.24.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[ 223/ 291]                 blk.24.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[ 224/ 291]               blk.24.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, converting to q6_K .. size =   112.00 MiB ->    45.94 MiB\n",
            "[ 225/ 291]              blk.24.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 226/ 291]               blk.24.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 227/ 291]                 blk.25.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[ 228/ 291]                 blk.25.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[ 229/ 291]                 blk.25.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[ 230/ 291]            blk.25.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[ 231/ 291]               blk.25.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[ 232/ 291]                 blk.25.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[ 233/ 291]               blk.25.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[ 234/ 291]              blk.25.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 235/ 291]               blk.25.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 236/ 291]                 blk.26.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[ 237/ 291]                 blk.26.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[ 238/ 291]                 blk.26.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[ 239/ 291]            blk.26.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[ 240/ 291]               blk.26.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[ 241/ 291]                 blk.26.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[ 242/ 291]               blk.26.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[ 243/ 291]              blk.26.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 244/ 291]               blk.26.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 245/ 291]                 blk.27.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[ 246/ 291]                 blk.27.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[ 247/ 291]                 blk.27.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[ 248/ 291]            blk.27.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[ 249/ 291]               blk.27.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[ 250/ 291]                 blk.27.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[ 251/ 291]               blk.27.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, converting to q6_K .. size =   112.00 MiB ->    45.94 MiB\n",
            "[ 252/ 291]              blk.27.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 253/ 291]               blk.27.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 254/ 291]                 blk.28.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[ 255/ 291]                 blk.28.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[ 256/ 291]                 blk.28.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[ 257/ 291]            blk.28.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[ 258/ 291]               blk.28.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[ 259/ 291]                 blk.28.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[ 260/ 291]               blk.28.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, converting to q6_K .. size =   112.00 MiB ->    45.94 MiB\n",
            "[ 261/ 291]              blk.28.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 262/ 291]               blk.28.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 263/ 291]                 blk.29.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[ 264/ 291]                 blk.29.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[ 265/ 291]                 blk.29.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[ 266/ 291]            blk.29.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[ 267/ 291]               blk.29.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[ 268/ 291]                 blk.29.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[ 269/ 291]               blk.29.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, converting to q6_K .. size =   112.00 MiB ->    45.94 MiB\n",
            "[ 270/ 291]              blk.29.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 271/ 291]               blk.29.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 272/ 291]                 blk.30.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[ 273/ 291]                 blk.30.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[ 274/ 291]                 blk.30.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[ 275/ 291]            blk.30.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[ 276/ 291]               blk.30.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[ 277/ 291]                 blk.30.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[ 278/ 291]               blk.30.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, converting to q6_K .. size =   112.00 MiB ->    45.94 MiB\n",
            "[ 279/ 291]              blk.30.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 280/ 291]               blk.30.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 281/ 291]                 blk.31.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[ 282/ 291]                 blk.31.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[ 283/ 291]                 blk.31.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[ 284/ 291]            blk.31.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[ 285/ 291]               blk.31.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[ 286/ 291]                 blk.31.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q4_K .. size =   112.00 MiB ->    31.50 MiB\n",
            "[ 287/ 291]               blk.31.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, converting to q6_K .. size =   112.00 MiB ->    45.94 MiB\n",
            "[ 288/ 291]              blk.31.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 289/ 291]               blk.31.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 290/ 291]                   output_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 291/ 291]                        output.weight - [ 4096, 128256,     1,     1], type =    f16, converting to q6_K .. size =  1002.00 MiB ->   410.98 MiB\n",
            "llama_model_quantize_internal: model size  = 15317.02 MB\n",
            "llama_model_quantize_internal: quant size  =  4685.30 MB\n",
            "\n",
            "main: quantize time = 974372.05 ms\n",
            "main:    total time = 974372.05 ms\n",
            "Unsloth: Conversion completed! Output location: ./vinci00/scrapegraphcompanion_finetuning_demo-GGUF/unsloth.Q4_K_M.gguf\n",
            "Unsloth: Uploading GGUF to Huggingface Hub...\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "22095323597f4e738b002ec171684e1f",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "unsloth.F16.gguf:   0%|          | 0.00/16.1G [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Saved GGUF to https://huggingface.co/vinci00/scrapegraphcompanion_finetuning_demo-GGUF\n",
            "Unsloth: Uploading GGUF to Huggingface Hub...\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "db7b862858c24e6c92cdc845e8e21ef9",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "unsloth.Q4_K_M.gguf:   0%|          | 0.00/4.92G [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Saved GGUF to https://huggingface.co/vinci00/scrapegraphcompanion_finetuning_demo-GGUF\n"
          ]
        }
      ],
      "source": [
        "model.push_to_hub_gguf(\n",
        "    f\"vinci00/scrapegraphcompanion_finetuning_GGUF\",\n",
        "    tokenizer,\n",
        "    quantization_method=\"q4_k_m\",\n",
        "    token=\"HF_TOKEN\"\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "5c89358602ff4789b389d7047cbabbb2",
            "c87965e2bc374a808959acd5be9aa342",
            "d8759b1e21664696b0e5e80e696537e4",
            "b298576478574d27b3e85badb1d74fda",
            "b8fff71974d74970b171407fd9c933e3",
            "7628f0680eac4bb896c13a359f1edf9f",
            "bd98493501ba4ad39c9f9302b469a517",
            "d22b91b94c5f434d8b2b00e836401963",
            "43ff15f706f949af918fd57596307d3b",
            "abd547704761494bbd76decf0c2708b5",
            "dc093f19352847d4ba4cf26b0a973108"
          ]
        },
        "id": "AYt93RI9I2zd",
        "outputId": "224afd88-d8d1-4017-935e-16d5b491227c"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Unsloth: You have 1 CPUs. Using `safe_serialization` is 10x slower.\n",
            "We shall switch to Pytorch saving, which will take 3 minutes and not 30 minutes.\n",
            "To force `safe_serialization`, set it to `None` instead.\n",
            "Unsloth: Kaggle/Colab has limited disk space. We need to delete the downloaded\n",
            "model which will save 4-16GB of disk space, allowing you to save on Kaggle/Colab.\n",
            "Unsloth: Will remove a cached repo with size 5.7G\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Unsloth: Merging 4bit and LoRA weights to 16bit...\n",
            "Unsloth: Will use up to 7.67 out of 12.67 RAM for saving.\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            " 50%|█████     | 16/32 [00:02<00:01, 10.20it/s]We will save to Disk and not RAM now.\n",
            "100%|██████████| 32/32 [01:20<00:00,  2.50s/it]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Unsloth: Saving tokenizer... Done.\n",
            "Unsloth: Saving model... This might take 5 minutes for Llama-7b...\n",
            "Unsloth: Saving vinci00/scrapegraphcompanion_finetuning_demo-GGUF/pytorch_model-00001-of-00004.bin...\n",
            "Unsloth: Saving vinci00/scrapegraphcompanion_finetuning_demo-GGUF/pytorch_model-00002-of-00004.bin...\n",
            "Unsloth: Saving vinci00/scrapegraphcompanion_finetuning_demo-GGUF/pytorch_model-00003-of-00004.bin...\n",
            "Unsloth: Saving vinci00/scrapegraphcompanion_finetuning_demo-GGUF/pytorch_model-00004-of-00004.bin...\n",
            "Done.\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Unsloth: Converting llama model. Can use fast conversion = False.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "==((====))==  Unsloth: Conversion from QLoRA to GGUF information\n",
            "   \\\\   /|    [0] Installing llama.cpp will take 3 minutes.\n",
            "O^O/ \\_/ \\    [1] Converting HF to GUUF 16bits will take 3 minutes.\n",
            "\\        /    [2] Converting GGUF 16bits to ['q5_k_m'] will take 10 minutes each.\n",
            " \"-____-\"     In total, you will have to wait at least 16 minutes.\n",
            "\n",
            "Unsloth: [0] Installing llama.cpp. This will take 3 minutes...\n",
            "Unsloth: [1] Converting model at vinci00/scrapegraphcompanion_finetuning_demo-GGUF into f16 GGUF format.\n",
            "The output location will be ./vinci00/scrapegraphcompanion_finetuning_demo-GGUF/unsloth.F16.gguf\n",
            "This will take 3 minutes...\n",
