Copy_of_Alpaca_+_Mistral_v3_7b_full_example.ipynb

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        "<a href=\"https://colab.research.google.com/github/feimaogit/A1111-Web-UI-Installer/blob/main/Copy_of_Alpaca_%2B_Mistral_v3_7b_full_example.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "source": [
        "# 🔥🔥🔥本项目代码由AI超元域频道制作，观看更多大模型微调视频请访问我的频道⬇\n",
        "# 👉👉👉[我的哔哩哔哩频道](https://space.bilibili.com/3493277319825652)\n",
        "# 👉👉👉[我的YouTube频道](https://www.youtube.com/@AIsuperdomain)"
      ],
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      "source": [
        "%%capture\n",
        "# Installs Unsloth, Xformers (Flash Attention) and all other packages!\n",
        "!pip install \"unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git\"\n",
        "!pip install --no-deps xformers trl peft accelerate bitsandbytes"
      ]
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        "id": "r2v_X2fA0Df5"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Unsloth: You passed in `unsloth/mistral-7b-v0.3` and `load_in_4bit = True`.\n",
            "We shall load `unsloth/mistral-7b-v0.3-bnb-4bit` for 4x faster loading.\n"
          ]
        },
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          "output_type": "display_data",
          "data": {
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          "text": [
            "==((====))==  Unsloth: Fast Mistral patching release 2024.5\n",
            "   \\\\   /|    GPU: Tesla T4. Max memory: 14.748 GB. Platform = Linux.\n",
            "O^O/ \\_/ \\    Pytorch: 2.3.0+cu121. CUDA = 7.5. CUDA Toolkit = 12.1.\n",
            "\\        /    Bfloat16 = FALSE. Xformers = 0.0.26.post1. FA = False.\n",
            " \"-____-\"     Free Apache license: http://github.com/unslothai/unsloth\n"
          ]
        },
        {
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          "metadata": {}
        },
        {
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You set `add_prefix_space`. The tokenizer needs to be converted from the slow tokenizers\n",
            "You are using the default legacy behaviour of the <class 'transformers.models.llama.tokenization_llama.LlamaTokenizer'>. This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thoroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565\n"
          ]
        }
      ],
      "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 = True # 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/mistral-7b-bnb-4bit\",\n",
        "    \"unsloth/mistral-7b-instruct-v0.2-bnb-4bit\",\n",
        "    \"unsloth/llama-2-7b-bnb-4bit\",\n",
        "    \"unsloth/llama-2-13b-bnb-4bit\",\n",
        "    \"unsloth/codellama-34b-bnb-4bit\",\n",
        "    \"unsloth/tinyllama-bnb-4bit\",\n",
        "    \"unsloth/gemma-7b-bnb-4bit\", # New Google 6 trillion tokens model 2.5x faster!\n",
        "    \"unsloth/gemma-2b-bnb-4bit\",\n",
        "] # More models at https://huggingface.co/unsloth\n",
        "\n",
        "model, tokenizer = FastLanguageModel.from_pretrained(\n",
        "    model_name = \"unsloth/mistral-7b-v0.3\", # Choose ANY! eg teknium/OpenHermes-2.5-Mistral-7B\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",
      "source": [
        "We now add LoRA adapters so we only need to update 1 to 10% of all parameters!"
      ],
      "metadata": {
        "id": "SXd9bTZd1aaL"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6bZsfBuZDeCL",
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          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a3b7a9ae-e2fa-4ff0-f28a-f45bc9c27fad"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Unsloth 2024.5 patched 32 layers with 32 QKV layers, 32 O layers and 32 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",
      "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 `ChatML` template for ShareGPT datasets, try our conversational [notebook](https://colab.research.google.com/drive/1Aau3lgPzeZKQ-98h69CCu1UJcvIBLmy2?usp=sharing).\n",
        "\n",
        "For text completions like novel writing, try this [notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing)."
      ],
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      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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      "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",
        "\n",
        "EOS_TOKEN = tokenizer.eos_token # Must add EOS_TOKEN\n",
        "def formatting_prompts_func(examples):\n",
        "    instructions = examples[\"instruction\"]\n",
        "    inputs       = examples[\"input\"]\n",
        "    outputs      = examples[\"output\"]\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(\"leo009/alpaca-cleaned-zh-cn\", split = \"train\")\n",
        "dataset = dataset.map(formatting_prompts_func, batched = True,)"
      ]
    },
    {
      "cell_type": "markdown",
      "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`!"
      ],
      "metadata": {
        "id": "idAEIeSQ3xdS"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "95_Nn-89DhsL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 123,
          "referenced_widgets": [
            "b5354630bfe74aed9f1e651a639dff59",
            "c2d227b1ab6e43eea829604d4605f4aa",
            "88ca05ab233f4df786006d4c4e38f22c",
            "0dd7df9b4ca641fd9f5b896ea671edf9",
            "9fa5668b75aa4c4daf87dae7fe4c6bcf",
            "e542e20896a14ae682f9d3882f3cc5b8",
            "fe02044b1ebb43ad9f1149239894ba13",
            "7f5f1804231b46a78163febcc515b5b3",
            "97b3a0679a594c968a7278791f993b35",
            "d8daeb433f814700a4802aba0cf5658c",
            "ea9da295a77147368d80362160b1fce3"
          ]
        },
        "outputId": "f6a55e4c-5a77-47da-d619-57301ab4c287"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/multiprocess/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
            "  self.pid = os.fork()\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map (num_proc=2):   0%|          | 0/51760 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b5354630bfe74aed9f1e651a639dff59"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "max_steps is given, it will override any value given in num_train_epochs\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 = dataset,\n",
        "    dataset_text_field = \"text\",\n",
        "    max_seq_length = max_seq_length,\n",
        "    dataset_num_proc = 2,\n",
        "    packing = False, # Can make training 5x faster for short sequences.\n",
        "    args = TrainingArguments(\n",
        "        per_device_train_batch_size = 2,\n",
        "        gradient_accumulation_steps = 4,\n",
        "        warmup_steps = 5,\n",
        "        max_steps = 60, # Set num_train_epochs = 1 for full training runs\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",
        "    ),\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2ejIt2xSNKKp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5681268c-3a86-4d5e-9e48-6a765769a821"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GPU = Tesla T4. Max memory = 14.748 GB.\n",
            "4.52 GB of memory reserved.\n"
          ]
        }
      ],
      "source": [
        "#@title Show current memory stats\n",
        "gpu_stats = torch.cuda.get_device_properties(0)\n",
        "start_gpu_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n",
        "max_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3)\n",
        "print(f\"GPU = {gpu_stats.name}. Max memory = {max_memory} GB.\")\n",
        "print(f\"{start_gpu_memory} GB of memory reserved.\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yqxqAZ7KJ4oL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "c859c48c-433b-45eb-c2b3-9188059ac6fb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "==((====))==  Unsloth - 2x faster free finetuning | Num GPUs = 1\n",
            "   \\\\   /|    Num examples = 51,760 | Num Epochs = 1\n",
            "O^O/ \\_/ \\    Batch size per device = 2 | Gradient Accumulation steps = 4\n",
            "\\        /    Total batch size = 8 | Total steps = 60\n",
            " \"-____-\"     Number of trainable parameters = 41,943,040\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='60' max='60' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [60/60 10:49, Epoch 0/1]\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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>1.595500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>1.931600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>1.532700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>1.720100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>1.395600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>1.356600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>1.186700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>1.240100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>1.176400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>1.291400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11</td>\n",
              "      <td>1.106100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12</td>\n",
              "      <td>1.050700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13</td>\n",
              "      <td>1.133700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14</td>\n",
              "      <td>1.212500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15</td>\n",
