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        "<a href=\"https://colab.research.google.com/github/0xZee/finetune_llama3-1_unsloth/blob/main/finetune_llama3_1_unsloth.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## UNSLOTH : `Fine-tunning llama 3.1 8b on Custom DataSet`\n",
        "> `Finetuned model` : https://huggingface.co/0xZee/llama3-1_0xZee_model\n",
        "\n",
        "> Finetuned from : `models--unsloth--meta-llama-3.1-8b-bnb-4bit`\n",
        "\n",
        "### Prerequis :\n",
        "\n",
        "> Install `Unsloth` on your own computer, `GPU` needed.\n",
        "\n",
        "> data prep (`Dataset`), train, run the model, & save it in `huggingface`.\n",
        "\n",
        "> `Llama-3.1 8b` trained on 15 trillion tokens with 128K long context lengths\n",
        "\n",
        "> [NEW] unsloth : Try 2x faster inference in a free Colab for Llama-3.1 8b Instruct here\n",
        "\n",
        "Notebook : https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing#scrollTo=IqM-T1RTzY6C"
      ],
      "metadata": {
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      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "M5_Ymv_mhfZH"
      },
      "outputs": [],
      "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<0.0.27\" \"trl<0.9.0\" peft accelerate bitsandbytes"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Supported :\n",
        "\n",
        "- support Llama, Mistral, Phi-3, Gemma, Yi, DeepSeek, Qwen, TinyLlama, Vicuna, Open Hermes etc\n",
        "- 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 method.\n",
        "- make Gemma-2 9b / 27b 2x faster! See our Gemma-2 9b notebook\n",
        "- To finetune and auto export to Ollama, try our Ollama notebook\n",
        "- make Mistral NeMo 12B 2x faster and fit in under 12GB of VRAM! Mistral NeMo notebook"
      ],
      "metadata": {
        "id": "JeGkP5dGjjK6"
      }
    },
    {
      "cell_type": "code",
      "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/Meta-Llama-3.1-8B-bnb-4bit\",      # Llama-3.1 15 trillion tokens model 2x faster!\n",
        "    \"unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit\",\n",
        "    \"unsloth/Meta-Llama-3.1-70B-bnb-4bit\",\n",
        "    \"unsloth/Meta-Llama-3.1-405B-bnb-4bit\",    # We also uploaded 4bit for 405b!\n",
        "    \"unsloth/Mistral-Nemo-Base-2407-bnb-4bit\", # New Mistral 12b 2x faster!\n",
        "    \"unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit\",\n",
        "    \"unsloth/mistral-7b-v0.3-bnb-4bit\",        # Mistral v3 2x faster!\n",
        "    \"unsloth/mistral-7b-instruct-v0.3-bnb-4bit\",\n",
        "    \"unsloth/Phi-3-mini-4k-instruct\",          # Phi-3 2x faster!d\n",
        "    \"unsloth/Phi-3-medium-4k-instruct\",\n",
        "    \"unsloth/gemma-2-9b-bnb-4bit\",\n",
        "    \"unsloth/gemma-2-27b-bnb-4bit\",            # Gemma 2x faster!\n",
        "] # More models at https://huggingface.co/unsloth\n",
        "\n",
        "model, tokenizer = FastLanguageModel.from_pretrained(\n",
        "    model_name = \"unsloth/Meta-Llama-3.1-8B\",\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",
        ")"
      ],
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      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n",
            "==((====))==  Unsloth: Fast Llama patching release 2024.8\n",
            "   \\\\   /|    GPU: Tesla T4. Max memory: 14.748 GB. Platform = Linux.\n",
            "O^O/ \\_/ \\    Pytorch: 2.3.1+cu121. CUDA = 7.5. CUDA Toolkit = 12.1.\n",
            "\\        /    Bfloat16 = FALSE. FA [Xformers = 0.0.26.post1. FA2 = False]\n",
            " \"-____-\"     Free Apache license: http://github.com/unslothai/unsloth\n",
            "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n"
          ]
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          "output_type": "display_data",
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            "text/plain": [
              "model.safetensors:   0%|          | 0.00/5.70G [00:00<?, ?B/s]"
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            "text/plain": [
              "generation_config.json:   0%|          | 0.00/184 [00:00<?, ?B/s]"
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              "tokenizer_config.json:   0%|          | 0.00/50.6k [00:00<?, ?B/s]"
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              "special_tokens_map.json:   0%|          | 0.00/459 [00:00<?, ?B/s]"
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    },
    {
      "cell_type": "markdown",
      "source": [
        "> `LoRA` Adapters : LoRA to optimize finetuning 1% of parameters adapted"
      ],
      "metadata": {
        "id": "KDfjjTgIkyFG"
      }
    },
    {
      "cell_type": "code",
      "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",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2Oi_zkLMj090",
        "outputId": "171b9be7-bdd1-490e-840d-b7d5e79cbd7d"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Unsloth 2024.8 patched 32 layers with 32 QKV layers, 32 O layers and 32 MLP layers.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## DATASET PREP\n",
        "\n",
        "We now use the Alpaca dataset from [yahma](https://huggingface.co/datasets/yahma/alpaca-cleaned), which is a filtered version of 52K of the original [Alpaca dataset](https://crfm.stanford.edu/2023/03/13/alpaca.html). You can replace this code section with your own data prep.\n",
        "\n",
        "**[NOTE]** To train only on completions (ignoring the user's input) read TRL's docs [here](https://huggingface.co/docs/trl/sft_trainer#train-on-completions-only).\n",
        "\n",
        "**[NOTE]** Remember to add the **EOS_TOKEN** to the tokenized output!! Otherwise you'll get infinite generations!\n",
        "\n",
        "If you want to use the `llama-3` template for ShareGPT datasets, try our conversational [notebook](https://colab.research.google.com/drive/1XamvWYinY6FOSX9GLvnqSjjsNflxdhNc?usp=sharing).\n",
        "\n",
        "For text completions like novel writing, try this [notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing)."
      ],
      "metadata": {
        "id": "NKV1KgsIlcU4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "alpaca_prompt = \"\"\"Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n",
        "\n",
