Cópia_de_Alpaca_+_TinyLlama_+_RoPE_Scaling_full_example.ipynb

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        "<a href=\"https://colab.research.google.com/github/RAMIKAN-BR/Aa/blob/main/C%C3%B3pia_de_Alpaca_%2B_TinyLlama_%2B_RoPE_Scaling_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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      "source": [
        "To run this, press \"Runtime\" and press \"Run all\" on a **free** Tesla T4 Google Colab instance!\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> Join our Discord if you need help!\n",
        "</div>\n",
        "\n",
        "To install Unsloth on your own computer, follow the installation instructions on our Github page [here](https://github.com/unslothai/unsloth#installation-instructions---conda).\n",
        "\n",
        "You will learn how to do [data prep](#Data), how to [train](#Train), how to [run the model](#Inference), & [how to save it](#Save) (eg for Llama.cpp).\n",
        "\n",
        "**[NOTE]** TinyLlama was trained on 2048 max tokens. With Unsloth, we can arbitrarily set the sequence length we want via `max_seq_length=4096`. We do RoPE Scaling internally to magically extend the maximum context size!"
      ]
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      "source": [
        "%%capture\n",
        "import torch\n",
        "major_version, minor_version = torch.cuda.get_device_capability()\n",
        "# Must install separately since Colab has torch 2.2.1, which breaks packages\n",
        "!pip install \"unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git\"\n",
        "if major_version >= 8:\n",
        "    # Use this for new GPUs like Ampere, Hopper GPUs (RTX 30xx, RTX 40xx, A100, H100, L40)\n",
        "    !pip install --no-deps packaging ninja einops flash-attn xformers trl peft accelerate bitsandbytes\n",
        "else:\n",
        "    # Use this for older GPUs (V100, Tesla T4, RTX 20xx)\n",
        "    !pip install --no-deps xformers trl peft accelerate bitsandbytes\n",
        "pass"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r2v_X2fA0Df5"
      },
      "source": [
        "* We support Llama, Mistral, CodeLlama, TinyLlama, Vicuna, Open Hermes etc\n",
        "* And Yi, Qwen ([llamafied](https://huggingface.co/models?sort=trending&search=qwen+llama)), Deepseek, all Llama, Mistral derived archs.\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",
        "* With [PR 26037](https://github.com/huggingface/transformers/pull/26037), we support downloading 4bit models **4x faster**! [Our repo](https://huggingface.co/unsloth) has Llama, Mistral 4bit models.\n",
        "* [**NEW**] We make Gemma 6 trillion tokens **2.5x faster**! See our [Gemma notebook](https://colab.research.google.com/drive/10NbwlsRChbma1v55m8LAPYG15uQv6HLo?usp=sharing)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "se74afmhAWKy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4168c0ec-fdea-415d-d224-9a2a35cc4429"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
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            "==((====))==  Unsloth: Fast Llama patching release 2024.4\n",
            "   \\\\   /|    GPU: Tesla T4. Max memory: 14.748 GB. Platform = Linux.\n",
            "O^O/ \\_/ \\    Pytorch: 2.2.1+cu121. CUDA = 7.5. CUDA Toolkit = 12.1.\n",
            "\\        /    Bfloat16 = FALSE. Xformers = 0.0.25.post1. FA = False.\n",
            " \"-____-\"     Free Apache license: http://github.com/unslothai/unsloth\n"
          ]
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          "output_type": "stream",
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          "text": [
            "Unsloth: unsloth/tinyllama-bnb-4bit can only handle sequence lengths of at most 2048.\n",
            "But with kaiokendev's RoPE scaling of 2.0, it can be magically be extended to 4096!\n",
            "Unused kwargs: ['quant_method']. These kwargs are not used in <class 'transformers.utils.quantization_config.BitsAndBytesConfig'>.\n"
          ]
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          "text": [
            "You set `add_prefix_space`. The tokenizer needs to be converted from the slow tokenizers\n"
          ]
        }
      ],
      "source": [
        "from unsloth import FastLanguageModel\n",
        "import torch\n",
        "max_seq_length = 4096 # 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 = \"Ramikan-BR/tinyllama_PY-CODER-bnb-4bit-lora_model-4k\", # \"unsloth/tinyllama\" for 16bit loading\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": "S3xvsMEWyJbZ"
      },
      "source": [
        "**[NOTE]** TinyLlama's internal maximum sequence length is 2048. We use RoPE Scaling to extend it to 4096 with Unsloth!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SXd9bTZd1aaL"
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      "source": [
        "We now add LoRA adapters so we only need to update 1 to 10% of all parameters!\n",
        "\n",
        "**[NOTE]** We set `gradient_checkpointing=False` ONLY for TinyLlama since Unsloth saves tonnes of memory usage. This does NOT work for `llama-2-7b` or `mistral-7b` since the memory usage will still exceed Tesla T4's 15GB. GC recomputes the forward pass during the backward pass, saving loads of memory.\n",
        "\n",
        "`**[IF YOU GET OUT OF MEMORY]**` set `gradient_checkpointing` to `True`."