              "      <td>1.074000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16</td>\n",
              "      <td>1.042900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>17</td>\n",
              "      <td>1.032400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>18</td>\n",
              "      <td>1.314400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>19</td>\n",
              "      <td>1.077700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>1.159800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>21</td>\n",
              "      <td>1.171900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>22</td>\n",
              "      <td>1.057700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>23</td>\n",
              "      <td>0.940100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>24</td>\n",
              "      <td>1.140000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>25</td>\n",
              "      <td>1.175400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>26</td>\n",
              "      <td>1.316800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>27</td>\n",
              "      <td>1.144300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>28</td>\n",
              "      <td>1.224600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>29</td>\n",
              "      <td>1.040000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>30</td>\n",
              "      <td>1.088100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>31</td>\n",
              "      <td>1.077000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>32</td>\n",
              "      <td>1.019000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>33</td>\n",
              "      <td>1.082700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>34</td>\n",
              "      <td>0.979600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>35</td>\n",
              "      <td>1.068900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>36</td>\n",
              "      <td>0.899500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>37</td>\n",
              "      <td>1.006200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>38</td>\n",
              "      <td>1.042300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>39</td>\n",
              "      <td>1.222600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>40</td>\n",
              "      <td>1.162700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>41</td>\n",
              "      <td>1.068400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>42</td>\n",
              "      <td>1.087300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>43</td>\n",
              "      <td>1.009500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>44</td>\n",
              "      <td>1.046900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>45</td>\n",
              "      <td>1.015900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>46</td>\n",
              "      <td>1.043300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>47</td>\n",
              "      <td>1.036800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>48</td>\n",
              "      <td>1.268500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>49</td>\n",
              "      <td>1.010800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>50</td>\n",
              "      <td>1.174200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>51</td>\n",
              "      <td>1.032100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>52</td>\n",
              "      <td>1.091400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>53</td>\n",
              "      <td>1.088900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>54</td>\n",
              "      <td>1.143100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>55</td>\n",
              "      <td>0.938100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>56</td>\n",
              "      <td>1.098000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>57</td>\n",
              "      <td>1.086500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>58</td>\n",
              "      <td>0.951700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>59</td>\n",
              "      <td>0.993500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>60</td>\n",
              "      <td>1.111100</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "trainer_stats = trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pCqnaKmlO1U9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "572202bc-76dd-43f8-d6d3-39af99d0cf52"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "666.46 seconds used for training.\n",
            "11.11 minutes used for training.\n",
            "Peak reserved memory = 5.877 GB.\n",
            "Peak reserved memory for training = 1.357 GB.\n",
            "Peak reserved memory % of max memory = 39.849 %.\n",
            "Peak reserved memory for training % of max memory = 9.201 %.\n"
          ]
        }
      ],
      "source": [
        "#@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",
      "source": [
        "<a name=\"Inference\"></a>\n",
        "### Inference\n",
        "Let's run the model! You can change the instruction and input - leave the output blank!"
      ],
      "metadata": {
        "id": "ekOmTR1hSNcr"
      }
    },
    {
      "cell_type": "code",
      "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",
        "        \"三种主要颜色是什么?\", # instruction\n",
        "        \"\", # 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)"
      ],
      "metadata": {
        "id": "kR3gIAX-SM2q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3b3caeed-7a7e-42cc-e3b5-c76b8a023d57"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['<s> 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三种主要颜色是什么?\\n\\n### Input:\\n\\n\\n### Response:\\n三种主要颜色是红色, 蓝色和黄色。</s>']"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "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!"
      ],
      "metadata": {
        "id": "CrSvZObor0lY"
      }
    },
    {
      "cell_type": "code",
      "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",
        "        \"三种主要颜色是什么?\", # 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)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e2pEuRb1r2Vg",
        "outputId": "60ac3ac1-1164-475e-8135-eca2a62b0fb2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<s> 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",
            "三种主要颜色是什么?\n",
            "\n",
            "### Input:\n",
            "\n",
            "\n",
            "### Response:\n",
            "三种主要颜色是红色, 蓝色和黄色。</s>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "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!"
      ],
      "metadata": {
        "id": "uMuVrWbjAzhc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.save_pretrained(\"lora_model\") # Local saving\n",
        "tokenizer.save_pretrained(\"lora_model\")\n",
        "# model.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving\n",
        "# tokenizer.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving"
      ],
      "metadata": {
        "id": "upcOlWe7A1vc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6e76052e-745d-41d9-b5fc-6026d27c99ff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('lora_model/tokenizer_config.json',\n",
              " 'lora_model/special_tokens_map.json',\n",
              " 'lora_model/tokenizer.model',\n",
              " 'lora_model/added_tokens.json',\n",
              " 'lora_model/tokenizer.json')"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now if you want to load the LoRA adapters we just saved for inference, set `False` to `True`:"
      ],
      "metadata": {
        "id": "AEEcJ4qfC7Lp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "if True:\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 = load_in_4bit,\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",
        "        \"给出三个保持健康的提示。\", # instruction\n",
        "        \"\", # input\n",
        "        \"\", # output - leave this blank for generation!\n",
        "    ),\n",
        "], return_tensors = \"pt\").to(\"cuda\")\n",
        "\n",
        "outputs = model.generate(**inputs, max_new_tokens = 512, use_cache = True)\n",
        "tokenizer.batch_decode(outputs)"
      ],
      "metadata": {
        "id": "MKX_XKs_BNZR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b722c5f3-13fc-4e91-de53-40954d6f0a1a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "==((====))==  Unsloth: Fast Mistral patching release 2024.5\n",
            "   \\\\   /|    GPU: Tesla T4. Max memory: 14.748 GB. Platform = Linux.\n",
            "O^O/ \\_/ \\    Pytorch: 2.3.0+cu121. CUDA = 7.5. CUDA Toolkit = 12.1.\n",
            "\\        /    Bfloat16 = FALSE. Xformers = 0.0.26.post1. FA = False.\n",
            " \"-____-\"     Free Apache license: http://github.com/unslothai/unsloth\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['<s> 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给出三个保持健康的提示。\\n\\n### Input:\\n\\n\\n### Response:\\n1 。 锻炼: 锻炼有助于提高身体的健康, 增加耐力, 减少脂肪, 并提高心脏健康。 \\n\\n2. 良好的营养: 良好的营养是保持健康的关键。 包括大量水果, 蔬菜, 蛋白质, 纤维, 脂肪, 碳水化合物和矿物质在内的健康食物。 \\n\\n3. 良好的睡眠: 睡眠是重要的, 因为它有助于恢复身体, 提高注意力, 提高心理健康。 建议每天至少 7 小时的睡眠。</s>']"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "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**."
