{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IqM-T1RTzY6C"
      },
      "source": [
        "To run this, press \"*Runtime*\" and press \"*Run all*\" on a **free** Tesla T4 Google Colab instance!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2eSvM9zX_2d3"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "!pip install unsloth\n",
        "# Also get the latest nightly Unsloth!\n",
        "!pip uninstall unsloth -y && pip install --upgrade --no-cache-dir \"unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git\"\n",
        "\n",
        "# Install Flash Attention 2 for softcapping support\n",
        "import torch\n",
        "if torch.cuda.get_device_capability()[0] >= 8:\n",
        "    !pip install --no-deps packaging ninja einops \"flash-attn>=2.6.3\""
      ]
    },
    {
      "cell_type": "code",
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        "id": "QmUBVEnvCDJv",
        "outputId": "10436b44-7e7b-4b8d-8d55-59ea9f5830bd"
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      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n",
            "==((====))==  Unsloth 2024.9.post3: Fast Gemma2 patching. Transformers = 4.44.2.\n",
            "   \\\\   /|    GPU: Tesla T4. Max memory: 14.748 GB. Platform = Linux.\n",
            "O^O/ \\_/ \\    Pytorch: 2.4.1+cu121. CUDA = 7.5. CUDA Toolkit = 12.1.\n",
            "\\        /    Bfloat16 = FALSE. FA [Xformers = 0.0.28.post1. FA2 = False]\n",
            " \"-____-\"     Free Apache license: http://github.com/unslothai/unsloth\n",
            "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n"
          ]
        },
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            "text/plain": [
              "model.safetensors:   0%|          | 0.00/2.22G [00:00<?, ?B/s]"
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              "generation_config.json:   0%|          | 0.00/190 [00:00<?, ?B/s]"
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              "tokenizer_config.json:   0%|          | 0.00/46.4k [00:00<?, ?B/s]"
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              "tokenizer.model:   0%|          | 0.00/4.24M [00:00<?, ?B/s]"
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              "special_tokens_map.json:   0%|          | 0.00/636 [00:00<?, ?B/s]"
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          },
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        },
        {
          "data": {
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      ],
      "source": [
        "from unsloth import FastLanguageModel\n",
        "import torch\n",
        "max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!\n",
        "dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+\n",
        "load_in_4bit = 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",
        "    \"unsloth/gemma-2-2b-bnb-4bit\",             # New small Gemma model!\n",
        "] # More models at https://huggingface.co/unsloth\n",
        "\n",
        "model, tokenizer = FastLanguageModel.from_pretrained(\n",
        "    model_name = \"unsloth/gemma-2-2b\",\n",
        "    max_seq_length = max_seq_length,\n",
        "    dtype = dtype,\n",
        "    load_in_4bit = load_in_4bit,\n",
        "    # token = \"hf_...\", # use one if using gated models like meta-llama/Llama-2-7b-hf\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SXd9bTZd1aaL"
      },
      "source": [
        "We now add LoRA adapters so we only need to update 1 to 10% of all parameters!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6bZsfBuZDeCL",
        "outputId": "222ba5d2-b328-435f-d240-fe109af81335"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Unsloth 2024.9.post3 patched 26 layers with 26 QKV layers, 26 O layers and 26 MLP layers.\n"
          ]
        }
      ],
      "source": [
        "model = FastLanguageModel.get_peft_model(\n",
        "    model,\n",
        "    r = 16, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128\n",
        "    target_modules = [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\n",
        "                      \"gate_proj\", \"up_proj\", \"down_proj\",],\n",
        "    lora_alpha = 16,\n",
        "    lora_dropout = 0, # Supports any, but = 0 is optimized\n",
        "    bias = \"none\",    # Supports any, but = \"none\" is optimized\n",
        "    # [NEW] \"unsloth\" uses 30% less VRAM, fits 2x larger batch sizes!\n",
        "    use_gradient_checkpointing = \"unsloth\", # True or \"unsloth\" for very long context\n",
        "    random_state = 3157,\n",
        "    use_rslora = False,  # We support rank stabilized LoRA\n",
        "    loftq_config = None, # And LoftQ\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vITh0KVJ10qX"
      },
      "source": [