      ]
    },
    {
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      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6bZsfBuZDeCL",
        "outputId": "463c2c8d-2db1-46fb-85cb-2c83afe89d82"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Unsloth 2024.4 patched 22 layers with 22 QKV layers, 22 O layers and 22 MLP layers.\n"
          ]
        }
      ],
      "source": [
        "model = FastLanguageModel.get_peft_model(\n",
        "    model,\n",
        "    r = 128, # 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 = 256,\n",
        "    lora_dropout = 0, # Currently only supports dropout = 0\n",
        "    bias = \"none\",    # Currently only supports bias = \"none\"\n",
        "    use_gradient_checkpointing = False, # @@@ IF YOU GET OUT OF MEMORY - set to True @@@\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 `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": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
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          "data": {
            "text/plain": [
              "Map:   0%|          | 0/3828 [00:00<?, ? examples/s]"
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            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from datasets import load_dataset\n",
        "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",
        "\n",
        "### Input:\n",
        "\n",
        "{}\n",
        "\n",
        "### Response:\n",
        "\n",
        "{}\"\"\"\n",
        "\n",
        "EOS_TOKEN = tokenizer.eos_token\n",
        "\n",
        "def formatting_prompts_func(examples):\n",
        "    instructions = examples[\"problem\"]  # Use \"problem\" como a instrução\n",
        "    inputs = examples[\"seed\"]  # Use \"seed\" como entrada adicional (contexto)\n",
        "    outputs = examples[\"solution\"]  # Use \"solution\" como saída esperada\n",
        "\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",
        "\n",
        "    return {\"text\": texts}\n",
        "\n",
        "from datasets import load_dataset\n",
        "\n",
        "dataset = load_dataset('json', data_files='/content/drive/MyDrive/data-oss_instruct-py-10%.jsonl', split='train')\n",
        "dataset = dataset.map(formatting_prompts_func, batched=True)"
      ]
    },
    {
      "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 1 full epoch which makes Alpaca run in 80ish minutes! We also support TRL's `DPOTrainer`! See our DPO tutorial on a free Google Colab instance [here](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121,
          "referenced_widgets": [
            "caa20246d17143d885dd157a0fa35bb3",
            "92bd6afa71414b2183dee1ed45c724e0",
            "14e975836acf42c0b693f54a9abc2771",
            "19d8a3ecbf7441abbcc8f85e166ffb1e",
            "1d6bf14fff914a59bee935101498d8a7",
            "436adeef40e444c89cde299e57834cc8",
            "0b366bb647e2451bb86f822daf8fc1c3",
            "9fb8fc2eaa3443a8af86ad050b34cd77",
            "6df7a1d4796f44d291736c1be212b39a",
            "477b56b881074bb9bc4a9634cc772012",
            "68f008982cb345478f4d56f64ab95b64"
          ]
        },
        "id": "95_Nn-89DhsL",
        "outputId": "a6c21210-62d9-4657-d7a3-d735a8757f85"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "PyTorch: setting up devices\n",
            "The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "caa20246d17143d885dd157a0fa35bb3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using auto half precision backend\n"
          ]
        }
      ],
      "source": [
        "from trl import SFTTrainer\n",
        "from transformers import TrainingArguments\n",
        "from transformers.utils import logging\n",
        "logging.set_verbosity_info()\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 = True, # Packs short sequences together to save time!\n",
        "    args = TrainingArguments(\n",
        "        per_device_train_batch_size = 2,\n",
        "        gradient_accumulation_steps = 16,\n",
        "        warmup_ratio = 0.1,\n",
        "        num_train_epochs = 3,\n",
        "        learning_rate = 2e-4,\n",
        "        fp16 = not torch.cuda.is_bf16_supported(),\n",
        "        bf16 = torch.cuda.is_bf16_supported(),\n",
        "        logging_steps = 1,\n",
        "        optim = \"adamw_8bit\",\n",
        "        weight_decay = 0.1,\n",
        "        lr_scheduler_type = \"linear\",\n",
        "        seed = 3407,\n",
        "        output_dir = \"outputs\",\n",
        "    ),\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "2ejIt2xSNKKp"
      },
      "outputs": [],
      "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": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "yqxqAZ7KJ4oL",
        "outputId": "e5a0b0b7-cce1-42e5-a559-62731dd0b58b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "==((====))==  Unsloth - 2x faster free finetuning | Num GPUs = 1\n",