      ],
      "metadata": {
        "id": "QQMjaNrjsU5_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "if True:\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\")"
      ],
      "metadata": {
        "id": "yFfaXG0WsQuE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "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."
      ],
      "metadata": {
        "id": "f422JgM9sdVT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Merge to 16bit\n",
        "if True: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"merged_16bit\",)\n",
        "if True: model.push_to_hub_merged(\"leo009/model\", tokenizer, save_method = \"merged_16bit\", token = \"\")\n",
        "\n",
        "# Merge to 4bit\n",
        "if True: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"merged_4bit\",)\n",
        "if True: model.push_to_hub_merged(\"leo009/model\", tokenizer, save_method = \"merged_4bit\", token = \"\")\n",
        "\n",
        "# Just LoRA adapters\n",
        "if True: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"lora\",)\n",
        "if True: model.push_to_hub_merged(\"leo009/model\", tokenizer, save_method = \"lora\", token = \"\")"
      ],
      "metadata": {
        "id": "iHjt_SMYsd3P"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "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."
      ],
      "metadata": {
        "id": "TCv4vXHd61i7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Save to 8bit Q8_0\n",
        "if True: model.save_pretrained_gguf(\"model\", tokenizer,)\n",
        "if True: model.push_to_hub_gguf(\"leo009/mistral-7b-v3\", tokenizer, token = \"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\")\n",
        "\n",
        "# Save to 16bit GGUF\n",
        "if True: model.save_pretrained_gguf(\"model\", tokenizer, quantization_method = \"f16\")\n",
        "if True: model.push_to_hub_gguf(\"leo009/mistral-7b-v3\", tokenizer, quantization_method = \"f16\", token = \"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\")\n",
        "\n",
        "# Save to q4_k_m GGUF\n",
        "if True: model.save_pretrained_gguf(\"model\", tokenizer, quantization_method = \"q4_k_m\")\n",
        "if True: model.push_to_hub_gguf(\"leo009/mistral-7b-v3\", tokenizer, quantization_method = \"q4_k_m\", token = \"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\")\n",
        "if True: model.push_to_hub_gguf(\"leo009/mistral-7b-v3\", tokenizer, quantization_method = \"q5_k_m\", token = \"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\")"
      ],
      "metadata": {
        "id": "FqfebeAdT073",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "acac9324bf2a4fa89786e7374604fd09",
            "3f49a240e14a4f048a84d10c0e9854ee",
            "b65cbb84013f4b469ba28a7e7271fba9",
            "95c2dd2afd5a496da2806bd018da8302",
            "1e6e507ee18847bcad60f430dd8531a1",
            "b655b169d1dd407aaacc3e430477a84b",
            "99f4b0cb6fa945e68e0b1c5f3524417a",
            "5eadf09851b8453c87c3098b89c710d1",
            "0cef0e6a910145c88a4b0a043feb2fe6",
            "d418c89355c74b709150eb9bc2e94af3",
            "8ff493222d2e40388cce42cb1035353f"
          ]
        },
        "outputId": "d29f97a9-4be4-4706-d6e0-7c997896a824"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "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 4.1G\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Unsloth: Merging 4bit and LoRA weights to 16bit...\n",
            "Unsloth: Will use up to 5.9 out of 12.67 RAM for saving.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            " 19%|█▉        | 6/32 [00:00<00:02,  9.34it/s]We will save to Disk and not RAM now.\n",
            "100%|██████████| 32/32 [02:30<00:00,  4.70s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Unsloth: Saving tokenizer... Done.\n",
            "Unsloth: Saving model... This might take 5 minutes for Llama-7b...\n",
            "Unsloth: Saving model/pytorch_model-00001-of-00003.bin...\n",
            "Unsloth: Saving model/pytorch_model-00002-of-00003.bin...\n",
            "Unsloth: Saving model/pytorch_model-00003-of-00003.bin...\n",
            "Done.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Unsloth: Converting mistral model. Can use fast conversion = True.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "==((====))==  Unsloth: Conversion from QLoRA to GGUF information\n",
            "   \\\\   /|    [0] Installing llama.cpp will take 3 minutes.\n",
            "O^O/ \\_/ \\    [1] Converting HF to GUUF 16bits will take 3 minutes.\n",
            "\\        /    [2] Converting GGUF 16bits to q8_0 will take 20 minutes.\n",
            " \"-____-\"     In total, you will have to wait around 26 minutes.\n",
            "\n",
            "Unsloth: [0] Installing llama.cpp. This will take 3 minutes...\n",
            "Unsloth: [1] Converting model at model into q8_0 GGUF format.\n",
            "The output location will be ./model-unsloth.Q8_0.gguf\n",
            "This will take 3 minutes...\n",
            "INFO:convert:Loading model file model/pytorch_model-00001-of-00003.bin\n",
            "INFO:convert:Loading model file model/pytorch_model-00001-of-00003.bin\n",
            "INFO:convert:Loading model file model/pytorch_model-00002-of-00003.bin\n",
            "INFO:convert:Loading model file model/pytorch_model-00003-of-00003.bin\n",
            "INFO:convert:model parameters count : 7248023552 (7B)\n",
            "INFO:convert:params = Params(n_vocab=32768, n_embd=4096, n_layer=32, n_ctx=32768, n_ff=14336, n_head=32, n_head_kv=8, n_experts=None, n_experts_used=None, f_norm_eps=1e-05, rope_scaling_type=None, f_rope_freq_base=1000000.0, f_rope_scale=None, n_orig_ctx=None, rope_finetuned=None, ftype=<GGMLFileType.MostlyQ8_0: 7>, path_model=PosixPath('model'))\n",
            "INFO:convert:Loaded vocab file PosixPath('model/tokenizer.model'), type 'spm'\n",
            "INFO:convert:Vocab info: <SentencePieceVocab with 32768 base tokens and 0 added tokens>\n",
            "INFO:convert:Special vocab info: <SpecialVocab with 0 merges, special tokens {'bos': 1, 'eos': 2, 'unk': 0, 'pad': 770}, add special tokens {'bos': True, 'eos': False}>\n",
            "INFO:convert:Writing model-unsloth.Q8_0.gguf, format 7\n",
            "WARNING:convert:Ignoring added_tokens.json since model matches vocab size without it.\n",
            "INFO:gguf.gguf_writer:gguf: This GGUF file is for Little Endian only\n",
            "INFO:gguf.vocab:Setting special token type bos to 1\n",
            "INFO:gguf.vocab:Setting special token type eos to 2\n",
            "INFO:gguf.vocab:Setting special token type unk to 0\n",
            "INFO:gguf.vocab:Setting special token type pad to 770\n",
            "INFO:gguf.vocab:Setting add_bos_token to True\n",
            "INFO:gguf.vocab:Setting add_eos_token to False\n",
            "/content/llama.cpp/convert.py:106: RuntimeWarning: invalid value encountered in divide\n",
            "  qs = (blocks / d[:, None]).round()\n",
            "INFO:convert:[  1/291] Writing tensor token_embd.weight                      | size  32768 x   4096  | type Q8_0 | T+  17\n",