        "### Instruction:\n",
        "{}\n",
        "\n",
        "### Input:\n",
        "{}\n",
        "\n",
        "### Response:\n",
        "{}\"\"\"\n",
        "\n",
        "EOS_TOKEN = tokenizer.eos_token # Must add EOS_TOKEN\n",
        "def formatting_prompts_func(examples):\n",
        "    instructions = examples[\"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(\"yahma/alpaca-cleaned\", split = \"train\")\n",
        "dataset = dataset.map(formatting_prompts_func, batched = True,)"
      ],
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      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/11.6k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6dcfb850f32747428c3bdfff94564119"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/44.3M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b3161165dde34b11885809b9f4957448"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/51760 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "71b5de73093a4500a2af47bfc4313700"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/51760 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5b6541c859d348b09f2da03ceb7d496f"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# TRAIN THE MODEL\n",
        "\n",
        "Now let's use Huggingface TRL's `SFTTrainer`!\n",
        "More docs here: [TRL SFT docs](https://huggingface.co/docs/trl/sft_trainer). We do 100 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": "xm4-8GiUl-Sw"
      }
    },
    {
      "cell_type": "code",
      "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",
        "        #num_train_epochs = 1, # Set this for 1 full training run.\n",
        "        #max_steps = None,\n",
        "        max_steps = 200,\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",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MxolnPyImA41",
        "outputId": "a8a359de-9449-46e9-c31b-514ee83925fa"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "max_steps is given, it will override any value given in num_train_epochs\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "> Show Memory stats"
      ],
      "metadata": {
        "id": "ThpQSg-tnIGt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "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.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3SZvpoFBnKhn",
        "outputId": "5da6afba-0334-40c9-8591-6213cdbf61b1"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GPU = Tesla T4. Max memory = 14.748 GB.\n",
            "5.984 GB of memory reserved.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "> Train the model"
      ],
      "metadata": {
        "id": "K1qMM4hxnPub"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "trainer_stats = trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "OVkyaWJanRmt",
        "outputId": "26e42af1-9aac-40ac-dc7d-7e09718d7c6d"
      },
      "execution_count": 9,
      "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 = 200\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='200' max='200' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [200/200 25:13, 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>1.817300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>2.304200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>1.689300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>1.938300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>1.657000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>1.621600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>1.187000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>1.264400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>1.099900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>1.186800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11</td>\n",
              "      <td>0.929500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12</td>\n",
              "      <td>0.957400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13</td>\n",
              "      <td>0.931600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14</td>\n",
              "      <td>1.049600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15</td>\n",
              "      <td>0.890100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16</td>\n",
              "      <td>0.900300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>17</td>\n",
              "      <td>1.007900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>18</td>\n",
              "      <td>1.255600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>19</td>\n",
              "      <td>1.016000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>0.879700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>21</td>\n",
              "      <td>0.937600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>22</td>\n",
              "      <td>1.010400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>23</td>\n",
              "      <td>0.889300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>24</td>\n",
              "      <td>0.980600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>25</td>\n",
              "      <td>1.060300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>26</td>\n",
              "      <td>1.015300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>27</td>\n",
              "      <td>1.038800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>28</td>\n",
              "      <td>0.868000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>29</td>\n",
              "      <td>0.824700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>30</td>\n",
              "      <td>0.874100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>31</td>\n",
              "      <td>0.844600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>32</td>\n",
              "      <td>0.864800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>33</td>\n",
              "      <td>0.981400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>34</td>\n",
              "      <td>0.854900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>35</td>\n",
              "      <td>0.964700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>36</td>\n",
              "      <td>0.857000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>37</td>\n",
              "      <td>0.868100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>38</td>\n",