            "INFO:hf-to-gguf:Loading model: scrapegraphcompanion_finetuning_demo-GGUF\n",
            "INFO:gguf.gguf_writer:gguf: This GGUF file is for Little Endian only\n",
            "INFO:hf-to-gguf:Set model parameters\n",
            "INFO:hf-to-gguf:gguf: context length = 8192\n",
            "INFO:hf-to-gguf:gguf: embedding length = 4096\n",
            "INFO:hf-to-gguf:gguf: feed forward length = 14336\n",
            "INFO:hf-to-gguf:gguf: head count = 32\n",
            "INFO:hf-to-gguf:gguf: key-value head count = 8\n",
            "INFO:hf-to-gguf:gguf: rope theta = 500000.0\n",
            "INFO:hf-to-gguf:gguf: rms norm epsilon = 1e-05\n",
            "INFO:hf-to-gguf:gguf: file type = 1\n",
            "INFO:hf-to-gguf:Set model tokenizer\n",
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
            "INFO:gguf.vocab:Adding 280147 merge(s).\n",
            "INFO:gguf.vocab:Setting special token type bos to 128000\n",
            "INFO:gguf.vocab:Setting special token type eos to 128009\n",
            "INFO:gguf.vocab:Setting special token type pad to 128255\n",
            "INFO:gguf.vocab:Setting chat_template to {% 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 %}{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>\n",
            "\n",
            "' }}{% endif %}\n",
            "INFO:hf-to-gguf:Exporting model...\n",
            "INFO:hf-to-gguf:gguf: loading model weight map from 'pytorch_model.bin.index.json'\n",
            "INFO:hf-to-gguf:gguf: loading model part 'pytorch_model-00001-of-00004.bin'\n",
            "INFO:hf-to-gguf:token_embd.weight,           torch.float16 --> F16, shape = {4096, 128256}\n",
            "INFO:hf-to-gguf:blk.0.attn_q.weight,         torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.0.attn_k.weight,         torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.0.attn_v.weight,         torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.0.attn_output.weight,    torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.0.ffn_gate.weight,       torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.0.ffn_up.weight,         torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.0.ffn_down.weight,       torch.float16 --> F16, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.0.attn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.0.ffn_norm.weight,       torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.1.attn_q.weight,         torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.1.attn_k.weight,         torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.1.attn_v.weight,         torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.1.attn_output.weight,    torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.1.ffn_gate.weight,       torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.1.ffn_up.weight,         torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.1.ffn_down.weight,       torch.float16 --> F16, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.1.attn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.1.ffn_norm.weight,       torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.2.attn_q.weight,         torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.2.attn_k.weight,         torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.2.attn_v.weight,         torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.2.attn_output.weight,    torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.2.ffn_gate.weight,       torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.2.ffn_up.weight,         torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.2.ffn_down.weight,       torch.float16 --> F16, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.2.attn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.2.ffn_norm.weight,       torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.3.attn_q.weight,         torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.3.attn_k.weight,         torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.3.attn_v.weight,         torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.3.attn_output.weight,    torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.3.ffn_gate.weight,       torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.3.ffn_up.weight,         torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.3.ffn_down.weight,       torch.float16 --> F16, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.3.attn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.3.ffn_norm.weight,       torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.4.attn_q.weight,         torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.4.attn_k.weight,         torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.4.attn_v.weight,         torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.4.attn_output.weight,    torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.4.ffn_gate.weight,       torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.4.ffn_up.weight,         torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.4.ffn_down.weight,       torch.float16 --> F16, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.4.attn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.4.ffn_norm.weight,       torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.5.attn_q.weight,         torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.5.attn_k.weight,         torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.5.attn_v.weight,         torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.5.attn_output.weight,    torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.5.ffn_gate.weight,       torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.5.ffn_up.weight,         torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.5.ffn_down.weight,       torch.float16 --> F16, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.5.attn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.5.ffn_norm.weight,       torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.6.attn_q.weight,         torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.6.attn_k.weight,         torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.6.attn_v.weight,         torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.6.attn_output.weight,    torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.6.ffn_gate.weight,       torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.6.ffn_up.weight,         torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.6.ffn_down.weight,       torch.float16 --> F16, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.6.attn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.6.ffn_norm.weight,       torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.7.attn_q.weight,         torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.7.attn_k.weight,         torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.7.attn_v.weight,         torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.7.attn_output.weight,    torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.7.ffn_gate.weight,       torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.7.ffn_up.weight,         torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.7.ffn_down.weight,       torch.float16 --> F16, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.7.attn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.7.ffn_norm.weight,       torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.8.attn_q.weight,         torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.8.attn_k.weight,         torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.8.attn_v.weight,         torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.8.attn_output.weight,    torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.8.ffn_gate.weight,       torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.8.ffn_up.weight,         torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.8.ffn_down.weight,       torch.float16 --> F16, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.8.attn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.8.ffn_norm.weight,       torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:gguf: loading model part 'pytorch_model-00002-of-00004.bin'\n",
            "INFO:hf-to-gguf:blk.9.attn_q.weight,         torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.9.attn_k.weight,         torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.9.attn_v.weight,         torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.9.attn_output.weight,    torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.9.ffn_gate.weight,       torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.9.ffn_up.weight,         torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.9.ffn_down.weight,       torch.float16 --> F16, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.9.attn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.9.ffn_norm.weight,       torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.10.attn_q.weight,        torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.10.attn_k.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.10.attn_v.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.10.attn_output.weight,   torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.10.ffn_gate.weight,      torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.10.ffn_up.weight,        torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.10.ffn_down.weight,      torch.float16 --> F16, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.10.attn_norm.weight,     torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.10.ffn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.11.attn_q.weight,        torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.11.attn_k.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.11.attn_v.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.11.attn_output.weight,   torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.11.ffn_gate.weight,      torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.11.ffn_up.weight,        torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.11.ffn_down.weight,      torch.float16 --> F16, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.11.attn_norm.weight,     torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.11.ffn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.12.attn_q.weight,        torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.12.attn_k.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.12.attn_v.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.12.attn_output.weight,   torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.12.ffn_gate.weight,      torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.12.ffn_up.weight,        torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.12.ffn_down.weight,      torch.float16 --> F16, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.12.attn_norm.weight,     torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.12.ffn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.13.attn_q.weight,        torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.13.attn_k.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.13.attn_v.