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  "cells": [
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      "cell_type": "code",
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        "id": "2eSvM9zX_2d3"
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      "source": [
        "%%capture\n",
        "!pip install unsloth\n",
        "# Also get the latest nightly Unsloth!\n",
        "!pip uninstall unsloth -y && pip install --upgrade --no-cache-dir \"unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git\"\n",
        "\n",
        "# Install Flash Attention 2 for softcapping support\n",
        "import torch\n",
        "if torch.cuda.get_device_capability()[0] >= 8:\n",
        "    !pip install --no-deps packaging ninja einops \"flash-attn>=2.6.3\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r2v_X2fA0Df5"
      },
      "source": [
        "* We support Llama, Mistral, Phi-3, Gemma, Yi, DeepSeek, Qwen, TinyLlama, Vicuna, Open Hermes etc\n",
        "* We support 16bit LoRA or 4bit QLoRA. Both 2x faster.\n",
        "* `max_seq_length` can be set to anything, since we do automatic RoPE Scaling via [kaiokendev's](https://kaiokendev.github.io/til) method.\n",
        "* [**NEW**] We make Gemma-2 9b / 27b **2x faster**! See our [Gemma-2 9b notebook](https://colab.research.google.com/drive/1vIrqH5uYDQwsJ4-OO3DErvuv4pBgVwk4?usp=sharing)\n",
        "* [**NEW**] To finetune and auto export to Ollama, try our [Ollama notebook](https://colab.research.google.com/drive/1WZDi7APtQ9VsvOrQSSC5DDtxq159j8iZ?usp=sharing)\n",
        "* [**NEW**] We make Mistral NeMo 12B 2x faster and fit in under 12GB of VRAM! [Mistral NeMo notebook](https://colab.research.google.com/drive/17d3U-CAIwzmbDRqbZ9NnpHxCkmXB6LZ0?usp=sharing)"
      ]
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      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n",
            "==((====))==  Unsloth 2024.9.post3: Fast Llama patching. Transformers = 4.45.1.\n",
            "   \\\\   /|    GPU: Tesla T4. Max memory: 14.748 GB. Platform = Linux.\n",
            "O^O/ \\_/ \\    Pytorch: 2.4.1+cu121. CUDA = 7.5. CUDA Toolkit = 12.1.\n",
            "\\        /    Bfloat16 = FALSE. FA [Xformers = 0.0.28.post1. FA2 = False]\n",
            " \"-____-\"     Free Apache license: http://github.com/unslothai/unsloth\n",
            "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n"
          ]
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              "model.safetensors:   0%|          | 0.00/2.47G [00:00<?, ?B/s]"
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              "tokenizer_config.json:   0%|          | 0.00/54.6k [00:00<?, ?B/s]"
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      ],
      "source": [
        "from unsloth import FastLanguageModel\n",
        "import torch\n",
        "max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!\n",
        "dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+\n",
        "load_in_4bit = False # Use 4bit quantization to reduce memory usage. Can be False.\n",
        "\n",
        "# # 4bit pre quantized models we support for 4x faster downloading + no OOMs.\n",
        "# fourbit_models = [\n",
        "#     \"unsloth/Meta-Llama-3.1-8B-bnb-4bit\",      # Llama-3.1 15 trillion tokens model 2x faster!\n",
        "#     \"unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit\",\n",
        "#     \"unsloth/Meta-Llama-3.1-70B-bnb-4bit\",\n",
        "#     \"unsloth/Meta-Llama-3.1-405B-bnb-4bit\",    # We also uploaded 4bit for 405b!\n",
        "#     \"unsloth/Mistral-Nemo-Base-2407-bnb-4bit\", # New Mistral 12b 2x faster!\n",
        "#     \"unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit\",\n",
        "#     \"unsloth/mistral-7b-v0.3-bnb-4bit\",        # Mistral v3 2x faster!\n",
        "#     \"unsloth/mistral-7b-instruct-v0.3-bnb-4bit\",\n",
        "#     \"unsloth/Phi-3-mini-4k-instruct\",          # Phi-3 2x faster!d\n",
        "#     \"unsloth/Phi-3-medium-4k-instruct\",\n",
        "#     \"unsloth/gemma-2-9b-bnb-4bit\",\n",
        "#     \"unsloth/gemma-2-27b-bnb-4bit\",            # Gemma 2x faster!\n",
        "#     \"unsloth/gemma-2-2b-it\",             # New small Gemma model!\n",
        "# ] # More models at https://huggingface.co/unsloth\n",
        "\n",
        "model, tokenizer = FastLanguageModel.from_pretrained(\n",
        "    model_name = \"unsloth/Llama-3.2-1B-Instruct\",\n",
        "    max_seq_length = max_seq_length,\n",
        "    dtype = dtype,\n",
        "    load_in_4bit = load_in_4bit,\n",
        "    # token = \"hf_...\", # use one if using gated models like meta-llama/Llama-2-7b-hf\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SXd9bTZd1aaL"
      },
      "source": [
        "We now add LoRA adapters so we only need to update 1 to 10% of all parameters!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6bZsfBuZDeCL",
        "outputId": "da119557-726e-4605-ea13-dd4cd0ec448c"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Unsloth 2024.9.post3 patched 16 layers with 16 QKV layers, 16 O layers and 16 MLP layers.\n"
          ]
        }
      ],
      "source": [
        "model = FastLanguageModel.get_peft_model(\n",
        "    model,\n",
        "    r = 16, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128\n",
        "    target_modules = [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\n",
        "                      \"gate_proj\", \"up_proj\", \"down_proj\",],\n",
        "    lora_alpha = 16,\n",
        "    lora_dropout = 0, # Supports any, but = 0 is optimized\n",
        "    bias = \"none\",    # Supports any, but = \"none\" is optimized\n",
        "    # [NEW] \"unsloth\" uses 30% less VRAM, fits 2x larger batch sizes!\n",
        "    use_gradient_checkpointing = \"unsloth\", # True or \"unsloth\" for very long context\n",
        "    random_state = 3407,\n",
        "    use_rslora = False,  # We support rank stabilized LoRA\n",
        "    loftq_config = None, # And LoftQ\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vITh0KVJ10qX"
      },
      "source": [
        "<a name=\"Data\"></a>\n",
        "### Data Prep\n",
        "We now use the Alpaca dataset from [yahma](https://huggingface.co/datasets/yahma/alpaca-cleaned), which is a filtered version of 52K of the original [Alpaca dataset](https://crfm.stanford.edu/2023/03/13/alpaca.html). You can replace this code section with your own data prep.\n",
        "\n",
        "**[NOTE]** To train only on completions (ignoring the user's input) read TRL's docs [here](https://huggingface.co/docs/trl/sft_trainer#train-on-completions-only).\n",
        "\n",
        "**[NOTE]** Remember to add the **EOS_TOKEN** to the tokenized output!! Otherwise you'll get infinite generations!\n",
        "\n",
        "If you want to use the `llama-3` template for ShareGPT datasets, try our conversational [notebook](https://colab.research.google.com/drive/1XamvWYinY6FOSX9GLvnqSjjsNflxdhNc?usp=sharing).\n",
        "\n",
        "For text completions like novel writing, try this [notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "LjY75GoYUCB8"
      },
      "outputs": [],
      "source": [
        "alpaca_prompt = \"\"\"Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n",
        "\n",
        "### Instruction:\n",
        "{}\n",
        "\n",
        "### Input:\n",
        "{}\n",
        "\n",
        "### Response:\n",
        "{}\"\"\"\n",
        "\n",
        "EOS_TOKEN = tokenizer.eos_token # Must add EOS_TOKEN\n",
        "def formatting_prompts_func(examples):\n",
        "    instructions = examples[\"prompt\"]\n",
        "    inputs       = examples[\"query\"]\n",
        "    outputs      = examples[\"response\"]\n",
        "    texts = []\n",
        "    for instruction, input, output in zip(instructions, inputs, outputs):\n",
        "        # Must add EOS_TOKEN, otherwise your generation will go on forever!\n",
        "        text = alpaca_prompt.format(instruction, input, output) + EOS_TOKEN\n",
        "        texts.append(text)\n",
        "    return { \"text\" : texts, }\n",
        "pass\n",
        "\n",
        "# from datasets import load_dataset\n",
        "# dataset = load_dataset(\"yahma/alpaca-cleaned\", split = \"train\")\n",
        "# dataset = dataset.map(formatting_prompts_func, batched = True,)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xUTnbqJUFDXc",
        "outputId": "2799c1c4-388e-47a9-8fc4-0a703734b038"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Training dataset size: 2256\n",
            "Test dataset size: 565\n"
          ]
        }
      ],
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "# Load the dataset\n",
        "dataset = load_dataset(\"/content\", data_files=\"restructured_dataset.json\")\n",
        "\n",
        "# Split the dataset into 80% training and 20% test\n",
        "split_ratio = 0.8\n",
        "train_test_split = dataset[\"train\"].train_test_split(test_size=1 - split_ratio, seed=42)  # Set seed for reproducibility\n",
        "\n",
        "# Get the train and test datasets\n",
        "train_dataset = train_test_split[\"train\"]\n",
        "test_dataset = train_test_split[\"test\"]\n",
        "\n",
        "# Output the sizes to verify\n",
        "print(f\"Training dataset size: {len(train_dataset)}\")\n",
        "print(f\"Test dataset size: {len(test_dataset)}\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "cgFqwIGpDAiY"
      },
      "outputs": [],
      "source": [
        "train_dataset = train_dataset.map(formatting_prompts_func, batched = True,)\n",
        "test_dataset = test_dataset.map(formatting_prompts_func, batched = True,)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "83mlFwUChiz7"
      },
      "outputs": [],
      "source": [
        "# train_dataset['text']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "idAEIeSQ3xdS"
      },
      "source": [
        "<a name=\"Train\"></a>\n",
        "### Train the model\n",
        "Now let's use Huggingface TRL's `SFTTrainer`! More docs here: [TRL SFT docs](https://huggingface.co/docs/trl/sft_trainer). We do 60 steps to speed things up, but you can set `num_train_epochs=1` for a full run, and turn off `max_steps=None`. We also support TRL's `DPOTrainer`!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "95_Nn-89DhsL",
        "outputId": "f144252e-5cef-46db-e040-5e657772ef7f"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1545: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1545: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "from trl import SFTTrainer\n",
        "from transformers import TrainingArguments\n",
        "from unsloth import is_bfloat16_supported\n",
        "\n",
        "trainer = SFTTrainer(\n",
        "    model = model,\n",
        "    tokenizer = tokenizer,\n",
        "    train_dataset = train_dataset,\n",
        "    eval_dataset=test_dataset,\n",
        "    dataset_text_field = \"text\",\n",
        "    max_seq_length = max_seq_length,\n",
        "    dataset_num_proc = 2,\n",
        "    packing = True, # Can make training 5x faster for short sequences.\n",
        "    args = TrainingArguments(\n",
        "        per_device_train_batch_size = 2,\n",
        "        per_device_eval_batch_size = 4,\n",
        "        gradient_accumulation_steps = 4,\n",
        "        warmup_steps = 5,\n",
        "        num_train_epochs = 2, # Set this for 1 full training run.\n",
        "        # max_steps = 60,\n",
        "        learning_rate = 2e-4,\n",
        "        fp16 = not is_bfloat16_supported(),\n",
        "        bf16 = is_bfloat16_supported(),\n",
        "        logging_steps = 1,\n",
        "        optim = \"adamw_8bit\",\n",
        "        weight_decay = 0.01,\n",
        "        lr_scheduler_type = \"linear\",\n",
        "        seed = 3407,\n",
        "        output_dir = \"outputs\",\n",
        "        evaluation_strategy=\"steps\"\n",
        "    ),\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "hkC1v_kaBUiW",
        "outputId": "ee9a3108-d05a-48a5-92a9-888257ea66ef"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "==((====))==  Unsloth - 2x faster free finetuning | Num GPUs = 1\n",
            "   \\\\   /|    Num examples = 277 | Num Epochs = 2\n",
            "O^O/ \\_/ \\    Batch size per device = 2 | Gradient Accumulation steps = 4\n",
            "\\        /    Total batch size = 8 | Total steps = 68\n",
            " \"-____-\"     Number of trainable parameters = 11,272,192\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='68' max='68' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [68/68 33:48, Epoch 1/2]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.750300</td>\n",
              "      <td>0.767613</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.709100</td>\n",
              "      <td>0.760781</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.685500</td>\n",
              "      <td>0.748784</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.652100</td>\n",
              "      <td>0.731342</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.711400</td>\n",
              "      <td>0.714547</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.774900</td>\n",
              "      <td>0.693540</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.665600</td>\n",
              "      <td>0.674969</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.735700</td>\n",
              "      <td>0.656129</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.620000</td>\n",
              "      <td>0.638963</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>0.591200</td>\n",
              "      <td>0.621351</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11</td>\n",
              "      <td>0.650800</td>\n",
              "      <td>0.604045</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12</td>\n",
              "      <td>0.587300</td>\n",
              "      <td>0.587853</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13</td>\n",
              "      <td>0.610400</td>\n",
              "      <td>0.573831</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14</td>\n",
              "      <td>0.544500</td>\n",
              "      <td>0.560170</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15</td>\n",
              "      <td>0.590700</td>\n",
              "      <td>0.545877</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16</td>\n",
              "      <td>0.492100</td>\n",
              "      <td>0.534258</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>17</td>\n",
              "      <td>0.520900</td>\n",
              "      <td>0.519963</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>18</td>\n",
              "      <td>0.527900</td>\n",
              "      <td>0.505541</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>19</td>\n",
              "      <td>0.539500</td>\n",
              "      <td>0.492883</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>0.508900</td>\n",
              "      <td>0.480199</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>21</td>\n",
              "      <td>0.462900</td>\n",
              "      <td>0.467022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>22</td>\n",
              "      <td>0.441700</td>\n",
              "      <td>0.453975</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>23</td>\n",
              "      <td>0.418200</td>\n",
              "      <td>0.442243</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>24</td>\n",
              "      <td>0.432400</td>\n",
              "      <td>0.430087</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>25</td>\n",
              "      <td>0.419300</td>\n",
              "      <td>0.418594</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>26</td>\n",
              "      <td>0.395600</td>\n",
              "      <td>0.407527</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>27</td>\n",
              "      <td>0.387800</td>\n",
              "      <td>0.396506</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>28</td>\n",
              "      <td>0.450600</td>\n",
              "      <td>0.384659</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>29</td>\n",
              "      <td>0.370400</td>\n",
              "      <td>0.373602</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>30</td>\n",
              "      <td>0.364500</td>\n",
              "      <td>0.363078</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>31</td>\n",
              "      <td>0.332300</td>\n",
              "      <td>0.353667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>32</td>\n",
              "      <td>0.305700</td>\n",
              "      <td>0.344543</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>33</td>\n",
              "      <td>0.322600</td>\n",
              "      <td>0.335432</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>34</td>\n",
              "      <td>0.338900</td>\n",
              "      <td>0.327199</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>35</td>\n",
              "      <td>0.331000</td>\n",
              "      <td>0.318517</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>36</td>\n",
              "      <td>0.349100</td>\n",
              "      <td>0.310108</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>37</td>\n",
              "      <td>0.252700</td>\n",
              "      <td>0.303383</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>38</td>\n",
              "      <td>0.294900</td>\n",
              "      <td>0.297450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>39</td>\n",
              "      <td>0.247400</td>\n",
              "      <td>0.289259</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>40</td>\n",
              "      <td>0.242800</td>\n",
              "      <td>0.281499</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>41</td>\n",
              "      <td>0.254700</td>\n",
              "      <td>0.275865</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>42</td>\n",
              "      <td>0.259000</td>\n",
              "      <td>0.270416</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>43</td>\n",
              "      <td>0.238900</td>\n",
              "      <td>0.264625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>44</td>\n",
              "      <td>0.239500</td>\n",
              "      <td>0.258969</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>45</td>\n",
              "      <td>0.223000</td>\n",
              "      <td>0.253462</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>46</td>\n",
              "      <td>0.207800</td>\n",
              "      <td>0.248274</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>47</td>\n",
              "      <td>0.251200</td>\n",
              "      <td>0.242153</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>48</td>\n",
              "      <td>0.200100</td>\n",
              "      <td>0.237188</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>49</td>\n",
              "      <td>0.214300</td>\n",
              "      <td>0.232814</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>50</td>\n",
              "      <td>0.199100</td>\n",
              "      <td>0.228829</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>51</td>\n",
              "      <td>0.226400</td>\n",
              "      <td>0.225165</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>52</td>\n",
              "      <td>0.197800</td>\n",
              "      <td>0.222183</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>53</td>\n",
              "      <td>0.222200</td>\n",
              "      <td>0.219091</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>54</td>\n",
              "      <td>0.222400</td>\n",
              "      <td>0.215774</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>55</td>\n",
              "      <td>0.193700</td>\n",
              "      <td>0.212546</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>56</td>\n",
              "      <td>0.205900</td>\n",
              "      <td>0.209754</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>57</td>\n",
              "      <td>0.216200</td>\n",
              "      <td>0.207039</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>58</td>\n",
              "      <td>0.196500</td>\n",
              "      <td>0.204481</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>59</td>\n",
              "      <td>0.207200</td>\n",
              "      <td>0.202018</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>60</td>\n",
              "      <td>0.176200</td>\n",
              "      <td>0.199767</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>61</td>\n",
              "      <td>0.160900</td>\n",
              "      <td>0.197782</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>62</td>\n",
              "      <td>0.169300</td>\n",
              "      <td>0.195964</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>63</td>\n",
              "      <td>0.185100</td>\n",
              "      <td>0.194448</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>64</td>\n",
              "      <td>0.182700</td>\n",
              "      <td>0.193181</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>65</td>\n",
              "      <td>0.171500</td>\n",
              "      <td>0.192146</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>66</td>\n",
              "      <td>0.164900</td>\n",
              "      <td>0.191384</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>67</td>\n",
              "      <td>0.192900</td>\n",
              "      <td>0.190831</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>68</td>\n",
              "      <td>0.209000</td>\n",
              "      <td>0.190555</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "trainer_stats=trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 440
        },
        "id": "yqxqAZ7KJ4oL",
        "outputId": "0c2d6e9e-bad5-4dae-ab40-6166961bf7a2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Training Losses: [0.7503, 0.7091, 0.6855, 0.6521, 0.7114, 0.7749, 0.6656, 0.7357, 0.62, 0.5912, 0.6508, 0.5873, 0.6104, 0.5445, 0.5907, 0.4921, 0.5209, 0.5279, 0.5395, 0.5089, 0.4629, 0.4417, 0.4182, 0.4324, 0.4193, 0.3956, 0.3878, 0.4506, 0.3704, 0.3645, 0.3323, 0.3057, 0.3226, 0.3389, 0.331, 0.3491, 0.2527, 0.2949, 0.2474, 0.2428, 0.2547, 0.259, 0.2389, 0.2395, 0.223, 0.2078, 0.2512, 0.2001, 0.2143, 0.1991, 0.2264, 0.1978, 0.2222, 0.2224, 0.1937, 0.2059, 0.2162, 0.1965, 0.2072, 0.1762, 0.1609, 0.1693, 0.1851, 0.1827, 0.1715, 0.1649, 0.1929, 0.209]\n",
            "Evaluation Losses: [0.767612636089325, 0.7607811093330383, 0.7487840056419373, 0.7313423156738281, 0.7145472764968872, 0.6935401558876038, 0.6749686598777771, 0.6561285853385925, 0.6389631032943726, 0.6213513016700745, 0.6040447950363159, 0.5878527164459229, 0.5738311409950256, 0.5601701736450195, 0.5458768606185913, 0.534257709980011, 0.5199625492095947, 0.5055410265922546, 0.49288254976272583, 0.4801987111568451, 0.4670219421386719, 0.45397475361824036, 0.442242830991745, 0.4300874173641205, 0.41859403252601624, 0.40752682089805603, 0.3965064585208893, 0.38465917110443115, 0.3736022412776947, 0.36307793855667114, 0.353667289018631, 0.34454306960105896, 0.3354315161705017, 0.32719936966896057, 0.3185167610645294, 0.31010758876800537, 0.30338314175605774, 0.2974502742290497, 0.2892588973045349, 0.2814987003803253, 0.2758648991584778, 0.27041569352149963, 0.26462453603744507, 0.2589690685272217, 0.2534623146057129, 0.2482735514640808, 0.24215327203273773, 0.23718804121017456, 0.23281413316726685, 0.22882941365242004, 0.2251654714345932, 0.22218288481235504, 0.21909120678901672, 0.21577446162700653, 0.21254558861255646, 0.20975379645824432, 0.207039475440979, 0.2044808268547058, 0.2020176351070404, 0.1997668743133545, 0.1977815479040146, 0.19596432149410248, 0.1944475919008255, 0.19318059086799622, 0.19214633107185364, 0.19138379395008087, 0.19083082675933838, 0.19055481255054474]\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# trainer.state.log_history\n",
        "train_losses = [log[\"loss\"] for log in trainer.state.log_history if \"loss\" in log]\n",
        "eval_losses = [log[\"eval_loss\"] for log in trainer.state.log_history if \"eval_loss\" in log]\n",
        "\n",
        "# Print out the losses to verify\n",
        "print(f\"Training Losses: {train_losses}\")\n",
        "print(f\"Evaluation Losses: {eval_losses}\")\n",
        "\n",
        "# Plot the losses using matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure(figsize=(10, 5))\n",
        "plt.plot(train_losses, label='Training Loss')\n",
        "plt.plot(eval_losses, label='Validation Loss')\n",
        "plt.title(\"Training and Validation Loss\")\n",
        "plt.xlabel(\"Iterations\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pCqnaKmlO1U9"
      },
      "outputs": [],
      "source": [
        "\n",
        "#@title Show final memory and time stats\n",
        "used_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n",
        "used_memory_for_lora = round(used_memory - start_gpu_memory, 3)\n",
        "used_percentage = round(used_memory         /max_memory*100, 3)\n",
        "lora_percentage = round(used_memory_for_lora/max_memory*100, 3)\n",
        "print(f\"{trainer_stats.metrics['train_runtime']} seconds used for training.\")\n",
        "print(f\"{round(trainer_stats.metrics['train_runtime']/60, 2)} minutes used for training.\")\n",
        "print(f\"Peak reserved memory = {used_memory} GB.\")\n",
        "print(f\"Peak reserved memory for training = {used_memory_for_lora} GB.\")\n",
        "print(f\"Peak reserved memory % of max memory = {used_percentage} %.\")\n",
        "print(f\"Peak reserved memory for training % of max memory = {lora_percentage} %.\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ekOmTR1hSNcr"
      },
      "source": [
        "<a name=\"Inference\"></a>\n",
        "### Inference\n",
        "Let's run the model! You can change the instruction and input - leave the output blank!\n",
        "\n",
        "**[NEW] Try 2x faster inference in a free Colab for Llama-3.1 8b Instruct [here](https://colab.research.google.com/drive/1T-YBVfnphoVc8E2E854qF3jdia2Ll2W2?usp=sharing)**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kR3gIAX-SM2q"
      },
      "outputs": [],
      "source": [
        "# alpaca_prompt = Copied from above\n",
        "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n",
        "inputs = tokenizer(\n",
        "[\n",
        "    alpaca_prompt.format(\n",
        "        \"Continue the fibonnaci sequence.\", # instruction\n",
        "        \"1, 1, 2, 3, 5, 8\", # input\n",
        "        \"\", # output - leave this blank for generation!\n",
        "    )\n",
        "], return_tensors = \"pt\").to(\"cuda\")\n",
        "\n",
        "outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)\n",
        "tokenizer.batch_decode(outputs)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CrSvZObor0lY"
      },
      "source": [