            "   \\\\   /|    Num examples = 967 | Num Epochs = 1\n",
            "O^O/ \\_/ \\    Batch size per device = 2 | Gradient Accumulation steps = 16\n",
            "\\        /    Total batch size = 32 | Total steps = 30\n",
            " \"-____-\"     Number of trainable parameters = 100,925,440\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='30' max='30' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [30/30 26:26, 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>1.737000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>1.738000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>1.384700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>1.086400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>1.009600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.921000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.830400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.808900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.774500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>0.759900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11</td>\n",
              "      <td>0.736100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12</td>\n",
              "      <td>0.721200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13</td>\n",
              "      <td>0.733200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14</td>\n",
              "      <td>0.701000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15</td>\n",
              "      <td>0.711700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16</td>\n",
              "      <td>0.701400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>17</td>\n",
              "      <td>0.689500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>18</td>\n",
              "      <td>0.678800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>19</td>\n",
              "      <td>0.675200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>0.680500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>21</td>\n",
              "      <td>0.685800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>22</td>\n",
              "      <td>0.681200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>23</td>\n",
              "      <td>0.672000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>24</td>\n",
              "      <td>0.679900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>25</td>\n",
              "      <td>0.675500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>26</td>\n",
              "      <td>0.666600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>27</td>\n",
              "      <td>0.687900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>28</td>\n",
              "      <td>0.653600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>29</td>\n",
              "      <td>0.672500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>30</td>\n",
              "      <td>0.660900</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "\n",
            "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "trainer_stats = trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 228
        },
        "id": "pCqnaKmlO1U9",
        "outputId": "e849c50b-304b-4d90-b07e-54170b09c6d6"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'start_gpu_memory' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-10-21a4da9893ae>\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#@title Show final memory and time stats\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mused_memory\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax_memory_reserved\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m1024\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m1024\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m1024\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mused_memory_for_lora\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mused_memory\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mstart_gpu_memory\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\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      4\u001b[0m \u001b[0mused_percentage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mused_memory\u001b[0m         \u001b[0;34m/\u001b[0m\u001b[0mmax_memory\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\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 \u001b[0mlora_percentage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mused_memory_for_lora\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mmax_memory\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'start_gpu_memory' is not defined"
          ]
        }
      ],
      "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",
      "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!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 211
        },
        "id": "kR3gIAX-SM2q",