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.13.attn_output.weight,   torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.13.ffn_gate.weight,      torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.13.ffn_up.weight,        torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.13.ffn_down.weight,      torch.float16 --> F16, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.13.attn_norm.weight,     torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.13.ffn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.14.attn_q.weight,        torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.14.attn_k.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.14.attn_v.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.14.attn_output.weight,   torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.14.ffn_gate.weight,      torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.14.ffn_up.weight,        torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.14.ffn_down.weight,      torch.float16 --> F16, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.14.attn_norm.weight,     torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.14.ffn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.15.attn_q.weight,        torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.15.attn_k.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.15.attn_v.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.15.attn_output.weight,   torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.15.ffn_gate.weight,      torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.15.ffn_up.weight,        torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.15.ffn_down.weight,      torch.float16 --> F16, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.15.attn_norm.weight,     torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.15.ffn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.16.attn_q.weight,        torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.16.attn_k.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.16.attn_v.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.16.attn_output.weight,   torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.16.ffn_gate.weight,      torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.16.ffn_up.weight,        torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.16.ffn_down.weight,      torch.float16 --> F16, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.16.attn_norm.weight,     torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.16.ffn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.17.attn_q.weight,        torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.17.attn_k.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.17.attn_v.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.17.attn_output.weight,   torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.17.ffn_gate.weight,      torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.17.ffn_up.weight,        torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.17.ffn_down.weight,      torch.float16 --> F16, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.17.attn_norm.weight,     torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.17.ffn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.18.attn_q.weight,        torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.18.attn_k.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.18.attn_v.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.18.attn_output.weight,   torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.18.ffn_gate.weight,      torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.18.ffn_up.weight,        torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.18.ffn_down.weight,      torch.float16 --> F16, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.18.attn_norm.weight,     torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.18.ffn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.19.attn_q.weight,        torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.19.attn_k.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.19.attn_v.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.19.attn_output.weight,   torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.19.ffn_gate.weight,      torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.19.ffn_up.weight,        torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.19.ffn_down.weight,      torch.float16 --> F16, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.19.attn_norm.weight,     torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.19.ffn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.20.attn_q.weight,        torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.20.attn_k.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.20.attn_v.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.20.attn_output.weight,   torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.20.ffn_gate.weight,      torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:gguf: loading model part 'pytorch_model-00003-of-00004.bin'\n",
            "INFO:hf-to-gguf:blk.20.ffn_up.weight,        torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.20.ffn_down.weight,      torch.float16 --> F16, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.20.attn_norm.weight,     torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.20.ffn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.21.attn_q.weight,        torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.21.attn_k.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.21.attn_v.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.21.attn_output.weight,   torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.21.ffn_gate.weight,      torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.21.ffn_up.weight,        torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.21.ffn_down.weight,      torch.float16 --> F16, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.21.attn_norm.weight,     torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.21.ffn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.22.attn_q.weight,        torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.22.attn_k.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.22.attn_v.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.22.attn_output.weight,   torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.22.ffn_gate.weight,      torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.22.ffn_up.weight,        torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.22.ffn_down.weight,      torch.float16 --> F16, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.22.attn_norm.weight,     torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.22.ffn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.23.attn_q.weight,        torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.23.attn_k.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.23.attn_v.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.23.attn_output.weight,   torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.23.ffn_gate.weight,      torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.23.ffn_up.weight,        torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.23.ffn_down.weight,      torch.float16 --> F16, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.23.attn_norm.weight,     torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.23.ffn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.24.attn_q.weight,        torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.24.attn_k.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.24.attn_v.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.24.attn_output.weight,   torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.24.ffn_gate.weight,      torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.24.ffn_up.weight,        torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.24.ffn_down.weight,      torch.float16 --> F16, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.24.attn_norm.weight,     torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.24.ffn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.25.attn_q.weight,        torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.25.attn_k.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.25.attn_v.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.25.attn_output.weight,   torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.25.ffn_gate.weight,      torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.25.ffn_up.weight,        torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.25.ffn_down.weight,      torch.float16 --> F16, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.25.attn_norm.weight,     torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.25.ffn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.26.attn_q.weight,        torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.26.attn_k.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.26.attn_v.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.26.attn_output.weight,   torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.26.ffn_gate.weight,      torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.26.ffn_up.weight,        torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.26.ffn_down.weight,      torch.float16 --> F16, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.26.attn_norm.weight,     torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.26.ffn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.27.attn_q.weight,        torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.27.attn_k.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.27.attn_v.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.27.attn_output.weight,   torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.27.ffn_gate.weight,      torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.27.ffn_up.weight,        torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.27.ffn_down.weight,      torch.float16 --> F16, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.27.attn_norm.weight,     torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.27.ffn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.28.attn_q.weight,        torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.28.attn_k.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.28.attn_v.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.28.attn_output.weight,   torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.28.ffn_gate.weight,      torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.28.ffn_up.weight,        torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.28.ffn_down.weight,      torch.float16 --> F16, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.28.attn_norm.weight,     torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.28.ffn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.29.attn_q.weight,        torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.29.attn_k.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.29.attn_v.