Alpaca_+_TinyLlama_+_RoPE_Scaling_full_example.ipynb

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        "<a href=\"https://colab.research.google.com/github/kaballas/AutoGPT/blob/master/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",
        "# Installs Unsloth, Xformers (Flash Attention) and all other packages!\n",
        "!pip install \"unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git\"\n",
        "!pip install --no-deps xformers \"trl<0.9.0\" peft accelerate bitsandbytes"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r2v_X2fA0Df5"
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      "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)"
      ]
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            "/usr/local/lib/python3.10/dist-packages/unsloth/__init__.py:67: UserWarning: CUDA is not linked properly.\n",
            "We shall run `ldconfig /usr/lib64-nvidia` to try to fix it.\n",
            "  warnings.warn(\n"
          ]
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            "==((====))==  Unsloth: Fast Llama patching release 2024.1\n",
            "   \\\\   /|    GPU: Tesla T4. Max memory: 14.748 GB\n",
            "O^O/ \\_/ \\    CUDA capability = 7.5. Xformers = 0.0.22.post7. FA = False.\n",
            "\\        /    Pytorch version: 2.1.0+cu121. CUDA Toolkit = 12.1\n",
            " \"-____-\"     bfloat16 = FALSE. Platform = Linux\n",
            "\n",
            "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",
            "You passed `quantization_config` to `from_pretrained` but the model you're loading already has a `quantization_config` attribute. The `quantization_config` attribute will be overwritten with the one you passed to `from_pretrained`.\n"
          ]
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          "metadata": {}
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      ],
      "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 = \"unsloth/tinyllama-bnb-4bit\", # \"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"
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      "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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      "outputs": [
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          "text": [
            "Unsloth 2024.1 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 = 32, # 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 = 32,\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"
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      "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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          "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": "8375b238b9084bceb04c8d67e148f9dc"
            }
          },
          "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": "12f020de361f41128fac402f89be26d8"
            }
          },
          "metadata": {}
        }
      ],
      "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\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,)"
      ]
    },
    {
      "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": 211,
          "referenced_widgets": [
            "47b91d628c824007ba2a899908543fc4",
            "544c9ffdfd6346d19865bf34d43e26a3",
            "0a88fd16d72442198803c4c082bdd5ab",
            "e245c09fe14a49929a8274fdc356fd25",
            "5cb07c2ef3be4a9f90cac8a53565040f",
            "93cfec82c4e24cd8bbf6d0044c92f9a4",
            "7aea5b2b4cb944efae3dc1de39b72f83",
            "444156136b864dd4896c9f83135772f6",
            "0da54be740b943c38a4aa805692b35f2",
            "3e5a6d4cd4bb45879ed67ab7d9cbcc16",
            "22a3640c1e344b5c922e488a9a2feaca"
          ]
        },
        "id": "95_Nn-89DhsL",
        "outputId": "0be04edc-0aff-481c-a52f-fce8283ac502"
      },
      "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",
            "loading file tokenizer.model from cache at /root/.cache/huggingface/hub/models--unsloth--tinyllama-bnb-4bit/snapshots/fc56510003ea9d49362400b8a362345150802c31/tokenizer.model\n",
            "loading file tokenizer.json from cache at /root/.cache/huggingface/hub/models--unsloth--tinyllama-bnb-4bit/snapshots/fc56510003ea9d49362400b8a362345150802c31/tokenizer.json\n",
            "loading file added_tokens.json from cache at None\n",
            "loading file special_tokens_map.json from cache at /root/.cache/huggingface/hub/models--unsloth--tinyllama-bnb-4bit/snapshots/fc56510003ea9d49362400b8a362345150802c31/special_tokens_map.json\n",
            "loading file tokenizer_config.json from cache at /root/.cache/huggingface/hub/models--unsloth--tinyllama-bnb-4bit/snapshots/fc56510003ea9d49362400b8a362345150802c31/tokenizer_config.json\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": "47b91d628c824007ba2a899908543fc4"
            }
          },
          "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 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 = True, # Packs short sequences together to save time!\n",
        "    args = TrainingArguments(\n",
        "        per_device_train_batch_size = 2,\n",
        "        gradient_accumulation_steps = 4,\n",
        "        warmup_ratio = 0.1,\n",
        "        num_train_epochs = 1,\n",
        "        learning_rate = 2e-5,\n",
        "        fp16 = not is_bfloat16_supported(),\n",
        "        bf16 = is_bfloat16_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",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2ejIt2xSNKKp",
        "outputId": "b58a5395-e0e3-43eb-b5dc-fd656d571a3d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GPU = Tesla T4. Max memory = 14.748 GB.\n",
            "0.879 GB of memory reserved.\n"
          ]
        }
      ],
      "source": [
        "#@title Show current memory stats\n",
        "gpu_stats = torch.cuda.get_device_properties(0)\n",
        "start_gpu_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n",
        "max_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3)\n",
        "print(f\"GPU = {gpu_stats.name}. Max memory = {max_memory} GB.\")\n",
        "print(f\"{start_gpu_memory} GB of memory reserved.\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "yqxqAZ7KJ4oL",
        "outputId": "985d469c-eff6-4d90-b0df-7e187efc0cf7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "***** Running training *****\n",
            "  Num examples = 3,000\n",
            "  Num Epochs = 1\n",
            "  Instantaneous batch size per device = 2\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 8\n",
            "  Gradient Accumulation steps = 4\n",
            "  Total optimization steps = 375\n",