        "outputId": "e8cfbd69-854e-476a-d4c7-6b7a55d11f28"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'FastLanguageModel' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-64c50bf8aa34>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# alpaca_prompt = Copied from above\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mFastLanguageModel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_inference\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# Enable native 2x faster inference\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m inputs = tokenizer(\n\u001b[1;32m      4\u001b[0m [\n\u001b[1;32m      5\u001b[0m     alpaca_prompt.format(\n",
            "\u001b[0;31mNameError\u001b[0m: name 'FastLanguageModel' is not defined"
          ]
        }
      ],
      "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",
      "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": "V2otZJcevdpZ"
      }
    },
    {
      "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",
        "        \"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)"
      ],
      "metadata": {
        "id": "QYvyvuj5vd7H",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 211
        },
        "outputId": "7a3eb8e0-861c-4b0c-b4a2-51f541b98a06"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'FastLanguageModel' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-2-54e7811fd479>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# alpaca_prompt = Copied from above\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mFastLanguageModel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_inference\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# Enable native 2x faster inference\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m inputs = tokenizer(\n\u001b[1;32m      4\u001b[0m [\n\u001b[1;32m      5\u001b[0m     alpaca_prompt.format(\n",
            "\u001b[0;31mNameError\u001b[0m: name 'FastLanguageModel' is not defined"
          ]
        }
      ]
    },
    {
      "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",
      "source": [
        "!pip install accelerate\n",
        "!pip install bitsandbytes\n",
        "!pip install bitsandbytes-cuda114 --force-reinstall\n",
        "from transformers import LlamaForCausalLM, LlamaTokenizer\n",
        "import torch\n",
        "\n",
        "# Caminho para salvar o checkpoint no Google Drive\n",
        "#checkpoint_path_gdrive = '/content/drive/MyDrive/checkpoint-500.pt'\n",
        "\n",
        "# Salva o checkpoint no Google Drive\n",
        "#torch.save(model.state_dict(), checkpoint_path_gdrive)\n",
        "\n",
        "\n",
        "# Carregue o checkpoint\n",
        "#checkpoint_path = \"outputs/checkpoint-500\"\n",
        "#model.load_state_dict(torch.load(checkpoint_path))\n",
        "\n",
        "# Salve o modelo localmente\n",
        "model.save_pretrained(\"lora_model\")\n",
        "\n",
        "# Faça o upload para o Hugging Face Hub\n",
        "model.push_to_hub(\"Ramikan-BR/tinyllama_PY-CODER-bnb-4bit-lora_model-4k\", token=\"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\")\n",
        "\n",
        "# Carregue o tokenizador do modelo base\n",
        "tokenizer = LlamaTokenizer.from_pretrained(\"unsloth/tinyllama\")\n",
        "\n",
        "# Salve o tokenizador localmente\n",
        "tokenizer.save_pretrained(\"tokenizer\")\n",
        "\n",
        "# Faça o upload do tokenizador para o Hugging Face Hub\n",
        "tokenizer.push_to_hub(\"Ramikan-BR/tinyllama_PY-CODER-bnb-4bit-lora_model-4k\", associated_package=\"tokenizer\")\n",
        "\n",
        "# Caminho para salvar o modelo no Google Drive\n",
        "model_path_gdrive = '/content/drive/MyDrive/modelo_final'\n",
        "\n",
        "# Salva o modelo no Google Drive\n",
        "model.save_pretrained(model_path_gdrive)\n",
        "\n",
        "# Caminho para salvar o tokenizador no Google Drive\n",
        "tokenizer_path_gdrive = '/content/drive/MyDrive/modelo_final/tokenizer'\n",
        "\n",
        "# Cria o diretório para o tokenizador, caso não exista\n",
        "os.makedirs(tokenizer_path_gdrive, exist_ok=True)\n",
        "\n",
        "# Salva o tokenizador no Google Drive\n",
        "tokenizer.save_pretrained(tokenizer_path_gdrive)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "84b40e7d7c434e8a8f35da72e7b0780b",
            "c43a876366b344708ad4d5c0c2d6df6f",
            "5a99b7932521452692b4fdb10585d28a",
            "22bd55bd5f03435fae438ea23646218b",
            "f3747c44abaf4b1eab3fb89b645f57c3",
            "9b54eaef63b24caea037b3da1684b445",
            "47e8a694c74f4c99a72b0551ea11225a",
            "601fe036a4884795b41b4b66384f50b1",
            "4bf23c9551a942719febeb263b5fd622",
            "6f7e748114054436bb378205d07e99b3",
            "cc8a644ed58e4266b1c8b2220bff3007",
            "a8b851fbc8254ac89465727ddd995d55",
            "d6edac82fffd4ea9bb557a15fdc3762d",
            "aec6c74ef7ed4dab9faa5b0346211d97",