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.29.attn_output.weight,   torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.29.ffn_gate.weight,      torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.29.ffn_up.weight,        torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.29.ffn_down.weight,      torch.float16 --> F16, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.29.attn_norm.weight,     torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.29.ffn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.30.attn_q.weight,        torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.30.attn_k.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.30.attn_v.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.30.attn_output.weight,   torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.30.ffn_gate.weight,      torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.30.ffn_up.weight,        torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.30.ffn_down.weight,      torch.float16 --> F16, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.30.attn_norm.weight,     torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.30.ffn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.31.attn_q.weight,        torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.31.attn_k.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.31.attn_v.weight,        torch.float16 --> F16, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.31.attn_output.weight,   torch.float16 --> F16, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.31.ffn_gate.weight,      torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.31.ffn_up.weight,        torch.float16 --> F16, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:gguf: loading model part 'pytorch_model-00004-of-00004.bin'\n",
            "INFO:hf-to-gguf:blk.31.ffn_down.weight,      torch.float16 --> F16, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.31.attn_norm.weight,     torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.31.ffn_norm.weight,      torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:output_norm.weight,          torch.float16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:output.weight,               torch.float16 --> F16, shape = {4096, 128256}\n",
            "INFO:gguf.gguf_writer:Writing the following files:\n",
            "INFO:gguf.gguf_writer:vinci00/scrapegraphcompanion_finetuning_demo-GGUF/unsloth.F16.gguf: n_tensors = 291, total_size = 16.1G\n",
            "Writing: 100%|██████████| 16.1G/16.1G [02:46<00:00, 96.4Mbyte/s]\n",
            "INFO:hf-to-gguf:Model successfully exported.\n",
            "Unsloth: Conversion completed! Output location: ./vinci00/scrapegraphcompanion_finetuning_demo-GGUF/unsloth.F16.gguf\n",
            "Unsloth: [2] Converting GGUF 16bit into q5_k_m. This will take 20 minutes...\n",
            "main: build = 3250 (6030c612)\n",
            "main: built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu\n",
            "main: quantizing './vinci00/scrapegraphcompanion_finetuning_demo-GGUF/unsloth.F16.gguf' to './vinci00/scrapegraphcompanion_finetuning_demo-GGUF/unsloth.Q5_K_M.gguf' as Q5_K_M using 4 threads\n",
            "llama_model_loader: loaded meta data with 23 key-value pairs and 291 tensors from ./vinci00/scrapegraphcompanion_finetuning_demo-GGUF/unsloth.F16.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              = scrapegraphcompanion_finetuning_demo-...\n",
            "llama_model_loader: - kv   2:                          llama.block_count u32              = 32\n",
            "llama_model_loader: - kv   3:                       llama.context_length u32              = 8192\n",
            "llama_model_loader: - kv   4:                     llama.embedding_length u32              = 4096\n",
            "llama_model_loader: - kv   5:                  llama.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              = 500000.000000\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              = 1\n",
            "llama_model_loader: - kv  11:                           llama.vocab_size u32              = 128256\n",
            "llama_model_loader: - kv  12:                 llama.rope.dimension_count u32              = 128\n",
            "llama_model_loader: - kv  13:                       tokenizer.ggml.model str              = gpt2\n",
            "llama_model_loader: - kv  14:                         tokenizer.ggml.pre str              = llama-bpe\n",
            "llama_model_loader: - kv  15:                      tokenizer.ggml.tokens arr[str,128256]  = [\"!\", \"\\\"\", \"#\", \"$\", \"%\", \"&\", \"'\", ...\n",
            "llama_model_loader: - kv  16:                  tokenizer.ggml.token_type arr[i32,128256]  = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...\n",
            "llama_model_loader: - kv  17:                      tokenizer.ggml.merges arr[str,280147]  = [\"Ġ Ġ\", \"Ġ ĠĠĠ\", \"ĠĠ ĠĠ\", \"...\n",
            "llama_model_loader: - kv  18:                tokenizer.ggml.bos_token_id u32              = 128000\n",
            "llama_model_loader: - kv  19:                tokenizer.ggml.eos_token_id u32              = 128009\n",
            "llama_model_loader: - kv  20:            tokenizer.ggml.padding_token_id u32              = 128255\n",
            "llama_model_loader: - kv  21:                    tokenizer.chat_template str              = {% set loop_messages = messages %}{% ...\n",
            "llama_model_loader: - kv  22:               general.quantization_version u32              = 2\n",
            "llama_model_loader: - type  f32:   65 tensors\n",
            "llama_model_loader: - type  f16:  226 tensors\n",
            "[   1/ 291]                    token_embd.weight - [ 4096, 128256,     1,     1], type =    f16, converting to q5_K .. size =  1002.00 MiB ->   344.44 MiB\n",
            "[   2/ 291]                  blk.0.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[   3/ 291]                  blk.0.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[   4/ 291]                  blk.0.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[   5/ 291]             blk.0.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[   6/ 291]                blk.0.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[   7/ 291]                  blk.0.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[   8/ 291]                blk.0.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, converting to q6_K .. size =   112.00 MiB ->    45.94 MiB\n",
            "[   9/ 291]               blk.0.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[  10/ 291]                blk.0.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[  11/ 291]                  blk.1.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[  12/ 291]                  blk.1.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[  13/ 291]                  blk.1.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[  14/ 291]             blk.1.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[  15/ 291]                blk.1.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[  16/ 291]                  blk.1.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[  17/ 291]                blk.1.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, converting to q6_K .. size =   112.00 MiB ->    45.94 MiB\n",
            "[  18/ 291]               blk.1.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[  19/ 291]                blk.1.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[  20/ 291]                  blk.2.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[  21/ 291]                  blk.2.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[  22/ 291]                  blk.2.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[  23/ 291]             blk.2.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[  24/ 291]                blk.2.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[  25/ 291]                  blk.2.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[  26/ 291]                blk.2.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, converting to q6_K .. size =   112.00 MiB ->    45.94 MiB\n",
            "[  27/ 291]               blk.2.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[  28/ 291]                blk.2.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[  29/ 291]                  blk.3.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[  30/ 291]                  blk.3.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[  31/ 291]                  blk.3.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[  32/ 291]             blk.3.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[  33/ 291]                blk.3.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[  34/ 291]                  blk.3.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[  35/ 291]                blk.3.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, converting to q6_K .. size =   112.00 MiB ->    45.94 MiB\n",
            "[  36/ 291]               blk.3.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[  37/ 291]                blk.3.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[  38/ 291]                  blk.4.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[  39/ 291]                  blk.4.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[  40/ 291]                  blk.4.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[  41/ 291]             blk.4.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[  42/ 291]                blk.4.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[  43/ 291]                  blk.4.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[  44/ 291]                blk.4.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[  45/ 291]               blk.4.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[  46/ 291]                blk.4.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[  47/ 291]                  blk.5.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[  48/ 291]                  blk.5.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[  49/ 291]                  blk.5.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[  50/ 291]             blk.5.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[  51/ 291]                blk.5.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[  52/ 291]                  blk.5.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[  53/ 291]                blk.5.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[  54/ 291]               blk.5.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[  55/ 291]                blk.5.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[  56/ 291]                  blk.6.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[  57/ 291]                  blk.6.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[  58/ 291]                  blk.6.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[  59/ 291]             blk.6.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[  60/ 291]                blk.6.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[  61/ 291]                  blk.6.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[  62/ 291]                blk.6.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, converting to q6_K .. size =   112.00 MiB ->    45.94 MiB\n",