              "      <td>0.750100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>39</td>\n",
              "      <td>1.081100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>40</td>\n",
              "      <td>1.170100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>41</td>\n",
              "      <td>0.898500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>42</td>\n",
              "      <td>0.977900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>43</td>\n",
              "      <td>0.947200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>44</td>\n",
              "      <td>0.907100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>45</td>\n",
              "      <td>0.914200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>46</td>\n",
              "      <td>0.968300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>47</td>\n",
              "      <td>0.864600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>48</td>\n",
              "      <td>1.192100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>49</td>\n",
              "      <td>0.901400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>50</td>\n",
              "      <td>1.029000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>51</td>\n",
              "      <td>1.002900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>52</td>\n",
              "      <td>0.910300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>53</td>\n",
              "      <td>0.979700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>54</td>\n",
              "      <td>1.149900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>55</td>\n",
              "      <td>0.773700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>56</td>\n",
              "      <td>1.010500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>57</td>\n",
              "      <td>0.887400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>58</td>\n",
              "      <td>0.831700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>59</td>\n",
              "      <td>0.849800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>60</td>\n",
              "      <td>0.901400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>61</td>\n",
              "      <td>0.883300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>62</td>\n",
              "      <td>1.012500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>63</td>\n",
              "      <td>0.949100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>64</td>\n",
              "      <td>0.919000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>65</td>\n",
              "      <td>0.847500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>66</td>\n",
              "      <td>0.976200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>67</td>\n",
              "      <td>1.001300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>68</td>\n",
              "      <td>0.889600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>69</td>\n",
              "      <td>1.020400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>70</td>\n",
              "      <td>0.832500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>71</td>\n",
              "      <td>0.913500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>72</td>\n",
              "      <td>1.001100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>73</td>\n",
              "      <td>0.869900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>74</td>\n",
              "      <td>0.901500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>75</td>\n",
              "      <td>0.893200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>76</td>\n",
              "      <td>0.809500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>77</td>\n",
              "      <td>0.847000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>78</td>\n",
              "      <td>0.910600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>79</td>\n",
              "      <td>0.934900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>80</td>\n",
              "      <td>1.026900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>81</td>\n",
              "      <td>0.924700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>82</td>\n",
              "      <td>0.721200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>83</td>\n",
              "      <td>0.806400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>84</td>\n",
              "      <td>0.939900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>85</td>\n",
              "      <td>0.986500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>86</td>\n",
              "      <td>0.875700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>87</td>\n",
              "      <td>0.958100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>88</td>\n",
              "      <td>1.127300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>89</td>\n",
              "      <td>0.968900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>90</td>\n",
              "      <td>1.064300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>91</td>\n",
              "      <td>1.103600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>92</td>\n",
              "      <td>1.102600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>93</td>\n",
              "      <td>0.889200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>94</td>\n",
              "      <td>1.036600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>95</td>\n",
              "      <td>0.893900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>96</td>\n",
              "      <td>0.900300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>97</td>\n",
              "      <td>0.801500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>98</td>\n",
              "      <td>1.032600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>99</td>\n",
              "      <td>0.845400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>0.944000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>101</td>\n",
              "      <td>0.996700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>102</td>\n",
              "      <td>0.932900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>103</td>\n",