            "ddac7cb09eef49c6af64f4b8f2a8efc8",
            "3e7bf6fe55734bbbb7173a3ef1f78bbc",
            "fbeb5548d237448ba17a4b806ffc998c",
            "a9d0e705d6c84bd498dcce7fe3d46c00",
            "cc921bb913924750a4b4697de2878636",
            "6298016cd8794c7097892e23001ebb5f",
            "c30f5dc4736c492e9cad036fcade3059",
            "aef6447e96354673bb2dba53546aac48"
          ]
        },
        "id": "YsqcvRpX2qxe",
        "outputId": "abb31de4-0e14-4b0b-f2dc-2b082558ec87"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: accelerate in /usr/local/lib/python3.10/dist-packages (0.29.3)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from accelerate) (1.25.2)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (24.0)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from accelerate) (5.9.5)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from accelerate) (6.0.1)\n",
            "Requirement already satisfied: torch>=1.10.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (2.2.1+cu121)\n",
            "Requirement already satisfied: huggingface-hub in /usr/local/lib/python3.10/dist-packages (from accelerate) (0.22.2)\n",
            "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from accelerate) (0.4.3)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.13.4)\n",
            "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (4.11.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (1.12)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.3)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.1.3)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2023.6.0)\n",
            "Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (23.7 MB)\n",
            "Collecting nvidia-cuda-runtime-cu12==12.1.105 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (823 kB)\n",
            "Collecting nvidia-cuda-cupti-cu12==12.1.105 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (14.1 MB)\n",
            "Collecting nvidia-cudnn-cu12==8.9.2.26 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl (731.7 MB)\n",
            "Collecting nvidia-cublas-cu12==12.1.3.1 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl (410.6 MB)\n",
            "Collecting nvidia-cufft-cu12==11.0.2.54 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl (121.6 MB)\n",
            "Collecting nvidia-curand-cu12==10.3.2.106 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl (56.5 MB)\n",
            "Collecting nvidia-cusolver-cu12==11.4.5.107 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl (124.2 MB)\n",
            "Collecting nvidia-cusparse-cu12==12.1.0.106 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl (196.0 MB)\n",
            "Collecting nvidia-nccl-cu12==2.19.3 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_nccl_cu12-2.19.3-py3-none-manylinux1_x86_64.whl (166.0 MB)\n",
            "Collecting nvidia-nvtx-cu12==12.1.105 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (99 kB)\n",
            "Requirement already satisfied: triton==2.2.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2.2.0)\n",
            "Collecting nvidia-nvjitlink-cu12 (from nvidia-cusolver-cu12==11.4.5.107->torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (21.1 MB)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->accelerate) (2.31.0)\n",
            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->accelerate) (4.66.2)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.10.0->accelerate) (2.1.5)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (2024.2.2)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.10.0->accelerate) (1.3.0)\n",
            "Installing collected packages: nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "xformers 0.0.25.post1 requires torch==2.2.2, but you have torch 2.2.1+cu121 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.19.3 nvidia-nvjitlink-cu12-12.4.127 nvidia-nvtx-cu12-12.1.105\n",
            "Requirement already satisfied: bitsandbytes in /usr/local/lib/python3.10/dist-packages (0.43.1)\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (from bitsandbytes) (2.2.1+cu121)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from bitsandbytes) (1.25.2)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (3.13.4)\n",
            "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (4.11.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (1.12)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (3.3)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (3.1.3)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (2023.6.0)\n",
            "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (8.9.2.26)\n",
            "Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (12.1.3.1)\n",
            "Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (11.0.2.54)\n",
            "Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (10.3.2.106)\n",
            "Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (11.4.5.107)\n",