        " You can also use a `TextStreamer` for continuous inference - so you can see the generation token by token, instead of waiting the whole time!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "e2pEuRb1r2Vg"
      },
      "outputs": [],
      "source": [
        "# alpaca_prompt = Copied from above\n",
        "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n",
        "inputs = tokenizer(\n",
        "[\n",
        "    alpaca_prompt.format(\n",
        "        \"Continue the fibonnaci sequence.\", # instruction\n",
        "        \"1, 1, 2, 3, 5, 8\", # input\n",
        "        \"\", # output - leave this blank for generation!\n",
        "    )\n",
        "], return_tensors = \"pt\").to(\"cuda\")\n",
        "\n",
        "from transformers import TextStreamer\n",
        "text_streamer = TextStreamer(tokenizer)\n",
        "_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xPiYHwXb8sbh",
        "outputId": "16fa9575-3ff6-4a18-acf7-b9ffbe878ec9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "('lora_model/tokenizer_config.json',\n",
              " 'lora_model/special_tokens_map.json',\n",
              " 'lora_model/tokenizer.json')"
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model.save_pretrained(\"lora_model\") # Local saving\n",
        "tokenizer.save_pretrained(\"lora_model\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        },
        "id": "thjsgd5f9HFm",
        "outputId": "0f6b8f4d-92f5-41eb-a4c4-ca7006875edd"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "67a334957642459fb5d5f310d88d7569",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "README.md:   0%|          | 0.00/582 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "  0%|          | 0/1 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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            "text/plain": [
              "adapter_model.safetensors:   0%|          | 0.00/45.1M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Saved model to https://huggingface.co/AiisNothing/llama-3.3-1b-it-adapters\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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              "  0%|          | 0/1 [00:00<?, ?it/s]"
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "09230f635d294fe69d56705f6e0bf8ae",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/17.2M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "model.push_to_hub(\"AiisNothing/llama-3.3-1b-it-adapters\", token = \"\") # Online saving\n",
        "tokenizer.push_to_hub(\"AiisNothing/llama-3.3-1b-it-adapters\", token = \"\") # Online saving"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uMuVrWbjAzhc"
      },
      "source": [
        "<a name=\"Save\"></a>\n",
        "### Saving, loading finetuned models\n",
        "To save the final model as LoRA adapters, either use Huggingface's `push_to_hub` for an online save or `save_pretrained` for a local save.\n",
        "\n",
        "**[NOTE]** This ONLY saves the LoRA adapters, and not the full model. To save to 16bit or GGUF, scroll down!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1qBZHJzdviPl"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "upcOlWe7A1vc"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 183
        },
        "id": "qXA53aNF1txG",
        "outputId": "2ce31833-0f74-4759-fb87-ce02cd0e4883"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "To https://huggingface.co/AiisNothing/gemma_lora_merged_fullonlylora\n",
            "   ddbb27c..78c895b  main -> main\n",
            "\n",
            "WARNING:huggingface_hub.repository:To https://huggingface.co/AiisNothing/gemma_lora_merged_fullonlylora\n",
            "   ddbb27c..78c895b  main -> main\n",
            "\n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'https://huggingface.co/AiisNothing/gemma_lora_merged_fullonlylora/commit/78c895b6a0d46cdf5c85c188de0725267640196e'"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "repo.git_add()\n",
        "# subprocess.run([\"git\", \"-C\", \"/content/lora_model1\",\"reset\", \"HEAD\", \"tokenizer.json\"], check=True)\n",
        "repo.git_commit(\"Initial commit of model folder\")\n",
        "repo.git_push()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AEEcJ4qfC7Lp"
      },
      "source": [
        "Now if you want to load the LoRA adapters we just saved for inference, set `False` to `True`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MKX_XKs_BNZR",
        "outputId": "7b791800-16fb-4a7b-84ca-9c23c349b150"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<bos>Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n",
            "\n",
            "### Instruction:\n",
            "What is a famous tall tower in Paris?\n",
            "\n",
            "### Input:\n",
            "\n",
            "\n",
            "### Response:\n",
            "The Eiffel Tower is a famous tall tower in Paris, France. It is located in the 5th arrondissement of Paris and is one of the most recognizable landmarks in the world. The tower was built for the 1889 World's Fair and is 324 meters tall. It is made of iron and has 1,665 steps. The tower is a symbol of Paris and is a popular tourist attraction.<eos>\n"
          ]
        }
      ],
      "source": [
        "if False:\n",
        "    from unsloth import FastLanguageModel\n",
        "    model, tokenizer = FastLanguageModel.from_pretrained(\n",
        "        model_name = \"lora_model\", # YOUR MODEL YOU USED FOR TRAINING\n",
        "        max_seq_length = max_seq_length,\n",
        "        dtype = dtype,\n",
        "        load_in_4bit = False,\n",
        "    )\n",
        "    FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n",
        "\n",
        "# alpaca_prompt = You MUST copy from above!\n",
        "\n",
        "inputs = tokenizer(\n",
        "[\n",
        "    alpaca_prompt.format(\n",
        "        \"What is a famous tall tower in Paris?\", # instruction\n",
        "        \"\", # input\n",
        "        \"\", # output - leave this blank for generation!\n",
        "    )\n",
        "], return_tensors = \"pt\").to(\"cuda\")\n",
        "\n",
        "from transformers import TextStreamer\n",
        "text_streamer = TextStreamer(tokenizer)\n",
        "_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QQMjaNrjsU5_"
      },
      "source": [
        "You can also use Hugging Face's `AutoModelForPeftCausalLM`. Only use this if you do not have `unsloth` installed. It can be hopelessly slow, since `4bit` model downloading is not supported, and Unsloth's **inference is 2x faster**."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yFfaXG0WsQuE"
      },
      "outputs": [],
      "source": [
        "if False:\n",
        "    # I highly do NOT suggest - use Unsloth if possible\n",
        "    from peft import AutoPeftModelForCausalLM\n",
        "    from transformers import AutoTokenizer\n",
        "    model = AutoPeftModelForCausalLM.from_pretrained(\n",
        "        \"lora_model\", # YOUR MODEL YOU USED FOR TRAINING\n",
        "        load_in_4bit = load_in_4bit,\n",
        "    )\n",
        "    tokenizer = AutoTokenizer.from_pretrained(\"lora_model\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f422JgM9sdVT"
      },
      "source": [
        "### Saving to float16 for VLLM\n",
        "\n",
        "We also support saving to `float16` directly. Select `merged_16bit` for float16 or `merged_4bit` for int4. We also allow `lora` adapters as a fallback. Use `push_to_hub_merged` to upload to your Hugging Face account! You can go to https://huggingface.co/settings/tokens for your personal tokens."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iHjt_SMYsd3P"
      },
      "outputs": [],
      "source": [
        "# Merge to 16bit\n",
        "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"merged_16bit\",)\n",
        "if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"merged_16bit\", token = \"\")\n",
        "\n",
        "# Merge to 4bit\n",
        "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"merged_4bit\",)\n",
        "if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"merged_4bit\", token = \"\")\n",
        "\n",
        "# Just LoRA adapters\n",
        "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"lora\",)\n",
        "if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"lora\", token = \"\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TCv4vXHd61i7"
      },
      "source": [
        "### GGUF / llama.cpp Conversion\n",
        "To save to `GGUF` / `llama.cpp`, we support it natively now! We clone `llama.cpp` and we default save it to `q8_0`. We allow all methods like `q4_k_m`. Use `save_pretrained_gguf` for local saving and `push_to_hub_gguf` for uploading to HF.\n",
        "\n",
        "Some supported quant methods (full list on our [Wiki page](https://github.com/unslothai/unsloth/wiki#gguf-quantization-options)):\n",
        "* `q8_0` - Fast conversion. High resource use, but generally acceptable.\n",
        "* `q4_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K.\n",
        "* `q5_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K.\n",
        "\n",
        "[**NEW**] To finetune and auto export to Ollama, try our [Ollama notebook](https://colab.research.google.com/drive/1WZDi7APtQ9VsvOrQSSC5DDtxq159j8iZ?usp=sharing)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FqfebeAdT073",
        "outputId": "eee4095a-2fb5-4898-ee2b-443bb92a3112"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "==((====))==  Unsloth 2024.9.post3: Fast Llama patching. Transformers = 4.45.1.\n",
            "   \\\\   /|    GPU: Tesla T4. Max memory: 14.748 GB. Platform = Linux.\n",
            "O^O/ \\_/ \\    Pytorch: 2.4.1+cu121. CUDA = 7.5. CUDA Toolkit = 12.1.\n",
            "\\        /    Bfloat16 = FALSE. FA [Xformers = 0.0.28.post1. FA2 = False]\n",
            " \"-____-\"     Free Apache license: http://github.com/unslothai/unsloth\n",
            "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n"
          ]
        },
        {
          "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 2.5G\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Unsloth: Merging 4bit and LoRA weights to 16bit...\n",
            "Unsloth: Will use up to 4.16 out of 12.67 RAM for saving.\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 16/16 [00:01<00:00,  9.81it/s]\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 AiisNothing/llama-3.3-1b-it-gguf/pytorch_model.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 GGUF 16bits will take 3 minutes.\n",
            "\\        /    [2] Converting GGUF 16bits to ['q6_k', 'q8_0', '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 AiisNothing/llama-3.3-1b-it-gguf into f16 GGUF format.\n",
            "The output location will be ./AiisNothing/llama-3.3-1b-it-gguf/unsloth.F16.gguf\n",
            "This will take 3 minutes...\n",
            "INFO:hf-to-gguf:Loading model: llama-3.3-1b-it-gguf\n",
            "INFO:gguf.gguf_writer:gguf: This GGUF file is for Little Endian only\n",
            "INFO:hf-to-gguf:Exporting model...\n",
            "INFO:hf-to-gguf:gguf: loading model part 'pytorch_model.bin'\n",
            "INFO:hf-to-gguf:token_embd.weight,           torch.float16 --> F16, shape = {2048, 128256}\n",
            "INFO:hf-to-gguf:blk.0.attn_q.weight,         torch.float16 --> F16, shape = {2048, 2048}\n",
            "INFO:hf-to-gguf:blk.0.attn_k.weight,         torch.float16 --> F16, shape = {2048, 512}\n",
            "INFO:hf-to-gguf:blk.0.attn_v.weight,         torch.float16 --> F16, shape = {2048, 512}\n",
            "INFO:hf-to-gguf:blk.0.attn_output.weight,    torch.float16 --> F16, shape = {2048, 2048}\n",
            "INFO:hf-to-gguf:blk.0.ffn_gate.weight,       torch.float16 --> F16, shape = {2048, 8192}\n",
            "INFO:hf-to-gguf:blk.0.ffn_up.weight,         torch.float16 --> F16, shape = {2048, 8192}\n",
            "INFO:hf-to-gguf:blk.0.ffn_down.weight,       torch.float16 --> F16, shape = {8192, 2048}\n",
            "INFO:hf-to-gguf:blk.0.attn_norm.weight,      torch.float16 --> F32, shape = {2048}\n",
            "INFO:hf-to-gguf:blk.0.ffn_norm.weight,       torch.float16 --> F32, shape = {2048}\n",
            "INFO:hf-to-gguf:blk.1.attn_q.weight,         torch.float16 --> F16, shape = {2048, 2048}\n",
            "INFO:hf-to-gguf:blk.1.attn_k.weight,         torch.float16 --> F16, shape = {2048, 512}\n",
            "INFO:hf-to-gguf:blk.1.attn_v.weight,         torch.float16 --> F16, shape = {2048, 512}\n",
            "INFO:hf-to-gguf:blk.1.attn_output.weight,    torch.float16 --> F16, shape = {2048, 2048}\n",
            "INFO:hf-to-gguf:blk.1.ffn_gate.weight,       torch.float16 --> F16, shape = {2048, 8192}\n",
            "INFO:hf-to-gguf:blk.1.ffn_up.weight,         torch.float16 --> F16, shape = {2048, 8192}\n",
            "INFO:hf-to-gguf:blk.1.ffn_down.weight,       torch.float16 --> F16, shape = {8192, 2048}\n",
            "INFO:hf-to-gguf:blk.1.attn_norm.weight,      torch.float16 --> F32, shape = {2048}\n",
            "INFO:hf-to-gguf:blk.1.ffn_norm.weight,       torch.float16 --> F32, shape = {2048}\n",
            "INFO:hf-to-gguf:blk.2.attn_q.weight,         torch.float16 --> F16, shape = {2048, 2048}\n",
            "INFO:hf-to-gguf:blk.2.attn_k.weight,         torch.float16 --> F16, shape = {2048, 512}\n",
            "INFO:hf-to-gguf:blk.2.attn_v.weight,         torch.float16 --> F16, shape = {2048, 512}\n",
            "INFO:hf-to-gguf:blk.2.attn_output.weight,    torch.float16 --> F16, shape = {2048, 2048}\n",
            "INFO:hf-to-gguf:blk.2.ffn_gate.weight,       torch.float16 --> F16, shape = {2048, 8192}\n",
            "INFO:hf-to-gguf:blk.2.ffn_up.weight,         torch.float16 --> F16, shape = {2048, 8192}\n",
            "INFO:hf-to-gguf:blk.2.ffn_down.weight,       torch.float16 --> F16, shape = {8192, 2048}\n",
            "INFO:hf-to-gguf:blk.2.attn_norm.weight,      torch.float16 --> F32, shape = {2048}\n",
            "INFO:hf-to-gguf:blk.2.ffn_norm.weight,       torch.float16 --> F32, shape = {2048}\n",
            "INFO:hf-to-gguf:blk.3.attn_q.weight,         torch.float16 --> F16, shape = {2048, 2048}\n",
            "INFO:hf-to-gguf:blk.3.attn_k.weight,         torch.float16 --> F16, shape = {2048, 512}\n",
            "INFO:hf-to-gguf:blk.3.attn_v.weight,         torch.float16 --> F16, shape = {2048, 512}\n",
            "INFO:hf-to-gguf:blk.3.attn_output.weight,    torch.float16 --> F16, shape = {2048, 2048}\n",
            "INFO:hf-to-gguf:blk.3.ffn_gate.weight,       torch.float16 --> F16, shape = {2048, 8192}\n",
            "INFO:hf-to-gguf:blk.3.ffn_up.weight,         torch.float16 --> F16, shape = {2048, 8192}\n",
            "INFO:hf-to-gguf:blk.3.ffn_down.weight,       torch.float16 --> F16, shape = {8192, 2048}\n",
            "INFO:hf-to-gguf:blk.3.attn_norm.weight,      torch.float16 --> F32, shape = {2048}\n",
            "INFO:hf-to-gguf:blk.3.ffn_norm.weight,       torch.float16 --> F32, shape = {2048}\n",
            "INFO:hf-to-gguf:blk.4.attn_q.weight,         torch.float16 --> F16, shape = {2048, 2048}\n",
            "INFO:hf-to-gguf:blk.4.attn_k.weight,         torch.float16 --> F16, shape = {2048, 512}\n",
            "INFO:hf-to-gguf:blk.4.attn_v.weight,         torch.float16 --> F16, shape = {2048, 512}\n",
            "INFO:hf-to-gguf:blk.4.attn_output.weight,    torch.float16 --> F16, shape = {2048, 2048}\n",
            "INFO:hf-to-gguf:blk.4.ffn_gate.weight,       torch.float16 --> F16, shape = {2048, 8192}\n",
            "INFO:hf-to-gguf:blk.4.ffn_up.weight,         torch.float16 --> F16, shape = {2048, 8192}\n",
            "INFO:hf-to-gguf:blk.4.ffn_down.weight,       torch.float16 --> F16, shape = {8192, 2048}\n",
            "INFO:hf-to-gguf:blk.4.attn_norm.weight,      torch.float16 --> F32, shape = {2048}\n",
            "INFO:hf-to-gguf:blk.4.ffn_norm.weight,       torch.float16 --> F32, shape = {2048}\n",
            "INFO:hf-to-gguf:blk.5.attn_q.weight,         torch.float16 --> F16, shape = {2048, 2048}\n",
            "INFO:hf-to-gguf:blk.5.attn_k.weight,         torch.float16 --> F16, shape = {2048, 512}\n",
            "INFO:hf-to-gguf:blk.5.attn_v.weight,         torch.float16 --> F16, shape = {2048, 512}\n",
            "INFO:hf-to-gguf:blk.5.attn_output.weight,    torch.float16 --> F16, shape = {2048, 2048}\n",
            "INFO:hf-to-gguf:blk.5.ffn_gate.weight,       torch.float16 --> F16, shape = {2048, 8192}\n",
            "INFO:hf-to-gguf:blk.5.ffn_up.weight,         torch.float16 --> F16, shape = {2048, 8192}\n",
            "INFO:hf-to-gguf:blk.5.ffn_down.weight,       torch.float16 --> F16, shape = {8192, 2048}\n",
            "INFO:hf-to-gguf:blk.5.attn_norm.weight,      torch.float16 --> F32, shape = {2048}\n",
            "INFO:hf-to-gguf:blk.5.ffn_norm.weight,       torch.float16 --> F32, shape = {2048}\n",
            "INFO:hf-to-gguf:blk.6.attn_q.weight,         torch.float16 --> F16, shape = {2048, 2048}\n",
            "INFO:hf-to-gguf:blk.6.attn_k.weight,         torch.float16 --> F16, shape = {2048, 512}\n",
            "INFO:hf-to-gguf:blk.6.attn_v.weight,         torch.float16 --> F16, shape = {2048, 512}\n",
            "INFO:hf-to-gguf:blk.6.attn_output.weight,    torch.float16 --> F16, shape = {2048, 2048}\n",
            "INFO:hf-to-gguf:blk.6.ffn_gate.weight,       torch.float16 --> F16, shape = {2048, 8192}\n",
            "INFO:hf-to-gguf:blk.6.ffn_up.weight,         torch.float16 --> F16, shape = {2048, 8192}\n",
            "INFO:hf-to-gguf:blk.6.ffn_down.weight,       torch.float16 --> F16, shape = {8192, 2048}\n",
            "INFO:hf-to-gguf:blk.6.attn_norm.weight,      torch.float16 --> F32, shape = {2048}\n",
            "INFO:hf-to-gguf:blk.6.ffn_norm.weight,       torch.float16 --> F32, shape = {2048}\n",
            "INFO:hf-to-gguf:blk.7.attn_q.weight,         torch.float16 --> F16, shape = {2048, 2048}\n",
            "INFO:hf-to-gguf:blk.7.attn_k.weight,         torch.float16 --> F16, shape = {2048, 512}\n",
            "INFO:hf-to-gguf:blk.7.attn_v.weight,         torch.float16 --> F16, shape = {2048, 512}\n",
            "INFO:hf-to-gguf:blk.7.attn_output.weight,    torch.float16 --> F16, shape = {2048, 2048}\n",
            "INFO:hf-to-gguf:blk.7.ffn_gate.weight,       torch.float16 --> F16, shape = {2048, 8192}\n",
            "INFO:hf-to-gguf:blk.7.ffn_up.weight,         torch.float16 --> F16, shape = {2048, 8192}\n",
            "INFO:hf-to-gguf:blk.7.ffn_down.weight,       torch.float16 --> F16, shape = {8192, 2048}\n",
            "INFO:hf-to-gguf:blk.7.attn_norm.weight,      torch.float16 --> F32, shape = {2048}\n",
            "INFO:hf-to-gguf:blk.7.ffn_norm.weight,       torch.float16 --> F32, shape = {2048}\n",
            "INFO:hf-to-gguf:blk.8.attn_q.weight,         torch.float16 --> F16, shape = {2048, 2048}\n",
            "INFO:hf-to-gguf:blk.8.attn_k.weight,         torch.float16 --> F16, shape = {2048, 512}\n",
            "INFO:hf-to-gguf:blk.8.attn_v.weight,         torch.float16 --> F16, shape = {2048, 512}\n",
            "INFO:hf-to-gguf:blk.8.attn_output.weight,    torch.float16 --> F16, shape = {2048, 2048}\n",
            "INFO:hf-to-gguf:blk.8.ffn_gate.weight,       torch.float16 --> F16, shape = {2048, 8192}\n",
            "INFO:hf-to-gguf:blk.8.ffn_up.weight,         torch.float16 --> F16, shape = {2048, 8192}\n",
            "INFO:hf-to-gguf:blk.8.ffn_down.weight,       torch.float16 --> F16, shape = {8192, 2048}\n",
            "INFO:hf-to-gguf:blk.8.attn_norm.weight,      torch.float16 --> F32, shape = {2048}\n",
            "INFO:hf-to-gguf:blk.8.ffn_norm.weight,       torch.float16 --> F32, shape = {2048}\n",
            "INFO:hf-to-gguf:blk.9.attn_q.weight,         torch.float16 --> F16, shape = {2048, 2048}\n",
            "INFO:hf-to-gguf:blk.9.attn_k.weight,         torch.float16 --> F16, shape = {2048, 512}\n",
            "INFO:hf-to-gguf:blk.9.attn_v.weight,         torch.float16 --> F16, shape = {2048, 512}\n",
            "INFO:hf-to-gguf:blk.9.attn_output.weight,    torch.float16 --> F16, shape = {2048, 2048}\n",
            "INFO:hf-to-gguf:blk.9.ffn_gate.weight,       torch.float16 --> F16, shape = {2048, 8192}\n",
            "INFO:hf-to-gguf:blk.9.ffn_up.weight,         torch.float16 --> F16, shape = {2048, 8192}\n",
            "INFO:hf-to-gguf:blk.9.ffn_down.weight,       torch.float16 --> F16, shape = {8192, 2048}\n",
            "INFO:hf-to-gguf:blk.9.attn_norm.weight,      torch.float16 --> F32, shape = {2048}\n",
            "INFO:hf-to-gguf:blk.9.ffn_norm.weight,       torch.float16 --> F32, shape = {2048}\n",
            "INFO:hf-to-gguf:blk.10.attn_q.weight,        torch.float16 --> F16, shape = {2048, 2048}\n",
            "INFO:hf-to-gguf:blk.10.attn_k.weight,        torch.float16 --> F16, shape = {2048, 512}\n",
            "INFO:hf-to-gguf:blk.10.attn_v.weight,        torch.float16 --> F16, shape = {2048, 512}\n",
            "INFO:hf-to-gguf:blk.10.attn_output.weight,   torch.float16 --> F16, shape = {2048, 2048}\n",
            "INFO:hf-to-gguf:blk.10.ffn_gate.weight,      torch.float16 --> F16, shape = {2048, 8192}\n",
            "INFO:hf-to-gguf:blk.10.ffn_up.weight,        torch.float16 --> F16, shape = {2048, 8192}\n",
            "INFO:hf-to-gguf:blk.10.ffn_down.weight,      torch.float16 --> F16, shape = {8192, 2048}\n",
            "INFO:hf-to-gguf:blk.10.attn_norm.weight,     torch.float16 --> F32, shape = {2048}\n",
            "INFO:hf-to-gguf:blk.10.ffn_norm.weight,      torch.float16 --> F32, shape = {2048}\n",
            "INFO:hf-to-gguf:blk.11.attn_q.weight,        torch.float16 --> F16, shape = {2048, 2048}\n",
            "INFO:hf-to-gguf:blk.11.attn_k.weight,        torch.float16 --> F16, shape = {2048, 512}\n",
            "INFO:hf-to-gguf:blk.11.attn_v.weight,        torch.float16 --> F16, shape = {2048, 512}\n",
            "INFO:hf-to-gguf:blk.11.attn_output.weight,   torch.float16 --> F16, shape = {2048, 2048}\n",
            "INFO:hf-to-gguf:blk.11.ffn_gate.weight,      torch.float16 --> F16, shape = {2048, 8192}\n",
            "INFO:hf-to-gguf:blk.11.ffn_up.weight,        torch.float16 --> F16, shape = {2048, 8192}\n",
            "INFO:hf-to-gguf:blk.11.ffn_down.weight,      torch.float16 --> F16, shape = {8192, 2048}\n",
            "INFO:hf-to-gguf:blk.11.attn_norm.weight,     torch.float16 --> F32, shape = {2048}\n",
            "INFO:hf-to-gguf:blk.11.ffn_norm.weight,      torch.float16 --> F32, shape = {2048}\n",
            "INFO:hf-to-gguf:blk.12.attn_q.weight,        torch.float16 --> F16, shape = {2048, 2048}\n",
            "INFO:hf-to-gguf:blk.12.attn_k.weight,        torch.float16 --> F16, shape = {2048, 512}\n",
            "INFO:hf-to-gguf:blk.12.attn_v.weight,        torch.float16 --> F16, shape = {2048, 512}\n",
            "INFO:hf-to-gguf:blk.12.attn_output.weight,   torch.float16 --> F16, shape = {2048, 2048}\n",
            "INFO:hf-to-gguf:blk.12.ffn_gate.weight,      torch.float16 --> F16, shape = {2048, 8192}\n",
            "INFO:hf-to-gguf:blk.12.ffn_up.weight,        torch.float16 --> F16, shape = {2048, 8192}\n",
            "INFO:hf-to-gguf:blk.12.ffn_down.weight,      torch.float16 --> F16, shape = {8192, 2048}\n",
            "INFO:hf-to-gguf:blk.12.attn_norm.weight,     torch.float16 --> F32, shape = {2048}\n",
            "INFO:hf-to-gguf:blk.12.ffn_norm.weight,      torch.float16 --> F32, shape = {2048}\n",
            "INFO:hf-to-gguf:blk.13.attn_q.weight,        torch.float16 --> F16, shape = {2048, 2048}\n",
            "INFO:hf-to-gguf:blk.13.attn_k.weight,        torch.float16 --> F16, shape = {2048, 512}\n",
            "INFO:hf-to-gguf:blk.13.attn_v.weight,        torch.float16 --> F16, shape = {2048, 512}\n",
            "INFO:hf-to-gguf:blk.13.attn_output.weight,   torch.float16 --> F16, shape = {2048, 2048}\n",
            "INFO:hf-to-gguf:blk.13.ffn_gate.weight,      torch.float16 --> F16, shape = {2048, 8192}\n",
            "INFO:hf-to-gguf:blk.13.ffn_up.weight,        torch.float16 --> F16, shape = {2048, 8192}\n",
            "INFO:hf-to-gguf:blk.13.ffn_down.weight,      torch.float16 --> F16, shape = {8192, 2048}\n",
            "INFO:hf-to-gguf:blk.13.attn_norm.weight,     torch.float16 --> F32, shape = {2048}\n",
            "INFO:hf-to-gguf:blk.13.ffn_norm.weight,      torch.float16 --> F32, shape = {2048}\n",
            "INFO:hf-to-gguf:blk.14.attn_q.weight,        torch.float16 --> F16, shape = {2048, 2048}\n",
            "INFO:hf-to-gguf:blk.14.attn_k.weight,        torch.float16 --> F16, shape = {2048, 512}\n",
            "INFO:hf-to-gguf:blk.14.attn_v.weight,        torch.float16 --> F16, shape = {2048, 512}\n",
            "INFO:hf-to-gguf:blk.14.attn_output.weight,   torch.float16 --> F16, shape = {2048, 2048}\n",
            "INFO:hf-to-gguf:blk.14.ffn_gate.weight,      torch.float16 --> F16, shape = {2048, 8192}\n",
            "INFO:hf-to-gguf:blk.14.ffn_up.weight,        torch.float16 --> F16, shape = {2048, 8192}\n",
            "INFO:hf-to-gguf:blk.14.ffn_down.weight,      torch.float16 --> F16, shape = {8192, 2048}\n",
            "INFO:hf-to-gguf:blk.14.attn_norm.weight,     torch.float16 --> F32, shape = {2048}\n",
            "INFO:hf-to-gguf:blk.14.ffn_norm.weight,      torch.float16 --> F32, shape = {2048}\n",
            "INFO:hf-to-gguf:blk.15.attn_q.weight,        torch.float16 --> F16, shape = {2048, 2048}\n",
            "INFO:hf-to-gguf:blk.15.attn_k.weight,        torch.float16 --> F16, shape = {2048, 512}\n",
            "INFO:hf-to-gguf:blk.15.attn_v.weight,        torch.float16 --> F16, shape = {2048, 512}\n",
            "INFO:hf-to-gguf:blk.15.attn_output.weight,   torch.float16 --> F16, shape = {2048, 2048}\n",
            "INFO:hf-to-gguf:blk.15.ffn_gate.weight,      torch.float16 --> F16, shape = {2048, 8192}\n",
            "INFO:hf-to-gguf:blk.15.ffn_up.weight,        torch.float16 --> F16, shape = {2048, 8192}\n",
            "INFO:hf-to-gguf:blk.15.ffn_down.weight,      torch.float16 --> F16, shape = {8192, 2048}\n",
            "INFO:hf-to-gguf:blk.15.attn_norm.weight,     torch.float16 --> F32, shape = {2048}\n",
            "INFO:hf-to-gguf:blk.15.ffn_norm.weight,      torch.float16 --> F32, shape = {2048}\n",
            "INFO:hf-to-gguf:output_norm.weight,          torch.float16 --> F32, shape = {2048}\n",
            "INFO:hf-to-gguf:Set meta model\n",
            "INFO:hf-to-gguf:Set model parameters\n",
            "INFO:hf-to-gguf:gguf: context length = 131072\n",
            "INFO:hf-to-gguf:gguf: embedding length = 2048\n",
            "INFO:hf-to-gguf:gguf: feed forward length = 8192\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",
            "WARNING:gguf.vocab:Adding merges requested but no merges found, output may be non-functional.\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 128004\n",
            "INFO:gguf.vocab:Setting chat_template to {{- bos_token }}\n",
            "{%- if custom_tools is defined %}\n",
            "    {%- set tools = custom_tools %}\n",
            "{%- endif %}\n",
            "{%- if not tools_in_user_message is defined %}\n",
            "    {%- set tools_in_user_message = true %}\n",
            "{%- endif %}\n",
            "{%- if not date_string is defined %}\n",