            "  Number of trainable parameters = 25,231,360\n",
            "You're using a LlamaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='375' max='375' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [375/375 1:23:39, Epoch 1/1]\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>2.280100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>2.243700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>2.245500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>2.243600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>2.302000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>2.287400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>2.283900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>2.144600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>2.237200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>2.245700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11</td>\n",
              "      <td>2.286200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12</td>\n",
              "      <td>2.281900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13</td>\n",
              "      <td>2.204400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14</td>\n",
              "      <td>2.158000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15</td>\n",
              "      <td>2.278200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16</td>\n",
              "      <td>2.168600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>17</td>\n",
              "      <td>2.147400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>18</td>\n",
              "      <td>2.119500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>19</td>\n",
              "      <td>2.122100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>2.099000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>21</td>\n",
              "      <td>2.072200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>22</td>\n",
              "      <td>2.066000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>23</td>\n",
              "      <td>2.073800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>24</td>\n",
              "      <td>2.000600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>25</td>\n",
              "      <td>2.043400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>26</td>\n",
              "      <td>1.992100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>27</td>\n",
              "      <td>2.020800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>28</td>\n",
              "      <td>1.972900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>29</td>\n",
              "      <td>1.984700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>30</td>\n",
              "      <td>1.991100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>31</td>\n",
              "      <td>1.973500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>32</td>\n",
              "      <td>1.964000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>33</td>\n",
              "      <td>1.896200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>34</td>\n",
              "      <td>1.872200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>35</td>\n",
              "      <td>1.862700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>36</td>\n",
              "      <td>1.855800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>37</td>\n",
              "      <td>1.792600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>38</td>\n",
              "      <td>1.849300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>39</td>\n",
              "      <td>1.745700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>40</td>\n",
              "      <td>1.734800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>41</td>\n",
              "      <td>1.646200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>42</td>\n",
              "      <td>1.701200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>43</td>\n",
              "      <td>1.705900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>44</td>\n",
              "      <td>1.702300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>45</td>\n",
              "      <td>1.650300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>46</td>\n",
              "      <td>1.601100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>47</td>\n",
              "      <td>1.639200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>48</td>\n",
              "      <td>1.633000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>49</td>\n",
              "      <td>1.612800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>50</td>\n",
              "      <td>1.638400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>51</td>\n",
              "      <td>1.593000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>52</td>\n",
              "      <td>1.549500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>53</td>\n",
              "      <td>1.500600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>54</td>\n",
              "      <td>1.511000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>55</td>\n",
              "      <td>1.489900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>56</td>\n",
              "      <td>1.526300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>57</td>\n",
              "      <td>1.447300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>58</td>\n",
              "      <td>1.454200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>59</td>\n",
              "      <td>1.454100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>60</td>\n",
              "      <td>1.482700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>61</td>\n",
              "      <td>1.403600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>62</td>\n",
              "      <td>1.416300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>63</td>\n",
              "      <td>1.422400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>64</td>\n",
              "      <td>1.380600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>65</td>\n",
              "      <td>1.425800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>66</td>\n",
              "      <td>1.379000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>67</td>\n",
              "      <td>1.404900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>68</td>\n",
              "      <td>1.415200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>69</td>\n",
              "      <td>1.362400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>70</td>\n",
              "      <td>1.368600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>71</td>\n",