            "[  63/ 291]               blk.6.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[  64/ 291]                blk.6.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[  65/ 291]                  blk.7.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[  66/ 291]                  blk.7.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[  67/ 291]                  blk.7.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[  68/ 291]             blk.7.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[  69/ 291]                blk.7.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[  70/ 291]                  blk.7.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[  71/ 291]                blk.7.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[  72/ 291]               blk.7.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[  73/ 291]                blk.7.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[  74/ 291]                  blk.8.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[  75/ 291]                  blk.8.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[  76/ 291]                  blk.8.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[  77/ 291]             blk.8.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[  78/ 291]                blk.8.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[  79/ 291]                  blk.8.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[  80/ 291]                blk.8.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[  81/ 291]               blk.8.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[  82/ 291]                blk.8.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[  83/ 291]                  blk.9.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[  84/ 291]                  blk.9.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[  85/ 291]                  blk.9.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[  86/ 291]             blk.9.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[  87/ 291]                blk.9.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[  88/ 291]                  blk.9.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[  89/ 291]                blk.9.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, converting to q6_K .. size =   112.00 MiB ->    45.94 MiB\n",
            "[  90/ 291]               blk.9.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[  91/ 291]                blk.9.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[  92/ 291]                 blk.10.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[  93/ 291]                 blk.10.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[  94/ 291]                 blk.10.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[  95/ 291]            blk.10.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[  96/ 291]               blk.10.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[  97/ 291]                 blk.10.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[  98/ 291]               blk.10.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[  99/ 291]              blk.10.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 100/ 291]               blk.10.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 101/ 291]                 blk.11.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[ 102/ 291]                 blk.11.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[ 103/ 291]                 blk.11.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[ 104/ 291]            blk.11.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[ 105/ 291]               blk.11.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 106/ 291]                 blk.11.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 107/ 291]               blk.11.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 108/ 291]              blk.11.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 109/ 291]               blk.11.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 110/ 291]                 blk.12.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[ 111/ 291]                 blk.12.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[ 112/ 291]                 blk.12.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[ 113/ 291]            blk.12.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[ 114/ 291]               blk.12.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 115/ 291]                 blk.12.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 116/ 291]               blk.12.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, converting to q6_K .. size =   112.00 MiB ->    45.94 MiB\n",
            "[ 117/ 291]              blk.12.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 118/ 291]               blk.12.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 119/ 291]                 blk.13.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[ 120/ 291]                 blk.13.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[ 121/ 291]                 blk.13.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[ 122/ 291]            blk.13.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[ 123/ 291]               blk.13.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 124/ 291]                 blk.13.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 125/ 291]               blk.13.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 126/ 291]              blk.13.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 127/ 291]               blk.13.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 128/ 291]                 blk.14.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[ 129/ 291]                 blk.14.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[ 130/ 291]                 blk.14.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[ 131/ 291]            blk.14.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[ 132/ 291]               blk.14.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 133/ 291]                 blk.14.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 134/ 291]               blk.14.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 135/ 291]              blk.14.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 136/ 291]               blk.14.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 137/ 291]                 blk.15.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[ 138/ 291]                 blk.15.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[ 139/ 291]                 blk.15.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[ 140/ 291]            blk.15.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[ 141/ 291]               blk.15.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 142/ 291]                 blk.15.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 143/ 291]               blk.15.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, converting to q6_K .. size =   112.00 MiB ->    45.94 MiB\n",
            "[ 144/ 291]              blk.15.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 145/ 291]               blk.15.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 146/ 291]                 blk.16.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[ 147/ 291]                 blk.16.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[ 148/ 291]                 blk.16.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[ 149/ 291]            blk.16.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[ 150/ 291]               blk.16.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 151/ 291]                 blk.16.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 152/ 291]               blk.16.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 153/ 291]              blk.16.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 154/ 291]               blk.16.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 155/ 291]                 blk.17.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[ 156/ 291]                 blk.17.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[ 157/ 291]                 blk.17.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[ 158/ 291]            blk.17.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[ 159/ 291]               blk.17.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 160/ 291]                 blk.17.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 161/ 291]               blk.17.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 162/ 291]              blk.17.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 163/ 291]               blk.17.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 164/ 291]                 blk.18.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[ 165/ 291]                 blk.18.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[ 166/ 291]                 blk.18.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[ 167/ 291]            blk.18.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[ 168/ 291]               blk.18.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 169/ 291]                 blk.18.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 170/ 291]               blk.18.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, converting to q6_K .. size =   112.00 MiB ->    45.94 MiB\n",
            "[ 171/ 291]              blk.18.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 172/ 291]               blk.18.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 173/ 291]                 blk.19.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[ 174/ 291]                 blk.19.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[ 175/ 291]                 blk.19.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[ 176/ 291]            blk.19.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[ 177/ 291]               blk.19.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 178/ 291]                 blk.19.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 179/ 291]               blk.19.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 180/ 291]              blk.19.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 181/ 291]               blk.19.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 182/ 291]                 blk.20.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[ 183/ 291]                 blk.20.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[ 184/ 291]                 blk.20.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[ 185/ 291]            blk.20.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[ 186/ 291]               blk.20.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 187/ 291]                 blk.20.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 188/ 291]               blk.20.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 189/ 291]              blk.20.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 190/ 291]               blk.20.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 191/ 291]                 blk.21.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[ 192/ 291]                 blk.21.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[ 193/ 291]                 blk.21.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[ 194/ 291]            blk.21.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[ 195/ 291]               blk.21.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 196/ 291]                 blk.21.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 197/ 291]               blk.21.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, converting to q6_K .. size =   112.00 MiB ->    45.94 MiB\n",