            "    {%- if strftime_now is defined %}\n",
            "        {%- set date_string = strftime_now(\"%d %b %Y\") %}\n",
            "    {%- else %}\n",
            "        {%- set date_string = \"26 Jul 2024\" %}\n",
            "    {%- endif %}\n",
            "{%- endif %}\n",
            "{%- if not tools is defined %}\n",
            "    {%- set tools = none %}\n",
            "{%- endif %}\n",
            "\n",
            "{#- This block extracts the system message, so we can slot it into the right place. #}\n",
            "{%- if messages[0]['role'] == 'system' %}\n",
            "    {%- set system_message = messages[0]['content']|trim %}\n",
            "    {%- set messages = messages[1:] %}\n",
            "{%- else %}\n",
            "    {%- set system_message = \"\" %}\n",
            "{%- endif %}\n",
            "\n",
            "{#- System message #}\n",
            "{{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n",
            "{%- if tools is not none %}\n",
            "    {{- \"Environment: ipython\\n\" }}\n",
            "{%- endif %}\n",
            "{{- \"Cutting Knowledge Date: December 2023\\n\" }}\n",
            "{{- \"Today Date: \" + date_string + \"\\n\\n\" }}\n",
            "{%- if tools is not none and not tools_in_user_message %}\n",
            "    {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n",
            "    {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n",
            "    {{- \"Do not use variables.\\n\\n\" }}\n",
            "    {%- for t in tools %}\n",
            "        {{- t | tojson(indent=4) }}\n",
            "        {{- \"\\n\\n\" }}\n",
            "    {%- endfor %}\n",
            "{%- endif %}\n",
            "{{- system_message }}\n",
            "{{- \"<|eot_id|>\" }}\n",
            "\n",
            "{#- Custom tools are passed in a user message with some extra guidance #}\n",
            "{%- if tools_in_user_message and not tools is none %}\n",
            "    {#- Extract the first user message so we can plug it in here #}\n",
            "    {%- if messages | length != 0 %}\n",
            "        {%- set first_user_message = messages[0]['content']|trim %}\n",
            "        {%- set messages = messages[1:] %}\n",
            "    {%- else %}\n",
            "        {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n",
            "{%- endif %}\n",
            "    {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n",
            "    {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n",
            "    {{- \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n",
            "    {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n",
            "    {{- \"Do not use variables.\\n\\n\" }}\n",
            "    {%- for t in tools %}\n",
            "        {{- t | tojson(indent=4) }}\n",
            "        {{- \"\\n\\n\" }}\n",
            "    {%- endfor %}\n",
            "    {{- first_user_message + \"<|eot_id|>\"}}\n",
            "{%- endif %}\n",
            "\n",
            "{%- for message in messages %}\n",
            "    {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n",
            "        {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'+ message['content'] | trim + '<|eot_id|>' }}\n",
            "    {%- elif 'tool_calls' in message %}\n",
            "        {%- if not message.tool_calls|length == 1 %}\n",
            "            {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n",
            "        {%- endif %}\n",
            "        {%- set tool_call = message.tool_calls[0].function %}\n",
            "        {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n",
            "        {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n",
            "        {{- '\"parameters\": ' }}\n",
            "        {{- tool_call.arguments | tojson }}\n",
            "        {{- \"}\" }}\n",
            "        {{- \"<|eot_id|>\" }}\n",
            "    {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n",
            "        {{- \"<|start_header_id|>ipython<|end_header_id|>\\n\\n\" }}\n",
            "        {%- if message.content is mapping or message.content is iterable %}\n",
            "            {{- message.content | tojson }}\n",
            "        {%- else %}\n",
            "            {{- message.content }}\n",
            "        {%- endif %}\n",
            "        {{- \"<|eot_id|>\" }}\n",
            "    {%- endif %}\n",
            "{%- endfor %}\n",
            "{%- if add_generation_prompt %}\n",
            "    {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n",
            "{%- endif %}\n",
            "\n",
            "INFO:hf-to-gguf:Set model quantization version\n",
            "INFO:gguf.gguf_writer:Writing the following files:\n",
            "INFO:gguf.gguf_writer:AiisNothing/llama-3.3-1b-it-gguf/unsloth.F16.gguf: n_tensors = 147, total_size = 2.5G\n",
            "Writing: 100%|██████████| 2.47G/2.47G [00:38<00:00, 64.6Mbyte/s]\n",
            "INFO:hf-to-gguf:Model successfully exported to AiisNothing/llama-3.3-1b-it-gguf/unsloth.F16.gguf\n",
            "Unsloth: Conversion completed! Output location: ./AiisNothing/llama-3.3-1b-it-gguf/unsloth.F16.gguf\n",
            "Unsloth: [2] Converting GGUF 16bit into q6_k. This will take 20 minutes...\n",
            "main: build = 3849 (8277a817)\n",
            "main: built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu\n",
            "main: quantizing './AiisNothing/llama-3.3-1b-it-gguf/unsloth.F16.gguf' to './AiisNothing/llama-3.3-1b-it-gguf/unsloth.Q6_K.gguf' as Q6_K using 4 threads\n",
            "llama_model_loader: loaded meta data with 29 key-value pairs and 147 tensors from ./AiisNothing/llama-3.3-1b-it-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.type str              = model\n",
            "llama_model_loader: - kv   2:                               general.name str              = Llama 3.2 1B Instruct\n",
            "llama_model_loader: - kv   3:                       general.organization str              = Unsloth\n",
            "llama_model_loader: - kv   4:                           general.finetune str              = Instruct\n",
            "llama_model_loader: - kv   5:                           general.basename str              = Llama-3.2\n",
            "llama_model_loader: - kv   6:                         general.size_label str              = 1B\n",
            "llama_model_loader: - kv   7:                          llama.block_count u32              = 16\n",
            "llama_model_loader: - kv   8:                       llama.context_length u32              = 131072\n",
            "llama_model_loader: - kv   9:                     llama.embedding_length u32              = 2048\n",
            "llama_model_loader: - kv  10:                  llama.feed_forward_length u32              = 8192\n",
            "llama_model_loader: - kv  11:                 llama.attention.head_count u32              = 32\n",
            "llama_model_loader: - kv  12:              llama.attention.head_count_kv u32              = 8\n",
            "llama_model_loader: - kv  13:                       llama.rope.freq_base f32              = 500000.000000\n",
            "llama_model_loader: - kv  14:     llama.attention.layer_norm_rms_epsilon f32              = 0.000010\n",
            "llama_model_loader: - kv  15:                 llama.attention.key_length u32              = 64\n",
            "llama_model_loader: - kv  16:               llama.attention.value_length u32              = 64\n",
            "llama_model_loader: - kv  17:                          general.file_type u32              = 1\n",
            "llama_model_loader: - kv  18:                           llama.vocab_size u32              = 128256\n",
            "llama_model_loader: - kv  19:                 llama.rope.dimension_count u32              = 64\n",
            "llama_model_loader: - kv  20:                       tokenizer.ggml.model str              = gpt2\n",
            "llama_model_loader: - kv  21:                         tokenizer.ggml.pre str              = llama-bpe\n",
            "llama_model_loader: - kv  22:                      tokenizer.ggml.tokens arr[str,128256]  = [\"!\", \"\\\"\", \"#\", \"$\", \"%\", \"&\", \"'\", ...\n",
            "llama_model_loader: - kv  23:                  tokenizer.ggml.token_type arr[i32,128256]  = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...\n",
            "llama_model_loader: - kv  24:                tokenizer.ggml.bos_token_id u32              = 128000\n",
            "llama_model_loader: - kv  25:                tokenizer.ggml.eos_token_id u32              = 128009\n",
            "llama_model_loader: - kv  26:            tokenizer.ggml.padding_token_id u32              = 128004\n",
            "llama_model_loader: - kv  27:                    tokenizer.chat_template str              = {{- bos_token }}\\n{%- if custom_tools ...\n",
            "llama_model_loader: - kv  28:               general.quantization_version u32              = 2\n",
            "llama_model_loader: - type  f32:   34 tensors\n",
            "llama_model_loader: - type  f16:  113 tensors\n",
            "[   1/ 147]                    rope_freqs.weight - [   32,     1,     1,     1], type =    f32, size =    0.000 MB\n",
            "[   2/ 147]                    token_embd.weight - [ 2048, 128256,     1,     1], type =    f16, converting to q6_K .. size =   501.00 MiB ->   205.49 MiB\n",
            "[   3/ 147]                  blk.0.attn_q.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[   4/ 147]                  blk.0.attn_k.weight - [ 2048,   512,     1,     1], type =    f16, converting to q6_K .. size =     2.00 MiB ->     0.82 MiB\n",
            "[   5/ 147]                  blk.0.attn_v.weight - [ 2048,   512,     1,     1], type =    f16, converting to q6_K .. size =     2.00 MiB ->     0.82 MiB\n",
            "[   6/ 147]             blk.0.attn_output.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[   7/ 147]                blk.0.ffn_gate.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q6_K .. size =    32.00 MiB ->    13.12 MiB\n",
            "[   8/ 147]                  blk.0.ffn_up.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q6_K .. size =    32.00 MiB ->    13.12 MiB\n",
            "[   9/ 147]                blk.0.ffn_down.weight - [ 8192,  2048,     1,     1], type =    f16, converting to q6_K .. size =    32.00 MiB ->    13.12 MiB\n",
            "[  10/ 147]               blk.0.attn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[  11/ 147]                blk.0.ffn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[  12/ 147]                  blk.1.attn_q.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[  13/ 147]                  blk.1.attn_k.weight - [ 2048,   512,     1,     1], type =    f16, converting to q6_K .. size =     2.00 MiB ->     0.82 MiB\n",
            "[  14/ 147]                  blk.1.attn_v.weight - [ 2048,   512,     1,     1], type =    f16, converting to q6_K .. size =     2.00 MiB ->     0.82 MiB\n",
            "[  15/ 147]             blk.1.attn_output.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[  16/ 147]                blk.1.ffn_gate.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q6_K .. size =    32.00 MiB ->    13.12 MiB\n",
            "[  17/ 147]                  blk.1.ffn_up.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q6_K .. size =    32.00 MiB ->    13.12 MiB\n",
            "[  18/ 147]                blk.1.ffn_down.weight - [ 8192,  2048,     1,     1], type =    f16, converting to q6_K .. size =    32.00 MiB ->    13.12 MiB\n",
            "[  19/ 147]               blk.1.attn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[  20/ 147]                blk.1.ffn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[  21/ 147]                  blk.2.attn_q.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[  22/ 147]                  blk.2.attn_k.weight - [ 2048,   512,     1,     1], type =    f16, converting to q6_K .. size =     2.00 MiB ->     0.82 MiB\n",
            "[  23/ 147]                  blk.2.attn_v.weight - [ 2048,   512,     1,     1], type =    f16, converting to q6_K .. size =     2.00 MiB ->     0.82 MiB\n",
            "[  24/ 147]             blk.2.attn_output.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[  25/ 147]                blk.2.ffn_gate.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q6_K .. size =    32.00 MiB ->    13.12 MiB\n",
            "[  26/ 147]                  blk.2.ffn_up.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q6_K .. size =    32.00 MiB ->    13.12 MiB\n",
            "[  27/ 147]                blk.2.ffn_down.weight - [ 8192,  2048,     1,     1], type =    f16, converting to q6_K .. size =    32.00 MiB ->    13.12 MiB\n",
            "[  28/ 147]               blk.2.attn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[  29/ 147]                blk.2.ffn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[  30/ 147]                  blk.3.attn_q.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[  31/ 147]                  blk.3.attn_k.weight - [ 2048,   512,     1,     1], type =    f16, converting to q6_K .. size =     2.00 MiB ->     0.82 MiB\n",
            "[  32/ 147]                  blk.3.attn_v.weight - [ 2048,   512,     1,     1], type =    f16, converting to q6_K .. size =     2.00 MiB ->     0.82 MiB\n",
            "[  33/ 147]             blk.3.attn_output.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[  34/ 147]                blk.3.ffn_gate.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q6_K .. size =    32.00 MiB ->    13.12 MiB\n",
            "[  35/ 147]                  blk.3.ffn_up.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q6_K .. size =    32.00 MiB ->    13.12 MiB\n",
            "[  36/ 147]                blk.3.ffn_down.weight - [ 8192,  2048,     1,     1], type =    f16, converting to q6_K .. size =    32.00 MiB ->    13.12 MiB\n",
            "[  37/ 147]               blk.3.attn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[  38/ 147]                blk.3.ffn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[  39/ 147]                  blk.4.attn_q.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[  40/ 147]                  blk.4.attn_k.weight - [ 2048,   512,     1,     1], type =    f16, converting to q6_K .. size =     2.00 MiB ->     0.82 MiB\n",
            "[  41/ 147]                  blk.4.attn_v.weight - [ 2048,   512,     1,     1], type =    f16, converting to q6_K .. size =     2.00 MiB ->     0.82 MiB\n",
            "[  42/ 147]             blk.4.attn_output.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[  43/ 147]                blk.4.ffn_gate.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q6_K .. size =    32.00 MiB ->    13.12 MiB\n",
            "[  44/ 147]                  blk.4.ffn_up.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q6_K .. size =    32.00 MiB ->    13.12 MiB\n",
            "[  45/ 147]                blk.4.ffn_down.weight - [ 8192,  2048,     1,     1], type =    f16, converting to q6_K .. size =    32.00 MiB ->    13.12 MiB\n",
            "[  46/ 147]               blk.4.attn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[  47/ 147]                blk.4.ffn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[  48/ 147]                  blk.5.attn_q.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[  49/ 147]                  blk.5.attn_k.weight - [ 2048,   512,     1,     1], type =    f16, converting to q6_K .. size =     2.00 MiB ->     0.82 MiB\n",
            "[  50/ 147]                  blk.5.attn_v.weight - [ 2048,   512,     1,     1], type =    f16, converting to q6_K .. size =     2.00 MiB ->     0.82 MiB\n",
            "[  51/ 147]             blk.5.attn_output.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[  52/ 147]                blk.5.ffn_gate.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q6_K .. size =    32.00 MiB ->    13.12 MiB\n",
            "[  53/ 147]                  blk.5.ffn_up.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q6_K .. size =    32.00 MiB ->    13.12 MiB\n",
            "[  54/ 147]                blk.5.ffn_down.weight - [ 8192,  2048,     1,     1], type =    f16, converting to q6_K .. size =    32.00 MiB ->    13.12 MiB\n",
            "[  55/ 147]               blk.5.attn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[  56/ 147]                blk.5.ffn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[  57/ 147]                  blk.6.attn_q.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[  58/ 147]                  blk.6.attn_k.weight - [ 2048,   512,     1,     1], type =    f16, converting to q6_K .. size =     2.00 MiB ->     0.82 MiB\n",
            "[  59/ 147]                  blk.6.attn_v.weight - [ 2048,   512,     1,     1], type =    f16, converting to q6_K .. size =     2.00 MiB ->     0.82 MiB\n",
            "[  60/ 147]             blk.6.attn_output.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[  61/ 147]                blk.6.ffn_gate.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q6_K .. size =    32.00 MiB ->    13.12 MiB\n",
            "[  62/ 147]                  blk.6.ffn_up.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q6_K .. size =    32.00 MiB ->    13.12 MiB\n",
            "[  63/ 147]                blk.6.ffn_down.weight - [ 8192,  2048,     1,     1], type =    f16, converting to q6_K .. size =    32.00 MiB ->    13.12 MiB\n",
            "[  64/ 147]               blk.6.attn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[  65/ 147]                blk.6.ffn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[  66/ 147]                  blk.7.attn_q.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[  67/ 147]                  blk.7.attn_k.weight - [ 2048,   512,     1,     1], type =    f16, converting to q6_K .. size =     2.00 MiB ->     0.82 MiB\n",
            "[  68/ 147]                  blk.7.attn_v.weight - [ 2048,   512,     1,     1], type =    f16, converting to q6_K .. size =     2.00 MiB ->     0.82 MiB\n",
            "[  69/ 147]             blk.7.attn_output.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[  70/ 147]                blk.7.ffn_gate.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q6_K .. size =    32.00 MiB ->    13.12 MiB\n",
            "[  71/ 147]                  blk.7.ffn_up.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q6_K .. size =    32.00 MiB ->    13.12 MiB\n",
            "[  72/ 147]                blk.7.ffn_down.weight - [ 8192,  2048,     1,     1], type =    f16, converting to q6_K .. size =    32.00 MiB ->    13.12 MiB\n",
            "[  73/ 147]               blk.7.attn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[  74/ 147]                blk.7.ffn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[  75/ 147]                  blk.8.attn_q.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[  76/ 147]                  blk.8.attn_k.weight - [ 2048,   512,     1,     1], type =    f16, converting to q6_K .. size =     2.00 MiB ->     0.82 MiB\n",
            "[  77/ 147]                  blk.8.attn_v.weight - [ 2048,   512,     1,     1], type =    f16, converting to q6_K .. size =     2.00 MiB ->     0.82 MiB\n",
            "[  78/ 147]             blk.8.attn_output.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[  79/ 147]                blk.8.ffn_gate.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q6_K .. size =    32.00 MiB ->    13.12 MiB\n",
            "[  80/ 147]                  blk.8.ffn_up.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q6_K .. size =    32.00 MiB ->    13.12 MiB\n",
            "[  81/ 147]                blk.8.ffn_down.weight - [ 8192,  2048,     1,     1], type =    f16, converting to q6_K .. size =    32.00 MiB ->    13.12 MiB\n",
            "[  82/ 147]               blk.8.attn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[  83/ 147]                blk.8.ffn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[  84/ 147]                  blk.9.attn_q.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[  85/ 147]                  blk.9.attn_k.weight - [ 2048,   512,     1,     1], type =    f16, converting to q6_K .. size =     2.00 MiB ->     0.82 MiB\n",
            "[  86/ 147]                  blk.9.attn_v.weight - [ 2048,   512,     1,     1], type =    f16, converting to q6_K .. size =     2.00 MiB ->     0.82 MiB\n",
            "[  87/ 147]             blk.9.attn_output.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[  88/ 147]                blk.9.ffn_gate.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q6_K .. size =    32.00 MiB ->    13.12 MiB\n",
            "[  89/ 147]                  blk.9.ffn_up.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q6_K .. size =    32.00 MiB ->    13.12 MiB\n",
            "[  90/ 147]                blk.9.ffn_down.weight - [ 8192,  2048,     1,     1], type =    f16, converting to q6_K .. size =    32.00 MiB ->    13.12 MiB\n",
            "[  91/ 147]               blk.9.attn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[  92/ 147]                blk.9.ffn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[  93/ 147]                 blk.10.attn_q.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[  94/ 147]                 blk.10.attn_k.weight - [ 2048,   512,     1,     1], type =    f16, converting to q6_K .. size =     2.00 MiB ->     0.82 MiB\n",
            "[  95/ 147]                 blk.10.attn_v.weight - [ 2048,   512,     1,     1], type =    f16, converting to q6_K .. size =     2.00 MiB ->     0.82 MiB\n",
            "[  96/ 147]            blk.10.attn_output.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[  97/ 147]               blk.10.ffn_gate.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q6_K .. size =    32.00 MiB ->    13.12 MiB\n",
            "[  98/ 147]                 blk.10.ffn_up.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q6_K .. size =    32.00 MiB ->    13.12 MiB\n",
            "[  99/ 147]               blk.10.ffn_down.weight - [ 8192,  2048,     1,     1], type =    f16, converting to q6_K .. size =    32.00 MiB ->    13.12 MiB\n",
            "[ 100/ 147]              blk.10.attn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[ 101/ 147]               blk.10.ffn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[ 102/ 147]                 blk.11.attn_q.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[ 103/ 147]                 blk.11.attn_k.weight - [ 2048,   512,     1,     1], type =    f16, converting to q6_K .. size =     2.00 MiB ->     0.82 MiB\n",
            "[ 104/ 147]                 blk.11.attn_v.weight - [ 2048,   512,     1,     1], type =    f16, converting to q6_K .. size =     2.00 MiB ->     0.82 MiB\n",
            "[ 105/ 147]            blk.11.attn_output.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[ 106/ 147]               blk.11.ffn_gate.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q6_K .. size =    32.00 MiB ->    13.12 MiB\n",
            "[ 107/ 147]                 blk.11.ffn_up.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q6_K .. size =    32.00 MiB ->    13.12 MiB\n",
            "[ 108/ 147]               blk.11.ffn_down.weight - [ 8192,  2048,     1,     1], type =    f16, converting to q6_K .. size =    32.00 MiB ->    13.12 MiB\n",
            "[ 109/ 147]              blk.11.attn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[ 110/ 147]               blk.11.ffn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[ 111/ 147]                 blk.12.attn_q.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[ 112/ 147]                 blk.12.attn_k.weight - [ 2048,   512,     1,     1], type =    f16, converting to q6_K .. size =     2.00 MiB ->     0.82 MiB\n",
            "[ 113/ 147]                 blk.12.attn_v.weight - [ 2048,   512,     1,     1], type =    f16, converting to q6_K .. size =     2.00 MiB ->     0.82 MiB\n",
            "[ 114/ 147]            blk.12.attn_output.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[ 115/ 147]               blk.12.ffn_gate.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q6_K .. size =    32.00 MiB ->    13.12 MiB\n",
            "[ 116/ 147]                 blk.12.ffn_up.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q6_K .. size =    32.00 MiB ->    13.12 MiB\n",
            "[ 117/ 147]               blk.12.ffn_down.weight - [ 8192,  2048,     1,     1], type =    f16, converting to q6_K .. size =    32.00 MiB ->    13.12 MiB\n",
            "[ 118/ 147]              blk.12.attn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[ 119/ 147]               blk.12.ffn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[ 120/ 147]                 blk.13.attn_q.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[ 121/ 147]                 blk.13.attn_k.weight - [ 2048,   512,     1,     1], type =    f16, converting to q6_K .. size =     2.00 MiB ->     0.82 MiB\n",
            "[ 122/ 147]                 blk.13.attn_v.weight - [ 2048,   512,     1,     1], type =    f16, converting to q6_K .. size =     2.00 MiB ->     0.82 MiB\n",
            "[ 123/ 147]            blk.13.attn_output.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[ 124/ 147]               blk.13.ffn_gate.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q6_K .. size =    32.00 MiB ->    13.12 MiB\n",
            "[ 125/ 147]                 blk.13.ffn_up.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q6_K .. size =    32.00 MiB ->    13.12 MiB\n",
            "[ 126/ 147]               blk.13.ffn_down.weight - [ 8192,  2048,     1,     1], type =    f16, converting to q6_K .. size =    32.00 MiB ->    13.12 MiB\n",
            "[ 127/ 147]              blk.13.attn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[ 128/ 147]               blk.13.ffn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[ 129/ 147]                 blk.14.attn_q.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[ 130/ 147]                 blk.14.attn_k.weight - [ 2048,   512,     1,     1], type =    f16, converting to q6_K .. size =     2.00 MiB ->     0.82 MiB\n",
            "[ 131/ 147]                 blk.14.attn_v.weight - [ 2048,   512,     1,     1], type =    f16, converting to q6_K .. size =     2.00 MiB ->     0.82 MiB\n",
            "[ 132/ 147]            blk.14.attn_output.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[ 133/ 147]               blk.14.ffn_gate.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q6_K .. size =    32.00 MiB ->    13.12 MiB\n",
            "[ 134/ 147]                 blk.14.ffn_up.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q6_K .. size =    32.00 MiB ->    13.12 MiB\n",
            "[ 135/ 147]               blk.14.ffn_down.weight - [ 8192,  2048,     1,     1], type =    f16, converting to q6_K .. size =    32.00 MiB ->    13.12 MiB\n",