              "      <td>1.412700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>72</td>\n",
              "      <td>1.388100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>73</td>\n",
              "      <td>1.352500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>74</td>\n",
              "      <td>1.365600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>75</td>\n",
              "      <td>1.332100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>76</td>\n",
              "      <td>1.363200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>77</td>\n",
              "      <td>1.379200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>78</td>\n",
              "      <td>1.328400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>79</td>\n",
              "      <td>1.289600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>80</td>\n",
              "      <td>1.340600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>81</td>\n",
              "      <td>1.325800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>82</td>\n",
              "      <td>1.277400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>83</td>\n",
              "      <td>1.294000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>84</td>\n",
              "      <td>1.292500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>85</td>\n",
              "      <td>1.285600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>86</td>\n",
              "      <td>1.272400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>87</td>\n",
              "      <td>1.223900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>88</td>\n",
              "      <td>1.296300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>89</td>\n",
              "      <td>1.313700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>90</td>\n",
              "      <td>1.285300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>91</td>\n",
              "      <td>1.290900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>92</td>\n",
              "      <td>1.232500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>93</td>\n",
              "      <td>1.242800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>94</td>\n",
              "      <td>1.240500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>95</td>\n",
              "      <td>1.227000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>96</td>\n",
              "      <td>1.198800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>97</td>\n",
              "      <td>1.224400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>98</td>\n",
              "      <td>1.271700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>99</td>\n",
              "      <td>1.205700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>1.251400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>101</td>\n",
              "      <td>1.207000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>102</td>\n",
              "      <td>1.249500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>103</td>\n",
              "      <td>1.225200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>104</td>\n",
              "      <td>1.228000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>105</td>\n",
              "      <td>1.191200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>106</td>\n",
              "      <td>1.255500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>107</td>\n",
              "      <td>1.194300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>108</td>\n",
              "      <td>1.184900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>109</td>\n",
              "      <td>1.182600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>110</td>\n",
              "      <td>1.191200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>111</td>\n",
              "      <td>1.250900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>112</td>\n",
              "      <td>1.213200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>113</td>\n",
              "      <td>1.146200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>114</td>\n",
              "      <td>1.177700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>115</td>\n",
              "      <td>1.217800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>116</td>\n",
              "      <td>1.245500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>117</td>\n",
              "      <td>1.154900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>118</td>\n",
              "      <td>1.205400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>119</td>\n",
              "      <td>1.155000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>120</td>\n",
              "      <td>1.176500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>121</td>\n",
              "      <td>1.152200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>122</td>\n",
              "      <td>1.203300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>123</td>\n",
              "      <td>1.194100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>124</td>\n",
              "      <td>1.222000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>125</td>\n",
              "      <td>1.153100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>126</td>\n",
              "      <td>1.172800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>127</td>\n",
              "      <td>1.191600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>128</td>\n",
              "      <td>1.215200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>129</td>\n",
              "      <td>1.207100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>130</td>\n",
              "      <td>1.200800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>131</td>\n",
              "      <td>1.177500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>132</td>\n",
              "      <td>1.140600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>133</td>\n",
              "      <td>1.141500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>134</td>\n",
              "      <td>1.146600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>135</td>\n",
              "      <td>1.122800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>136</td>\n",
              "      <td>1.152900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>137</td>\n",
              "      <td>1.190700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>138</td>\n",
              "      <td>1.154700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>139</td>\n",