              "      <td>0.973500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>104</td>\n",
              "      <td>0.844200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>105</td>\n",
              "      <td>0.794100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>106</td>\n",
              "      <td>1.015100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>107</td>\n",
              "      <td>0.990100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>108</td>\n",
              "      <td>0.881500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>109</td>\n",
              "      <td>0.812200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>110</td>\n",
              "      <td>0.864700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>111</td>\n",
              "      <td>0.793000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>112</td>\n",
              "      <td>0.792700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>113</td>\n",
              "      <td>0.878100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>114</td>\n",
              "      <td>0.817400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>115</td>\n",
              "      <td>0.786000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>116</td>\n",
              "      <td>0.932900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>117</td>\n",
              "      <td>0.960100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>118</td>\n",
              "      <td>0.950200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>119</td>\n",
              "      <td>0.935100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>120</td>\n",
              "      <td>0.970900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>121</td>\n",
              "      <td>0.827300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>122</td>\n",
              "      <td>0.798200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>123</td>\n",
              "      <td>0.930400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>124</td>\n",
              "      <td>0.960100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>125</td>\n",
              "      <td>1.012000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>126</td>\n",
              "      <td>0.799800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>127</td>\n",
              "      <td>0.943900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>128</td>\n",
              "      <td>0.893300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>129</td>\n",
              "      <td>0.909000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>130</td>\n",
              "      <td>0.850400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>131</td>\n",
              "      <td>0.918600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>132</td>\n",
              "      <td>0.841100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>133</td>\n",
              "      <td>0.956100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>134</td>\n",
              "      <td>0.879600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>135</td>\n",
              "      <td>0.958000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>136</td>\n",
              "      <td>0.939200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>137</td>\n",
              "      <td>0.815900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>138</td>\n",
              "      <td>0.952700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>139</td>\n",
              "      <td>0.702700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>140</td>\n",
              "      <td>0.909400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>141</td>\n",
              "      <td>0.796000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>142</td>\n",
              "      <td>1.090900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>143</td>\n",
              "      <td>0.949800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>144</td>\n",
              "      <td>0.873700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>145</td>\n",
              "      <td>0.935200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>146</td>\n",
              "      <td>0.962600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>147</td>\n",
              "      <td>1.134500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>148</td>\n",
              "      <td>1.080000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>149</td>\n",
              "      <td>0.987600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>150</td>\n",
              "      <td>1.021400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>151</td>\n",
              "      <td>0.857900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>152</td>\n",
              "      <td>0.842600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>153</td>\n",
              "      <td>0.962000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>154</td>\n",
              "      <td>0.947900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>155</td>\n",
              "      <td>0.768000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>156</td>\n",
              "      <td>0.984900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>157</td>\n",
              "      <td>0.977200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>158</td>\n",
              "      <td>0.828100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>159</td>\n",
              "      <td>0.923000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>160</td>\n",
              "      <td>0.931800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>161</td>\n",
              "      <td>0.862800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>162</td>\n",
              "      <td>0.911600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>163</td>\n",
              "      <td>0.943600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>164</td>\n",
              "      <td>0.946500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>165</td>\n",
              "      <td>0.912300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>166</td>\n",
              "      <td>0.846300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>167</td>\n",
              "      <td>0.735200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>168</td>\n",