            "[ 136/ 147]              blk.14.attn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[ 137/ 147]               blk.14.ffn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[ 138/ 147]                 blk.15.attn_q.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[ 139/ 147]                 blk.15.attn_k.weight - [ 2048,   512,     1,     1], type =    f16, converting to q6_K .. size =     2.00 MiB ->     0.82 MiB\n",
            "[ 140/ 147]                 blk.15.attn_v.weight - [ 2048,   512,     1,     1], type =    f16, converting to q6_K .. size =     2.00 MiB ->     0.82 MiB\n",
            "[ 141/ 147]            blk.15.attn_output.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q6_K .. size =     8.00 MiB ->     3.28 MiB\n",
            "[ 142/ 147]               blk.15.ffn_gate.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q6_K .. size =    32.00 MiB ->    13.12 MiB\n",
            "[ 143/ 147]                 blk.15.ffn_up.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q6_K .. size =    32.00 MiB ->    13.12 MiB\n",
            "[ 144/ 147]               blk.15.ffn_down.weight - [ 8192,  2048,     1,     1], type =    f16, converting to q6_K .. size =    32.00 MiB ->    13.12 MiB\n",
            "[ 145/ 147]              blk.15.attn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[ 146/ 147]               blk.15.ffn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[ 147/ 147]                   output_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "llama_model_quantize_internal: model size  =  2357.26 MB\n",
            "llama_model_quantize_internal: quant size  =   967.00 MB\n",
            "\n",
            "main: quantize time = 56268.67 ms\n",
            "main:    total time = 56268.67 ms\n",
            "Unsloth: Conversion completed! Output location: ./AiisNothing/llama-3.3-1b-it-gguf/unsloth.Q6_K.gguf\n",
            "Unsloth: [2] Converting GGUF 16bit into q8_0. This will take 20 minutes...\n",
            "main: build = 3849 (8277a817)\n",
            "main: built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu\n",
            "main: quantizing './AiisNothing/llama-3.3-1b-it-gguf/unsloth.F16.gguf' to './AiisNothing/llama-3.3-1b-it-gguf/unsloth.Q8_0.gguf' as Q8_0 using 4 threads\n",
            "llama_model_loader: loaded meta data with 29 key-value pairs and 147 tensors from ./AiisNothing/llama-3.3-1b-it-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.type str              = model\n",
            "llama_model_loader: - kv   2:                               general.name str              = Llama 3.2 1B Instruct\n",
            "llama_model_loader: - kv   3:                       general.organization str              = Unsloth\n",
            "llama_model_loader: - kv   4:                           general.finetune str              = Instruct\n",
            "llama_model_loader: - kv   5:                           general.basename str              = Llama-3.2\n",
            "llama_model_loader: - kv   6:                         general.size_label str              = 1B\n",
            "llama_model_loader: - kv   7:                          llama.block_count u32              = 16\n",
            "llama_model_loader: - kv   8:                       llama.context_length u32              = 131072\n",
            "llama_model_loader: - kv   9:                     llama.embedding_length u32              = 2048\n",
            "llama_model_loader: - kv  10:                  llama.feed_forward_length u32              = 8192\n",
            "llama_model_loader: - kv  11:                 llama.attention.head_count u32              = 32\n",
            "llama_model_loader: - kv  12:              llama.attention.head_count_kv u32              = 8\n",
            "llama_model_loader: - kv  13:                       llama.rope.freq_base f32              = 500000.000000\n",
            "llama_model_loader: - kv  14:     llama.attention.layer_norm_rms_epsilon f32              = 0.000010\n",
            "llama_model_loader: - kv  15:                 llama.attention.key_length u32              = 64\n",
            "llama_model_loader: - kv  16:               llama.attention.value_length u32              = 64\n",
            "llama_model_loader: - kv  17:                          general.file_type u32              = 1\n",
            "llama_model_loader: - kv  18:                           llama.vocab_size u32              = 128256\n",
            "llama_model_loader: - kv  19:                 llama.rope.dimension_count u32              = 64\n",
            "llama_model_loader: - kv  20:                       tokenizer.ggml.model str              = gpt2\n",
            "llama_model_loader: - kv  21:                         tokenizer.ggml.pre str              = llama-bpe\n",
            "llama_model_loader: - kv  22:                      tokenizer.ggml.tokens arr[str,128256]  = [\"!\", \"\\\"\", \"#\", \"$\", \"%\", \"&\", \"'\", ...\n",
            "llama_model_loader: - kv  23:                  tokenizer.ggml.token_type arr[i32,128256]  = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...\n",
            "llama_model_loader: - kv  24:                tokenizer.ggml.bos_token_id u32              = 128000\n",
            "llama_model_loader: - kv  25:                tokenizer.ggml.eos_token_id u32              = 128009\n",
            "llama_model_loader: - kv  26:            tokenizer.ggml.padding_token_id u32              = 128004\n",
            "llama_model_loader: - kv  27:                    tokenizer.chat_template str              = {{- bos_token }}\\n{%- if custom_tools ...\n",
            "llama_model_loader: - kv  28:               general.quantization_version u32              = 2\n",
            "llama_model_loader: - type  f32:   34 tensors\n",
            "llama_model_loader: - type  f16:  113 tensors\n",
            "[   1/ 147]                    rope_freqs.weight - [   32,     1,     1,     1], type =    f32, size =    0.000 MB\n",
            "[   2/ 147]                    token_embd.weight - [ 2048, 128256,     1,     1], type =    f16, converting to q8_0 .. size =   501.00 MiB ->   266.16 MiB\n",
            "[   3/ 147]                  blk.0.attn_q.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q8_0 .. size =     8.00 MiB ->     4.25 MiB\n",
            "[   4/ 147]                  blk.0.attn_k.weight - [ 2048,   512,     1,     1], type =    f16, converting to q8_0 .. size =     2.00 MiB ->     1.06 MiB\n",
            "[   5/ 147]                  blk.0.attn_v.weight - [ 2048,   512,     1,     1], type =    f16, converting to q8_0 .. size =     2.00 MiB ->     1.06 MiB\n",
            "[   6/ 147]             blk.0.attn_output.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q8_0 .. size =     8.00 MiB ->     4.25 MiB\n",
            "[   7/ 147]                blk.0.ffn_gate.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q8_0 .. size =    32.00 MiB ->    17.00 MiB\n",
            "[   8/ 147]                  blk.0.ffn_up.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q8_0 .. size =    32.00 MiB ->    17.00 MiB\n",
            "[   9/ 147]                blk.0.ffn_down.weight - [ 8192,  2048,     1,     1], type =    f16, converting to q8_0 .. size =    32.00 MiB ->    17.00 MiB\n",
            "[  10/ 147]               blk.0.attn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[  11/ 147]                blk.0.ffn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[  12/ 147]                  blk.1.attn_q.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q8_0 .. size =     8.00 MiB ->     4.25 MiB\n",
            "[  13/ 147]                  blk.1.attn_k.weight - [ 2048,   512,     1,     1], type =    f16, converting to q8_0 .. size =     2.00 MiB ->     1.06 MiB\n",
            "[  14/ 147]                  blk.1.attn_v.weight - [ 2048,   512,     1,     1], type =    f16, converting to q8_0 .. size =     2.00 MiB ->     1.06 MiB\n",
            "[  15/ 147]             blk.1.attn_output.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q8_0 .. size =     8.00 MiB ->     4.25 MiB\n",
            "[  16/ 147]                blk.1.ffn_gate.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q8_0 .. size =    32.00 MiB ->    17.00 MiB\n",
            "[  17/ 147]                  blk.1.ffn_up.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q8_0 .. size =    32.00 MiB ->    17.00 MiB\n",
            "[  18/ 147]                blk.1.ffn_down.weight - [ 8192,  2048,     1,     1], type =    f16, converting to q8_0 .. size =    32.00 MiB ->    17.00 MiB\n",
            "[  19/ 147]               blk.1.attn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[  20/ 147]                blk.1.ffn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[  21/ 147]                  blk.2.attn_q.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q8_0 .. size =     8.00 MiB ->     4.25 MiB\n",
            "[  22/ 147]                  blk.2.attn_k.weight - [ 2048,   512,     1,     1], type =    f16, converting to q8_0 .. size =     2.00 MiB ->     1.06 MiB\n",
            "[  23/ 147]                  blk.2.attn_v.weight - [ 2048,   512,     1,     1], type =    f16, converting to q8_0 .. size =     2.00 MiB ->     1.06 MiB\n",
            "[  24/ 147]             blk.2.attn_output.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q8_0 .. size =     8.00 MiB ->     4.25 MiB\n",
            "[  25/ 147]                blk.2.ffn_gate.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q8_0 .. size =    32.00 MiB ->    17.00 MiB\n",
            "[  26/ 147]                  blk.2.ffn_up.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q8_0 .. size =    32.00 MiB ->    17.00 MiB\n",
            "[  27/ 147]                blk.2.ffn_down.weight - [ 8192,  2048,     1,     1], type =    f16, converting to q8_0 .. size =    32.00 MiB ->    17.00 MiB\n",
            "[  28/ 147]               blk.2.attn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[  29/ 147]                blk.2.ffn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[  30/ 147]                  blk.3.attn_q.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q8_0 .. size =     8.00 MiB ->     4.25 MiB\n",
            "[  31/ 147]                  blk.3.attn_k.weight - [ 2048,   512,     1,     1], type =    f16, converting to q8_0 .. size =     2.00 MiB ->     1.06 MiB\n",
            "[  32/ 147]                  blk.3.attn_v.weight - [ 2048,   512,     1,     1], type =    f16, converting to q8_0 .. size =     2.00 MiB ->     1.06 MiB\n",
            "[  33/ 147]             blk.3.attn_output.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q8_0 .. size =     8.00 MiB ->     4.25 MiB\n",
            "[  34/ 147]                blk.3.ffn_gate.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q8_0 .. size =    32.00 MiB ->    17.00 MiB\n",
            "[  35/ 147]                  blk.3.ffn_up.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q8_0 .. size =    32.00 MiB ->    17.00 MiB\n",
            "[  36/ 147]                blk.3.ffn_down.weight - [ 8192,  2048,     1,     1], type =    f16, converting to q8_0 .. size =    32.00 MiB ->    17.00 MiB\n",
            "[  37/ 147]               blk.3.attn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[  38/ 147]                blk.3.ffn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[  39/ 147]                  blk.4.attn_q.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q8_0 .. size =     8.00 MiB ->     4.25 MiB\n",
            "[  40/ 147]                  blk.4.attn_k.weight - [ 2048,   512,     1,     1], type =    f16, converting to q8_0 .. size =     2.00 MiB ->     1.06 MiB\n",
            "[  41/ 147]                  blk.4.attn_v.weight - [ 2048,   512,     1,     1], type =    f16, converting to q8_0 .. size =     2.00 MiB ->     1.06 MiB\n",
            "[  42/ 147]             blk.4.attn_output.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q8_0 .. size =     8.00 MiB ->     4.25 MiB\n",
            "[  43/ 147]                blk.4.ffn_gate.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q8_0 .. size =    32.00 MiB ->    17.00 MiB\n",
            "[  44/ 147]                  blk.4.ffn_up.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q8_0 .. size =    32.00 MiB ->    17.00 MiB\n",
            "[  45/ 147]                blk.4.ffn_down.weight - [ 8192,  2048,     1,     1], type =    f16, converting to q8_0 .. size =    32.00 MiB ->    17.00 MiB\n",
            "[  46/ 147]               blk.4.attn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[  47/ 147]                blk.4.ffn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[  48/ 147]                  blk.5.attn_q.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q8_0 .. size =     8.00 MiB ->     4.25 MiB\n",
            "[  49/ 147]                  blk.5.attn_k.weight - [ 2048,   512,     1,     1], type =    f16, converting to q8_0 .. size =     2.00 MiB ->     1.06 MiB\n",
            "[  50/ 147]                  blk.5.attn_v.weight - [ 2048,   512,     1,     1], type =    f16, converting to q8_0 .. size =     2.00 MiB ->     1.06 MiB\n",
            "[  51/ 147]             blk.5.attn_output.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q8_0 .. size =     8.00 MiB ->     4.25 MiB\n",
            "[  52/ 147]                blk.5.ffn_gate.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q8_0 .. size =    32.00 MiB ->    17.00 MiB\n",
            "[  53/ 147]                  blk.5.ffn_up.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q8_0 .. size =    32.00 MiB ->    17.00 MiB\n",
            "[  54/ 147]                blk.5.ffn_down.weight - [ 8192,  2048,     1,     1], type =    f16, converting to q8_0 .. size =    32.00 MiB ->    17.00 MiB\n",
            "[  55/ 147]               blk.5.attn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[  56/ 147]                blk.5.ffn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[  57/ 147]                  blk.6.attn_q.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q8_0 .. size =     8.00 MiB ->     4.25 MiB\n",
            "[  58/ 147]                  blk.6.attn_k.weight - [ 2048,   512,     1,     1], type =    f16, converting to q8_0 .. size =     2.00 MiB ->     1.06 MiB\n",
            "[  59/ 147]                  blk.6.attn_v.weight - [ 2048,   512,     1,     1], type =    f16, converting to q8_0 .. size =     2.00 MiB ->     1.06 MiB\n",
            "[  60/ 147]             blk.6.attn_output.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q8_0 .. size =     8.00 MiB ->     4.25 MiB\n",
            "[  61/ 147]                blk.6.ffn_gate.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q8_0 .. size =    32.00 MiB ->    17.00 MiB\n",
            "[  62/ 147]                  blk.6.ffn_up.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q8_0 .. size =    32.00 MiB ->    17.00 MiB\n",
            "[  63/ 147]                blk.6.ffn_down.weight - [ 8192,  2048,     1,     1], type =    f16, converting to q8_0 .. size =    32.00 MiB ->    17.00 MiB\n",
            "[  64/ 147]               blk.6.attn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[  65/ 147]                blk.6.ffn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[  66/ 147]                  blk.7.attn_q.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q8_0 .. size =     8.00 MiB ->     4.25 MiB\n",
            "[  67/ 147]                  blk.7.attn_k.weight - [ 2048,   512,     1,     1], type =    f16, converting to q8_0 .. size =     2.00 MiB ->     1.06 MiB\n",
            "[  68/ 147]                  blk.7.attn_v.weight - [ 2048,   512,     1,     1], type =    f16, converting to q8_0 .. size =     2.00 MiB ->     1.06 MiB\n",
            "[  69/ 147]             blk.7.attn_output.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q8_0 .. size =     8.00 MiB ->     4.25 MiB\n",
            "[  70/ 147]                blk.7.ffn_gate.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q8_0 .. size =    32.00 MiB ->    17.00 MiB\n",
            "[  71/ 147]                  blk.7.ffn_up.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q8_0 .. size =    32.00 MiB ->    17.00 MiB\n",
            "[  72/ 147]                blk.7.ffn_down.weight - [ 8192,  2048,     1,     1], type =    f16, converting to q8_0 .. size =    32.00 MiB ->    17.00 MiB\n",
            "[  73/ 147]               blk.7.attn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[  74/ 147]                blk.7.ffn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[  75/ 147]                  blk.8.attn_q.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q8_0 .. size =     8.00 MiB ->     4.25 MiB\n",
            "[  76/ 147]                  blk.8.attn_k.weight - [ 2048,   512,     1,     1], type =    f16, converting to q8_0 .. size =     2.00 MiB ->     1.06 MiB\n",
            "[  77/ 147]                  blk.8.attn_v.weight - [ 2048,   512,     1,     1], type =    f16, converting to q8_0 .. size =     2.00 MiB ->     1.06 MiB\n",
            "[  78/ 147]             blk.8.attn_output.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q8_0 .. size =     8.00 MiB ->     4.25 MiB\n",
            "[  79/ 147]                blk.8.ffn_gate.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q8_0 .. size =    32.00 MiB ->    17.00 MiB\n",
            "[  80/ 147]                  blk.8.ffn_up.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q8_0 .. size =    32.00 MiB ->    17.00 MiB\n",
            "[  81/ 147]                blk.8.ffn_down.weight - [ 8192,  2048,     1,     1], type =    f16, converting to q8_0 .. size =    32.00 MiB ->    17.00 MiB\n",
            "[  82/ 147]               blk.8.attn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[  83/ 147]                blk.8.ffn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[  84/ 147]                  blk.9.attn_q.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q8_0 .. size =     8.00 MiB ->     4.25 MiB\n",
            "[  85/ 147]                  blk.9.attn_k.weight - [ 2048,   512,     1,     1], type =    f16, converting to q8_0 .. size =     2.00 MiB ->     1.06 MiB\n",
            "[  86/ 147]                  blk.9.attn_v.weight - [ 2048,   512,     1,     1], type =    f16, converting to q8_0 .. size =     2.00 MiB ->     1.06 MiB\n",
            "[  87/ 147]             blk.9.attn_output.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q8_0 .. size =     8.00 MiB ->     4.25 MiB\n",
            "[  88/ 147]                blk.9.ffn_gate.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q8_0 .. size =    32.00 MiB ->    17.00 MiB\n",
            "[  89/ 147]                  blk.9.ffn_up.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q8_0 .. size =    32.00 MiB ->    17.00 MiB\n",
            "[  90/ 147]                blk.9.ffn_down.weight - [ 8192,  2048,     1,     1], type =    f16, converting to q8_0 .. size =    32.00 MiB ->    17.00 MiB\n",
            "[  91/ 147]               blk.9.attn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[  92/ 147]                blk.9.ffn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[  93/ 147]                 blk.10.attn_q.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q8_0 .. size =     8.00 MiB ->     4.25 MiB\n",
            "[  94/ 147]                 blk.10.attn_k.weight - [ 2048,   512,     1,     1], type =    f16, converting to q8_0 .. size =     2.00 MiB ->     1.06 MiB\n",
            "[  95/ 147]                 blk.10.attn_v.weight - [ 2048,   512,     1,     1], type =    f16, converting to q8_0 .. size =     2.00 MiB ->     1.06 MiB\n",
            "[  96/ 147]            blk.10.attn_output.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q8_0 .. size =     8.00 MiB ->     4.25 MiB\n",
            "[  97/ 147]               blk.10.ffn_gate.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q8_0 .. size =    32.00 MiB ->    17.00 MiB\n",
            "[  98/ 147]                 blk.10.ffn_up.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q8_0 .. size =    32.00 MiB ->    17.00 MiB\n",
            "[  99/ 147]               blk.10.ffn_down.weight - [ 8192,  2048,     1,     1], type =    f16, converting to q8_0 .. size =    32.00 MiB ->    17.00 MiB\n",
            "[ 100/ 147]              blk.10.attn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[ 101/ 147]               blk.10.ffn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[ 102/ 147]                 blk.11.attn_q.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q8_0 .. size =     8.00 MiB ->     4.25 MiB\n",
            "[ 103/ 147]                 blk.11.attn_k.weight - [ 2048,   512,     1,     1], type =    f16, converting to q8_0 .. size =     2.00 MiB ->     1.06 MiB\n",
            "[ 104/ 147]                 blk.11.attn_v.weight - [ 2048,   512,     1,     1], type =    f16, converting to q8_0 .. size =     2.00 MiB ->     1.06 MiB\n",
            "[ 105/ 147]            blk.11.attn_output.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q8_0 .. size =     8.00 MiB ->     4.25 MiB\n",
            "[ 106/ 147]               blk.11.ffn_gate.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q8_0 .. size =    32.00 MiB ->    17.00 MiB\n",
            "[ 107/ 147]                 blk.11.ffn_up.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q8_0 .. size =    32.00 MiB ->    17.00 MiB\n",
            "[ 108/ 147]               blk.11.ffn_down.weight - [ 8192,  2048,     1,     1], type =    f16, converting to q8_0 .. size =    32.00 MiB ->    17.00 MiB\n",
            "[ 109/ 147]              blk.11.attn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[ 110/ 147]               blk.11.ffn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[ 111/ 147]                 blk.12.attn_q.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q8_0 .. size =     8.00 MiB ->     4.25 MiB\n",
            "[ 112/ 147]                 blk.12.attn_k.weight - [ 2048,   512,     1,     1], type =    f16, converting to q8_0 .. size =     2.00 MiB ->     1.06 MiB\n",
            "[ 113/ 147]                 blk.12.attn_v.weight - [ 2048,   512,     1,     1], type =    f16, converting to q8_0 .. size =     2.00 MiB ->     1.06 MiB\n",
            "[ 114/ 147]            blk.12.attn_output.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q8_0 .. size =     8.00 MiB ->     4.25 MiB\n",
            "[ 115/ 147]               blk.12.ffn_gate.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q8_0 .. size =    32.00 MiB ->    17.00 MiB\n",
            "[ 116/ 147]                 blk.12.ffn_up.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q8_0 .. size =    32.00 MiB ->    17.00 MiB\n",
            "[ 117/ 147]               blk.12.ffn_down.weight - [ 8192,  2048,     1,     1], type =    f16, converting to q8_0 .. size =    32.00 MiB ->    17.00 MiB\n",
            "[ 118/ 147]              blk.12.attn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[ 119/ 147]               blk.12.ffn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[ 120/ 147]                 blk.13.attn_q.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q8_0 .. size =     8.00 MiB ->     4.25 MiB\n",
            "[ 121/ 147]                 blk.13.attn_k.weight - [ 2048,   512,     1,     1], type =    f16, converting to q8_0 .. size =     2.00 MiB ->     1.06 MiB\n",
            "[ 122/ 147]                 blk.13.attn_v.weight - [ 2048,   512,     1,     1], type =    f16, converting to q8_0 .. size =     2.00 MiB ->     1.06 MiB\n",
            "[ 123/ 147]            blk.13.attn_output.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q8_0 .. size =     8.00 MiB ->     4.25 MiB\n",
            "[ 124/ 147]               blk.13.ffn_gate.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q8_0 .. size =    32.00 MiB ->    17.00 MiB\n",
            "[ 125/ 147]                 blk.13.ffn_up.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q8_0 .. size =    32.00 MiB ->    17.00 MiB\n",
            "[ 126/ 147]               blk.13.ffn_down.weight - [ 8192,  2048,     1,     1], type =    f16, converting to q8_0 .. size =    32.00 MiB ->    17.00 MiB\n",
            "[ 127/ 147]              blk.13.attn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[ 128/ 147]               blk.13.ffn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[ 129/ 147]                 blk.14.attn_q.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q8_0 .. size =     8.00 MiB ->     4.25 MiB\n",
            "[ 130/ 147]                 blk.14.attn_k.weight - [ 2048,   512,     1,     1], type =    f16, converting to q8_0 .. size =     2.00 MiB ->     1.06 MiB\n",
            "[ 131/ 147]                 blk.14.attn_v.weight - [ 2048,   512,     1,     1], type =    f16, converting to q8_0 .. size =     2.00 MiB ->     1.06 MiB\n",
            "[ 132/ 147]            blk.14.attn_output.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q8_0 .. size =     8.00 MiB ->     4.25 MiB\n",
            "[ 133/ 147]               blk.14.ffn_gate.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q8_0 .. size =    32.00 MiB ->    17.00 MiB\n",
            "[ 134/ 147]                 blk.14.ffn_up.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q8_0 .. size =    32.00 MiB ->    17.00 MiB\n",
            "[ 135/ 147]               blk.14.ffn_down.weight - [ 8192,  2048,     1,     1], type =    f16, converting to q8_0 .. size =    32.00 MiB ->    17.00 MiB\n",
            "[ 136/ 147]              blk.14.attn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[ 137/ 147]               blk.14.ffn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[ 138/ 147]                 blk.15.attn_q.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q8_0 .. size =     8.00 MiB ->     4.25 MiB\n",
            "[ 139/ 147]                 blk.15.attn_k.weight - [ 2048,   512,     1,     1], type =    f16, converting to q8_0 .. size =     2.00 MiB ->     1.06 MiB\n",
            "[ 140/ 147]                 blk.15.attn_v.weight - [ 2048,   512,     1,     1], type =    f16, converting to q8_0 .. size =     2.00 MiB ->     1.06 MiB\n",
            "[ 141/ 147]            blk.15.attn_output.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q8_0 .. size =     8.00 MiB ->     4.25 MiB\n",
            "[ 142/ 147]               blk.15.ffn_gate.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q8_0 .. size =    32.00 MiB ->    17.00 MiB\n",
            "[ 143/ 147]                 blk.15.ffn_up.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q8_0 .. size =    32.00 MiB ->    17.00 MiB\n",
            "[ 144/ 147]               blk.15.ffn_down.weight - [ 8192,  2048,     1,     1], type =    f16, converting to q8_0 .. size =    32.00 MiB ->    17.00 MiB\n",
            "[ 145/ 147]              blk.15.attn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[ 146/ 147]               blk.15.ffn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[ 147/ 147]                   output_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "llama_model_quantize_internal: model size  =  2357.26 MB\n",
            "llama_model_quantize_internal: quant size  =  1252.41 MB\n",
            "\n",
            "main: quantize time = 27830.45 ms\n",
            "main:    total time = 27830.45 ms\n",