              "      <td>1.183800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>140</td>\n",
              "      <td>1.160500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>141</td>\n",
              "      <td>1.096000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>142</td>\n",
              "      <td>1.124700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>143</td>\n",
              "      <td>1.121000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>144</td>\n",
              "      <td>1.182000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>145</td>\n",
              "      <td>1.144500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>146</td>\n",
              "      <td>1.182200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>147</td>\n",
              "      <td>1.151000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>148</td>\n",
              "      <td>1.152600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>149</td>\n",
              "      <td>1.224500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>150</td>\n",
              "      <td>1.116600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>151</td>\n",
              "      <td>1.149500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>152</td>\n",
              "      <td>1.162200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>153</td>\n",
              "      <td>1.099100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>154</td>\n",
              "      <td>1.119100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>155</td>\n",
              "      <td>1.142200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>156</td>\n",
              "      <td>1.188800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>157</td>\n",
              "      <td>1.135000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>158</td>\n",
              "      <td>1.159000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>159</td>\n",
              "      <td>1.125200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>160</td>\n",
              "      <td>1.183500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>161</td>\n",
              "      <td>1.123200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>162</td>\n",
              "      <td>1.139300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>163</td>\n",
              "      <td>1.129700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>164</td>\n",
              "      <td>1.111700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>165</td>\n",
              "      <td>1.093400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>166</td>\n",
              "      <td>1.139300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>167</td>\n",
              "      <td>1.125600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>168</td>\n",
              "      <td>1.100800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>169</td>\n",
              "      <td>1.137200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>170</td>\n",
              "      <td>1.087700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>171</td>\n",
              "      <td>1.052200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>172</td>\n",
              "      <td>1.153600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>173</td>\n",
              "      <td>1.132200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>174</td>\n",
              "      <td>1.127100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>175</td>\n",
              "      <td>1.125800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>176</td>\n",
              "      <td>1.120600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>177</td>\n",
              "      <td>1.123200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>178</td>\n",
              "      <td>1.148700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>179</td>\n",
              "      <td>1.128300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>180</td>\n",
              "      <td>1.154800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>181</td>\n",
              "      <td>1.101900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>182</td>\n",
              "      <td>1.150900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>183</td>\n",
              "      <td>1.085300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>184</td>\n",
              "      <td>1.152900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>185</td>\n",
              "      <td>1.141800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>186</td>\n",
              "      <td>1.090200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>187</td>\n",
              "      <td>1.167600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>188</td>\n",
              "      <td>1.109800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>189</td>\n",
              "      <td>1.059100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>190</td>\n",
              "      <td>1.071300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>191</td>\n",
              "      <td>1.111100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>192</td>\n",
              "      <td>1.146600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>193</td>\n",
              "      <td>1.125800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>194</td>\n",
              "      <td>1.082200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>195</td>\n",
              "      <td>1.112300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>196</td>\n",
              "      <td>1.159800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>197</td>\n",
              "      <td>1.097600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>198</td>\n",
              "      <td>1.107900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>199</td>\n",
              "      <td>1.114800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200</td>\n",
              "      <td>1.103600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>201</td>\n",
              "      <td>1.082200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>202</td>\n",
              "      <td>1.080200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>203</td>\n",
              "      <td>1.103900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>204</td>\n",
              "      <td>1.120600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>205</td>\n",
              "      <td>1.106400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>206</td>\n",
              "      <td>1.123900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>207</td>\n",