            "INFO:convert:[  2/291] Writing tensor blk.0.attn_q.weight                    | size   4096 x   4096  | type Q8_0 | T+  17\n",
            "INFO:convert:[  3/291] Writing tensor blk.0.attn_k.weight                    | size   1024 x   4096  | type Q8_0 | T+  17\n",
            "INFO:convert:[  4/291] Writing tensor blk.0.attn_v.weight                    | size   1024 x   4096  | type Q8_0 | T+  17\n",
            "INFO:convert:[  5/291] Writing tensor blk.0.attn_output.weight               | size   4096 x   4096  | type Q8_0 | T+  19\n",
            "INFO:convert:[  6/291] Writing tensor blk.0.ffn_gate.weight                  | size  14336 x   4096  | type Q8_0 | T+  27\n",
            "INFO:convert:[  7/291] Writing tensor blk.0.ffn_up.weight                    | size  14336 x   4096  | type Q8_0 | T+  28\n",
            "INFO:convert:[  8/291] Writing tensor blk.0.ffn_down.weight                  | size   4096 x  14336  | type Q8_0 | T+  29\n",
            "INFO:convert:[  9/291] Writing tensor blk.0.attn_norm.weight                 | size   4096           | type F32  | T+  33\n",
            "INFO:convert:[ 10/291] Writing tensor blk.0.ffn_norm.weight                  | size   4096           | type F32  | T+  33\n",
            "INFO:convert:[ 11/291] Writing tensor blk.1.attn_q.weight                    | size   4096 x   4096  | type Q8_0 | T+  34\n",
            "INFO:convert:[ 12/291] Writing tensor blk.1.attn_k.weight                    | size   1024 x   4096  | type Q8_0 | T+  36\n",
            "INFO:convert:[ 13/291] Writing tensor blk.1.attn_v.weight                    | size   1024 x   4096  | type Q8_0 | T+  37\n",
            "INFO:convert:[ 14/291] Writing tensor blk.1.attn_output.weight               | size   4096 x   4096  | type Q8_0 | T+  37\n",
            "INFO:convert:[ 15/291] Writing tensor blk.1.ffn_gate.weight                  | size  14336 x   4096  | type Q8_0 | T+  46\n",
            "INFO:convert:[ 16/291] Writing tensor blk.1.ffn_up.weight                    | size  14336 x   4096  | type Q8_0 | T+  47\n",
            "INFO:convert:[ 17/291] Writing tensor blk.1.ffn_down.weight                  | size   4096 x  14336  | type Q8_0 | T+  48\n",
            "INFO:convert:[ 18/291] Writing tensor blk.1.attn_norm.weight                 | size   4096           | type F32  | T+  55\n",
            "INFO:convert:[ 19/291] Writing tensor blk.1.ffn_norm.weight                  | size   4096           | type F32  | T+  55\n",
            "INFO:convert:[ 20/291] Writing tensor blk.2.attn_q.weight                    | size   4096 x   4096  | type Q8_0 | T+  55\n",
            "INFO:convert:[ 21/291] Writing tensor blk.2.attn_k.weight                    | size   1024 x   4096  | type Q8_0 | T+  72\n",
            "INFO:convert:[ 22/291] Writing tensor blk.2.attn_v.weight                    | size   1024 x   4096  | type Q8_0 | T+  83\n",
            "INFO:convert:[ 23/291] Writing tensor blk.2.attn_output.weight               | size   4096 x   4096  | type Q8_0 | T+  83\n",
            "INFO:convert:[ 24/291] Writing tensor blk.2.ffn_gate.weight                  | size  14336 x   4096  | type Q8_0 | T+  83\n",
            "INFO:convert:[ 25/291] Writing tensor blk.2.ffn_up.weight                    | size  14336 x   4096  | type Q8_0 | T+  84\n",
            "INFO:convert:[ 26/291] Writing tensor blk.2.ffn_down.weight                  | size   4096 x  14336  | type Q8_0 | T+  88\n",
            "INFO:convert:[ 27/291] Writing tensor blk.2.attn_norm.weight                 | size   4096           | type F32  | T+  89\n",
            "INFO:convert:[ 28/291] Writing tensor blk.2.ffn_norm.weight                  | size   4096           | type F32  | T+  89\n",
            "INFO:convert:[ 29/291] Writing tensor blk.3.attn_q.weight                    | size   4096 x   4096  | type Q8_0 | T+  91\n",
            "INFO:convert:[ 30/291] Writing tensor blk.3.attn_k.weight                    | size   1024 x   4096  | type Q8_0 | T+  92\n",
            "INFO:convert:[ 31/291] Writing tensor blk.3.attn_v.weight                    | size   1024 x   4096  | type Q8_0 | T+  92\n",
            "INFO:convert:[ 32/291] Writing tensor blk.3.attn_output.weight               | size   4096 x   4096  | type Q8_0 | T+  93\n",
            "INFO:convert:[ 33/291] Writing tensor blk.3.ffn_gate.weight                  | size  14336 x   4096  | type Q8_0 | T+ 100\n",
            "INFO:convert:[ 34/291] Writing tensor blk.3.ffn_up.weight                    | size  14336 x   4096  | type Q8_0 | T+ 102\n",
            "INFO:convert:[ 35/291] Writing tensor blk.3.ffn_down.weight                  | size   4096 x  14336  | type Q8_0 | T+ 103\n",
            "INFO:convert:[ 36/291] Writing tensor blk.3.attn_norm.weight                 | size   4096           | type F32  | T+ 103\n",
            "INFO:convert:[ 37/291] Writing tensor blk.3.ffn_norm.weight                  | size   4096           | type F32  | T+ 104\n",
            "INFO:convert:[ 38/291] Writing tensor blk.4.attn_q.weight                    | size   4096 x   4096  | type Q8_0 | T+ 106\n",
            "INFO:convert:[ 39/291] Writing tensor blk.4.attn_k.weight                    | size   1024 x   4096  | type Q8_0 | T+ 107\n",
            "INFO:convert:[ 40/291] Writing tensor blk.4.attn_v.weight                    | size   1024 x   4096  | type Q8_0 | T+ 107\n",
            "INFO:convert:[ 41/291] Writing tensor blk.4.attn_output.weight               | size   4096 x   4096  | type Q8_0 | T+ 108\n",
            "INFO:convert:[ 42/291] Writing tensor blk.4.ffn_gate.weight                  | size  14336 x   4096  | type Q8_0 | T+ 115\n",
            "INFO:convert:[ 43/291] Writing tensor blk.4.ffn_up.weight                    | size  14336 x   4096  | type Q8_0 | T+ 118\n",
            "INFO:convert:[ 44/291] Writing tensor blk.4.ffn_down.weight                  | size   4096 x  14336  | type Q8_0 | T+ 119\n",
            "INFO:convert:[ 45/291] Writing tensor blk.4.attn_norm.weight                 | size   4096           | type F32  | T+ 146\n",
            "INFO:convert:[ 46/291] Writing tensor blk.4.ffn_norm.weight                  | size   4096           | type F32  | T+ 146\n",
            "INFO:convert:[ 47/291] Writing tensor blk.5.attn_q.weight                    | size   4096 x   4096  | type Q8_0 | T+ 146\n",
            "INFO:convert:[ 48/291] Writing tensor blk.5.attn_k.weight                    | size   1024 x   4096  | type Q8_0 | T+ 155\n",
            "INFO:convert:[ 49/291] Writing tensor blk.5.attn_v.weight                    | size   1024 x   4096  | type Q8_0 | T+ 157\n",
            "INFO:convert:[ 50/291] Writing tensor blk.5.attn_output.weight               | size   4096 x   4096  | type Q8_0 | T+ 158\n",
            "INFO:convert:[ 51/291] Writing tensor blk.5.ffn_gate.weight                  | size  14336 x   4096  | type Q8_0 | T+ 158\n",
            "INFO:convert:[ 52/291] Writing tensor blk.5.ffn_up.weight                    | size  14336 x   4096  | type Q8_0 | T+ 163\n",