              "      <td>1.072400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>169</td>\n",
              "      <td>0.783700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>170</td>\n",
              "      <td>0.866200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>171</td>\n",
              "      <td>0.849900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>172</td>\n",
              "      <td>1.208500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>173</td>\n",
              "      <td>0.904900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>174</td>\n",
              "      <td>1.007300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>175</td>\n",
              "      <td>0.894600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>176</td>\n",
              "      <td>0.842100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>177</td>\n",
              "      <td>0.905800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>178</td>\n",
              "      <td>0.958300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>179</td>\n",
              "      <td>0.931900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>180</td>\n",
              "      <td>0.932100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>181</td>\n",
              "      <td>0.890100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>182</td>\n",
              "      <td>1.020300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>183</td>\n",
              "      <td>0.832400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>184</td>\n",
              "      <td>0.870500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>185</td>\n",
              "      <td>0.992400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>186</td>\n",
              "      <td>0.927900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>187</td>\n",
              "      <td>1.013900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>188</td>\n",
              "      <td>0.793400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>189</td>\n",
              "      <td>0.802000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>190</td>\n",
              "      <td>0.895600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>191</td>\n",
              "      <td>0.804800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>192</td>\n",
              "      <td>0.830800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>193</td>\n",
              "      <td>0.898000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>194</td>\n",
              "      <td>1.037500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>195</td>\n",
              "      <td>0.762700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>196</td>\n",
              "      <td>0.999800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>197</td>\n",
              "      <td>0.903800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>198</td>\n",
              "      <td>0.777600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>199</td>\n",
              "      <td>0.905000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200</td>\n",
              "      <td>0.940700</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "> Show memory stats after training"
      ],
      "metadata": {
        "id": "69fyyb7DnejZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "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} %.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7zYsuRxpne-h",
        "outputId": "ffd1ea0f-2da4-4be9-8d9c-686fbe71e1da"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1530.26 seconds used for training.\n",
            "25.5 minutes used for training.\n",
            "Peak reserved memory = 7.926 GB.\n",
            "Peak reserved memory for training = 1.942 GB.\n",
            "Peak reserved memory % of max memory = 53.743 %.\n",
            "Peak reserved memory for training % of max memory = 13.168 %.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# `INFERENCE` OF THE FINETUNED MODE"
      ],
      "metadata": {
        "id": "TMgVpmbComQT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "> Simple Inference"
      ],
      "metadata": {
        "id": "Kyb11dU-o3-5"
      }
    },
    {
      "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",
        "outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)\n",
        "tokenizer.batch_decode(outputs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ldzykN81otS0",
        "outputId": "f0e95ad8-9ec8-4f3a-9ba9-9c52ff01ae8d"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['<|begin_of_text|>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:\\nContinue the fibonnaci sequence.\\n\\n### Input:\\n1, 1, 2, 3, 5, 8\\n\\n### Response:\\n13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584, 4181, 6765<|end_of_text|>']"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "> Streaming inference"
      ],
      "metadata": {
        "id": "mzj-rjMpo64f"
      }
    },
    {
      "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",
        "        \"List the last kings in France\", # 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": "lrSGh9BDo8qP",
        "outputId": "54dc2d02-c0f2-4b6d-9f42-d8c9d761a425"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<|begin_of_text|>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",
            "List the last kings in France\n",
            "\n",
            "### Input:\n",
            "\n",
            "\n",
            "### Response:\n",
            "The last kings in France were Louis XVI (1774-1792) and Louis XVII (1793-1795). Louis XVI was overthrown and executed during the French Revolution, and Louis XVII, his son, died in captivity.<|end_of_text|>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# `SAVE`, `LOAD` FINETUNED MODEL : `llama3-1_0xZee_model`\n",
        "\n",
        "Saving Fine-Tuned Model to `llama3-1_0xZee_model` in local & `Huggingface` Online"
      ],
      "metadata": {
        "id": "DcdYSQNUpLGe"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "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": "4it9fiEQpoDC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# HF TOKEN TO SAVE MODEL ONLINE\n",
        "from google.colab import userdata\n",
        "HF_API = userdata.get('HF_API')"