            "Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (12.1.0.106)\n",
            "Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (2.19.3)\n",
            "Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (12.1.105)\n",
            "Requirement already satisfied: triton==2.2.0 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (2.2.0)\n",
            "Requirement already satisfied: nvidia-nvjitlink-cu12 in /usr/local/lib/python3.10/dist-packages (from nvidia-cusolver-cu12==11.4.5.107->torch->bitsandbytes) (12.4.127)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch->bitsandbytes) (2.1.5)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch->bitsandbytes) (1.3.0)\n",
            "Collecting bitsandbytes-cuda114\n",
            "  Downloading bitsandbytes_cuda114-0.26.0.post2-py3-none-any.whl (4.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.3/4.3 MB\u001b[0m \u001b[31m249.7 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: bitsandbytes-cuda114\n",
            "Successfully installed bitsandbytes-cuda114-0.26.0.post2\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "bitsandbytes"
                ]
              },
              "id": "56d51b2e9ae84f6b840d34b836bfe917"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--unsloth--tinyllama-bnb-4bit/snapshots/82c9ac484c6a4191514431e172bf62c482e0a1b0/config.json\n",
            "Model config LlamaConfig {\n",
            "  \"_name_or_path\": \"unsloth/tinyllama\",\n",
            "  \"architectures\": [\n",
            "    \"LlamaForCausalLM\"\n",
            "  ],\n",
            "  \"attention_bias\": false,\n",
            "  \"attention_dropout\": 0.0,\n",
            "  \"bos_token_id\": 1,\n",
            "  \"eos_token_id\": 2,\n",
            "  \"hidden_act\": \"silu\",\n",
            "  \"hidden_size\": 2048,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"intermediate_size\": 5632,\n",
            "  \"max_position_embeddings\": 2048,\n",
            "  \"model_type\": \"llama\",\n",
            "  \"num_attention_heads\": 32,\n",
            "  \"num_hidden_layers\": 22,\n",
            "  \"num_key_value_heads\": 4,\n",
            "  \"pad_token_id\": 0,\n",
            "  \"pretraining_tp\": 1,\n",
            "  \"quantization_config\": {\n",
            "    \"bnb_4bit_compute_dtype\": \"bfloat16\",\n",
            "    \"bnb_4bit_quant_type\": \"nf4\",\n",
            "    \"bnb_4bit_use_double_quant\": true,\n",
            "    \"llm_int8_enable_fp32_cpu_offload\": false,\n",
            "    \"llm_int8_has_fp16_weight\": false,\n",
            "    \"llm_int8_skip_modules\": null,\n",
            "    \"llm_int8_threshold\": 6.0,\n",
            "    \"load_in_4bit\": true,\n",
            "    \"load_in_8bit\": false,\n",
            "    \"quant_method\": \"bitsandbytes\"\n",
            "  },\n",
            "  \"rms_norm_eps\": 1e-05,\n",
            "  \"rope_scaling\": null,\n",
            "  \"rope_theta\": 10000.0,\n",
            "  \"tie_word_embeddings\": false,\n",
            "  \"torch_dtype\": \"bfloat16\",\n",
            "  \"transformers_version\": \"4.40.0\",\n",
            "  \"use_cache\": true,\n",
            "  \"vocab_size\": 32000\n",
            "}\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "README.md:   0%|          | 0.00/575 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "84b40e7d7c434e8a8f35da72e7b0780b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--unsloth--tinyllama-bnb-4bit/snapshots/82c9ac484c6a4191514431e172bf62c482e0a1b0/config.json\n",
            "Model config LlamaConfig {\n",
            "  \"_name_or_path\": \"unsloth/tinyllama\",\n",
            "  \"architectures\": [\n",
            "    \"LlamaForCausalLM\"\n",
            "  ],\n",
            "  \"attention_bias\": false,\n",
            "  \"attention_dropout\": 0.0,\n",
            "  \"bos_token_id\": 1,\n",
            "  \"eos_token_id\": 2,\n",
            "  \"hidden_act\": \"silu\",\n",
            "  \"hidden_size\": 2048,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"intermediate_size\": 5632,\n",
            "  \"max_position_embeddings\": 2048,\n",
            "  \"model_type\": \"llama\",\n",
            "  \"num_attention_heads\": 32,\n",
            "  \"num_hidden_layers\": 22,\n",
            "  \"num_key_value_heads\": 4,\n",
            "  \"pad_token_id\": 0,\n",
            "  \"pretraining_tp\": 1,\n",
            "  \"quantization_config\": {\n",
            "    \"bnb_4bit_compute_dtype\": \"bfloat16\",\n",
            "    \"bnb_4bit_quant_type\": \"nf4\",\n",
            "    \"bnb_4bit_use_double_quant\": true,\n",
            "    \"llm_int8_enable_fp32_cpu_offload\": false,\n",
            "    \"llm_int8_has_fp16_weight\": false,\n",
            "    \"llm_int8_skip_modules\": null,\n",
            "    \"llm_int8_threshold\": 6.0,\n",
            "    \"load_in_4bit\": true,\n",
            "    \"load_in_8bit\": false,\n",