            "Unsloth: Conversion completed! Output location: ./AiisNothing/llama-3.3-1b-it-gguf/unsloth.Q8_0.gguf\n",
            "Unsloth: [2] Converting GGUF 16bit into q4_k_m. This will take 20 minutes...\n",
            "main: build = 3849 (8277a817)\n",
            "main: built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu\n",
            "main: quantizing './AiisNothing/llama-3.3-1b-it-gguf/unsloth.F16.gguf' to './AiisNothing/llama-3.3-1b-it-gguf/unsloth.Q4_K_M.gguf' as Q4_K_M using 4 threads\n",
            "llama_model_loader: loaded meta data with 29 key-value pairs and 147 tensors from ./AiisNothing/llama-3.3-1b-it-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.type str              = model\n",
            "llama_model_loader: - kv   2:                               general.name str              = Llama 3.2 1B Instruct\n",
            "llama_model_loader: - kv   3:                       general.organization str              = Unsloth\n",
            "llama_model_loader: - kv   4:                           general.finetune str              = Instruct\n",
            "llama_model_loader: - kv   5:                           general.basename str              = Llama-3.2\n",
            "llama_model_loader: - kv   6:                         general.size_label str              = 1B\n",
            "llama_model_loader: - kv   7:                          llama.block_count u32              = 16\n",
            "llama_model_loader: - kv   8:                       llama.context_length u32              = 131072\n",
            "llama_model_loader: - kv   9:                     llama.embedding_length u32              = 2048\n",
            "llama_model_loader: - kv  10:                  llama.feed_forward_length u32              = 8192\n",
            "llama_model_loader: - kv  11:                 llama.attention.head_count u32              = 32\n",
            "llama_model_loader: - kv  12:              llama.attention.head_count_kv u32              = 8\n",
            "llama_model_loader: - kv  13:                       llama.rope.freq_base f32              = 500000.000000\n",
            "llama_model_loader: - kv  14:     llama.attention.layer_norm_rms_epsilon f32              = 0.000010\n",
            "llama_model_loader: - kv  15:                 llama.attention.key_length u32              = 64\n",
            "llama_model_loader: - kv  16:               llama.attention.value_length u32              = 64\n",
            "llama_model_loader: - kv  17:                          general.file_type u32              = 1\n",
            "llama_model_loader: - kv  18:                           llama.vocab_size u32              = 128256\n",
            "llama_model_loader: - kv  19:                 llama.rope.dimension_count u32              = 64\n",
            "llama_model_loader: - kv  20:                       tokenizer.ggml.model str              = gpt2\n",
            "llama_model_loader: - kv  21:                         tokenizer.ggml.pre str              = llama-bpe\n",
            "llama_model_loader: - kv  22:                      tokenizer.ggml.tokens arr[str,128256]  = [\"!\", \"\\\"\", \"#\", \"$\", \"%\", \"&\", \"'\", ...\n",
            "llama_model_loader: - kv  23:                  tokenizer.ggml.token_type arr[i32,128256]  = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...\n",
            "llama_model_loader: - kv  24:                tokenizer.ggml.bos_token_id u32              = 128000\n",
            "llama_model_loader: - kv  25:                tokenizer.ggml.eos_token_id u32              = 128009\n",
            "llama_model_loader: - kv  26:            tokenizer.ggml.padding_token_id u32              = 128004\n",
            "llama_model_loader: - kv  27:                    tokenizer.chat_template str              = {{- bos_token }}\\n{%- if custom_tools ...\n",
            "llama_model_loader: - kv  28:               general.quantization_version u32              = 2\n",
            "llama_model_loader: - type  f32:   34 tensors\n",
            "llama_model_loader: - type  f16:  113 tensors\n",
            "[   1/ 147]                    rope_freqs.weight - [   32,     1,     1,     1], type =    f32, size =    0.000 MB\n",
            "[   2/ 147]                    token_embd.weight - [ 2048, 128256,     1,     1], type =    f16, converting to q6_K .. size =   501.00 MiB ->   205.49 MiB\n",
            "[   3/ 147]                  blk.0.attn_q.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[   4/ 147]                  blk.0.attn_k.weight - [ 2048,   512,     1,     1], type =    f16, converting to q4_K .. size =     2.00 MiB ->     0.56 MiB\n",
            "[   5/ 147]                  blk.0.attn_v.weight - [ 2048,   512,     1,     1], type =    f16, converting to q6_K .. size =     2.00 MiB ->     0.82 MiB\n",
            "[   6/ 147]             blk.0.attn_output.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[   7/ 147]                blk.0.ffn_gate.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[   8/ 147]                  blk.0.ffn_up.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[   9/ 147]                blk.0.ffn_down.weight - [ 8192,  2048,     1,     1], type =    f16, converting to q6_K .. size =    32.00 MiB ->    13.12 MiB\n",
            "[  10/ 147]               blk.0.attn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[  11/ 147]                blk.0.ffn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[  12/ 147]                  blk.1.attn_q.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[  13/ 147]                  blk.1.attn_k.weight - [ 2048,   512,     1,     1], type =    f16, converting to q4_K .. size =     2.00 MiB ->     0.56 MiB\n",
            "[  14/ 147]                  blk.1.attn_v.weight - [ 2048,   512,     1,     1], type =    f16, converting to q6_K .. size =     2.00 MiB ->     0.82 MiB\n",
            "[  15/ 147]             blk.1.attn_output.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[  16/ 147]                blk.1.ffn_gate.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[  17/ 147]                  blk.1.ffn_up.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[  18/ 147]                blk.1.ffn_down.weight - [ 8192,  2048,     1,     1], type =    f16, converting to q6_K .. size =    32.00 MiB ->    13.12 MiB\n",
            "[  19/ 147]               blk.1.attn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[  20/ 147]                blk.1.ffn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[  21/ 147]                  blk.2.attn_q.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[  22/ 147]                  blk.2.attn_k.weight - [ 2048,   512,     1,     1], type =    f16, converting to q4_K .. size =     2.00 MiB ->     0.56 MiB\n",
            "[  23/ 147]                  blk.2.attn_v.weight - [ 2048,   512,     1,     1], type =    f16, converting to q4_K .. size =     2.00 MiB ->     0.56 MiB\n",
            "[  24/ 147]             blk.2.attn_output.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[  25/ 147]                blk.2.ffn_gate.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[  26/ 147]                  blk.2.ffn_up.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[  27/ 147]                blk.2.ffn_down.weight - [ 8192,  2048,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[  28/ 147]               blk.2.attn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[  29/ 147]                blk.2.ffn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[  30/ 147]                  blk.3.attn_q.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[  31/ 147]                  blk.3.attn_k.weight - [ 2048,   512,     1,     1], type =    f16, converting to q4_K .. size =     2.00 MiB ->     0.56 MiB\n",
            "[  32/ 147]                  blk.3.attn_v.weight - [ 2048,   512,     1,     1], type =    f16, converting to q4_K .. size =     2.00 MiB ->     0.56 MiB\n",
            "[  33/ 147]             blk.3.attn_output.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[  34/ 147]                blk.3.ffn_gate.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[  35/ 147]                  blk.3.ffn_up.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[  36/ 147]                blk.3.ffn_down.weight - [ 8192,  2048,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[  37/ 147]               blk.3.attn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[  38/ 147]                blk.3.ffn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[  39/ 147]                  blk.4.attn_q.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[  40/ 147]                  blk.4.attn_k.weight - [ 2048,   512,     1,     1], type =    f16, converting to q4_K .. size =     2.00 MiB ->     0.56 MiB\n",
            "[  41/ 147]                  blk.4.attn_v.weight - [ 2048,   512,     1,     1], type =    f16, converting to q6_K .. size =     2.00 MiB ->     0.82 MiB\n",
            "[  42/ 147]             blk.4.attn_output.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[  43/ 147]                blk.4.ffn_gate.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[  44/ 147]                  blk.4.ffn_up.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[  45/ 147]                blk.4.ffn_down.weight - [ 8192,  2048,     1,     1], type =    f16, converting to q6_K .. size =    32.00 MiB ->    13.12 MiB\n",
            "[  46/ 147]               blk.4.attn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[  47/ 147]                blk.4.ffn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[  48/ 147]                  blk.5.attn_q.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[  49/ 147]                  blk.5.attn_k.weight - [ 2048,   512,     1,     1], type =    f16, converting to q4_K .. size =     2.00 MiB ->     0.56 MiB\n",
            "[  50/ 147]                  blk.5.attn_v.weight - [ 2048,   512,     1,     1], type =    f16, converting to q4_K .. size =     2.00 MiB ->     0.56 MiB\n",
            "[  51/ 147]             blk.5.attn_output.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[  52/ 147]                blk.5.ffn_gate.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[  53/ 147]                  blk.5.ffn_up.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[  54/ 147]                blk.5.ffn_down.weight - [ 8192,  2048,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[  55/ 147]               blk.5.attn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[  56/ 147]                blk.5.ffn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[  57/ 147]                  blk.6.attn_q.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[  58/ 147]                  blk.6.attn_k.weight - [ 2048,   512,     1,     1], type =    f16, converting to q4_K .. size =     2.00 MiB ->     0.56 MiB\n",
            "[  59/ 147]                  blk.6.attn_v.weight - [ 2048,   512,     1,     1], type =    f16, converting to q4_K .. size =     2.00 MiB ->     0.56 MiB\n",
            "[  60/ 147]             blk.6.attn_output.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[  61/ 147]                blk.6.ffn_gate.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[  62/ 147]                  blk.6.ffn_up.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[  63/ 147]                blk.6.ffn_down.weight - [ 8192,  2048,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[  64/ 147]               blk.6.attn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[  65/ 147]                blk.6.ffn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[  66/ 147]                  blk.7.attn_q.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[  67/ 147]                  blk.7.attn_k.weight - [ 2048,   512,     1,     1], type =    f16, converting to q4_K .. size =     2.00 MiB ->     0.56 MiB\n",
            "[  68/ 147]                  blk.7.attn_v.weight - [ 2048,   512,     1,     1], type =    f16, converting to q6_K .. size =     2.00 MiB ->     0.82 MiB\n",
            "[  69/ 147]             blk.7.attn_output.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[  70/ 147]                blk.7.ffn_gate.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[  71/ 147]                  blk.7.ffn_up.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[  72/ 147]                blk.7.ffn_down.weight - [ 8192,  2048,     1,     1], type =    f16, converting to q6_K .. size =    32.00 MiB ->    13.12 MiB\n",
            "[  73/ 147]               blk.7.attn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[  74/ 147]                blk.7.ffn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[  75/ 147]                  blk.8.attn_q.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[  76/ 147]                  blk.8.attn_k.weight - [ 2048,   512,     1,     1], type =    f16, converting to q4_K .. size =     2.00 MiB ->     0.56 MiB\n",
            "[  77/ 147]                  blk.8.attn_v.weight - [ 2048,   512,     1,     1], type =    f16, converting to q4_K .. size =     2.00 MiB ->     0.56 MiB\n",
            "[  78/ 147]             blk.8.attn_output.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[  79/ 147]                blk.8.ffn_gate.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[  80/ 147]                  blk.8.ffn_up.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[  81/ 147]                blk.8.ffn_down.weight - [ 8192,  2048,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[  82/ 147]               blk.8.attn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[  83/ 147]                blk.8.ffn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[  84/ 147]                  blk.9.attn_q.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[  85/ 147]                  blk.9.attn_k.weight - [ 2048,   512,     1,     1], type =    f16, converting to q4_K .. size =     2.00 MiB ->     0.56 MiB\n",
            "[  86/ 147]                  blk.9.attn_v.weight - [ 2048,   512,     1,     1], type =    f16, converting to q4_K .. size =     2.00 MiB ->     0.56 MiB\n",
            "[  87/ 147]             blk.9.attn_output.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[  88/ 147]                blk.9.ffn_gate.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[  89/ 147]                  blk.9.ffn_up.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[  90/ 147]                blk.9.ffn_down.weight - [ 8192,  2048,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[  91/ 147]               blk.9.attn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[  92/ 147]                blk.9.ffn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[  93/ 147]                 blk.10.attn_q.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[  94/ 147]                 blk.10.attn_k.weight - [ 2048,   512,     1,     1], type =    f16, converting to q4_K .. size =     2.00 MiB ->     0.56 MiB\n",
            "[  95/ 147]                 blk.10.attn_v.weight - [ 2048,   512,     1,     1], type =    f16, converting to q6_K .. size =     2.00 MiB ->     0.82 MiB\n",
            "[  96/ 147]            blk.10.attn_output.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[  97/ 147]               blk.10.ffn_gate.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[  98/ 147]                 blk.10.ffn_up.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[  99/ 147]               blk.10.ffn_down.weight - [ 8192,  2048,     1,     1], type =    f16, converting to q6_K .. size =    32.00 MiB ->    13.12 MiB\n",
            "[ 100/ 147]              blk.10.attn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[ 101/ 147]               blk.10.ffn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[ 102/ 147]                 blk.11.attn_q.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[ 103/ 147]                 blk.11.attn_k.weight - [ 2048,   512,     1,     1], type =    f16, converting to q4_K .. size =     2.00 MiB ->     0.56 MiB\n",
            "[ 104/ 147]                 blk.11.attn_v.weight - [ 2048,   512,     1,     1], type =    f16, converting to q4_K .. size =     2.00 MiB ->     0.56 MiB\n",
            "[ 105/ 147]            blk.11.attn_output.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[ 106/ 147]               blk.11.ffn_gate.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[ 107/ 147]                 blk.11.ffn_up.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[ 108/ 147]               blk.11.ffn_down.weight - [ 8192,  2048,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[ 109/ 147]              blk.11.attn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[ 110/ 147]               blk.11.ffn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[ 111/ 147]                 blk.12.attn_q.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[ 112/ 147]                 blk.12.attn_k.weight - [ 2048,   512,     1,     1], type =    f16, converting to q4_K .. size =     2.00 MiB ->     0.56 MiB\n",
            "[ 113/ 147]                 blk.12.attn_v.weight - [ 2048,   512,     1,     1], type =    f16, converting to q4_K .. size =     2.00 MiB ->     0.56 MiB\n",
            "[ 114/ 147]            blk.12.attn_output.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[ 115/ 147]               blk.12.ffn_gate.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[ 116/ 147]                 blk.12.ffn_up.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[ 117/ 147]               blk.12.ffn_down.weight - [ 8192,  2048,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[ 118/ 147]              blk.12.attn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[ 119/ 147]               blk.12.ffn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[ 120/ 147]                 blk.13.attn_q.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[ 121/ 147]                 blk.13.attn_k.weight - [ 2048,   512,     1,     1], type =    f16, converting to q4_K .. size =     2.00 MiB ->     0.56 MiB\n",
            "[ 122/ 147]                 blk.13.attn_v.weight - [ 2048,   512,     1,     1], type =    f16, converting to q6_K .. size =     2.00 MiB ->     0.82 MiB\n",
            "[ 123/ 147]            blk.13.attn_output.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[ 124/ 147]               blk.13.ffn_gate.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[ 125/ 147]                 blk.13.ffn_up.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[ 126/ 147]               blk.13.ffn_down.weight - [ 8192,  2048,     1,     1], type =    f16, converting to q6_K .. size =    32.00 MiB ->    13.12 MiB\n",
            "[ 127/ 147]              blk.13.attn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[ 128/ 147]               blk.13.ffn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[ 129/ 147]                 blk.14.attn_q.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[ 130/ 147]                 blk.14.attn_k.weight - [ 2048,   512,     1,     1], type =    f16, converting to q4_K .. size =     2.00 MiB ->     0.56 MiB\n",
            "[ 131/ 147]                 blk.14.attn_v.weight - [ 2048,   512,     1,     1], type =    f16, converting to q6_K .. size =     2.00 MiB ->     0.82 MiB\n",
            "[ 132/ 147]            blk.14.attn_output.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[ 133/ 147]               blk.14.ffn_gate.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[ 134/ 147]                 blk.14.ffn_up.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[ 135/ 147]               blk.14.ffn_down.weight - [ 8192,  2048,     1,     1], type =    f16, converting to q6_K .. size =    32.00 MiB ->    13.12 MiB\n",
            "[ 136/ 147]              blk.14.attn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[ 137/ 147]               blk.14.ffn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[ 138/ 147]                 blk.15.attn_q.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[ 139/ 147]                 blk.15.attn_k.weight - [ 2048,   512,     1,     1], type =    f16, converting to q4_K .. size =     2.00 MiB ->     0.56 MiB\n",
            "[ 140/ 147]                 blk.15.attn_v.weight - [ 2048,   512,     1,     1], type =    f16, converting to q6_K .. size =     2.00 MiB ->     0.82 MiB\n",
            "[ 141/ 147]            blk.15.attn_output.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
            "[ 142/ 147]               blk.15.ffn_gate.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[ 143/ 147]                 blk.15.ffn_up.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
            "[ 144/ 147]               blk.15.ffn_down.weight - [ 8192,  2048,     1,     1], type =    f16, converting to q6_K .. size =    32.00 MiB ->    13.12 MiB\n",
            "[ 145/ 147]              blk.15.attn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[ 146/ 147]               blk.15.ffn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "[ 147/ 147]                   output_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
            "llama_model_quantize_internal: model size  =  2357.26 MB\n",
            "llama_model_quantize_internal: quant size  =   762.81 MB\n",
            "\n",
            "main: quantize time = 124341.12 ms\n",
            "main:    total time = 124341.12 ms\n",
            "Unsloth: Conversion completed! Output location: ./AiisNothing/llama-3.3-1b-it-gguf/unsloth.Q4_K_M.gguf\n",
            "Unsloth: Uploading GGUF to Huggingface Hub...\n",
            "Saved GGUF to https://huggingface.co/AiisNothing/llama-3.3-1b-it-gguf\n",
            "Unsloth: Uploading GGUF to Huggingface Hub...\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "No files have been modified since last commit. Skipping to prevent empty commit.\n",
            "WARNING:huggingface_hub.hf_api:No files have been modified since last commit. Skipping to prevent empty commit.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Saved GGUF to https://huggingface.co/AiisNothing/llama-3.3-1b-it-gguf\n",
            "Unsloth: Uploading GGUF to Huggingface Hub...\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "No files have been modified since last commit. Skipping to prevent empty commit.\n",
            "WARNING:huggingface_hub.hf_api:No files have been modified since last commit. Skipping to prevent empty commit.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Saved GGUF to https://huggingface.co/AiisNothing/llama-3.3-1b-it-gguf\n",
            "Unsloth: Uploading GGUF to Huggingface Hub...\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "No files have been modified since last commit. Skipping to prevent empty commit.\n",
            "WARNING:huggingface_hub.hf_api:No files have been modified since last commit. Skipping to prevent empty commit.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Saved GGUF to https://huggingface.co/AiisNothing/llama-3.3-1b-it-gguf\n"
          ]
        }
      ],
      "source": [
        "from unsloth import FastLanguageModel\n",
        "model, tokenizer = FastLanguageModel.from_pretrained(\n",
        "    model_name = \"lora_model\", # YOUR MODEL YOU USED FOR TRAINING\n",
        "    max_seq_length = max_seq_length,\n",
        "    dtype = dtype,\n",
        "    load_in_4bit = False,\n",
        ")\n",
        "# Save to 8bit Q8_0\n",
        "if False: model.save_pretrained_gguf(\"model\", tokenizer,)\n",
        "# Remember to go to https://huggingface.co/settings/tokens for a token!\n",
        "# And change hf to your username!\n",
        "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, token = \"\")\n",
        "\n",
        "# Save to 16bit GGUF\n",
        "if False: model.save_pretrained_gguf(\"model\", tokenizer, quantization_method = \"q6_k\")\n",
        "if False: model.push_to_hub_gguf(\"AiisNothing/qwen2-0.5_unsloth_lora_merged_gguf_Q6_K\", tokenizer, quantization_method = \"q6_k\", token = \"\")\n",
        "\n",
        "# Save to q4_k_m GGUF\n",
        "if False: model.save_pretrained_gguf(\"model\", tokenizer, quantization_method = \"q4_k_m\")\n",
        "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, quantization_method = \"q4_k_m\", token = \"\")\n",
        "\n",
        "# Save to multiple GGUF options - much faster if you want multiple!\n",
        "if True:\n",
        "    model.push_to_hub_gguf(\n",
        "        \"AiisNothing/llama-3.3-1b-it-gguf\", # Change hf to your username!\n",
        "        tokenizer,\n",
        "        quantization_method = [\"q6_k\", \"q8_0\", \"q4_k_m\",],\n",
        "        token = \"\", # Get a token at https://huggingface.co/settings/tokens\n",
        "    )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "500d5aa1c006450287b84eeabf0fb8e9",
            "991e06a60db846ae964382c4690ed276",
            "4e707b75c94e44888540961131845d0a",
            "ba3175fd7be54de49ee9e5db09f5efff",
            "7852757821ec40c2b406908907a6fdea",
            "893e2e8a45ee4a558fb826b6a570d0d3",
            "691c6f17e0f74d83a1c61fdd32f5c25c",
            "7e430009219145098df36e1c6b0fd7e1",
            "7d752e85d81041abbe24d422220fc352",
            "c7932f40703d41b68c923db12dd596f6",
            "c8bc2abc22564c009c111db62bdcd479",
            "8e5dbf0fbf294882b1b2569ff2dd47f1",
            "fa197a8301304038bd47e1c5c62a19a7",
            "859d9613899f4d34a0a66ab96371cb22",
            "c5c66be744114e788f1f0b7f8cf8239a",
            "060f69752ecf451d9017a5b3f3a5ffd5",
            "763c0ccf82ed445985f428552c201f33",
            "b31bdfe8a70b425f8e4b84f3c1e9b23e",
            "fd0c73a4b4f74ef0b4126f15c6ef8f0f",
            "5f3fcfd1aeca49858afd07c172e8169d",
            "042c340d75ca4288be4a74edd3e3dce9",
            "d193b20e589e4c269c5dbea980f1708b",
            "7701d6c6ae924d698c1437a39dbc31af",
            "0e0070eb9a4c49109e95b0394ea7e34e",
            "86e8abe780ce4ea8965a7d04a2a370e2",
            "194a1ad517e64ea591c028c80362c51c",
            "dc4838f860b243cdaefb8557e3b921f7",
            "a69dcd9943ad481cb94b3edcf99d4e55",
            "aab13546285f48ee9108d0b3e34f03b9",
            "23f1c347562e4dd3bc0270ec042ecec3",
            "b158cb0025f249b88cf7a86644b1c60c",
            "9dc08784f7974e6faed9fb0a44eec830",
            "f1c561ac695a46e7ba67b23a07d60ea3",
            "50231df56d4a4af38beef24685f81881",
            "75c045ade6d2416485737067a45bacc4",
            "c6b25ce6420f4836ac821c8071e85700",
            "3bc03d7c71f9418c839df4b0e1560bf8",
            "a8f3d604419f4403ad891e024754850b",
            "f8864adbc4a74606b19b6674e77d43cc",
            "df898333eeb646d5b98aaefd7a96fd13",
            "90ba8c13a8674528922751b2c4e630c3",
            "7c0a0c74b03142fcb4e969a06115f412",
            "ae1813504ff74d7d92de5a6d2ce8d1ca",
            "5553de8cc5a54efa804b274ff9b514eb"
          ]
        },
        "id": "pSKrg9wOBFXa",
        "outputId": "98e9d735-e18c-474b-8951-3416a7f5253f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Unsloth: Merging 4bit and LoRA weights to 16bit...\n",
            "Unsloth: Will use up to 4.07 out of 12.67 RAM for saving.\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 24/24 [00:00<00:00, 228.35it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Unsloth: Saving tokenizer..."