            "[ 198/ 291]              blk.21.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 199/ 291]               blk.21.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 200/ 291]                 blk.22.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[ 201/ 291]                 blk.22.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[ 202/ 291]                 blk.22.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[ 203/ 291]            blk.22.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[ 204/ 291]               blk.22.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 205/ 291]                 blk.22.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 206/ 291]               blk.22.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 207/ 291]              blk.22.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 208/ 291]               blk.22.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 209/ 291]                 blk.23.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[ 210/ 291]                 blk.23.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[ 211/ 291]                 blk.23.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[ 212/ 291]            blk.23.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[ 213/ 291]               blk.23.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 214/ 291]                 blk.23.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 215/ 291]               blk.23.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 216/ 291]              blk.23.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 217/ 291]               blk.23.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 218/ 291]                 blk.24.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[ 219/ 291]                 blk.24.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[ 220/ 291]                 blk.24.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[ 221/ 291]            blk.24.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[ 222/ 291]               blk.24.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 223/ 291]                 blk.24.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 224/ 291]               blk.24.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, converting to q6_K .. size =   112.00 MiB ->    45.94 MiB\n",
            "[ 225/ 291]              blk.24.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 226/ 291]               blk.24.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 227/ 291]                 blk.25.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[ 228/ 291]                 blk.25.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[ 229/ 291]                 blk.25.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[ 230/ 291]            blk.25.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[ 231/ 291]               blk.25.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 232/ 291]                 blk.25.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 233/ 291]               blk.25.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 234/ 291]              blk.25.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 235/ 291]               blk.25.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 236/ 291]                 blk.26.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[ 237/ 291]                 blk.26.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[ 238/ 291]                 blk.26.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[ 239/ 291]            blk.26.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[ 240/ 291]               blk.26.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 241/ 291]                 blk.26.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 242/ 291]               blk.26.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 243/ 291]              blk.26.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 244/ 291]               blk.26.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 245/ 291]                 blk.27.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[ 246/ 291]                 blk.27.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[ 247/ 291]                 blk.27.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[ 248/ 291]            blk.27.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[ 249/ 291]               blk.27.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 250/ 291]                 blk.27.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 251/ 291]               blk.27.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, converting to q6_K .. size =   112.00 MiB ->    45.94 MiB\n",
            "[ 252/ 291]              blk.27.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 253/ 291]               blk.27.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 254/ 291]                 blk.28.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[ 255/ 291]                 blk.28.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[ 256/ 291]                 blk.28.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[ 257/ 291]            blk.28.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[ 258/ 291]               blk.28.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 259/ 291]                 blk.28.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 260/ 291]               blk.28.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, converting to q6_K .. size =   112.00 MiB ->    45.94 MiB\n",
            "[ 261/ 291]              blk.28.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 262/ 291]               blk.28.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 263/ 291]                 blk.29.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[ 264/ 291]                 blk.29.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[ 265/ 291]                 blk.29.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[ 266/ 291]            blk.29.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[ 267/ 291]               blk.29.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 268/ 291]                 blk.29.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 269/ 291]               blk.29.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, converting to q6_K .. size =   112.00 MiB ->    45.94 MiB\n",
            "[ 270/ 291]              blk.29.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 271/ 291]               blk.29.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 272/ 291]                 blk.30.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[ 273/ 291]                 blk.30.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[ 274/ 291]                 blk.30.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[ 275/ 291]            blk.30.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[ 276/ 291]               blk.30.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 277/ 291]                 blk.30.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 278/ 291]               blk.30.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, converting to q6_K .. size =   112.00 MiB ->    45.94 MiB\n",
            "[ 279/ 291]              blk.30.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 280/ 291]               blk.30.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 281/ 291]                 blk.31.attn_q.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[ 282/ 291]                 blk.31.attn_k.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q5_K .. size =     8.00 MiB ->     2.75 MiB\n",
            "[ 283/ 291]                 blk.31.attn_v.weight - [ 4096,  1024,     1,     1], type =    f16, converting to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[ 284/ 291]            blk.31.attn_output.weight - [ 4096,  4096,     1,     1], type =    f16, converting to q5_K .. size =    32.00 MiB ->    11.00 MiB\n",
            "[ 285/ 291]               blk.31.ffn_gate.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 286/ 291]                 blk.31.ffn_up.weight - [ 4096, 14336,     1,     1], type =    f16, converting to q5_K .. size =   112.00 MiB ->    38.50 MiB\n",
            "[ 287/ 291]               blk.31.ffn_down.weight - [14336,  4096,     1,     1], type =    f16, converting to q6_K .. size =   112.00 MiB ->    45.94 MiB\n",
            "[ 288/ 291]              blk.31.attn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 289/ 291]               blk.31.ffn_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 290/ 291]                   output_norm.weight - [ 4096,     1,     1,     1], type =    f32, size =    0.016 MB\n",
            "[ 291/ 291]                        output.weight - [ 4096, 128256,     1,     1], type =    f16, converting to q6_K .. size =  1002.00 MiB ->   410.98 MiB\n",
            "llama_model_quantize_internal: model size  = 15317.02 MB\n",
            "llama_model_quantize_internal: quant size  =  5459.93 MB\n",
            "\n",
            "main: quantize time = 862056.99 ms\n",
            "main:    total time = 862056.99 ms\n",
            "Unsloth: Conversion completed! Output location: ./vinci00/scrapegraphcompanion_finetuning_demo-GGUF/unsloth.Q5_K_M.gguf\n",
            "Unsloth: Uploading GGUF to Huggingface Hub...\n",
            "Saved GGUF to https://huggingface.co/vinci00/scrapegraphcompanion_finetuning_demo-GGUF\n",
            "Unsloth: Uploading GGUF to Huggingface Hub...\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5c89358602ff4789b389d7047cbabbb2",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "unsloth.Q5_K_M.gguf:   0%|          | 0.00/5.73G [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Saved GGUF to https://huggingface.co/vinci00/scrapegraphcompanion_finetuning_demo-GGUF\n"
          ]
        }
      ],
      "source": [
        "model.push_to_hub_gguf(\n",
        "    f\"vinci00/scrapegraphcompanion_finetuning_GGUF\",\n",
        "    tokenizer,\n",
        "    quantization_method=\"q5_k_m\",\n",
        "    token=\"HF_TOKEN\"\n",
        ")"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "01d39db694b64ae6b302e106d437869e": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_dde8956e22904712b707741dc9c4f0e4",
            "placeholder": "​",
            "style": "IPY_MODEL_a2dd12d89b78403a93c98d6ad593ea18",