              "      <td>1.118700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>208</td>\n",
              "      <td>1.070800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>209</td>\n",
              "      <td>1.096500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>210</td>\n",
              "      <td>1.107800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>211</td>\n",
              "      <td>1.084000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>212</td>\n",
              "      <td>1.138900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>213</td>\n",
              "      <td>1.082100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>214</td>\n",
              "      <td>1.101900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>215</td>\n",
              "      <td>1.080700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>216</td>\n",
              "      <td>1.124300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>217</td>\n",
              "      <td>1.082500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>218</td>\n",
              "      <td>1.098300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>219</td>\n",
              "      <td>1.089400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>220</td>\n",
              "      <td>1.090600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>221</td>\n",
              "      <td>1.109700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>222</td>\n",
              "      <td>1.084900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>223</td>\n",
              "      <td>1.062900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>224</td>\n",
              "      <td>1.090400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>225</td>\n",
              "      <td>1.119900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>226</td>\n",
              "      <td>1.122600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>227</td>\n",
              "      <td>1.106500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>228</td>\n",
              "      <td>1.068300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>229</td>\n",
              "      <td>1.148000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>230</td>\n",
              "      <td>1.120300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>231</td>\n",
              "      <td>1.051000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>232</td>\n",
              "      <td>1.115600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>233</td>\n",
              "      <td>1.070800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>234</td>\n",
              "      <td>1.124800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>235</td>\n",
              "      <td>1.071500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>236</td>\n",
              "      <td>1.083000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>237</td>\n",
              "      <td>1.081800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>238</td>\n",
              "      <td>1.045200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>239</td>\n",
              "      <td>1.127600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>240</td>\n",
              "      <td>1.120100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>241</td>\n",
              "      <td>1.089800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>242</td>\n",
              "      <td>1.173400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>243</td>\n",
              "      <td>1.100400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>244</td>\n",
              "      <td>1.107100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>245</td>\n",
              "      <td>1.121700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>246</td>\n",
              "      <td>1.037800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>247</td>\n",
              "      <td>1.103900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>248</td>\n",
              "      <td>1.112700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>249</td>\n",
              "      <td>1.134600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>250</td>\n",
              "      <td>1.108800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>251</td>\n",
              "      <td>1.100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>252</td>\n",
              "      <td>1.056000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>253</td>\n",
              "      <td>1.089300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>254</td>\n",
              "      <td>1.078600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>255</td>\n",
              "      <td>1.084300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>256</td>\n",
              "      <td>1.073000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>257</td>\n",
              "      <td>1.062500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>258</td>\n",
              "      <td>1.110800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>259</td>\n",
              "      <td>1.074600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>260</td>\n",
              "      <td>1.151400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>261</td>\n",
              "      <td>1.104700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>262</td>\n",
              "      <td>1.099700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>263</td>\n",
              "      <td>1.084100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>264</td>\n",
              "      <td>1.077900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>265</td>\n",
              "      <td>1.078400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>266</td>\n",
              "      <td>1.046300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>267</td>\n",
              "      <td>1.042800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>268</td>\n",
              "      <td>1.090200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>269</td>\n",
              "      <td>1.042600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>270</td>\n",
              "      <td>1.112700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>271</td>\n",
              "      <td>1.115300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>272</td>\n",
              "      <td>1.125800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>273</td>\n",
              "      <td>1.092600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>274</td>\n",