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            " Done.\n",
            "Unsloth: Saving model... This might take 5 minutes for Llama-7b...\n",
            "Unsloth: Saving AiisNothing/qwen2-0.5_unsloth_lora_merged_gguf_Q4_K_M/pytorch_model.bin...\n",
            "Done.\n",
            "==((====))==  Unsloth: Conversion from QLoRA to GGUF information\n",
            "   \\\\   /|    [0] Installing llama.cpp will take 3 minutes.\n",
            "O^O/ \\_/ \\    [1] Converting HF to GGUF 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 AiisNothing/qwen2-0.5_unsloth_lora_merged_gguf_Q4_K_M into f16 GGUF format.\n",
            "The output location will be ./AiisNothing/qwen2-0.5_unsloth_lora_merged_gguf_Q4_K_M/unsloth.F16.gguf\n",
            "This will take 3 minutes...\n",
            "INFO:hf-to-gguf:Loading model: qwen2-0.5_unsloth_lora_merged_gguf_Q4_K_M\n",
            "INFO:gguf.gguf_writer:gguf: This GGUF file is for Little Endian only\n",
            "INFO:hf-to-gguf:Exporting model...\n",
            "INFO:hf-to-gguf:gguf: loading model part 'pytorch_model.bin'\n",
            "INFO:hf-to-gguf:token_embd.weight,         torch.float16 --> F16, shape = {896, 151936}\n",
            "INFO:hf-to-gguf:blk.0.attn_q.weight,       torch.float16 --> F16, shape = {896, 896}\n",
            "INFO:hf-to-gguf:blk.0.attn_k.weight,       torch.float16 --> F16, shape = {896, 128}\n",
            "INFO:hf-to-gguf:blk.0.attn_v.weight,       torch.float16 --> F16, shape = {896, 128}\n",
            "INFO:hf-to-gguf:blk.0.attn_output.weight,  torch.float16 --> F16, shape = {896, 896}\n",
            "INFO:hf-to-gguf:blk.0.ffn_gate.weight,     torch.float16 --> F16, shape = {896, 4864}\n",
            "INFO:hf-to-gguf:blk.0.ffn_up.weight,       torch.float16 --> F16, shape = {896, 4864}\n",
            "INFO:hf-to-gguf:blk.0.ffn_down.weight,     torch.float16 --> F16, shape = {4864, 896}\n",
            "INFO:hf-to-gguf:blk.0.attn_norm.weight,    torch.float16 --> F32, shape = {896}\n",
            "INFO:hf-to-gguf:blk.0.ffn_norm.weight,     torch.float16 --> F32, shape = {896}\n",
            "INFO:hf-to-gguf:blk.1.attn_q.weight,       torch.float16 --> F16, shape = {896, 896}\n",
            "INFO:hf-to-gguf:blk.1.attn_k.weight,       torch.float16 --> F16, shape = {896, 128}\n",
            "INFO:hf-to-gguf:blk.1.attn_v.weight,       torch.float16 --> F16, shape = {896, 128}\n",
            "INFO:hf-to-gguf:blk.1.attn_output.weight,  torch.float16 --> F16, shape = {896, 896}\n",
            "INFO:hf-to-gguf:blk.1.ffn_gate.weight,     torch.float16 --> F16, shape = {896, 4864}\n",
            "INFO:hf-to-gguf:blk.1.ffn_up.weight,       torch.float16 --> F16, shape = {896, 4864}\n",
            "INFO:hf-to-gguf:blk.1.ffn_down.weight,     torch.float16 --> F16, shape = {4864, 896}\n",
            "INFO:hf-to-gguf:blk.1.attn_norm.weight,    torch.float16 --> F32, shape = {896}\n",
            "INFO:hf-to-gguf:blk.1.ffn_norm.weight,     torch.float16 --> F32, shape = {896}\n",
            "INFO:hf-to-gguf:blk.2.attn_q.weight,       torch.float16 --> F16, shape = {896, 896}\n",
            "INFO:hf-to-gguf:blk.2.attn_k.weight,       torch.float16 --> F16, shape = {896, 128}\n",
            "INFO:hf-to-gguf:blk.2.attn_v.weight,       torch.float16 --> F16, shape = {896, 128}\n",
            "INFO:hf-to-gguf:blk.2.attn_output.weight,  torch.float16 --> F16, shape = {896, 896}\n",
            "INFO:hf-to-gguf:blk.2.ffn_gate.weight,     torch.float16 --> F16, shape = {896, 4864}\n",
            "INFO:hf-to-gguf:blk.2.ffn_up.weight,       torch.float16 --> F16, shape = {896, 4864}\n",
            "INFO:hf-to-gguf:blk.2.ffn_down.weight,     torch.float16 --> F16, shape = {4864, 896}\n",
            "INFO:hf-to-gguf:blk.2.attn_norm.weight,    torch.float16 --> F32, shape = {896}\n",
            "INFO:hf-to-gguf:blk.2.ffn_norm.weight,     torch.float16 --> F32, shape = {896}\n",
            "INFO:hf-to-gguf:blk.3.attn_q.weight,       torch.float16 --> F16, shape = {896, 896}\n",
            "INFO:hf-to-gguf:blk.3.attn_k.weight,       torch.float16 --> F16, shape = {896, 128}\n",
            "INFO:hf-to-gguf:blk.3.attn_v.weight,       torch.float16 --> F16, shape = {896, 128}\n",
            "INFO:hf-to-gguf:blk.3.attn_output.weight,  torch.float16 --> F16, shape = {896, 896}\n",
            "INFO:hf-to-gguf:blk.3.ffn_gate.weight,     torch.float16 --> F16, shape = {896, 4864}\n",
            "INFO:hf-to-gguf:blk.3.ffn_up.weight,       torch.float16 --> F16, shape = {896, 4864}\n",
            "INFO:hf-to-gguf:blk.3.ffn_down.weight,     torch.float16 --> F16, shape = {4864, 896}\n",
            "INFO:hf-to-gguf:blk.3.attn_norm.weight,    torch.float16 --> F32, shape = {896}\n",
            "INFO:hf-to-gguf:blk.3.ffn_norm.weight,     torch.float16 --> F32, shape = {896}\n",
            "INFO:hf-to-gguf:blk.4.attn_q.weight,       torch.float16 --> F16, shape = {896, 896}\n",
            "INFO:hf-to-gguf:blk.4.attn_k.weight,       torch.float16 --> F16, shape = {896, 128}\n",
            "INFO:hf-to-gguf:blk.4.attn_v.weight,       torch.float16 --> F16, shape = {896, 128}\n",
            "INFO:hf-to-gguf:blk.4.attn_output.weight,  torch.float16 --> F16, shape = {896, 896}\n",
            "INFO:hf-to-gguf:blk.4.ffn_gate.weight,     torch.float16 --> F16, shape = {896, 4864}\n",
            "INFO:hf-to-gguf:blk.4.ffn_up.weight,       torch.float16 --> F16, shape = {896, 4864}\n",
            "INFO:hf-to-gguf:blk.4.ffn_down.weight,     torch.float16 --> F16, shape = {4864, 896}\n",
            "INFO:hf-to-gguf:blk.4.attn_norm.weight,    torch.float16 --> F32, shape = {896}\n",
            "INFO:hf-to-gguf:blk.4.ffn_norm.weight,     torch.float16 --> F32, shape = {896}\n",
            "INFO:hf-to-gguf:blk.5.attn_q.weight,       torch.float16 --> F16, shape = {896, 896}\n",
            "INFO:hf-to-gguf:blk.5.attn_k.weight,       torch.float16 --> F16, shape = {896, 128}\n",
            "INFO:hf-to-gguf:blk.5.attn_v.weight,       torch.float16 --> F16, shape = {896, 128}\n",
            "INFO:hf-to-gguf:blk.5.attn_output.weight,  torch.float16 --> F16, shape = {896, 896}\n",
            "INFO:hf-to-gguf:blk.5.ffn_gate.weight,     torch.float16 --> F16, shape = {896, 4864}\n",
            "INFO:hf-to-gguf:blk.5.ffn_up.weight,       torch.float16 --> F16, shape = {896, 4864}\n",
            "INFO:hf-to-gguf:blk.5.ffn_down.weight,     torch.float16 --> F16, shape = {4864, 896}\n",
            "INFO:hf-to-gguf:blk.5.attn_norm.weight,    torch.float16 --> F32, shape = {896}\n",
            "INFO:hf-to-gguf:blk.5.ffn_norm.weight,     torch.float16 --> F32, shape = {896}\n",
            "INFO:hf-to-gguf:blk.6.attn_q.weight,       torch.float16 --> F16, shape = {896, 896}\n",
            "INFO:hf-to-gguf:blk.6.attn_k.weight,       torch.float16 --> F16, shape = {896, 128}\n",
            "INFO:hf-to-gguf:blk.6.attn_v.weight,       torch.float16 --> F16, shape = {896, 128}\n",
            "INFO:hf-to-gguf:blk.6.attn_output.weight,  torch.float16 --> F16, shape = {896, 896}\n",
            "INFO:hf-to-gguf:blk.6.ffn_gate.weight,     torch.float16 --> F16, shape = {896, 4864}\n",
            "INFO:hf-to-gguf:blk.6.ffn_up.weight,       torch.float16 --> F16, shape = {896, 4864}\n",
            "INFO:hf-to-gguf:blk.6.ffn_down.weight,     torch.float16 --> F16, shape = {4864, 896}\n",
            "INFO:hf-to-gguf:blk.6.attn_norm.weight,    torch.float16 --> F32, shape = {896}\n",
            "INFO:hf-to-gguf:blk.6.ffn_norm.weight,     torch.float16 --> F32, shape = {896}\n",
            "INFO:hf-to-gguf:blk.7.attn_q.weight,       torch.float16 --> F16, shape = {896, 896}\n",
            "INFO:hf-to-gguf:blk.7.attn_k.weight,       torch.float16 --> F16, shape = {896, 128}\n",
            "INFO:hf-to-gguf:blk.7.attn_v.weight,       torch.float16 --> F16, shape = {896, 128}\n",
            "INFO:hf-to-gguf:blk.7.attn_output.weight,  torch.float16 --> F16, shape = {896, 896}\n",
            "INFO:hf-to-gguf:blk.7.ffn_gate.weight,     torch.float16 --> F16, shape = {896, 4864}\n",
            "INFO:hf-to-gguf:blk.7.ffn_up.weight,       torch.float16 --> F16, shape = {896, 4864}\n",
            "INFO:hf-to-gguf:blk.7.ffn_down.weight,     torch.float16 --> F16, shape = {4864, 896}\n",
            "INFO:hf-to-gguf:blk.7.attn_norm.weight,    torch.float16 --> F32, shape = {896}\n",
            "INFO:hf-to-gguf:blk.7.ffn_norm.weight,     torch.float16 --> F32, shape = {896}\n",
            "INFO:hf-to-gguf:blk.8.attn_q.weight,       torch.float16 --> F16, shape = {896, 896}\n",
            "INFO:hf-to-gguf:blk.8.attn_k.weight,       torch.float16 --> F16, shape = {896, 128}\n",
            "INFO:hf-to-gguf:blk.8.attn_v.weight,       torch.float16 --> F16, shape = {896, 128}\n",
            "INFO:hf-to-gguf:blk.8.attn_output.weight,  torch.float16 --> F16, shape = {896, 896}\n",
            "INFO:hf-to-gguf:blk.8.ffn_gate.weight,     torch.float16 --> F16, shape = {896, 4864}\n",
            "INFO:hf-to-gguf:blk.8.ffn_up.weight,       torch.float16 --> F16, shape = {896, 4864}\n",
            "INFO:hf-to-gguf:blk.8.ffn_down.weight,     torch.float16 --> F16, shape = {4864, 896}\n",
            "INFO:hf-to-gguf:blk.8.attn_norm.weight,    torch.float16 --> F32, shape = {896}\n",
            "INFO:hf-to-gguf:blk.8.ffn_norm.weight,     torch.float16 --> F32, shape = {896}\n",
            "INFO:hf-to-gguf:blk.9.attn_q.weight,       torch.float16 --> F16, shape = {896, 896}\n",
            "INFO:hf-to-gguf:blk.9.attn_k.weight,       torch.float16 --> F16, shape = {896, 128}\n",
            "INFO:hf-to-gguf:blk.9.attn_v.weight,       torch.float16 --> F16, shape = {896, 128}\n",
            "INFO:hf-to-gguf:blk.9.attn_output.weight,  torch.float16 --> F16, shape = {896, 896}\n",
            "INFO:hf-to-gguf:blk.9.ffn_gate.weight,     torch.float16 --> F16, shape = {896, 4864}\n",
            "INFO:hf-to-gguf:blk.9.ffn_up.weight,       torch.float16 --> F16, shape = {896, 4864}\n",
            "INFO:hf-to-gguf:blk.9.ffn_down.weight,     torch.float16 --> F16, shape = {4864, 896}\n",
            "INFO:hf-to-gguf:blk.9.attn_norm.weight,    torch.float16 --> F32, shape = {896}\n",
            "INFO:hf-to-gguf:blk.9.ffn_norm.weight,     torch.float16 --> F32, shape = {896}\n",
            "INFO:hf-to-gguf:blk.10.attn_q.weight,      torch.float16 --> F16, shape = {896, 896}\n",
            "INFO:hf-to-gguf:blk.10.attn_k.weight,      torch.float16 --> F16, shape = {896, 128}\n",
            "INFO:hf-to-gguf:blk.10.attn_v.weight,      torch.float16 --> F16, shape = {896, 128}\n",
            "INFO:hf-to-gguf:blk.10.attn_output.weight, torch.float16 --> F16, shape = {896, 896}\n",
            "INFO:hf-to-gguf:blk.10.ffn_gate.weight,    torch.float16 --> F16, shape = {896, 4864}\n",
            "INFO:hf-to-gguf:blk.10.ffn_up.weight,      torch.float16 --> F16, shape = {896, 4864}\n",
            "INFO:hf-to-gguf:blk.10.ffn_down.weight,    torch.float16 --> F16, shape = {4864, 896}\n",
            "INFO:hf-to-gguf:blk.10.attn_norm.weight,   torch.float16 --> F32, shape = {896}\n",
            "INFO:hf-to-gguf:blk.10.ffn_norm.weight,    torch.float16 --> F32, shape = {896}\n",
            "INFO:hf-to-gguf:blk.11.attn_q.weight,      torch.float16 --> F16, shape = {896, 896}\n",
            "INFO:hf-to-gguf:blk.11.attn_k.weight,      torch.float16 --> F16, shape = {896, 128}\n",
            "INFO:hf-to-gguf:blk.11.attn_v.weight,      torch.float16 --> F16, shape = {896, 128}\n",
            "INFO:hf-to-gguf:blk.11.attn_output.weight, torch.float16 --> F16, shape = {896, 896}\n",
            "INFO:hf-to-gguf:blk.11.ffn_gate.weight,    torch.float16 --> F16, shape = {896, 4864}\n",
            "INFO:hf-to-gguf:blk.11.ffn_up.weight,      torch.float16 --> F16, shape = {896, 4864}\n",
            "INFO:hf-to-gguf:blk.11.ffn_down.weight,    torch.float16 --> F16, shape = {4864, 896}\n",
            "INFO:hf-to-gguf:blk.11.attn_norm.weight,   torch.float16 --> F32, shape = {896}\n",
            "INFO:hf-to-gguf:blk.11.ffn_norm.weight,    torch.float16 --> F32, shape = {896}\n",
            "INFO:hf-to-gguf:blk.12.attn_q.weight,      torch.float16 --> F16, shape = {896, 896}\n",
            "INFO:hf-to-gguf:blk.12.attn_k.weight,      torch.float16 --> F16, shape = {896, 128}\n",
            "INFO:hf-to-gguf:blk.12.attn_v.weight,      torch.float16 --> F16, shape = {896, 128}\n",
            "INFO:hf-to-gguf:blk.12.attn_output.weight, torch.float16 --> F16, shape = {896, 896}\n",
            "INFO:hf-to-gguf:blk.12.ffn_gate.weight,    torch.float16 --> F16, shape = {896, 4864}\n",
            "INFO:hf-to-gguf:blk.12.ffn_up.weight,      torch.float16 --> F16, shape = {896, 4864}\n",
            "INFO:hf-to-gguf:blk.12.ffn_down.weight,    torch.float16 --> F16, shape = {4864, 896}\n",
            "INFO:hf-to-gguf:blk.12.attn_norm.weight,   torch.float16 --> F32, shape = {896}\n",
            "INFO:hf-to-gguf:blk.12.ffn_norm.weight,    torch.float16 --> F32, shape = {896}\n",
            "INFO:hf-to-gguf:blk.13.attn_q.weight,      torch.float16 --> F16, shape = {896, 896}\n",
            "INFO:hf-to-gguf:blk.13.attn_k.weight,      torch.float16 --> F16, shape = {896, 128}\n",
            "INFO:hf-to-gguf:blk.13.attn_v.weight,      torch.float16 --> F16, shape = {896, 128}\n",
            "INFO:hf-to-gguf:blk.13.attn_output.weight, torch.float16 --> F16, shape = {896, 896}\n",
            "INFO:hf-to-gguf:blk.13.ffn_gate.weight,    torch.float16 --> F16, shape = {896, 4864}\n",
            "INFO:hf-to-gguf:blk.13.ffn_up.weight,      torch.float16 --> F16, shape = {896, 4864}\n",
            "INFO:hf-to-gguf:blk.13.ffn_down.weight,    torch.float16 --> F16, shape = {4864, 896}\n",
            "INFO:hf-to-gguf:blk.13.attn_norm.weight,   torch.float16 --> F32, shape = {896}\n",
            "INFO:hf-to-gguf:blk.13.ffn_norm.weight,    torch.float16 --> F32, shape = {896}\n",
            "INFO:hf-to-gguf:blk.14.attn_q.weight,      torch.float16 --> F16, shape = {896, 896}\n",
            "INFO:hf-to-gguf:blk.14.attn_k.weight,      torch.float16 --> F16, shape = {896, 128}\n",
            "INFO:hf-to-gguf:blk.14.attn_v.weight,      torch.float16 --> F16, shape = {896, 128}\n",
            "INFO:hf-to-gguf:blk.14.attn_output.weight, torch.float16 --> F16, shape = {896, 896}\n",
            "INFO:hf-to-gguf:blk.14.ffn_gate.weight,    torch.float16 --> F16, shape = {896, 4864}\n",
            "INFO:hf-to-gguf:blk.14.ffn_up.weight,      torch.float16 --> F16, shape = {896, 4864}\n",
            "INFO:hf-to-gguf:blk.14.ffn_down.weight,    torch.float16 --> F16, shape = {4864, 896}\n",
            "INFO:hf-to-gguf:blk.14.attn_norm.weight,   torch.float16 --> F32, shape = {896}\n",
            "INFO:hf-to-gguf:blk.14.ffn_norm.weight,    torch.float16 --> F32, shape = {896}\n",
            "INFO:hf-to-gguf:blk.15.attn_q.weight,      torch.float16 --> F16, shape = {896, 896}\n",
            "INFO:hf-to-gguf:blk.15.attn_k.weight,      torch.float16 --> F16, shape = {896, 128}\n",
            "INFO:hf-to-gguf:blk.15.attn_v.weight,      torch.float16 --> F16, shape = {896, 128}\n",
            "INFO:hf-to-gguf:blk.15.attn_output.weight, torch.float16 --> F16, shape = {896, 896}\n",
            "INFO:hf-to-gguf:blk.15.ffn_gate.weight,    torch.float16 --> F16, shape = {896, 4864}\n",
            "INFO:hf-to-gguf:blk.15.ffn_up.weight,      torch.float16 --> F16, shape = {896, 4864}\n",
            "INFO:hf-to-gguf:blk.15.ffn_down.weight,    torch.float16 --> F16, shape = {4864, 896}\n",
            "INFO:hf-to-gguf:blk.15.attn_norm.weight,   torch.float16 --> F32, shape = {896}\n",
            "INFO:hf-to-gguf:blk.15.ffn_norm.weight,    torch.float16 --> F32, shape = {896}\n",
            "INFO:hf-to-gguf:blk.16.attn_q.weight,      torch.float16 --> F16, shape = {896, 896}\n",
            "INFO:hf-to-gguf:blk.16.attn_k.weight,      torch.float16 --> F16, shape = {896, 128}\n",
            "INFO:hf-to-gguf:blk.16.attn_v.weight,      torch.float16 --> F16, shape = {896, 128}\n",
            "INFO:hf-to-gguf:blk.16.attn_output.weight, torch.float16 --> F16, shape = {896, 896}\n",
            "INFO:hf-to-gguf:blk.16.ffn_gate.weight,    torch.float16 --> F16, shape = {896, 4864}\n",
            "INFO:hf-to-gguf:blk.16.ffn_up.weight,      torch.float16 --> F16, shape = {896, 4864}\n",
            "INFO:hf-to-gguf:blk.16.ffn_down.weight,    torch.float16 --> F16, shape = {4864, 896}\n",
            "INFO:hf-to-gguf:blk.16.attn_norm.weight,   torch.float16 --> F32, shape = {896}\n",
            "INFO:hf-to-gguf:blk.16.ffn_norm.weight,    torch.float16 --> F32, shape = {896}\n",
            "INFO:hf-to-gguf:blk.17.attn_q.weight,      torch.float16 --> F16, shape = {896, 896}\n",
            "INFO:hf-to-gguf:blk.17.attn_k.weight,      torch.float16 --> F16, shape = {896, 128}\n",
            "INFO:hf-to-gguf:blk.17.attn_v.weight,      torch.float16 --> F16, shape = {896, 128}\n",
            "INFO:hf-to-gguf:blk.17.attn_output.weight, torch.float16 --> F16, shape = {896, 896}\n",
            "INFO:hf-to-gguf:blk.17.ffn_gate.weight,    torch.float16 --> F16, shape = {896, 4864}\n",
            "INFO:hf-to-gguf:blk.17.ffn_up.weight,      torch.float16 --> F16, shape = {896, 4864}\n",
            "INFO:hf-to-gguf:blk.17.ffn_down.weight,    torch.float16 --> F16, shape = {4864, 896}\n",
            "INFO:hf-to-gguf:blk.17.attn_norm.weight,   torch.float16 --> F32, shape = {896}\n",
            "INFO:hf-to-gguf:blk.17.ffn_norm.weight,    torch.float16 --> F32, shape = {896}\n",
            "INFO:hf-to-gguf:blk.18.attn_q.weight,      torch.float16 --> F16, shape = {896, 896}\n",
            "INFO:hf-to-gguf:blk.18.attn_k.weight,      torch.float16 --> F16, shape = {896, 128}\n",
            "INFO:hf-to-gguf:blk.18.attn_v.weight,      torch.float16 --> F16, shape = {896, 128}\n",
            "INFO:hf-to-gguf:blk.18.attn_output.weight, torch.float16 --> F16, shape = {896, 896}\n",
            "INFO:hf-to-gguf:blk.18.ffn_gate.weight,    torch.float16 --> F16, shape = {896, 4864}\n",
            "INFO:hf-to-gguf:blk.18.ffn_up.weight,      torch.float16 --> F16, shape = {896, 4864}\n",
            "INFO:hf-to-gguf:blk.18.ffn_down.weight,    torch.float16 --> F16, shape = {4864, 896}\n",
            "INFO:hf-to-gguf:blk.18.attn_norm.weight,   torch.float16 --> F32, shape = {896}\n",
            "INFO:hf-to-gguf:blk.18.ffn_norm.weight,    torch.float16 --> F32, shape = {896}\n",
            "INFO:hf-to-gguf:blk.19.attn_q.weight,      torch.float16 --> F16, shape = {896, 896}\n",
            "INFO:hf-to-gguf:blk.19.attn_k.weight,      torch.float16 --> F16, shape = {896, 128}\n",
            "INFO:hf-to-gguf:blk.19.attn_v.weight,      torch.float16 --> F16, shape = {896, 128}\n",
            "INFO:hf-to-gguf:blk.19.attn_output.weight, torch.float16 --> F16, shape = {896, 896}\n",
            "INFO:hf-to-gguf:blk.19.ffn_gate.weight,    torch.float16 --> F16, shape = {896, 4864}\n",
            "INFO:hf-to-gguf:blk.19.ffn_up.weight,      torch.float16 --> F16, shape = {896, 4864}\n",
            "INFO:hf-to-gguf:blk.19.ffn_down.weight,    torch.float16 --> F16, shape = {4864, 896}\n",
            "INFO:hf-to-gguf:blk.19.attn_norm.weight,   torch.float16 --> F32, shape = {896}\n",
            "INFO:hf-to-gguf:blk.19.ffn_norm.weight,    torch.float16 --> F32, shape = {896}\n",
            "INFO:hf-to-gguf:blk.20.attn_q.weight,      torch.float16 --> F16, shape = {896, 896}\n",
            "INFO:hf-to-gguf:blk.20.attn_k.weight,      torch.float16 --> F16, shape = {896, 128}\n",
            "INFO:hf-to-gguf:blk.20.attn_v.weight,      torch.float16 --> F16, shape = {896, 128}\n",
            "INFO:hf-to-gguf:blk.20.attn_output.weight, torch.float16 --> F16, shape = {896, 896}\n",
            "INFO:hf-to-gguf:blk.20.ffn_gate.weight,    torch.float16 --> F16, shape = {896, 4864}\n",
            "INFO:hf-to-gguf:blk.20.ffn_up.weight,      torch.float16 --> F16, shape = {896, 4864}\n",
            "INFO:hf-to-gguf:blk.20.ffn_down.weight,    torch.float16 --> F16, shape = {4864, 896}\n",
            "INFO:hf-to-gguf:blk.20.attn_norm.weight,   torch.float16 --> F32, shape = {896}\n",
            "INFO:hf-to-gguf:blk.20.ffn_norm.weight,    torch.float16 --> F32, shape = {896}\n",
            "INFO:hf-to-gguf:blk.21.attn_q.weight,      torch.float16 --> F16, shape = {896, 896}\n",
            "INFO:hf-to-gguf:blk.21.attn_k.weight,      torch.float16 --> F16, shape = {896, 128}\n",
            "INFO:hf-to-gguf:blk.21.attn_v.weight,      torch.float16 --> F16, shape = {896, 128}\n",
            "INFO:hf-to-gguf:blk.21.attn_output.weight, torch.float16 --> F16, shape = {896, 896}\n",
            "INFO:hf-to-gguf:blk.21.ffn_gate.weight,    torch.float16 --> F16, shape = {896, 4864}\n",
            "INFO:hf-to-gguf:blk.21.ffn_up.weight,      torch.float16 --> F16, shape = {896, 4864}\n",
            "INFO:hf-to-gguf:blk.21.ffn_down.weight,    torch.float16 --> F16, shape = {4864, 896}\n",
            "INFO:hf-to-gguf:blk.21.attn_norm.weight,   torch.float16 --> F32, shape = {896}\n",
            "INFO:hf-to-gguf:blk.21.ffn_norm.weight,    torch.float16 --> F32, shape = {896}\n",
            "INFO:hf-to-gguf:blk.22.attn_q.weight,      torch.float16 --> F16, shape = {896, 896}\n",
            "INFO:hf-to-gguf:blk.22.attn_k.weight,      torch.float16 --> F16, shape = {896, 128}\n",
            "INFO:hf-to-gguf:blk.22.attn_v.weight,      torch.float16 --> F16, shape = {896, 128}\n",
            "INFO:hf-to-gguf:blk.22.attn_output.weight, torch.float16 --> F16, shape = {896, 896}\n",
            "INFO:hf-to-gguf:blk.22.ffn_gate.weight,    torch.float16 --> F16, shape = {896, 4864}\n",
            "INFO:hf-to-gguf:blk.22.ffn_up.weight,      torch.float16 --> F16, shape = {896, 4864}\n",
            "INFO:hf-to-gguf:blk.22.ffn_down.weight,    torch.float16 --> F16, shape = {4864, 896}\n",
            "INFO:hf-to-gguf:blk.22.attn_norm.weight,   torch.float16 --> F32, shape = {896}\n",
            "INFO:hf-to-gguf:blk.22.ffn_norm.weight,    torch.float16 --> F32, shape = {896}\n",
            "INFO:hf-to-gguf:blk.23.attn_q.weight,      torch.float16 --> F16, shape = {896, 896}\n",
            "INFO:hf-to-gguf:blk.23.attn_k.weight,      torch.float16 --> F16, shape = {896, 128}\n",
            "INFO:hf-to-gguf:blk.23.attn_v.weight,      torch.float16 --> F16, shape = {896, 128}\n",
            "INFO:hf-to-gguf:blk.23.attn_output.weight, torch.float16 --> F16, shape = {896, 896}\n",
            "INFO:hf-to-gguf:blk.23.ffn_gate.weight,    torch.float16 --> F16, shape = {896, 4864}\n",
            "INFO:hf-to-gguf:blk.23.ffn_up.weight,      torch.float16 --> F16, shape = {896, 4864}\n",
            "INFO:hf-to-gguf:blk.23.ffn_down.weight,    torch.float16 --> F16, shape = {4864, 896}\n",
            "INFO:hf-to-gguf:blk.23.attn_norm.weight,   torch.float16 --> F32, shape = {896}\n",
            "INFO:hf-to-gguf:blk.23.ffn_norm.weight,    torch.float16 --> F32, shape = {896}\n",
            "INFO:hf-to-gguf:output_norm.weight,        torch.float16 --> F32, shape = {896}\n",
            "INFO:hf-to-gguf:Set meta model\n",
            "INFO:hf-to-gguf:Set model parameters\n",
            "INFO:hf-to-gguf:gguf: context length = 32768\n",
            "INFO:hf-to-gguf:gguf: embedding length = 896\n",
            "INFO:hf-to-gguf:gguf: feed forward length = 4864\n",
            "INFO:hf-to-gguf:gguf: head count = 14\n",
            "INFO:hf-to-gguf:gguf: key-value head count = 2\n",
            "INFO:hf-to-gguf:gguf: rope theta = 1000000.0\n",
            "INFO:hf-to-gguf:gguf: rms norm epsilon = 1e-06\n",
            "INFO:hf-to-gguf:gguf: file type = 1\n",
            "INFO:hf-to-gguf:Set model tokenizer\n",
            "INFO:gguf.vocab:Adding 151387 merge(s).\n",
            "INFO:gguf.vocab:Setting special token type eos to 151645\n",
            "INFO:gguf.vocab:Setting special token type pad to 151643\n",
            "INFO:gguf.vocab:Setting special token type bos to 151643\n",
            "INFO:gguf.vocab:Setting chat_template to {% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system\n",
            "You are a helpful assistant.<|im_end|>\n",
            "' }}{% endif %}{{'<|im_start|>' + message['role'] + '\n",
            "' + message['content'] + '<|im_end|>' + '\n",
            "'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n",
            "' }}{% endif %}\n",
            "INFO:hf-to-gguf:Set model quantization version\n",
            "INFO:gguf.gguf_writer:Writing the following files:\n",
            "INFO:gguf.gguf_writer:AiisNothing/qwen2-0.5_unsloth_lora_merged_gguf_Q4_K_M/unsloth.F16.gguf: n_tensors = 218, total_size = 988.1M\n",
            "Writing: 100%|██████████| 988M/988M [00:16<00:00, 59.9Mbyte/s]\n",
            "INFO:hf-to-gguf:Model successfully exported to AiisNothing/qwen2-0.5_unsloth_lora_merged_gguf_Q4_K_M/unsloth.F16.gguf\n",
            "Unsloth: Conversion completed! Output location: ./AiisNothing/qwen2-0.5_unsloth_lora_merged_gguf_Q4_K_M/unsloth.F16.gguf\n",
            "Unsloth: [2] Converting GGUF 16bit into q4_k_m. This will take 20 minutes...\n",
            "main: build = 3798 (41f47787)\n",
            "main: built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu\n",
            "main: quantizing './AiisNothing/qwen2-0.5_unsloth_lora_merged_gguf_Q4_K_M/unsloth.F16.gguf' to './AiisNothing/qwen2-0.5_unsloth_lora_merged_gguf_Q4_K_M/unsloth.Q4_K_M.gguf' as Q4_K_M using 4 threads\n",
            "llama_model_loader: loaded meta data with 25 key-value pairs and 218 tensors from ./AiisNothing/qwen2-0.5_unsloth_lora_merged_gguf_Q4_K_M/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              = qwen2\n",
            "llama_model_loader: - kv   1:                               general.type str              = model\n",
            "llama_model_loader: - kv   2:                               general.name str              = Lora_Model\n",
            "llama_model_loader: - kv   3:                           general.finetune str              = 0.5_unsloth_lora_merged_gguf_Q4_K_M\n",
            "llama_model_loader: - kv   4:                           general.basename str              = qwen2\n",
            "llama_model_loader: - kv   5:                         general.size_label str              = 494M\n",
            "llama_model_loader: - kv   6:                          qwen2.block_count u32              = 24\n",
            "llama_model_loader: - kv   7:                       qwen2.context_length u32              = 32768\n",
            "llama_model_loader: - kv   8:                     qwen2.embedding_length u32              = 896\n",
            "llama_model_loader: - kv   9:                  qwen2.feed_forward_length u32              = 4864\n",
            "llama_model_loader: - kv  10:                 qwen2.attention.head_count u32              = 14\n",