            "INFO:convert:[ 53/291] Writing tensor blk.5.ffn_down.weight                  | size   4096 x  14336  | type Q8_0 | T+ 165\n",
            "INFO:convert:[ 54/291] Writing tensor blk.5.attn_norm.weight                 | size   4096           | type F32  | T+ 165\n",
            "INFO:convert:[ 55/291] Writing tensor blk.5.ffn_norm.weight                  | size   4096           | type F32  | T+ 165\n",
            "INFO:convert:[ 56/291] Writing tensor blk.6.attn_q.weight                    | size   4096 x   4096  | type Q8_0 | T+ 165\n",
            "INFO:convert:[ 57/291] Writing tensor blk.6.attn_k.weight                    | size   1024 x   4096  | type Q8_0 | T+ 171\n",
            "INFO:convert:[ 58/291] Writing tensor blk.6.attn_v.weight                    | size   1024 x   4096  | type Q8_0 | T+ 173\n",
            "INFO:convert:[ 59/291] Writing tensor blk.6.attn_output.weight               | size   4096 x   4096  | type Q8_0 | T+ 173\n",
            "INFO:convert:[ 60/291] Writing tensor blk.6.ffn_gate.weight                  | size  14336 x   4096  | type Q8_0 | T+ 173\n",
            "INFO:convert:[ 61/291] Writing tensor blk.6.ffn_up.weight                    | size  14336 x   4096  | type Q8_0 | T+ 178\n",
            "INFO:convert:[ 62/291] Writing tensor blk.6.ffn_down.weight                  | size   4096 x  14336  | type Q8_0 | T+ 180\n",
            "INFO:convert:[ 63/291] Writing tensor blk.6.attn_norm.weight                 | size   4096           | type F32  | T+ 180\n",
            "INFO:convert:[ 64/291] Writing tensor blk.6.ffn_norm.weight                  | size   4096           | type F32  | T+ 180\n",
            "INFO:convert:[ 65/291] Writing tensor blk.7.attn_q.weight                    | size   4096 x   4096  | type Q8_0 | T+ 180\n",
            "INFO:convert:[ 66/291] Writing tensor blk.7.attn_k.weight                    | size   1024 x   4096  | type Q8_0 | T+ 184\n",
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            "INFO:convert:[285/291] Writing tensor blk.31.ffn_gate.weight                 | size  14336 x   4096  | type Q8_0 | T+ 606\n",
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            "INFO:convert:[289/291] Writing tensor blk.31.ffn_norm.weight                 | size   4096           | type F32  | T+ 609\n",
            "INFO:convert:[290/291] Writing tensor output_norm.weight                     | size   4096           | type F32  | T+ 609\n",
            "INFO:convert:[291/291] Writing tensor output.weight                          | size  32768 x   4096  | type Q8_0 | T+ 619\n",
            "INFO:convert:Wrote model-unsloth.Q8_0.gguf\n",
            "Unsloth: Conversion completed! Output location: ./model-unsloth.Q8_0.gguf\n",
            "Unsloth: Merging 4bit and LoRA weights to 16bit...\n",
            "Unsloth: Will use up to 6.21 out of 12.67 RAM for saving.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 32/32 [04:42<00:00,  8.82s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Unsloth: Saving tokenizer... Done.\n",
            "Unsloth: Saving model... This might take 5 minutes for Llama-7b...\n",
            "Unsloth: Saving leo009/mistral-7b-v3/pytorch_model-00001-of-00003.bin...\n",
            "Unsloth: Saving leo009/mistral-7b-v3/pytorch_model-00002-of-00003.bin...\n",
            "Unsloth: Saving leo009/mistral-7b-v3/pytorch_model-00003-of-00003.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 GUUF 16bits will take 3 minutes.\n",
            "\\        /    [2] Converting GGUF 16bits to q8_0 will take 20 minutes.\n",
            " \"-____-\"     In total, you will have to wait around 26 minutes.\n",
            "\n",
            "Unsloth: [0] Installing llama.cpp. This will take 3 minutes...\n",
            "Unsloth: [1] Converting model at leo009/mistral-7b-v3 into q8_0 GGUF format.\n",
            "The output location will be ./leo009/mistral-7b-v3-unsloth.Q8_0.gguf\n",
            "This will take 3 minutes...\n",
            "INFO:convert:Loading model file leo009/mistral-7b-v3/pytorch_model-00001-of-00003.bin\n",
            "INFO:convert:Loading model file leo009/mistral-7b-v3/pytorch_model-00001-of-00003.bin\n",
            "INFO:convert:Loading model file leo009/mistral-7b-v3/pytorch_model-00002-of-00003.bin\n",
            "INFO:convert:Loading model file leo009/mistral-7b-v3/pytorch_model-00003-of-00003.bin\n",
            "INFO:convert:model parameters count : 7248023552 (7B)\n",
            "INFO:convert:params = Params(n_vocab=32768, n_embd=4096, n_layer=32, n_ctx=32768, n_ff=14336, n_head=32, n_head_kv=8, n_experts=None, n_experts_used=None, f_norm_eps=1e-05, rope_scaling_type=None, f_rope_freq_base=1000000.0, f_rope_scale=None, n_orig_ctx=None, rope_finetuned=None, ftype=<GGMLFileType.MostlyQ8_0: 7>, path_model=PosixPath('leo009/mistral-7b-v3'))\n",
            "INFO:convert:Loaded vocab file PosixPath('leo009/mistral-7b-v3/tokenizer.model'), type 'spm'\n",
            "INFO:convert:Vocab info: <SentencePieceVocab with 32768 base tokens and 0 added tokens>\n",
            "INFO:convert:Special vocab info: <SpecialVocab with 0 merges, special tokens {'bos': 1, 'eos': 2, 'unk': 0, 'pad': 770}, add special tokens {'bos': True, 'eos': False}>\n",
            "INFO:convert:Writing leo009/mistral-7b-v3-unsloth.Q8_0.gguf, format 7\n",
            "WARNING:convert:Ignoring added_tokens.json since model matches vocab size without it.\n",
            "INFO:gguf.gguf_writer:gguf: This GGUF file is for Little Endian only\n",
            "INFO:gguf.vocab:Setting special token type bos to 1\n",
            "INFO:gguf.vocab:Setting special token type eos to 2\n",
            "INFO:gguf.vocab:Setting special token type unk to 0\n",
            "INFO:gguf.vocab:Setting special token type pad to 770\n",
            "INFO:gguf.vocab:Setting add_bos_token to True\n",
            "INFO:gguf.vocab:Setting add_eos_token to False\n",
            "/content/llama.cpp/convert.py:106: RuntimeWarning: invalid value encountered in divide\n",
            "  qs = (blocks / d[:, None]).round()\n",
            "INFO:convert:[  1/291] Writing tensor token_embd.weight                      | size  32768 x   4096  | type Q8_0 | T+  16\n",
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            "INFO:convert:[250/291] Writing tensor blk.27.ffn_up.weight                   | size  14336 x   4096  | type Q8_0 | T+ 487\n",
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            "INFO:convert:[252/291] Writing tensor blk.27.attn_norm.weight                | size   4096           | type F32  | T+ 489\n",
            "INFO:convert:[253/291] Writing tensor blk.27.ffn_norm.weight                 | size   4096           | type F32  | T+ 489\n",
            "INFO:convert:[254/291] Writing tensor blk.28.attn_q.weight                   | size   4096 x   4096  | type Q8_0 | T+ 491\n",
            "INFO:convert:[255/291] Writing tensor blk.28.attn_k.weight                   | size   1024 x   4096  | type Q8_0 | T+ 493\n",
            "INFO:convert:[256/291] Writing tensor blk.28.attn_v.weight                   | size   1024 x   4096  | type Q8_0 | T+ 493\n",