              "      <td>1.115700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>275</td>\n",
              "      <td>1.101700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>276</td>\n",
              "      <td>1.081200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>277</td>\n",
              "      <td>1.094400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>278</td>\n",
              "      <td>1.057900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>279</td>\n",
              "      <td>1.060400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>280</td>\n",
              "      <td>1.133200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>281</td>\n",
              "      <td>1.053900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>282</td>\n",
              "      <td>1.102300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>283</td>\n",
              "      <td>1.075500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>284</td>\n",
              "      <td>1.115100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>285</td>\n",
              "      <td>1.029300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>286</td>\n",
              "      <td>1.038500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>287</td>\n",
              "      <td>1.055000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>288</td>\n",
              "      <td>1.110800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>289</td>\n",
              "      <td>1.116200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>290</td>\n",
              "      <td>1.050500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>291</td>\n",
              "      <td>1.073800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>292</td>\n",
              "      <td>1.075900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>293</td>\n",
              "      <td>1.131600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>294</td>\n",
              "      <td>1.141400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>295</td>\n",
              "      <td>1.140300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>296</td>\n",
              "      <td>1.096600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>297</td>\n",
              "      <td>1.079900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>298</td>\n",
              "      <td>1.061700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>299</td>\n",
              "      <td>1.091000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>300</td>\n",
              "      <td>1.075700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>301</td>\n",
              "      <td>1.123300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>302</td>\n",
              "      <td>1.098200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>303</td>\n",
              "      <td>1.118200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>304</td>\n",
              "      <td>1.089600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>305</td>\n",
              "      <td>1.102400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>306</td>\n",
              "      <td>1.078900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>307</td>\n",
              "      <td>1.074900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>308</td>\n",
              "      <td>1.037700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>309</td>\n",
              "      <td>1.069100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>310</td>\n",
              "      <td>1.071500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>311</td>\n",
              "      <td>1.088100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>312</td>\n",
              "      <td>1.074900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>313</td>\n",
              "      <td>1.101600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>314</td>\n",
              "      <td>1.097200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>315</td>\n",
              "      <td>1.041500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>316</td>\n",
              "      <td>1.117300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>317</td>\n",
              "      <td>1.065400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>318</td>\n",
              "      <td>1.090700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>319</td>\n",
              "      <td>1.095100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>320</td>\n",
              "      <td>1.108600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>321</td>\n",
              "      <td>1.089400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>322</td>\n",
              "      <td>1.083800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>323</td>\n",
              "      <td>1.059400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>324</td>\n",
              "      <td>1.091200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>325</td>\n",
              "      <td>1.063700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>326</td>\n",
              "      <td>1.045400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>327</td>\n",
              "      <td>1.042900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>328</td>\n",
              "      <td>1.136300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>329</td>\n",
              "      <td>1.086600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>330</td>\n",
              "      <td>1.067300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>331</td>\n",
              "      <td>1.053600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>332</td>\n",
              "      <td>1.096500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>333</td>\n",
              "      <td>1.104300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>334</td>\n",
              "      <td>1.040800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>335</td>\n",
              "      <td>1.090800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>336</td>\n",
              "      <td>1.069800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>337</td>\n",
              "      <td>1.025400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>338</td>\n",
              "      <td>1.030300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>339</td>\n",
              "      <td>1.049500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>340</td>\n",
              "      <td>1.081800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>341</td>\n",
              "      <td>1.090200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>342</td>\n",