      ],
      "metadata": {
        "id": "QyKIAsAcquor"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Saving Model to local and HuggingFace Online"
      ],
      "metadata": {
        "id": "RijLbLL9q9tw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "> `Saving` model as LoRA adapters"
      ],
      "metadata": {
        "id": "CK7pGRz0tRii"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.save_pretrained(\"llama3-1_0xZee_model\") # Local saving\n",
        "tokenizer.save_pretrained(\"llama3-1_0xZee_model\")\n",
        "model.push_to_hub(\"0xZee/llama3-1_0xZee_model\", token = HF_API) # Online saving\n",
        "tokenizer.push_to_hub(\"0xZee/llama3-1_0xZee_model\", token = HF_API) # Online saving"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 131,
          "referenced_widgets": [
            "e46393be7b604214a73001973f10b16f",
            "70510a6f059b45edbc9b8abfaf273050",
            "b705dcecebfd42dcab6d16061c2fc084",
            "7754180a8951447a9eafb46486589546",
            "0041e790f15844e3878d0b376497c8d4",
            "3e29d88dab8c46fab0f345a0cc2112ec",
            "6771b3ad37294b8db345248e643b0094",
            "0e4c1e73469443c29f084f7bb65a6121",
            "479e695d56fe4f5eace1a3fd2690e85a",
            "f219bcf3bb3c42e7bbe1de88aaf25bfd",
            "ae3b7031ea224350ac251d122c08c517",
            "fbf9cd7bd09a4e20ac0402492188595f",
            "513acc52f2c440d3abe57416ab2a93cc",
            "418c8705b48f42fdb6f0f1d08121dc2c",
            "b50b5256cfaf4fa4bd62632096cb12e6",
            "657dfdfc6ad0496c9b80473a7aeb7ee6",
            "c4e62e8ff5f44e55b62524f347c7b690",
            "644a21495650411ab5bfd33df568e00c",
            "eb8a53fa40694cd3b798a72079aff91a",
            "16e509aa14014a5b9d984eff050576a4",
            "e26b3f72ae9a4a01aa726ad0bc1d2a9b",
            "900f2d848bf34a6f8fb0fc41b2ffec32",
            "d14fd3ab74344dd29e76b7109b8b2fed",
            "54279c47fb1041789434dcde23158436",
            "8b7ab74390a943f78c435269fea58c90",
            "b147f1c844d447dea1d869131d1fa530",
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        },
        "id": "299BK_rppKiD",
        "outputId": "c5aa9d40-f380-4e0f-a100-88ef9cccd470"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "README.md:   0%|          | 0.00/586 [00:00<?, ?B/s]"
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              "  0%|          | 0/1 [00:00<?, ?it/s]"
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          "data": {
            "text/plain": [
              "adapter_model.safetensors:   0%|          | 0.00/168M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "d14fd3ab74344dd29e76b7109b8b2fed"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saved model to https://huggingface.co/0xZee/llama3-1_0xZee_model\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "> to `load` the LoRA adapters we just saved for inference, set `False` to `True`:"
      ],
      "metadata": {
        "id": "M5SYp8patT5Q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "if False:\n",
        "    from unsloth import FastLanguageModel\n",
        "    model, tokenizer = FastLanguageModel.from_pretrained(\n",
        "        model_name = \"lama3-1_0xZee_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 = 128)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qSeB1wxHtcpM",
        "outputId": "ae64b8d8-a01f-4b8c-bb58-8cc2ccff5050"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<|begin_of_text|>Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n",
            "\n",
            "### Instruction:\n",
            "What is a famous tall tower in Paris?\n",
            "\n",
            "### Input:\n",
            "\n",
            "\n",
            "### Response:\n",
            "One of the most famous and iconic tall towers in Paris is the Eiffel Tower.<|end_of_text|>\n"
          ]
        }
      ]
    },
    {
      "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": "spMxQW3yv5DJ"
      }
    },
    {
      "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",
        "        \"llama3-1_0xZee_model\", # YOUR MODEL YOU USED FOR TRAINING\n",
        "        load_in_4bit = load_in_4bit,\n",
        "    )\n",
        "    tokenizer = AutoTokenizer.from_pretrained(\"llama3-1_0xZee_model\")"
      ],
      "metadata": {
        "id": "fJ7pIsNlv8HP"
      },
      "execution_count": 18,
      "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": "CHwv3BjXwp4z"
      }
    },
    {
      "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 = HF_API)\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": "nWcyeMfgwqq2"
      },
      "execution_count": 21,
      "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.\n",
        "\n",
        "[**NEW**] To finetune and auto export to Ollama, try our [Ollama notebook](https://colab.research.google.com/drive/1WZDi7APtQ9VsvOrQSSC5DDtxq159j8iZ?usp=sharing)"
      ],
      "metadata": {
        "id": "eia_G2eXwvQ4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Save to 8bit Q8_0\n",
        "if False: model.save_pretrained_gguf(\"model\", tokenizer,)\n",
        "# Remember to go to https://huggingface.co/settings/tokens for a token!\n",
        "# And change hf to your username!\n",
        "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, token = \"\")\n",
        "\n",
        "# Save to 16bit GGUF\n",
        "if False: model.save_pretrained_gguf(\"model\", tokenizer, quantization_method = \"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 = \"\")\n",
        "\n",
        "# Save to multiple GGUF options - much faster if you want multiple!\n",
        "if False:\n",
        "    model.push_to_hub_gguf(\n",
        "        \"hf/model\", # Change hf to your username!\n",
        "        tokenizer,\n",
        "        quantization_method = [\"q4_k_m\", \"q8_0\", \"q5_k_m\",],\n",
        "        token = \"\", # Get a token at https://huggingface.co/settings/tokens\n",
        "    )"
      ],
      "metadata": {
        "id": "zwSWsbxbwwPF"
      },
      "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).\n",
        "\n",
        "**[NEW] Try 2x faster inference in a free Colab for Llama-3.1 8b Instruct [here](https://colab.research.google.com/drive/1T-YBVfnphoVc8E2E854qF3jdia2Ll2W2?usp=sharing)**"
      ],
      "metadata": {
        "id": "3Am1Shq9w3bu"
      }
    }
  ]
}