            "INFO:convert:[257/291] Writing tensor blk.28.attn_output.weight              | size   4096 x   4096  | type Q8_0 | T+ 493\n",
            "INFO:convert:[258/291] Writing tensor blk.28.ffn_gate.weight                 | size  14336 x   4096  | type Q8_0 | T+ 502\n",
            "INFO:convert:[259/291] Writing tensor blk.28.ffn_up.weight                   | size  14336 x   4096  | type Q8_0 | T+ 503\n",
            "INFO:convert:[260/291] Writing tensor blk.28.ffn_down.weight                 | size   4096 x  14336  | type Q8_0 | T+ 504\n",
            "INFO:convert:[261/291] Writing tensor blk.28.attn_norm.weight                | size   4096           | type F32  | T+ 504\n",
            "INFO:convert:[262/291] Writing tensor blk.28.ffn_norm.weight                 | size   4096           | type F32  | T+ 505\n",
            "INFO:convert:[263/291] Writing tensor blk.29.attn_q.weight                   | size   4096 x   4096  | type Q8_0 | T+ 507\n",
            "INFO:convert:[264/291] Writing tensor blk.29.attn_k.weight                   | size   1024 x   4096  | type Q8_0 | T+ 508\n",
            "INFO:convert:[265/291] Writing tensor blk.29.attn_v.weight                   | size   1024 x   4096  | type Q8_0 | T+ 508\n",
            "INFO:convert:[266/291] Writing tensor blk.29.attn_output.weight              | size   4096 x   4096  | type Q8_0 | T+ 508\n",
            "INFO:convert:[267/291] Writing tensor blk.29.ffn_gate.weight                 | size  14336 x   4096  | type Q8_0 | T+ 517\n",
            "INFO:convert:[268/291] Writing tensor blk.29.ffn_up.weight                   | size  14336 x   4096  | type Q8_0 | T+ 519\n",
            "INFO:convert:[269/291] Writing tensor blk.29.ffn_down.weight                 | size   4096 x  14336  | type Q8_0 | T+ 521\n",
            "INFO:convert:[270/291] Writing tensor blk.29.attn_norm.weight                | size   4096           | type F32  | T+ 521\n",
            "INFO:convert:[271/291] Writing tensor blk.29.ffn_norm.weight                 | size   4096           | type F32  | T+ 521\n",
            "INFO:convert:[272/291] Writing tensor blk.30.attn_q.weight                   | size   4096 x   4096  | type Q8_0 | T+ 523\n",
            "INFO:convert:[273/291] Writing tensor blk.30.attn_k.weight                   | size   1024 x   4096  | type Q8_0 | T+ 523\n",
            "INFO:convert:[274/291] Writing tensor blk.30.attn_v.weight                   | size   1024 x   4096  | type Q8_0 | T+ 524\n",
            "INFO:convert:[275/291] Writing tensor blk.30.attn_output.weight              | size   4096 x   4096  | type Q8_0 | T+ 525\n",
            "INFO:convert:[276/291] Writing tensor blk.30.ffn_gate.weight                 | size  14336 x   4096  | type Q8_0 | T+ 533\n",
            "INFO:convert:[277/291] Writing tensor blk.30.ffn_up.weight                   | size  14336 x   4096  | type Q8_0 | T+ 534\n",
            "INFO:convert:[278/291] Writing tensor blk.30.ffn_down.weight                 | size   4096 x  14336  | type Q8_0 | T+ 536\n",
            "INFO:convert:[279/291] Writing tensor blk.30.attn_norm.weight                | size   4096           | type F32  | T+ 536\n",
            "INFO:convert:[280/291] Writing tensor blk.30.ffn_norm.weight                 | size   4096           | type F32  | T+ 536\n",
            "INFO:convert:[281/291] Writing tensor blk.31.attn_q.weight                   | size   4096 x   4096  | type Q8_0 | T+ 538\n",
            "INFO:convert:[282/291] Writing tensor blk.31.attn_k.weight                   | size   1024 x   4096  | type Q8_0 | T+ 538\n",
            "INFO:convert:[283/291] Writing tensor blk.31.attn_v.weight                   | size   1024 x   4096  | type Q8_0 | T+ 540\n",
            "INFO:convert:[284/291] Writing tensor blk.31.attn_output.weight              | size   4096 x   4096  | type Q8_0 | T+ 540\n",
            "INFO:convert:[285/291] Writing tensor blk.31.ffn_gate.weight                 | size  14336 x   4096  | type Q8_0 | T+ 549\n",
            "INFO:convert:[286/291] Writing tensor blk.31.ffn_up.weight                   | size  14336 x   4096  | type Q8_0 | T+ 551\n",
            "INFO:convert:[287/291] Writing tensor blk.31.ffn_down.weight                 | size   4096 x  14336  | type Q8_0 | T+ 552\n",
            "INFO:convert:[288/291] Writing tensor blk.31.attn_norm.weight                | size   4096           | type F32  | T+ 553\n",
            "INFO:convert:[289/291] Writing tensor blk.31.ffn_norm.weight                 | size   4096           | type F32  | T+ 553\n",
            "INFO:convert:[290/291] Writing tensor output_norm.weight                     | size   4096           | type F32  | T+ 553\n",
            "INFO:convert:[291/291] Writing tensor output.weight                          | size  32768 x   4096  | type Q8_0 | T+ 560\n",
            "INFO:convert:Wrote leo009/mistral-7b-v3-unsloth.Q8_0.gguf\n",
            "Unsloth: Conversion completed! Output location: ./leo009/mistral-7b-v3-unsloth.Q8_0.gguf\n",
            "Unsloth: Uploading GGUF to Huggingface Hub...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "mistral-7b-v3-unsloth.Q8_0.gguf:   0%|          | 0.00/7.70G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "acac9324bf2a4fa89786e7374604fd09"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saved GGUF to https://huggingface.co/leo009/mistral-7b-v3\n",
            "Unsloth: Merging 4bit and LoRA weights to 16bit...\n",
            "Unsloth: Will use up to 6.09 out of 12.67 RAM for saving.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            " 84%|████████▍ | 27/32 [01:04<00:11,  2.39s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Unsloth: Merging 4bit and LoRA weights to 16bit...\n",
            "Unsloth: Will use up to 5.68 out of 12.67 RAM for saving.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "  0%|          | 0/32 [00:01<?, ?it/s]\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "RuntimeError",
          "evalue": "[enforce fail at inline_container.cc:595] . unexpected pos 576 vs 470",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/serialization.py\u001b[0m in \u001b[0;36msave\u001b[0;34m(obj, f, pickle_module, pickle_protocol, _use_new_zipfile_serialization, _disable_byteorder_record)\u001b[0m\n\u001b[1;32m    627\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0m_open_zipfile_writer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mopened_zipfile\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 628\u001b[0;31m             \u001b[0m_save\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mopened_zipfile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpickle_module\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpickle_protocol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_disable_byteorder_record\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    629\u001b[0m             \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/serialization.py\u001b[0m in \u001b[0;36m_save\u001b[0;34m(obj, zip_file, pickle_module, pickle_protocol, _disable_byteorder_record)\u001b[0m\n\u001b[1;32m    