            "value": " 741/741 [00:00&lt;00:00, 47.6kB/s]"
          }
        },
        "0204b48fbc8e4cc7963c6b40f9a9aead": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "057f98642bc7436296bb87aa0f77957f": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "07fc3456e15a443cbaf0ec30092b413e": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "09bb6db398904d089393da0c92f83464": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "0f477da56da943dc9a63bb9269b30fa2": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "FloatProgressModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_11fec579fe39409ea4f05e76cc085250",
            "max": 9085698,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_2f8d156247b64cd3ba644a3305a6d8cd",
            "value": 9085698
          }
        },
        "10e58236474f451bb4f2c94f39c9ed97": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "11fec579fe39409ea4f05e76cc085250": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "15bf3d4590444efba34a0fa66fb08456": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_44a033cf82e54735abbf8152da00eb18",
            "placeholder": "​",
            "style": "IPY_MODEL_67218b31f35b40bd99dd49a90000523c",
            "value": "model.safetensors: 100%"
          }
        },
        "161e9ce7e6404168b60c9f2ac9216720": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "18d526d19d244e7e9481443da7d1b507": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_5f9392255f6546848b1c054f0ba5b167",
            "placeholder": "​",
            "style": "IPY_MODEL_86c91cfebf54425798174b579f9f599d",
            "value": "unsloth.F16.gguf: 100%"
          }
        },
        "1c843fd7dc0a4d409486793054b84a92": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HBoxModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_30ce4896850441b48b53c334fc9842f6",
              "IPY_MODEL_c3d41bdfa4794b258b0c5fb37b729e16",
              "IPY_MODEL_b08b687487a44ed3a68a4503511093a2"
            ],
            "layout": "IPY_MODEL_a010aed00d864b53a24ff4a6fbbd1a51"
          }
        },
        "2146ef0871384889aac2099f16fea801": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "22095323597f4e738b002ec171684e1f": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HBoxModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_18d526d19d244e7e9481443da7d1b507",
              "IPY_MODEL_af7c7c09b4e14e77817691e56dbd06a1",
              "IPY_MODEL_921e34c298df419aaef88b4e6bd22d91"
            ],
            "layout": "IPY_MODEL_a40fede526bf44299292963005ff01f5"
          }
        },
        "2535aea91b274f07bbb4b6bf4ce4e27b": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "2addd5e4788d48909b921b93f1c3828f": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "2f8d156247b64cd3ba644a3305a6d8cd": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "ProgressStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "30ce4896850441b48b53c334fc9842f6": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_552e38070c8946c8a26b54c71c49b6a1",
            "placeholder": "​",
            "style": "IPY_MODEL_539b957c3baf4c17882d4e76821475bc",
            "value": "config.json: 100%"
          }
        },
        "31f3f80281f2404580f1bf73cb7aa7ef": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "32595dbb6c814f98ab19dfaa543e39cb": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "ProgressStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "33fc12c71bdb465eb8fada84189f88f4": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "ProgressStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "37bd782766f54afaaec8d83cb647121d": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HBoxModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_feac171ff30c4ecdb84bffcedd8c7327",
              "IPY_MODEL_e467ba547f914a368a7a662c59f313c5",
              "IPY_MODEL_60fc1db0944b4d2897c47c830c42b1ad"
            ],
            "layout": "IPY_MODEL_38202de1459c4e72a6d68a508dfd701e"
          }
        },
        "38202de1459c4e72a6d68a508dfd701e": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "3a261c2ac3e34ce79f848df489c7dcf5": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "3de4f30bb23240148b31895ebcba8dce": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HBoxModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_540e616f0d0249819c7cb83fc7b436f8",
              "IPY_MODEL_9fcfa88c0eb04a66af71b21f4e7e873f",
              "IPY_MODEL_875e0842e7854789b72ef584b720a39e"
            ],
            "layout": "IPY_MODEL_057f98642bc7436296bb87aa0f77957f"
          }
        },
        "3f17a472484545e8bb8f6e88256e9c28": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "ProgressStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "3f95030236cd4cf593e906683e3e598b": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "43ff15f706f949af918fd57596307d3b": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "ProgressStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "44a033cf82e54735abbf8152da00eb18": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "45d7a2c66f5a4d8ca89994b80b45906a": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "47e97f71876d44558c17ea96879a6913": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "4938c9d7f6c7473e875b09eb31615c82": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "517c757324f9442d8b065dcccd0ea745": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "522e02fd045b4b8cb2d622a01b5e50f8": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "539b957c3baf4c17882d4e76821475bc": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "540e616f0d0249819c7cb83fc7b436f8": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_8007c549d7eb4d3488aadc6cbcec9741",
            "placeholder": "​",
            "style": "IPY_MODEL_d07e15ef0d754eddbde18590d44f07c1",
            "value": "adapter_model.safetensors: 100%"
          }
        },
        "552e38070c8946c8a26b54c71c49b6a1": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "55e532a8435c41e8ac79199c049ea8f4": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "574b4b5fc5fd484a812344679050aca6": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HBoxModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_d682fc2eac2544a89415f6eddb6fc720",
              "IPY_MODEL_73a31e438d5e46aa904690b4a3a2f164",
              "IPY_MODEL_737392e0ee2442fb9f1c2b3edb008717"
            ],
            "layout": "IPY_MODEL_8fd94269facf407a8b21e373d6808c57"
          }
        },
        "57d021ed8da24cd2a8bd018096d39e20": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "5bd94572ec9942a1b5304824152ffe1e": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HBoxModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_ce6efc24ee134e149b5ba9d5e7f6da8d",
              "IPY_MODEL_f5f5ea7ff25a45c5b897ec16b8e64f35",
              "IPY_MODEL_01d39db694b64ae6b302e106d437869e"
            ],
            "layout": "IPY_MODEL_161e9ce7e6404168b60c9f2ac9216720"
          }
        },
        "5c89358602ff4789b389d7047cbabbb2": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HBoxModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_c87965e2bc374a808959acd5be9aa342",
              "IPY_MODEL_d8759b1e21664696b0e5e80e696537e4",
              "IPY_MODEL_b298576478574d27b3e85badb1d74fda"
            ],
            "layout": "IPY_MODEL_b8fff71974d74970b171407fd9c933e3"
          }
        },
        "5f437ce1e879426a91b32bc55db5c1d9": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "5f9392255f6546848b1c054f0ba5b167": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "60fc1db0944b4d2897c47c830c42b1ad": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_d047abddd9c04478bc8c3dadeeb52367",
            "placeholder": "​",
            "style": "IPY_MODEL_3f95030236cd4cf593e906683e3e598b",
            "value": " 459/459 [00:00&lt;00:00, 23.9kB/s]"
          }
        },
        "621f6b9ec9b34c9d9dd4e38a12dbd789": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "67218b31f35b40bd99dd49a90000523c": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "6dac9af4bad34e53b5395fcc5dd625b8": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "FloatProgressModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_c2a96d96b576402e9b9bba883cac643b",
            "max": 4920734048,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_3f17a472484545e8bb8f6e88256e9c28",
            "value": 4920734048
          }
        },
        "6e07290041254408b8a135803e3e7aa7": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "737392e0ee2442fb9f1c2b3edb008717": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_bec9a6baa8a8482d9f368b4dd4dcd54d",
            "placeholder": "​",
            "style": "IPY_MODEL_31f3f80281f2404580f1bf73cb7aa7ef",
            "value": " 131/131 [00:00&lt;00:00, 7.32kB/s]"
          }
        },
        "73a31e438d5e46aa904690b4a3a2f164": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "FloatProgressModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_be469d7a77454482b87d8a38b4bd8aa5",
            "max": 131,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_df11493595434f95a892b82ae7fc0310",
            "value": 131
          }
        },
        "7628f0680eac4bb896c13a359f1edf9f": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "7a125a56e21246f3b95843338c4940e3": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "7a5d591e9d9448beb473cbdee311af2a": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "8007c549d7eb4d3488aadc6cbcec9741": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "86c91cfebf54425798174b579f9f599d": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "875e0842e7854789b72ef584b720a39e": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_55e532a8435c41e8ac79199c049ea8f4",
            "placeholder": "​",
            "style": "IPY_MODEL_7a5d591e9d9448beb473cbdee311af2a",
            "value": " 168M/168M [00:01&lt;00:00, 184MB/s]"
          }
        },
        "8d681616d7a44d49a02841b277c99510": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "8dc82de26dfc4078ae43cf13ee9fba4e": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "8e835337fda441928e6d82e5c5e6d636": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "8fd94269facf407a8b21e373d6808c57": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "911d4b61e771416f8da8c2cf7c2fc006": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_d4d9f4a08e3f451a9c82bf93f379aad5",
            "placeholder": "​",
            "style": "IPY_MODEL_2146ef0871384889aac2099f16fea801",
            "value": " 5.70G/5.70G [00:48&lt;00:00, 189MB/s]"
          }
        },
        "912317468871410dad4a61f70a157840": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "FloatProgressModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_0204b48fbc8e4cc7963c6b40f9a9aead",
            "max": 51052,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_e3c8727734b94393a2a8b677d259c44d",
            "value": 51052
          }
        },
        "921e34c298df419aaef88b4e6bd22d91": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_09bb6db398904d089393da0c92f83464",
            "placeholder": "​",
            "style": "IPY_MODEL_a4d29e96dc234e13b3c2cba54ffc49de",
            "value": " 16.1G/16.1G [06:47&lt;00:00, 53.9MB/s]"
          }
        },