            "llama_model_loader: - kv  11:              qwen2.attention.head_count_kv u32              = 2\n",
            "llama_model_loader: - kv  12:                       qwen2.rope.freq_base f32              = 1000000.000000\n",
            "llama_model_loader: - kv  13:     qwen2.attention.layer_norm_rms_epsilon f32              = 0.000001\n",
            "llama_model_loader: - kv  14:                          general.file_type u32              = 1\n",
            "llama_model_loader: - kv  15:                       tokenizer.ggml.model str              = gpt2\n",
            "llama_model_loader: - kv  16:                         tokenizer.ggml.pre str              = qwen2\n",
            "llama_model_loader: - kv  17:                      tokenizer.ggml.tokens arr[str,151936]  = [\"!\", \"\\\"\", \"#\", \"$\", \"%\", \"&\", \"'\", ...\n",
            "llama_model_loader: - kv  18:                  tokenizer.ggml.token_type arr[i32,151936]  = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...\n",
            "llama_model_loader: - kv  19:                      tokenizer.ggml.merges arr[str,151387]  = [\"Ġ Ġ\", \"ĠĠ ĠĠ\", \"i n\", \"Ġ t\",...\n",
            "llama_model_loader: - kv  20:                tokenizer.ggml.eos_token_id u32              = 151645\n",
            "llama_model_loader: - kv  21:            tokenizer.ggml.padding_token_id u32              = 151643\n",
            "llama_model_loader: - kv  22:                tokenizer.ggml.bos_token_id u32              = 151643\n",
            "llama_model_loader: - kv  23:                    tokenizer.chat_template str              = {% for message in messages %}{% if lo...\n",
            "llama_model_loader: - kv  24:               general.quantization_version u32              = 2\n",
            "llama_model_loader: - type  f32:   49 tensors\n",
            "llama_model_loader: - type  f16:  169 tensors\n",
            "[   1/ 218]                    token_embd.weight - [  896, 151936,     1,     1], type =    f16, converting to q8_0 .. size =   259.66 MiB ->   137.94 MiB\n",
            "[   2/ 218]                  blk.0.attn_q.weight - [  896,   896,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 896 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     1.53 MiB ->     0.53 MiB\n",
            "[   3/ 218]                  blk.0.attn_k.weight - [  896,   128,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 128 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     0.22 MiB ->     0.08 MiB\n",
            "[   4/ 218]                  blk.0.attn_v.weight - [  896,   128,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 128 are not divisible by 256, required for q6_K - using fallback quantization q8_0\n",
            "converting to q8_0 .. size =     0.22 MiB ->     0.12 MiB\n",
            "[   5/ 218]             blk.0.attn_output.weight - [  896,   896,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 896 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     1.53 MiB ->     0.53 MiB\n",
            "[   6/ 218]                blk.0.ffn_gate.weight - [  896,  4864,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 4864 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     8.31 MiB ->     2.86 MiB\n",
            "[   7/ 218]                  blk.0.ffn_up.weight - [  896,  4864,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 4864 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     8.31 MiB ->     2.86 MiB\n",
            "[   8/ 218]                blk.0.ffn_down.weight - [ 4864,   896,     1,     1], type =    f16, converting to q6_K .. size =     8.31 MiB ->     3.41 MiB\n",
            "[   9/ 218]               blk.0.attn_norm.weight - [  896,     1,     1,     1], type =    f32, size =    0.003 MB\n",
            "[  10/ 218]                blk.0.ffn_norm.weight - [  896,     1,     1,     1], type =    f32, size =    0.003 MB\n",
            "[  11/ 218]                  blk.1.attn_q.weight - [  896,   896,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 896 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     1.53 MiB ->     0.53 MiB\n",
            "[  12/ 218]                  blk.1.attn_k.weight - [  896,   128,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 128 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     0.22 MiB ->     0.08 MiB\n",
            "[  13/ 218]                  blk.1.attn_v.weight - [  896,   128,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 128 are not divisible by 256, required for q6_K - using fallback quantization q8_0\n",
            "converting to q8_0 .. size =     0.22 MiB ->     0.12 MiB\n",
            "[  14/ 218]             blk.1.attn_output.weight - [  896,   896,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 896 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     1.53 MiB ->     0.53 MiB\n",
            "[  15/ 218]                blk.1.ffn_gate.weight - [  896,  4864,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 4864 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     8.31 MiB ->     2.86 MiB\n",
            "[  16/ 218]                  blk.1.ffn_up.weight - [  896,  4864,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 4864 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     8.31 MiB ->     2.86 MiB\n",
            "[  17/ 218]                blk.1.ffn_down.weight - [ 4864,   896,     1,     1], type =    f16, converting to q6_K .. size =     8.31 MiB ->     3.41 MiB\n",
            "[  18/ 218]               blk.1.attn_norm.weight - [  896,     1,     1,     1], type =    f32, size =    0.003 MB\n",
            "[  19/ 218]                blk.1.ffn_norm.weight - [  896,     1,     1,     1], type =    f32, size =    0.003 MB\n",
            "[  20/ 218]                  blk.2.attn_q.weight - [  896,   896,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 896 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     1.53 MiB ->     0.53 MiB\n",
            "[  21/ 218]                  blk.2.attn_k.weight - [  896,   128,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 128 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     0.22 MiB ->     0.08 MiB\n",
            "[  22/ 218]                  blk.2.attn_v.weight - [  896,   128,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 128 are not divisible by 256, required for q6_K - using fallback quantization q8_0\n",
            "converting to q8_0 .. size =     0.22 MiB ->     0.12 MiB\n",
            "[  23/ 218]             blk.2.attn_output.weight - [  896,   896,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 896 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     1.53 MiB ->     0.53 MiB\n",
            "[  24/ 218]                blk.2.ffn_gate.weight - [  896,  4864,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 4864 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     8.31 MiB ->     2.86 MiB\n",
            "[  25/ 218]                  blk.2.ffn_up.weight - [  896,  4864,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 4864 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     8.31 MiB ->     2.86 MiB\n",
            "[  26/ 218]                blk.2.ffn_down.weight - [ 4864,   896,     1,     1], type =    f16, converting to q6_K .. size =     8.31 MiB ->     3.41 MiB\n",
            "[  27/ 218]               blk.2.attn_norm.weight - [  896,     1,     1,     1], type =    f32, size =    0.003 MB\n",
            "[  28/ 218]                blk.2.ffn_norm.weight - [  896,     1,     1,     1], type =    f32, size =    0.003 MB\n",
            "[  29/ 218]                  blk.3.attn_q.weight - [  896,   896,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 896 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     1.53 MiB ->     0.53 MiB\n",
            "[  30/ 218]                  blk.3.attn_k.weight - [  896,   128,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 128 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     0.22 MiB ->     0.08 MiB\n",
            "[  31/ 218]                  blk.3.attn_v.weight - [  896,   128,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 128 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     0.22 MiB ->     0.08 MiB\n",
            "[  32/ 218]             blk.3.attn_output.weight - [  896,   896,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 896 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     1.53 MiB ->     0.53 MiB\n",
            "[  33/ 218]                blk.3.ffn_gate.weight - [  896,  4864,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 4864 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     8.31 MiB ->     2.86 MiB\n",
            "[  34/ 218]                  blk.3.ffn_up.weight - [  896,  4864,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 4864 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     8.31 MiB ->     2.86 MiB\n",
            "[  35/ 218]                blk.3.ffn_down.weight - [ 4864,   896,     1,     1], type =    f16, converting to q4_K .. size =     8.31 MiB ->     2.34 MiB\n",
            "[  36/ 218]               blk.3.attn_norm.weight - [  896,     1,     1,     1], type =    f32, size =    0.003 MB\n",
            "[  37/ 218]                blk.3.ffn_norm.weight - [  896,     1,     1,     1], type =    f32, size =    0.003 MB\n",
            "[  38/ 218]                  blk.4.attn_q.weight - [  896,   896,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 896 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     1.53 MiB ->     0.53 MiB\n",
            "[  39/ 218]                  blk.4.attn_k.weight - [  896,   128,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 128 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     0.22 MiB ->     0.08 MiB\n",
            "[  40/ 218]                  blk.4.attn_v.weight - [  896,   128,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 128 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     0.22 MiB ->     0.08 MiB\n",
            "[  41/ 218]             blk.4.attn_output.weight - [  896,   896,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 896 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     1.53 MiB ->     0.53 MiB\n",
            "[  42/ 218]                blk.4.ffn_gate.weight - [  896,  4864,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 4864 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     8.31 MiB ->     2.86 MiB\n",
            "[  43/ 218]                  blk.4.ffn_up.weight - [  896,  4864,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 4864 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     8.31 MiB ->     2.86 MiB\n",
            "[  44/ 218]                blk.4.ffn_down.weight - [ 4864,   896,     1,     1], type =    f16, converting to q4_K .. size =     8.31 MiB ->     2.34 MiB\n",
            "[  45/ 218]               blk.4.attn_norm.weight - [  896,     1,     1,     1], type =    f32, size =    0.003 MB\n",
            "[  46/ 218]                blk.4.ffn_norm.weight - [  896,     1,     1,     1], type =    f32, size =    0.003 MB\n",
            "[  47/ 218]                  blk.5.attn_q.weight - [  896,   896,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 896 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     1.53 MiB ->     0.53 MiB\n",
            "[  48/ 218]                  blk.5.attn_k.weight - [  896,   128,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 128 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     0.22 MiB ->     0.08 MiB\n",
            "[  49/ 218]                  blk.5.attn_v.weight - [  896,   128,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 128 are not divisible by 256, required for q6_K - using fallback quantization q8_0\n",
            "converting to q8_0 .. size =     0.22 MiB ->     0.12 MiB\n",
            "[  50/ 218]             blk.5.attn_output.weight - [  896,   896,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 896 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     1.53 MiB ->     0.53 MiB\n",
            "[  51/ 218]                blk.5.ffn_gate.weight - [  896,  4864,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 4864 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     8.31 MiB ->     2.86 MiB\n",
            "[  52/ 218]                  blk.5.ffn_up.weight - [  896,  4864,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 4864 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     8.31 MiB ->     2.86 MiB\n",
            "[  53/ 218]                blk.5.ffn_down.weight - [ 4864,   896,     1,     1], type =    f16, converting to q6_K .. size =     8.31 MiB ->     3.41 MiB\n",
            "[  54/ 218]               blk.5.attn_norm.weight - [  896,     1,     1,     1], type =    f32, size =    0.003 MB\n",
            "[  55/ 218]                blk.5.ffn_norm.weight - [  896,     1,     1,     1], type =    f32, size =    0.003 MB\n",
            "[  56/ 218]                  blk.6.attn_q.weight - [  896,   896,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 896 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     1.53 MiB ->     0.53 MiB\n",
            "[  57/ 218]                  blk.6.attn_k.weight - [  896,   128,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 128 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     0.22 MiB ->     0.08 MiB\n",
            "[  58/ 218]                  blk.6.attn_v.weight - [  896,   128,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 128 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     0.22 MiB ->     0.08 MiB\n",
            "[  59/ 218]             blk.6.attn_output.weight - [  896,   896,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 896 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     1.53 MiB ->     0.53 MiB\n",
            "[  60/ 218]                blk.6.ffn_gate.weight - [  896,  4864,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 4864 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     8.31 MiB ->     2.86 MiB\n",
            "[  61/ 218]                  blk.6.ffn_up.weight - [  896,  4864,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 4864 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     8.31 MiB ->     2.86 MiB\n",
            "[  62/ 218]                blk.6.ffn_down.weight - [ 4864,   896,     1,     1], type =    f16, converting to q4_K .. size =     8.31 MiB ->     2.34 MiB\n",
            "[  63/ 218]               blk.6.attn_norm.weight - [  896,     1,     1,     1], type =    f32, size =    0.003 MB\n",
            "[  64/ 218]                blk.6.ffn_norm.weight - [  896,     1,     1,     1], type =    f32, size =    0.003 MB\n",
            "[  65/ 218]                  blk.7.attn_q.weight - [  896,   896,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 896 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     1.53 MiB ->     0.53 MiB\n",
            "[  66/ 218]                  blk.7.attn_k.weight - [  896,   128,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 128 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     0.22 MiB ->     0.08 MiB\n",
            "[  67/ 218]                  blk.7.attn_v.weight - [  896,   128,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 128 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     0.22 MiB ->     0.08 MiB\n",
            "[  68/ 218]             blk.7.attn_output.weight - [  896,   896,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 896 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     1.53 MiB ->     0.53 MiB\n",
            "[  69/ 218]                blk.7.ffn_gate.weight - [  896,  4864,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 4864 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     8.31 MiB ->     2.86 MiB\n",
            "[  70/ 218]                  blk.7.ffn_up.weight - [  896,  4864,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 4864 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     8.31 MiB ->     2.86 MiB\n",
            "[  71/ 218]                blk.7.ffn_down.weight - [ 4864,   896,     1,     1], type =    f16, converting to q4_K .. size =     8.31 MiB ->     2.34 MiB\n",
            "[  72/ 218]               blk.7.attn_norm.weight - [  896,     1,     1,     1], type =    f32, size =    0.003 MB\n",
            "[  73/ 218]                blk.7.ffn_norm.weight - [  896,     1,     1,     1], type =    f32, size =    0.003 MB\n",
            "[  74/ 218]                  blk.8.attn_q.weight - [  896,   896,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 896 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     1.53 MiB ->     0.53 MiB\n",
            "[  75/ 218]                  blk.8.attn_k.weight - [  896,   128,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 128 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     0.22 MiB ->     0.08 MiB\n",
            "[  76/ 218]                  blk.8.attn_v.weight - [  896,   128,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 128 are not divisible by 256, required for q6_K - using fallback quantization q8_0\n",
            "converting to q8_0 .. size =     0.22 MiB ->     0.12 MiB\n",
            "[  77/ 218]             blk.8.attn_output.weight - [  896,   896,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 896 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     1.53 MiB ->     0.53 MiB\n",
            "[  78/ 218]                blk.8.ffn_gate.weight - [  896,  4864,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 4864 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     8.31 MiB ->     2.86 MiB\n",
            "[  79/ 218]                  blk.8.ffn_up.weight - [  896,  4864,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 4864 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     8.31 MiB ->     2.86 MiB\n",
            "[  80/ 218]                blk.8.ffn_down.weight - [ 4864,   896,     1,     1], type =    f16, converting to q6_K .. size =     8.31 MiB ->     3.41 MiB\n",
            "[  81/ 218]               blk.8.attn_norm.weight - [  896,     1,     1,     1], type =    f32, size =    0.003 MB\n",
            "[  82/ 218]                blk.8.ffn_norm.weight - [  896,     1,     1,     1], type =    f32, size =    0.003 MB\n",
            "[  83/ 218]                  blk.9.attn_q.weight - [  896,   896,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 896 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     1.53 MiB ->     0.53 MiB\n",
            "[  84/ 218]                  blk.9.attn_k.weight - [  896,   128,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 128 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     0.22 MiB ->     0.08 MiB\n",
            "[  85/ 218]                  blk.9.attn_v.weight - [  896,   128,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 128 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     0.22 MiB ->     0.08 MiB\n",
            "[  86/ 218]             blk.9.attn_output.weight - [  896,   896,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 896 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     1.53 MiB ->     0.53 MiB\n",
            "[  87/ 218]                blk.9.ffn_gate.weight - [  896,  4864,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 4864 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     8.31 MiB ->     2.86 MiB\n",
            "[  88/ 218]                  blk.9.ffn_up.weight - [  896,  4864,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 4864 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     8.31 MiB ->     2.86 MiB\n",
            "[  89/ 218]                blk.9.ffn_down.weight - [ 4864,   896,     1,     1], type =    f16, converting to q4_K .. size =     8.31 MiB ->     2.34 MiB\n",
            "[  90/ 218]               blk.9.attn_norm.weight - [  896,     1,     1,     1], type =    f32, size =    0.003 MB\n",
            "[  91/ 218]                blk.9.ffn_norm.weight - [  896,     1,     1,     1], type =    f32, size =    0.003 MB\n",
            "[  92/ 218]                 blk.10.attn_q.weight - [  896,   896,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 896 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     1.53 MiB ->     0.53 MiB\n",
            "[  93/ 218]                 blk.10.attn_k.weight - [  896,   128,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 128 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     0.22 MiB ->     0.08 MiB\n",
            "[  94/ 218]                 blk.10.attn_v.weight - [  896,   128,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 128 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     0.22 MiB ->     0.08 MiB\n",
            "[  95/ 218]            blk.10.attn_output.weight - [  896,   896,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 896 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     1.53 MiB ->     0.53 MiB\n",
            "[  96/ 218]               blk.10.ffn_gate.weight - [  896,  4864,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 4864 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     8.31 MiB ->     2.86 MiB\n",
            "[  97/ 218]                 blk.10.ffn_up.weight - [  896,  4864,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 4864 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     8.31 MiB ->     2.86 MiB\n",
            "[  98/ 218]               blk.10.ffn_down.weight - [ 4864,   896,     1,     1], type =    f16, converting to q4_K .. size =     8.31 MiB ->     2.34 MiB\n",
            "[  99/ 218]              blk.10.attn_norm.weight - [  896,     1,     1,     1], type =    f32, size =    0.003 MB\n",
            "[ 100/ 218]               blk.10.ffn_norm.weight - [  896,     1,     1,     1], type =    f32, size =    0.003 MB\n",
            "[ 101/ 218]                 blk.11.attn_q.weight - [  896,   896,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 896 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     1.53 MiB ->     0.53 MiB\n",
            "[ 102/ 218]                 blk.11.attn_k.weight - [  896,   128,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 128 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     0.22 MiB ->     0.08 MiB\n",
            "[ 103/ 218]                 blk.11.attn_v.weight - [  896,   128,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 128 are not divisible by 256, required for q6_K - using fallback quantization q8_0\n",
            "converting to q8_0 .. size =     0.22 MiB ->     0.12 MiB\n",
            "[ 104/ 218]            blk.11.attn_output.weight - [  896,   896,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 896 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     1.53 MiB ->     0.53 MiB\n",
            "[ 105/ 218]               blk.11.ffn_gate.weight - [  896,  4864,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 4864 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     8.31 MiB ->     2.86 MiB\n",
            "[ 106/ 218]                 blk.11.ffn_up.weight - [  896,  4864,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 4864 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     8.31 MiB ->     2.86 MiB\n",
            "[ 107/ 218]               blk.11.ffn_down.weight - [ 4864,   896,     1,     1], type =    f16, converting to q6_K .. size =     8.31 MiB ->     3.41 MiB\n",
            "[ 108/ 218]              blk.11.attn_norm.weight - [  896,     1,     1,     1], type =    f32, size =    0.003 MB\n",
            "[ 109/ 218]               blk.11.ffn_norm.weight - [  896,     1,     1,     1], type =    f32, size =    0.003 MB\n",
            "[ 110/ 218]                 blk.12.attn_q.weight - [  896,   896,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 896 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     1.53 MiB ->     0.53 MiB\n",
            "[ 111/ 218]                 blk.12.attn_k.weight - [  896,   128,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 128 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     0.22 MiB ->     0.08 MiB\n",
            "[ 112/ 218]                 blk.12.attn_v.weight - [  896,   128,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 128 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     0.22 MiB ->     0.08 MiB\n",
            "[ 113/ 218]            blk.12.attn_output.weight - [  896,   896,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 896 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     1.53 MiB ->     0.53 MiB\n",
            "[ 114/ 218]               blk.12.ffn_gate.weight - [  896,  4864,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 4864 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     8.31 MiB ->     2.86 MiB\n",
            "[ 115/ 218]                 blk.12.ffn_up.weight - [  896,  4864,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 4864 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     8.31 MiB ->     2.86 MiB\n",
            "[ 116/ 218]               blk.12.ffn_down.weight - [ 4864,   896,     1,     1], type =    f16, converting to q4_K .. size =     8.31 MiB ->     2.34 MiB\n",
            "[ 117/ 218]              blk.12.attn_norm.weight - [  896,     1,     1,     1], type =    f32, size =    0.003 MB\n",
            "[ 118/ 218]               blk.12.ffn_norm.weight - [  896,     1,     1,     1], type =    f32, size =    0.003 MB\n",
            "[ 119/ 218]                 blk.13.attn_q.weight - [  896,   896,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 896 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     1.53 MiB ->     0.53 MiB\n",
            "[ 120/ 218]                 blk.13.attn_k.weight - [  896,   128,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 128 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     0.22 MiB ->     0.08 MiB\n",
            "[ 121/ 218]                 blk.13.attn_v.weight - [  896,   128,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 128 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     0.22 MiB ->     0.08 MiB\n",
            "[ 122/ 218]            blk.13.attn_output.weight - [  896,   896,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 896 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     1.53 MiB ->     0.53 MiB\n",
            "[ 123/ 218]               blk.13.ffn_gate.weight - [  896,  4864,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 4864 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     8.31 MiB ->     2.86 MiB\n",
            "[ 124/ 218]                 blk.13.ffn_up.weight - [  896,  4864,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 4864 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     8.31 MiB ->     2.86 MiB\n",