            "    \"quant_method\": \"bitsandbytes\"\n",
            "  },\n",
            "  \"rms_norm_eps\": 1e-05,\n",
            "  \"rope_scaling\": null,\n",
            "  \"rope_theta\": 10000.0,\n",
            "  \"tie_word_embeddings\": false,\n",
            "  \"torch_dtype\": \"bfloat16\",\n",
            "  \"transformers_version\": \"4.40.0\",\n",
            "  \"use_cache\": true,\n",
            "  \"vocab_size\": 32000\n",
            "}\n",
            "\n",
            "Uploading the following files to Ramikan-BR/tinyllama_PY-CODER-bnb-4bit-lora_model-4k: adapter_config.json,README.md,adapter_model.safetensors\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "adapter_model.safetensors:   0%|          | 0.00/404M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a8b851fbc8254ac89465727ddd995d55"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saved model to https://huggingface.co/Ramikan-BR/tinyllama_PY-CODER-bnb-4bit-lora_model-4k\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--unsloth--tinyllama-bnb-4bit/snapshots/82c9ac484c6a4191514431e172bf62c482e0a1b0/config.json\n",
            "Model config LlamaConfig {\n",
            "  \"_name_or_path\": \"unsloth/tinyllama\",\n",
            "  \"architectures\": [\n",
            "    \"LlamaForCausalLM\"\n",
            "  ],\n",
            "  \"attention_bias\": false,\n",
            "  \"attention_dropout\": 0.0,\n",
            "  \"bos_token_id\": 1,\n",
            "  \"eos_token_id\": 2,\n",
            "  \"hidden_act\": \"silu\",\n",
            "  \"hidden_size\": 2048,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"intermediate_size\": 5632,\n",
            "  \"max_position_embeddings\": 2048,\n",
            "  \"model_type\": \"llama\",\n",
            "  \"num_attention_heads\": 32,\n",
            "  \"num_hidden_layers\": 22,\n",
            "  \"num_key_value_heads\": 4,\n",
            "  \"pad_token_id\": 0,\n",
            "  \"pretraining_tp\": 1,\n",
            "  \"quantization_config\": {\n",
            "    \"bnb_4bit_compute_dtype\": \"bfloat16\",\n",
            "    \"bnb_4bit_quant_type\": \"nf4\",\n",
            "    \"bnb_4bit_use_double_quant\": true,\n",
            "    \"llm_int8_enable_fp32_cpu_offload\": false,\n",
            "    \"llm_int8_has_fp16_weight\": false,\n",
            "    \"llm_int8_skip_modules\": null,\n",
            "    \"llm_int8_threshold\": 6.0,\n",
            "    \"load_in_4bit\": true,\n",
            "    \"load_in_8bit\": false,\n",
            "    \"quant_method\": \"bitsandbytes\"\n",
            "  },\n",
            "  \"rms_norm_eps\": 1e-05,\n",
            "  \"rope_scaling\": null,\n",
            "  \"rope_theta\": 10000.0,\n",
            "  \"tie_word_embeddings\": false,\n",
            "  \"torch_dtype\": \"bfloat16\",\n",
            "  \"transformers_version\": \"4.40.0\",\n",
            "  \"use_cache\": true,\n",
            "  \"vocab_size\": 32000\n",
            "}\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "upcOlWe7A1vc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 637
        },
        "outputId": "f83b7262-e3bc-4274-d47c-612aeeb7262e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Unused kwargs: ['quant_method']. These kwargs are not used in <class 'transformers.utils.quantization_config.BitsAndBytesConfig'>.\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "ImportError",
          "evalue": "Using `bitsandbytes` 8-bit quantization requires Accelerate: `pip install accelerate` and the latest version of bitsandbytes: `pip install -i https://pypi.org/simple/ bitsandbytes`",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-10-1bc816ac9ba9>\u001b[0m in \u001b[0;36m<cell line: 4>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mmodel_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"unsloth/tinyllama-bnb-4bit\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mtokenizer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLlamaTokenizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_pretrained\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLlamaForCausalLM\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_pretrained\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_path\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      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave_pretrained\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"lora_model\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# Local saving\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/transformers/modeling_utils.py\u001b[0m in \u001b[0;36mfrom_pretrained\u001b[0;34m(cls, pretrained_model_name_or_path, config, cache_dir, ignore_mismatched_sizes, force_download, local_files_only, token, revision, use_safetensors, *model_args, **kwargs)\u001b[0m\n\u001b[1;32m   