        "<a name=\"Data\"></a>\n",
        "### Data Prep\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EbUIVm1jLt6P"
      },
      "outputs": [],
      "source": [
        "from datasets import Dataset\n",
        "import pandas as pd\n",
        "dataset = Dataset.from_pandas(pd.read_csv('train.csv'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
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        },
        "id": "LjY75GoYUCB8",
        "outputId": "c3722c4f-6f58-4c56-f36f-6233dc92ae3b"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "17c04d54063249cbb7ca949fce48442b",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/2700 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "my_prompt = \"\"\"\n",
        "### Instruction:\n",
        "{}\n",
        "\n",
        "### Response:\n",
        "{}\"\"\"\n",
        "\n",
        "\n",
        "EOS_TOKEN = tokenizer.eos_token # Must add EOS_TOKEN\n",
        "def formatting_prompts_func(examples):\n",
        "    instructions = examples[\"instruction\"]\n",
        "    outputs      = examples[\"output\"]\n",
        "    texts = []\n",
        "    for instruction, output in zip(instructions, outputs):\n",
        "        # Must add EOS_TOKEN, otherwise your generation will go on forever!\n",
        "        text = my_prompt.format(instruction, 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 60 steps to speed things up, but you can set `num_train_epochs=1` for a full run, and turn off `max_steps=None`. We also support TRL's `DPOTrainer`!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
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        },
        "id": "95_Nn-89DhsL",
        "outputId": "1d9946b8-fabb-49b1-d08c-9be1df0aff4a"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a6001deecd1b441cad4fb9ef73dea593",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map (num_proc=2):   0%|          | 0/2700 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "max_steps is given, it will override any value given in num_train_epochs\n"
          ]
        }
      ],
      "source": [
        "from trl import SFTTrainer\n",
        "from transformers import TrainingArguments\n",
        "from unsloth import is_bfloat16_supported\n",
        "\n",
        "trainer = SFTTrainer(\n",
        "    model = model,\n",
        "    tokenizer = tokenizer,\n",
        "    train_dataset = dataset,\n",
        "    dataset_text_field = \"text\",\n",
        "    max_seq_length = max_seq_length,\n",
        "    dataset_num_proc = 2,\n",
        "    packing = False, # Can make training 5x faster for short sequences.\n",
        "    args = TrainingArguments(\n",
        "        per_device_train_batch_size = 2,\n",
        "        gradient_accumulation_steps = 4,\n",
        "        warmup_steps = 5,\n",
        "        # num_train_epochs = 1, # Set this for 1 full training run.\n",
        "        max_steps = 3000,\n",
        "        learning_rate = 2e-4,\n",
        "        fp16 = not is_bfloat16_supported(),\n",
        "        bf16 = is_bfloat16_supported(),\n",
        "        logging_steps = 100,\n",
        "        optim = \"adamw_8bit\",\n",
        "        weight_decay = 0.01,\n",
        "        lr_scheduler_type = \"linear\",\n",
        "        seed = 3407,\n",
        "        output_dir = \"outputs\",\n",
        "    ),\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "yqxqAZ7KJ4oL",
        "outputId": "b4e7ff26-27c6-4197-c71e-81ee8b738b0a"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "==((====))==  Unsloth - 2x faster free finetuning | Num GPUs = 1\n",
            "   \\\\   /|    Num examples = 2,700 | Num Epochs = 9\n",
            "O^O/ \\_/ \\    Batch size per device = 2 | Gradient Accumulation steps = 4\n",
            "\\        /    Total batch size = 8 | Total steps = 3,000\n",
            " \"-____-\"     Number of trainable parameters = 20,766,720\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='3000' max='3000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [3000/3000 1:27:56, Epoch 8/9]\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>100</td>\n",
              "      <td>0.809900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200</td>\n",
              "      <td>0.471200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>300</td>\n",
              "      <td>0.452000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>400</td>\n",
              "      <td>0.419900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>0.389400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>600</td>\n",
              "      <td>0.396300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>700</td>\n",
              "      <td>0.376600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>800</td>\n",