            "[ 125/ 218]               blk.13.ffn_down.weight - [ 4864,   896,     1,     1], type =    f16, converting to q4_K .. size =     8.31 MiB ->     2.34 MiB\n",
            "[ 126/ 218]              blk.13.attn_norm.weight - [  896,     1,     1,     1], type =    f32, size =    0.003 MB\n",
            "[ 127/ 218]               blk.13.ffn_norm.weight - [  896,     1,     1,     1], type =    f32, size =    0.003 MB\n",
            "[ 128/ 218]                 blk.14.attn_q.weight - [  896,   896,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 896 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     1.53 MiB ->     0.53 MiB\n",
            "[ 129/ 218]                 blk.14.attn_k.weight - [  896,   128,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 128 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     0.22 MiB ->     0.08 MiB\n",
            "[ 130/ 218]                 blk.14.attn_v.weight - [  896,   128,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 128 are not divisible by 256, required for q6_K - using fallback quantization q8_0\n",
            "converting to q8_0 .. size =     0.22 MiB ->     0.12 MiB\n",
            "[ 131/ 218]            blk.14.attn_output.weight - [  896,   896,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 896 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     1.53 MiB ->     0.53 MiB\n",
            "[ 132/ 218]               blk.14.ffn_gate.weight - [  896,  4864,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 4864 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     8.31 MiB ->     2.86 MiB\n",
            "[ 133/ 218]                 blk.14.ffn_up.weight - [  896,  4864,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 4864 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     8.31 MiB ->     2.86 MiB\n",
            "[ 134/ 218]               blk.14.ffn_down.weight - [ 4864,   896,     1,     1], type =    f16, converting to q6_K .. size =     8.31 MiB ->     3.41 MiB\n",
            "[ 135/ 218]              blk.14.attn_norm.weight - [  896,     1,     1,     1], type =    f32, size =    0.003 MB\n",
            "[ 136/ 218]               blk.14.ffn_norm.weight - [  896,     1,     1,     1], type =    f32, size =    0.003 MB\n",
            "[ 137/ 218]                 blk.15.attn_q.weight - [  896,   896,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 896 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     1.53 MiB ->     0.53 MiB\n",
            "[ 138/ 218]                 blk.15.attn_k.weight - [  896,   128,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 128 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     0.22 MiB ->     0.08 MiB\n",
            "[ 139/ 218]                 blk.15.attn_v.weight - [  896,   128,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 128 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     0.22 MiB ->     0.08 MiB\n",
            "[ 140/ 218]            blk.15.attn_output.weight - [  896,   896,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 896 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     1.53 MiB ->     0.53 MiB\n",
            "[ 141/ 218]               blk.15.ffn_gate.weight - [  896,  4864,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 4864 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     8.31 MiB ->     2.86 MiB\n",
            "[ 142/ 218]                 blk.15.ffn_up.weight - [  896,  4864,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 4864 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     8.31 MiB ->     2.86 MiB\n",
            "[ 143/ 218]               blk.15.ffn_down.weight - [ 4864,   896,     1,     1], type =    f16, converting to q4_K .. size =     8.31 MiB ->     2.34 MiB\n",
            "[ 144/ 218]              blk.15.attn_norm.weight - [  896,     1,     1,     1], type =    f32, size =    0.003 MB\n",
            "[ 145/ 218]               blk.15.ffn_norm.weight - [  896,     1,     1,     1], type =    f32, size =    0.003 MB\n",
            "[ 146/ 218]                 blk.16.attn_q.weight - [  896,   896,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 896 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     1.53 MiB ->     0.53 MiB\n",
            "[ 147/ 218]                 blk.16.attn_k.weight - [  896,   128,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 128 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     0.22 MiB ->     0.08 MiB\n",
            "[ 148/ 218]                 blk.16.attn_v.weight - [  896,   128,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 128 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     0.22 MiB ->     0.08 MiB\n",
            "[ 149/ 218]            blk.16.attn_output.weight - [  896,   896,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 896 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     1.53 MiB ->     0.53 MiB\n",
            "[ 150/ 218]               blk.16.ffn_gate.weight - [  896,  4864,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 4864 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     8.31 MiB ->     2.86 MiB\n",
            "[ 151/ 218]                 blk.16.ffn_up.weight - [  896,  4864,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 4864 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     8.31 MiB ->     2.86 MiB\n",
            "[ 152/ 218]               blk.16.ffn_down.weight - [ 4864,   896,     1,     1], type =    f16, converting to q4_K .. size =     8.31 MiB ->     2.34 MiB\n",
            "[ 153/ 218]              blk.16.attn_norm.weight - [  896,     1,     1,     1], type =    f32, size =    0.003 MB\n",
            "[ 154/ 218]               blk.16.ffn_norm.weight - [  896,     1,     1,     1], type =    f32, size =    0.003 MB\n",
            "[ 155/ 218]                 blk.17.attn_q.weight - [  896,   896,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 896 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     1.53 MiB ->     0.53 MiB\n",
            "[ 156/ 218]                 blk.17.attn_k.weight - [  896,   128,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 128 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     0.22 MiB ->     0.08 MiB\n",
            "[ 157/ 218]                 blk.17.attn_v.weight - [  896,   128,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 128 are not divisible by 256, required for q6_K - using fallback quantization q8_0\n",
            "converting to q8_0 .. size =     0.22 MiB ->     0.12 MiB\n",
            "[ 158/ 218]            blk.17.attn_output.weight - [  896,   896,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 896 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     1.53 MiB ->     0.53 MiB\n",
            "[ 159/ 218]               blk.17.ffn_gate.weight - [  896,  4864,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 4864 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     8.31 MiB ->     2.86 MiB\n",
            "[ 160/ 218]                 blk.17.ffn_up.weight - [  896,  4864,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 4864 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     8.31 MiB ->     2.86 MiB\n",
            "[ 161/ 218]               blk.17.ffn_down.weight - [ 4864,   896,     1,     1], type =    f16, converting to q6_K .. size =     8.31 MiB ->     3.41 MiB\n",
            "[ 162/ 218]              blk.17.attn_norm.weight - [  896,     1,     1,     1], type =    f32, size =    0.003 MB\n",
            "[ 163/ 218]               blk.17.ffn_norm.weight - [  896,     1,     1,     1], type =    f32, size =    0.003 MB\n",
            "[ 164/ 218]                 blk.18.attn_q.weight - [  896,   896,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 896 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     1.53 MiB ->     0.53 MiB\n",
            "[ 165/ 218]                 blk.18.attn_k.weight - [  896,   128,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 128 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     0.22 MiB ->     0.08 MiB\n",
            "[ 166/ 218]                 blk.18.attn_v.weight - [  896,   128,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 128 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     0.22 MiB ->     0.08 MiB\n",
            "[ 167/ 218]            blk.18.attn_output.weight - [  896,   896,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 896 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     1.53 MiB ->     0.53 MiB\n",
            "[ 168/ 218]               blk.18.ffn_gate.weight - [  896,  4864,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 4864 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     8.31 MiB ->     2.86 MiB\n",
            "[ 169/ 218]                 blk.18.ffn_up.weight - [  896,  4864,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 4864 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     8.31 MiB ->     2.86 MiB\n",
            "[ 170/ 218]               blk.18.ffn_down.weight - [ 4864,   896,     1,     1], type =    f16, converting to q4_K .. size =     8.31 MiB ->     2.34 MiB\n",
            "[ 171/ 218]              blk.18.attn_norm.weight - [  896,     1,     1,     1], type =    f32, size =    0.003 MB\n",
            "[ 172/ 218]               blk.18.ffn_norm.weight - [  896,     1,     1,     1], type =    f32, size =    0.003 MB\n",
            "[ 173/ 218]                 blk.19.attn_q.weight - [  896,   896,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 896 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     1.53 MiB ->     0.53 MiB\n",
            "[ 174/ 218]                 blk.19.attn_k.weight - [  896,   128,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 128 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     0.22 MiB ->     0.08 MiB\n",
            "[ 175/ 218]                 blk.19.attn_v.weight - [  896,   128,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 128 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     0.22 MiB ->     0.08 MiB\n",
            "[ 176/ 218]            blk.19.attn_output.weight - [  896,   896,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 896 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     1.53 MiB ->     0.53 MiB\n",
            "[ 177/ 218]               blk.19.ffn_gate.weight - [  896,  4864,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 4864 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     8.31 MiB ->     2.86 MiB\n",
            "[ 178/ 218]                 blk.19.ffn_up.weight - [  896,  4864,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 4864 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     8.31 MiB ->     2.86 MiB\n",
            "[ 179/ 218]               blk.19.ffn_down.weight - [ 4864,   896,     1,     1], type =    f16, converting to q4_K .. size =     8.31 MiB ->     2.34 MiB\n",
            "[ 180/ 218]              blk.19.attn_norm.weight - [  896,     1,     1,     1], type =    f32, size =    0.003 MB\n",
            "[ 181/ 218]               blk.19.ffn_norm.weight - [  896,     1,     1,     1], type =    f32, size =    0.003 MB\n",
            "[ 182/ 218]                 blk.20.attn_q.weight - [  896,   896,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 896 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     1.53 MiB ->     0.53 MiB\n",
            "[ 183/ 218]                 blk.20.attn_k.weight - [  896,   128,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 128 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     0.22 MiB ->     0.08 MiB\n",
            "[ 184/ 218]                 blk.20.attn_v.weight - [  896,   128,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 128 are not divisible by 256, required for q6_K - using fallback quantization q8_0\n",
            "converting to q8_0 .. size =     0.22 MiB ->     0.12 MiB\n",
            "[ 185/ 218]            blk.20.attn_output.weight - [  896,   896,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 896 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     1.53 MiB ->     0.53 MiB\n",
            "[ 186/ 218]               blk.20.ffn_gate.weight - [  896,  4864,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 4864 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     8.31 MiB ->     2.86 MiB\n",
            "[ 187/ 218]                 blk.20.ffn_up.weight - [  896,  4864,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 4864 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     8.31 MiB ->     2.86 MiB\n",
            "[ 188/ 218]               blk.20.ffn_down.weight - [ 4864,   896,     1,     1], type =    f16, converting to q6_K .. size =     8.31 MiB ->     3.41 MiB\n",
            "[ 189/ 218]              blk.20.attn_norm.weight - [  896,     1,     1,     1], type =    f32, size =    0.003 MB\n",
            "[ 190/ 218]               blk.20.ffn_norm.weight - [  896,     1,     1,     1], type =    f32, size =    0.003 MB\n",
            "[ 191/ 218]                 blk.21.attn_q.weight - [  896,   896,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 896 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     1.53 MiB ->     0.53 MiB\n",
            "[ 192/ 218]                 blk.21.attn_k.weight - [  896,   128,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 128 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     0.22 MiB ->     0.08 MiB\n",
            "[ 193/ 218]                 blk.21.attn_v.weight - [  896,   128,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 128 are not divisible by 256, required for q6_K - using fallback quantization q8_0\n",
            "converting to q8_0 .. size =     0.22 MiB ->     0.12 MiB\n",
            "[ 194/ 218]            blk.21.attn_output.weight - [  896,   896,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 896 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     1.53 MiB ->     0.53 MiB\n",
            "[ 195/ 218]               blk.21.ffn_gate.weight - [  896,  4864,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 4864 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     8.31 MiB ->     2.86 MiB\n",
            "[ 196/ 218]                 blk.21.ffn_up.weight - [  896,  4864,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 4864 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     8.31 MiB ->     2.86 MiB\n",
            "[ 197/ 218]               blk.21.ffn_down.weight - [ 4864,   896,     1,     1], type =    f16, converting to q6_K .. size =     8.31 MiB ->     3.41 MiB\n",
            "[ 198/ 218]              blk.21.attn_norm.weight - [  896,     1,     1,     1], type =    f32, size =    0.003 MB\n",
            "[ 199/ 218]               blk.21.ffn_norm.weight - [  896,     1,     1,     1], type =    f32, size =    0.003 MB\n",
            "[ 200/ 218]                 blk.22.attn_q.weight - [  896,   896,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 896 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     1.53 MiB ->     0.53 MiB\n",
            "[ 201/ 218]                 blk.22.attn_k.weight - [  896,   128,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 128 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     0.22 MiB ->     0.08 MiB\n",
            "[ 202/ 218]                 blk.22.attn_v.weight - [  896,   128,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 128 are not divisible by 256, required for q6_K - using fallback quantization q8_0\n",
            "converting to q8_0 .. size =     0.22 MiB ->     0.12 MiB\n",
            "[ 203/ 218]            blk.22.attn_output.weight - [  896,   896,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 896 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     1.53 MiB ->     0.53 MiB\n",
            "[ 204/ 218]               blk.22.ffn_gate.weight - [  896,  4864,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 4864 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     8.31 MiB ->     2.86 MiB\n",
            "[ 205/ 218]                 blk.22.ffn_up.weight - [  896,  4864,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 4864 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     8.31 MiB ->     2.86 MiB\n",
            "[ 206/ 218]               blk.22.ffn_down.weight - [ 4864,   896,     1,     1], type =    f16, converting to q6_K .. size =     8.31 MiB ->     3.41 MiB\n",
            "[ 207/ 218]              blk.22.attn_norm.weight - [  896,     1,     1,     1], type =    f32, size =    0.003 MB\n",
            "[ 208/ 218]               blk.22.ffn_norm.weight - [  896,     1,     1,     1], type =    f32, size =    0.003 MB\n",
            "[ 209/ 218]                 blk.23.attn_q.weight - [  896,   896,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 896 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     1.53 MiB ->     0.53 MiB\n",
            "[ 210/ 218]                 blk.23.attn_k.weight - [  896,   128,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 128 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     0.22 MiB ->     0.08 MiB\n",
            "[ 211/ 218]                 blk.23.attn_v.weight - [  896,   128,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 128 are not divisible by 256, required for q6_K - using fallback quantization q8_0\n",
            "converting to q8_0 .. size =     0.22 MiB ->     0.12 MiB\n",
            "[ 212/ 218]            blk.23.attn_output.weight - [  896,   896,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 896 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     1.53 MiB ->     0.53 MiB\n",
            "[ 213/ 218]               blk.23.ffn_gate.weight - [  896,  4864,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 4864 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     8.31 MiB ->     2.86 MiB\n",
            "[ 214/ 218]                 blk.23.ffn_up.weight - [  896,  4864,     1,     1], type =    f16, \n",
            "\n",
            "llama_tensor_get_type : tensor cols 896 x 4864 are not divisible by 256, required for q4_K - using fallback quantization q5_0\n",
            "converting to q5_0 .. size =     8.31 MiB ->     2.86 MiB\n",
            "[ 215/ 218]               blk.23.ffn_down.weight - [ 4864,   896,     1,     1], type =    f16, converting to q6_K .. size =     8.31 MiB ->     3.41 MiB\n",
            "[ 216/ 218]              blk.23.attn_norm.weight - [  896,     1,     1,     1], type =    f32, size =    0.003 MB\n",
            "[ 217/ 218]               blk.23.ffn_norm.weight - [  896,     1,     1,     1], type =    f32, size =    0.003 MB\n",
            "[ 218/ 218]                   output_norm.weight - [  896,     1,     1,     1], type =    f32, size =    0.003 MB\n",
            "llama_model_quantize_internal: model size  =   942.32 MB\n",
            "llama_model_quantize_internal: quant size  =   373.60 MB\n",
            "llama_model_quantize_internal: WARNING: 144 of 168 tensor(s) required fallback quantization\n",
            "\n",
            "main: quantize time = 23624.52 ms\n",
            "main:    total time = 23624.52 ms\n",
            "Unsloth: Conversion completed! Output location: ./AiisNothing/qwen2-0.5_unsloth_lora_merged_gguf_Q4_K_M/unsloth.Q4_K_M.gguf\n",
            "Unsloth: Uploading GGUF to Huggingface Hub...\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "500d5aa1c006450287b84eeabf0fb8e9",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8e5dbf0fbf294882b1b2569ff2dd47f1",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "unsloth.F16.gguf:   0%|          | 0.00/994M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Saved GGUF to https://huggingface.co/AiisNothing/qwen2-0.5_unsloth_lora_merged_gguf_Q4_K_M\n",
            "Unsloth: Uploading GGUF to Huggingface Hub...\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7701d6c6ae924d698c1437a39dbc31af",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "50231df56d4a4af38beef24685f81881",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "unsloth.Q4_K_M.gguf:   0%|          | 0.00/398M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "No files have been modified since last commit. Skipping to prevent empty commit.\n",
            "WARNING:huggingface_hub.hf_api:No files have been modified since last commit. Skipping to prevent empty commit.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Saved GGUF to https://huggingface.co/AiisNothing/qwen2-0.5_unsloth_lora_merged_gguf_Q4_K_M\n"
          ]
        }
      ],
      "source": [
        "# model.push_to_hub(\"AiisNothing/qwen2-0.5_unsloth_lora_merged\", token = \"\") # Online saving\n",
        "# tokenizer.push_to_hub(\"AiisNothing/qwen2-0.5_unsloth_lora_merged\", token = \"\") # Online saving\n",
        "model.push_to_hub_gguf(\"AiisNothing/qwen2-0.5_unsloth_lora_merged_gguf_Q4_K_M\", tokenizer, quantization_method = \"q4_k_m\", token = \"\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 219
        },
        "id": "wom_tGB_Gj7R",
        "outputId": "09ff0827-0863-4fee-b0fe-7ae2ced8f7ce"
      },
      "outputs": [
        {
          "ename": "TypeError",
          "evalue": "Llama.from_pretrained() missing 1 required positional argument: 'repo_id'",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-24-f11946c0d1fe>\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mllama_cpp\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mLlama\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m llm = Llama.from_pretrained(\n\u001b[0m\u001b[1;32m      4\u001b[0m         \u001b[0mfilename\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"/content/model/unsloth.Q4_K_M.gguf\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m )\n",
            "\u001b[0;31mTypeError\u001b[0m: Llama.from_pretrained() missing 1 required positional argument: 'repo_id'"
          ]
        }
      ],
      "source": [
        "from llama_cpp import Llama\n",
        "\n",
        "llm = Llama.from_pretrained(\n",
        "\tfilename=\"/content/model/unsloth.Q4_K_M.gguf\",\n",
        ")\n",
        "\n",
        "llm.create_chat_completion(\n",
        "\tmessages = [\n",
        "\t\t{\n",
        "\t\t\t\"role\": \"user\",\n",
        "\t\t\t\"content\": \"What is the capital of France?\"\n",
        "\t\t}\n",
        "\t]\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OIo823ZiLJeo"
      },
      "outputs": [],
      "source": [
        "import requests\n",
        "\n",
        "API_URL = \"https://api-inference.huggingface.co/models/black-forest-labs/FLUX.1-dev\"\n",
        "headers = {\"Authorization\": \"Bearer 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\"}\n",
        "\n",
        "def query(payload):\n",
        "\tresponse = requests.post(API_URL, headers=headers, json=payload)\n",
        "\treturn response.content\n",
        "image_bytes = query({\n",
        "\t\"inputs\": \"view of new delhi\",\n",
        "})\n",
        "# You can access the image with PIL.Image for example\n",
        "import io\n",
        "from PIL import Image\n",
        "image = Image.open(io.BytesIO(image_bytes))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BHvpYXoOLoeP"
      },
      "outputs": [],
      "source": [
        "image"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bDp0zNpwe6U_"
      },
      "source": [
        "Now, use the `model-unsloth.gguf` file or `model-unsloth-Q4_K_M.gguf` file in `llama.cpp` or a UI based system like `GPT4All`. You can install GPT4All by going [here](https://gpt4all.io/index.html).\n",
        "\n",
        "**[NEW] Try 2x faster inference in a free Colab for Llama-3.1 8b Instruct [here](https://colab.research.google.com/drive/1T-YBVfnphoVc8E2E854qF3jdia2Ll2W2?usp=sharing)**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zt9CHJqO6p30"
      },
      "source": [
        "And we're done! If you have any questions on Unsloth, we have a [Discord](https://discord.gg/u54VK8m8tk) channel! If you find any bugs or want to keep updated with the latest LLM stuff, or need help, join projects etc, feel free to join our Discord!\n",
        "\n",
        "Some other links:\n",
        "1. Zephyr DPO 2x faster [free Colab](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing)\n",
        "2. Llama 7b 2x faster [free Colab](https://colab.research.google.com/drive/1lBzz5KeZJKXjvivbYvmGarix9Ao6Wxe5?usp=sharing)\n",
        "3. TinyLlama 4x faster full Alpaca 52K in 1 hour [free Colab](https://colab.research.google.com/drive/1AZghoNBQaMDgWJpi4RbffGM1h6raLUj9?usp=sharing)\n",
        "4. CodeLlama 34b 2x faster [A100 on Colab](https://colab.research.google.com/drive/1y7A0AxE3y8gdj4AVkl2aZX47Xu3P1wJT?usp=sharing)\n",
        "5. Mistral 7b [free Kaggle version](https://www.kaggle.com/code/danielhanchen/kaggle-mistral-7b-unsloth-notebook)\n",
        "6. We also did a [blog](https://huggingface.co/blog/unsloth-trl) with 🤗 HuggingFace, and we're in the TRL [docs](https://huggingface.co/docs/trl/main/en/sft_trainer#accelerate-fine-tuning-2x-using-unsloth)!\n",
        "7. `ChatML` for ShareGPT datasets, [conversational notebook](https://colab.research.google.com/drive/1Aau3lgPzeZKQ-98h69CCu1UJcvIBLmy2?usp=sharing)\n",
        "8. Text completions like novel writing [notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing)\n",
        "9. [**NEW**] We make Phi-3 Medium / Mini **2x faster**! See our [Phi-3 Medium notebook](https://colab.research.google.com/drive/1hhdhBa1j_hsymiW9m-WzxQtgqTH_NHqi?usp=sharing)\n",
        "10. [**NEW**] We make Gemma-2 9b / 27b **2x faster**! See our [Gemma-2 9b notebook](https://colab.research.google.com/drive/1vIrqH5uYDQwsJ4-OO3DErvuv4pBgVwk4?usp=sharing)\n",
        "11. [**NEW**] To finetune and auto export to Ollama, try our [Ollama notebook](https://colab.research.google.com/drive/1WZDi7APtQ9VsvOrQSSC5DDtxq159j8iZ?usp=sharing)\n",
        "12. [**NEW**] We make Mistral NeMo 12B 2x faster and fit in under 12GB of VRAM! [Mistral NeMo notebook](https://colab.research.google.com/drive/17d3U-CAIwzmbDRqbZ9NnpHxCkmXB6LZ0?usp=sharing)\n",
        "13. [**NEW**] Llama 3.1 8b, 70b and 405b is here! We make it 2x faster and use 60% less VRAM. [Llama 3.1 8b notebook](https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing)\n",
        "\n",
        "<div class=\"align-center\">\n",
        "  <a href=\"https://github.com/unslothai/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png\" width=\"115\"></a>\n",
        "  <a href=\"https://discord.gg/u54VK8m8tk\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Discord.png\" width=\"145\"></a>\n",
        "  <a href=\"https://ko-fi.com/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Kofi button.png\" width=\"145\"></a></a> Support our work if you can! Thanks!\n",
        "</div>"
      ]
    }
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