3163\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3164\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mhf_quantizer\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;32m-> 3165\u001b[0;31m             hf_quantizer.validate_environment(\n\u001b[0m\u001b[1;32m   3166\u001b[0m                 \u001b[0mtorch_dtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtorch_dtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfrom_tf\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfrom_tf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfrom_flax\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfrom_flax\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice_map\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdevice_map\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3167\u001b[0m             )\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/quantizers/quantizer_bnb_4bit.py\u001b[0m in \u001b[0;36mvalidate_environment\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     60\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mvalidate_environment\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[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     61\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mis_accelerate_available\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mis_bitsandbytes_available\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[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 62\u001b[0;31m             raise ImportError(\n\u001b[0m\u001b[1;32m     63\u001b[0m                 \u001b[0;34m\"Using `bitsandbytes` 8-bit quantization requires Accelerate: `pip install accelerate` \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     64\u001b[0m                 \u001b[0;34m\"and the latest version of bitsandbytes: `pip install -i https://pypi.org/simple/ bitsandbytes`\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mImportError\u001b[0m: Using `bitsandbytes` 8-bit quantization requires Accelerate: `pip install accelerate` and the latest version of bitsandbytes: `pip install -i https://pypi.org/simple/ bitsandbytes`",
            "",
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"
          ],
          "errorDetails": {
            "actions": [
              {
                "action": "open_url",
                "actionText": "Open Examples",
                "url": "/notebooks/snippets/importing_libraries.ipynb"
              }
            ]
          }
        }
      ],
      "source": [
        "model.save_pretrained(\"lora_model\") # Local saving\n",
        "model.push_to_hub(\"Ramikan-BR/tinyllama_PY-CODER-bnb-4bit-lora_model-4k\", token = \"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\") # Online saving"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now if you want to load the LoRA adapters we just saved for inference, set `False` to `True`:"
      ],
      "metadata": {
        "id": "3CgqR2B0vmCt"
      }
    },
    {
      "cell_type": "code",
      "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 = 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",
        "        \"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 = 64)"
      ],
      "metadata": {
        "id": "Yle1gGB3vmWK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "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": "8m76iItmvni0"
      }
    },
    {
      "cell_type": "code",
      "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\")"
      ],
      "metadata": {
        "id": "wcMqKxzcvouj"
      },
      "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": "xwCTbEUavpoC"
      }
    },
    {
      "cell_type": "code",
      "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 = \"\")"
      ],
      "metadata": {
        "id": "gJKx0osWvqzz"
      },
      "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": "mhc9u6HAvr3b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Save to 8bit Q8_0\n",
        "if False: model.save_pretrained_gguf(\"model\", tokenizer,)\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 = \"f16\")\n",
        "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, quantization_method = \"f16\", 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 = \"\")"
      ],
      "metadata": {
        "id": "2_TmxAoavvYW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "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": "SvFui8YuvZ1R"
      }
    },
    {
      "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. Mistral 7b 2x faster [free Colab](https://colab.research.google.com/drive/1Dyauq4kTZoLewQ1cApceUQVNcnnNTzg_?usp=sharing)\n",
        "3. Llama 7b 2x faster [free Colab](https://colab.research.google.com/drive/1lBzz5KeZJKXjvivbYvmGarix9Ao6Wxe5?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. Gemma 6 trillion tokens is 2.5x faster! [free Colab](https://colab.research.google.com/drive/10NbwlsRChbma1v55m8LAPYG15uQv6HLo?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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