        "96076b40ab0d4430a2b9f33c60f591ed": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "97cccec196e74e99ba0ce2f88bf4dc9c": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "9ae67e6b90a2410e82fa27a593249628": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "9cc778faed39403f8109cbfbf57d9eab": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "9d4da438959e48babfa3c16d87652ee7": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HBoxModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_b8d92478a04e4ce195872f6de1618d4f",
              "IPY_MODEL_0f477da56da943dc9a63bb9269b30fa2",
              "IPY_MODEL_cf38a9d55572468d851ccdcf3a596ccf"
            ],
            "layout": "IPY_MODEL_9ae67e6b90a2410e82fa27a593249628"
          }
        },
        "9dbd0882aad74c2e90c3fd5645de9d87": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "9fcfa88c0eb04a66af71b21f4e7e873f": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "FloatProgressModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_97cccec196e74e99ba0ce2f88bf4dc9c",
            "max": 167832240,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_cc48d4fa7cf14e7e9ac4e893bd68f2fe",
            "value": 167832240
          }
        },
        "a010aed00d864b53a24ff4a6fbbd1a51": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "a2dd12d89b78403a93c98d6ad593ea18": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "a3072f76ae5c40de8a1ec37a6c7fa244": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "FloatProgressModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_acf19e4f681b4569b04600715fe66939",
            "max": 5702746403,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_32595dbb6c814f98ab19dfaa543e39cb",
            "value": 5702746403
          }
        },
        "a393b5d0aa3f43ae82d4c7d0f7bc1a66": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "ProgressStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "a40fede526bf44299292963005ff01f5": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "a4d29e96dc234e13b3c2cba54ffc49de": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "abd547704761494bbd76decf0c2708b5": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "acf19e4f681b4569b04600715fe66939": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "af7c7c09b4e14e77817691e56dbd06a1": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "FloatProgressModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_ced21e2a523448059021d7a88a2ce1a5",
            "max": 16068890976,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_a393b5d0aa3f43ae82d4c7d0f7bc1a66",
            "value": 16068890976
          }
        },
        "b08b687487a44ed3a68a4503511093a2": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_8d681616d7a44d49a02841b277c99510",
            "placeholder": "​",
            "style": "IPY_MODEL_57d021ed8da24cd2a8bd018096d39e20",
            "value": " 1.15k/1.15k [00:00&lt;00:00, 38.7kB/s]"
          }
        },
        "b139db24666645728ec84a03d16fe8df": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_07fc3456e15a443cbaf0ec30092b413e",
            "placeholder": "​",
            "style": "IPY_MODEL_10e58236474f451bb4f2c94f39c9ed97",
            "value": " 51.1k/51.1k [00:00&lt;00:00, 823kB/s]"
          }
        },
        "b298576478574d27b3e85badb1d74fda": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_abd547704761494bbd76decf0c2708b5",
            "placeholder": "​",
            "style": "IPY_MODEL_dc093f19352847d4ba4cf26b0a973108",
            "value": " 5.73G/5.73G [02:17&lt;00:00, 36.6MB/s]"
          }
        },
        "b8d92478a04e4ce195872f6de1618d4f": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_cf0fb6bc6acb409cbcd1d4419e8799e6",
            "placeholder": "​",
            "style": "IPY_MODEL_8dc82de26dfc4078ae43cf13ee9fba4e",
            "value": "tokenizer.json: 100%"
          }
        },
        "b8fff71974d74970b171407fd9c933e3": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "bd98493501ba4ad39c9f9302b469a517": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "be469d7a77454482b87d8a38b4bd8aa5": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "bec9a6baa8a8482d9f368b4dd4dcd54d": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "c13a730f6ef946f982d18d36774dc307": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HBoxModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_f0540232e78d4c82b1c73d9b3ab53803",
              "IPY_MODEL_912317468871410dad4a61f70a157840",
              "IPY_MODEL_b139db24666645728ec84a03d16fe8df"
            ],
            "layout": "IPY_MODEL_e41f64356aa647b08845ed44765d3fa6"
          }
        },
        "c2a96d96b576402e9b9bba883cac643b": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "c3d41bdfa4794b258b0c5fb37b729e16": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "FloatProgressModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_7a125a56e21246f3b95843338c4940e3",
            "max": 1149,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_e09c0b7063d04307a4b6f9b28aa6837e",
            "value": 1149
          }
        },
        "c4ac912053ed4c0e86466dbd8bea84da": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "c87965e2bc374a808959acd5be9aa342": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_7628f0680eac4bb896c13a359f1edf9f",
            "placeholder": "​",
            "style": "IPY_MODEL_bd98493501ba4ad39c9f9302b469a517",
            "value": "unsloth.Q5_K_M.gguf: 100%"
          }
        },
        "cc48d4fa7cf14e7e9ac4e893bd68f2fe": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "ProgressStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "ce6efc24ee134e149b5ba9d5e7f6da8d": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_3a261c2ac3e34ce79f848df489c7dcf5",
            "placeholder": "​",
            "style": "IPY_MODEL_8e835337fda441928e6d82e5c5e6d636",
            "value": "adapter_config.json: 100%"
          }
        },
        "ced21e2a523448059021d7a88a2ce1a5": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "cf0fb6bc6acb409cbcd1d4419e8799e6": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "cf38a9d55572468d851ccdcf3a596ccf": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_2535aea91b274f07bbb4b6bf4ce4e27b",
            "placeholder": "​",
            "style": "IPY_MODEL_47e97f71876d44558c17ea96879a6913",
            "value": " 9.09M/9.09M [00:00&lt;00:00, 47.0MB/s]"
          }
        },
        "d047abddd9c04478bc8c3dadeeb52367": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "d07e15ef0d754eddbde18590d44f07c1": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "d22b91b94c5f434d8b2b00e836401963": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "d4cec1617bbc48ffb3e9f9b24b29e893": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_96076b40ab0d4430a2b9f33c60f591ed",
            "placeholder": "​",
            "style": "IPY_MODEL_5f437ce1e879426a91b32bc55db5c1d9",
            "value": "unsloth.Q4_K_M.gguf: 100%"
          }
        },
        "d4d9f4a08e3f451a9c82bf93f379aad5": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "d682fc2eac2544a89415f6eddb6fc720": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_eef0bcb4e52a4a0f95b30aaf34e16ccd",
            "placeholder": "​",
            "style": "IPY_MODEL_621f6b9ec9b34c9d9dd4e38a12dbd789",
            "value": "generation_config.json: 100%"
          }
        },
        "d8759b1e21664696b0e5e80e696537e4": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "FloatProgressModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_d22b91b94c5f434d8b2b00e836401963",
            "max": 5732987232,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_43ff15f706f949af918fd57596307d3b",
            "value": 5732987232
          }
        },
        "db7b862858c24e6c92cdc845e8e21ef9": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HBoxModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_d4cec1617bbc48ffb3e9f9b24b29e893",
              "IPY_MODEL_6dac9af4bad34e53b5395fcc5dd625b8",
              "IPY_MODEL_e7b403265f7347c2872f4b827f1f7a92"
            ],
            "layout": "IPY_MODEL_e10364ab6c0c436687fd3ef7624bce9c"
          }
        },
        "dc093f19352847d4ba4cf26b0a973108": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "dde8956e22904712b707741dc9c4f0e4": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "df11493595434f95a892b82ae7fc0310": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "ProgressStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "e09c0b7063d04307a4b6f9b28aa6837e": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "ProgressStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "e10364ab6c0c436687fd3ef7624bce9c": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "e3c8727734b94393a2a8b677d259c44d": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "ProgressStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "e41f64356aa647b08845ed44765d3fa6": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "e467ba547f914a368a7a662c59f313c5": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "FloatProgressModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_2addd5e4788d48909b921b93f1c3828f",
            "max": 459,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_33fc12c71bdb465eb8fada84189f88f4",
            "value": 459
          }
        },
        "e7b403265f7347c2872f4b827f1f7a92": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_522e02fd045b4b8cb2d622a01b5e50f8",
            "placeholder": "​",
            "style": "IPY_MODEL_4938c9d7f6c7473e875b09eb31615c82",
            "value": " 4.92G/4.92G [02:00&lt;00:00, 37.5MB/s]"
          }
        },
        "e7c4700333a44b77bb34d26704db01b6": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "ProgressStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "eef0bcb4e52a4a0f95b30aaf34e16ccd": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "f0540232e78d4c82b1c73d9b3ab53803": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_9cc778faed39403f8109cbfbf57d9eab",
            "placeholder": "​",
            "style": "IPY_MODEL_9dbd0882aad74c2e90c3fd5645de9d87",
            "value": "tokenizer_config.json: 100%"
          }
        },
        "f5f5ea7ff25a45c5b897ec16b8e64f35": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "FloatProgressModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_517c757324f9442d8b065dcccd0ea745",
            "max": 741,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_e7c4700333a44b77bb34d26704db01b6",
            "value": 741
          }
        },
        "fb220a32bbbd457f80300563490ab852": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HBoxModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_15bf3d4590444efba34a0fa66fb08456",
              "IPY_MODEL_a3072f76ae5c40de8a1ec37a6c7fa244",
              "IPY_MODEL_911d4b61e771416f8da8c2cf7c2fc006"
            ],
            "layout": "IPY_MODEL_45d7a2c66f5a4d8ca89994b80b45906a"
          }
        },
        "feac171ff30c4ecdb84bffcedd8c7327": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_6e07290041254408b8a135803e3e7aa7",
            "placeholder": "​",
            "style": "IPY_MODEL_c4ac912053ed4c0e86466dbd8bea84da",
            "value": "special_tokens_map.json: 100%"
          }
        }
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}