861\u001b[0m         \u001b[0mnum_bytes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstorage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnbytes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 862\u001b[0;31m         \u001b[0mzip_file\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite_record\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstorage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_bytes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    863\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mRuntimeError\u001b[0m: [enforce fail at inline_container.cc:764] . PytorchStreamWriter failed writing file data/0: file write failed",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/unsloth/save.py\u001b[0m in \u001b[0;36munsloth_save_pretrained_gguf\u001b[0;34m(self, save_directory, tokenizer, quantization_method, first_conversion, push_to_hub, token, private, is_main_process, state_dict, save_function, max_shard_size, safe_serialization, variant, save_peft_format, tags, temporary_location, maximum_memory_usage)\u001b[0m\n\u001b[1;32m   1365\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1366\u001b[0;31m             \u001b[0mnew_save_directory\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mold_username\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0munsloth_save_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0marguments\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1367\u001b[0m             \u001b[0mmakefile\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py\u001b[0m in \u001b[0;36mdecorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    114\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mctx_factory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 115\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    116\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/unsloth/save.py\u001b[0m in \u001b[0;36munsloth_save_model\u001b[0;34m(model, tokenizer, save_directory, save_method, push_to_hub, token, is_main_process, state_dict, save_function, max_shard_size, safe_serialization, variant, save_peft_format, use_temp_dir, commit_message, private, create_pr, revision, commit_description, tags, temporary_location, maximum_memory_usage)\u001b[0m\n\u001b[1;32m    543\u001b[0m                 \u001b[0mfilename\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtemporary_location\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34mf\"{name}.pt\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 544\u001b[0;31m                 \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mW\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpickle_module\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpickle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpickle_protocol\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpickle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mHIGHEST_PROTOCOL\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    545\u001b[0m                 \u001b[0mstate_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmap_location\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"cpu\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmmap\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/serialization.py\u001b[0m in \u001b[0;36msave\u001b[0;34m(obj, f, pickle_module, pickle_protocol, _use_new_zipfile_serialization, _disable_byteorder_record)\u001b[0m\n\u001b[1;32m    626\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0m_use_new_zipfile_serialization\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 627\u001b[0;31m         \u001b[0;32mwith\u001b[0m \u001b[0m_open_zipfile_writer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mopened_zipfile\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    628\u001b[0m             \u001b[0m_save\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mopened_zipfile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpickle_module\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpickle_protocol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_disable_byteorder_record\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/serialization.py\u001b[0m in \u001b[0;36m__exit__\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m    474\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__exit__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 475\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfile_like\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite_end_of_file\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    476\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfile_stream\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mRuntimeError\u001b[0m: [enforce fail at inline_container.cc:595] . unexpected pos 576 vs 470",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/serialization.py\u001b[0m in \u001b[0;36msave\u001b[0;34m(obj, f, pickle_module, pickle_protocol, _use_new_zipfile_serialization, _disable_byteorder_record)\u001b[0m\n\u001b[1;32m    627\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0m_open_zipfile_writer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mopened_zipfile\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 628\u001b[0;31m             \u001b[0m_save\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mopened_zipfile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpickle_module\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpickle_protocol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_disable_byteorder_record\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    629\u001b[0m             \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/serialization.py\u001b[0m in \u001b[0;36m_save\u001b[0;34m(obj, zip_file, pickle_module, pickle_protocol, _disable_byteorder_record)\u001b[0m\n\u001b[1;32m    861\u001b[0m         \u001b[0mnum_bytes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstorage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnbytes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 862\u001b[0;31m         \u001b[0mzip_file\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite_record\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstorage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_bytes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    863\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mRuntimeError\u001b[0m: [enforce fail at inline_container.cc:764] . PytorchStreamWriter failed writing file data/0: file write failed",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-15-bf05c73c1432>\u001b[0m in \u001b[0;36m<cell line: 6>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m# Save to 16bit