              "      <td>1.065400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>343</td>\n",
              "      <td>1.068600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>344</td>\n",
              "      <td>1.111300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>345</td>\n",
              "      <td>1.091700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>346</td>\n",
              "      <td>1.062700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>347</td>\n",
              "      <td>1.090900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>348</td>\n",
              "      <td>1.099900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>349</td>\n",
              "      <td>1.037900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>350</td>\n",
              "      <td>1.108200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>351</td>\n",
              "      <td>1.102100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>352</td>\n",
              "      <td>1.085800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>353</td>\n",
              "      <td>1.054900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>354</td>\n",
              "      <td>1.087700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>355</td>\n",
              "      <td>1.103500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>356</td>\n",
              "      <td>1.063600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>357</td>\n",
              "      <td>1.080600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>358</td>\n",
              "      <td>1.095600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>359</td>\n",
              "      <td>1.054600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>360</td>\n",
              "      <td>1.056100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>361</td>\n",
              "      <td>1.093400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>362</td>\n",
              "      <td>1.044400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>363</td>\n",
              "      <td>1.052700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>364</td>\n",
              "      <td>1.051300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>365</td>\n",
              "      <td>1.060500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>366</td>\n",
              "      <td>1.063400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>367</td>\n",
              "      <td>1.044900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>368</td>\n",
              "      <td>1.108100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>369</td>\n",
              "      <td>1.074500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>370</td>\n",
              "      <td>1.038300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>371</td>\n",
              "      <td>1.047300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>372</td>\n",
              "      <td>1.072900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>373</td>\n",
              "      <td>1.064600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>374</td>\n",
              "      <td>1.104100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>375</td>\n",
              "      <td>1.074300</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/"
        },
        "id": "pCqnaKmlO1U9",
        "outputId": "ecb21ea9-b0f5-48ab-9145-5baedb68a203"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5034.6413 seconds used for training.\n",
            "83.91 minutes used for training.\n",
            "Peak reserved memory = 13.508 GB.\n",
            "Peak reserved memory for training = 12.629 GB.\n",
            "Peak reserved memory % of max memory = 91.592 %.\n",
            "Peak reserved memory for training % of max memory = 85.632 %.\n"
          ]
        }
      ],
      "source": [
        "#@title Show final memory and time stats\n",
        "used_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n",
        "used_memory_for_lora = round(used_memory - start_gpu_memory, 3)\n",
        "used_percentage = round(used_memory         /max_memory*100, 3)\n",
        "lora_percentage = round(used_memory_for_lora/max_memory*100, 3)\n",
        "print(f\"{trainer_stats.metrics['train_runtime']} seconds used for training.\")\n",
        "print(f\"{round(trainer_stats.metrics['train_runtime']/60, 2)} minutes used for training.\")\n",
        "print(f\"Peak reserved memory = {used_memory} GB.\")\n",
        "print(f\"Peak reserved memory for training = {used_memory_for_lora} GB.\")\n",
        "print(f\"Peak reserved memory % of max memory = {used_percentage} %.\")\n",
        "print(f\"Peak reserved memory for training % of max memory = {lora_percentage} %.\")"
      ]
    },
    {
      "cell_type": "markdown",
      "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/"
        },
        "id": "kR3gIAX-SM2q",
        "outputId": "2283bb0c-d568-4c92-8352-094ba12eaf06"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['<s> Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\\n\\n### Instruction:\\nContinue the fibonnaci sequence.\\n\\n### Input:\\n1, 1, 2, 3, 5, 8\\n\\n### Response:\\nThe fibonacci sequence is a sequence of numbers that can be generated by adding the previous two numbers and then subtracting the previous number from the previous number. The first number in the sequence is 1, and the second number is 1. The third number in the sequence is 1 + 1 = 2, the fourth number is 1 + 2 = 3, and so on. The sequence can be continued by adding 1 + 1 = 2, 1 + 2 = 3, and so on.</s>']"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "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"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uMuVrWbjAzhc"
      },
      "source": [
        "<a name=\"Save\"></a>\n",
        "### Saving, loading finetuned models\n",
        "To save the final model as LoRA adapters, either use Huggingface's `push_to_hub` for an online save or `save_pretrained` for a local save.\n",
        "\n",
        "**[NOTE]** This ONLY saves the LoRA adapters, and not the full model. To save to 16bit or GGUF, scroll down!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "upcOlWe7A1vc"
      },
      "outputs": [],
      "source": [
        "model.save_pretrained(\"lora_model\") # Local saving\n",
        "tokenizer.save_pretrained(\"lora_model\")\n",
        "# model.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving\n",
        "# tokenizer.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving"
      ]
    },
    {
      "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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