GGUF\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave_pretrained_gguf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"model\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtokenizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mquantization_method\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"f16\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpush_to_hub_gguf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"leo009/mistral-7b-v3\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtokenizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mquantization_method\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"f16\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtoken\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"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\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/unsloth/save.py\u001b[0m in \u001b[0;36munsloth_save_pretrained_gguf\u001b[0;34m(self, save_directory, tokenizer, quantization_method, first_conversion, push_to_hub, token, private, is_main_process, state_dict, save_function, max_shard_size, safe_serialization, variant, save_peft_format, tags, temporary_location, maximum_memory_usage)\u001b[0m\n\u001b[1;32m   1380\u001b[0m                 \u001b[0mgit_clone\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1381\u001b[0m                 \u001b[0mmakefile\u001b[0m  \u001b[0;34m=\u001b[0m \u001b[0minstall_llama_cpp_make_non_blocking\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1382\u001b[0;31m                 \u001b[0mnew_save_directory\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mold_username\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0munsloth_save_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0marguments\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1383\u001b[0m                 \u001b[0mpython_install\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1384\u001b[0m             \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py\u001b[0m in \u001b[0;36mdecorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    113\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdecorate_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    114\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mctx_factory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 115\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    116\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    117\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mdecorate_context\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/unsloth/save.py\u001b[0m in \u001b[0;36munsloth_save_model\u001b[0;34m(model, tokenizer, save_directory, save_method, push_to_hub, token, is_main_process, state_dict, save_function, max_shard_size, safe_serialization, variant, save_peft_format, use_temp_dir, commit_message, private, create_pr, revision, commit_description, tags, temporary_location, maximum_memory_usage)\u001b[0m\n\u001b[1;32m    542\u001b[0m                 \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwarning_once\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"We will save to Disk and not RAM now.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    543\u001b[0m                 \u001b[0mfilename\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtemporary_location\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34mf\"{name}.pt\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 544\u001b[0;31m                 \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mW\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpickle_module\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpickle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpickle_protocol\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpickle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mHIGHEST_PROTOCOL\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    545\u001b[0m                 \u001b[0mstate_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmap_location\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"cpu\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmmap\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    546\u001b[0m         \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/serialization.py\u001b[0m in \u001b[0;36msave\u001b[0;34m(obj, f, pickle_module, pickle_protocol, _use_new_zipfile_serialization, _disable_byteorder_record)\u001b[0m\n\u001b[1;32m    625\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    626\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0m_use_new_zipfile_serialization\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 627\u001b[0;31m         \u001b[0;32mwith\u001b[0m \u001b[0m_open_zipfile_writer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mopened_zipfile\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    628\u001b[0m             \u001b[0m_save\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mopened_zipfile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpickle_module\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpickle_protocol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_disable_byteorder_record\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    629\u001b[0m             \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/serialization.py\u001b[0m in \u001b[0;36m__exit__\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m    473\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    474\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__exit__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 475\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfile_like\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite_end_of_file\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    476\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfile_stream\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    477\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfile_stream\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mRuntimeError\u001b[0m: [enforce fail at inline_container.cc:595] . unexpected pos 576 vs 470"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "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)."
      ],
      "metadata": {
        "id": "bDp0zNpwe6U_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 🔥🔥🔥本项目代码由AI超元域频道制作，观看更多大模型微调视频请访问我的频道⬇\n",
        "# 👉👉👉[我的哔哩哔哩频道](https://space.bilibili.com/3493277319825652)\n",
        "# 👉👉👉[我的YouTube频道](https://www.youtube.com/@AIsuperdomain)"
      ],
      "metadata": {
        "id": "Zt9CHJqO6p30"
      }
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "widgets": {
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        "18ab5780db774501899de00d392818a2": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_ab8e5087b05d4d52a6e01c27d805c215",
              "IPY_MODEL_c596c133d0f34029898a4897b1ac303b",
              "IPY_MODEL_e2359b4fa8ff44f783fea6880faff683"
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            "layout": "IPY_MODEL_1992ef5e421d45c48c9b555872257473"
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        "ab8e5087b05d4d52a6e01c27d805c215": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
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            "_model_module": "@jupyter-widgets/controls",
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