              "      <td>0.331700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>900</td>\n",
              "      <td>0.338700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>0.335900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1100</td>\n",
              "      <td>0.282800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1200</td>\n",
              "      <td>0.281600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1300</td>\n",
              "      <td>0.283500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1400</td>\n",
              "      <td>0.259900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1500</td>\n",
              "      <td>0.230700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1600</td>\n",
              "      <td>0.235800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1700</td>\n",
              "      <td>0.232200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1800</td>\n",
              "      <td>0.197000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1900</td>\n",
              "      <td>0.199700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2000</td>\n",
              "      <td>0.203000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2100</td>\n",
              "      <td>0.175900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2200</td>\n",
              "      <td>0.171800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2300</td>\n",
              "      <td>0.171600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2400</td>\n",
              "      <td>0.160500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2500</td>\n",
              "      <td>0.144100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2600</td>\n",
              "      <td>0.145600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2700</td>\n",
              "      <td>0.146000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2800</td>\n",
              "      <td>0.119600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2900</td>\n",
              "      <td>0.118700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3000</td>\n",
              "      <td>0.118800</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "trainer_stats = trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pCqnaKmlO1U9",
        "outputId": "21875fd4-df3b-4ca0-904c-0164cfd40d37"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "5340.1034 seconds used for training.\n",
            "89.0 minutes used for training.\n",
            "Peak reserved memory = 5.273 GB.\n",
            "Peak reserved memory for training = 2.576 GB.\n",
            "Peak reserved memory % of max memory = 35.754 %.\n",
            "Peak reserved memory for training % of max memory = 17.467 %.\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": "db3058a9-3435-4d14-932f-e6ae4587033d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['<bos>\\n### Instruction:\\n서울시 20대 남성, 여행지 추천\\n\\n### Response:\\n유사한 사람들의 만족도를 고려하였을 때 추천드리는 여행지는 마니 주네입니다.<eos>']"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# alpaca_prompt = Copied from above\n",
        "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n",
        "inputs = tokenizer(\n",
        "[\n",
        "    my_prompt.format(\n",
        "        \"서울시 20대 남성, 여행지 추천\", # 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": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lg60IgwoD4xD",
        "outputId": "1768ab35-e93b-4f66-a9b1-66dadf93f1ff"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['<bos>\\n### Instruction:\\n30대 남성을 위한 역사 관련 여행지 추천 해줘.\\n\\n### Response:\\n광교호수 공원에 가보셨나요? 가보시지 않으셨다면, 광교호수 공원을 추천드립니다.<eos>']"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# alpaca_prompt = Copied from above\n",
        "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n",
        "inputs = tokenizer(\n",
        "[\n",
        "    my_prompt.format(\n",
        "        \"30대 남성을 위한 역사 관련 여행지 추천 해줘.\", # 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": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T88NmfQ4EABS",
        "outputId": "c1775675-1e4b-4521-9035-505fae76c497"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['<bos>\\n### Instruction:\\n20, 30대 여성이 좋아할 만한 인생샷 여행지 하나 골라줘\\n\\n### Response:\\n유사한 사람들의 만족도를 고려하였을 때 추천드리는 여행지는 송도 현대 아울렛 + 드림 consentimiento입니다.<eos>']"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# alpaca_prompt = Copied from above\n",
        "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n",
        "inputs = tokenizer(\n",
        "[\n",
        "    my_prompt.format(\n",
        "        \"20, 30대 여성이 좋아할 만한 인생샷 여행지 하나 골라줘\", # instruction\n",
        "        # \"1, 1, 2, 3, 5, 8\", # input\n",
        "        \"\", # output - leave this blank for generation!\n",
        "    )\n",
        "], return_tensors = \"pt\").to(\"cuda\")\n",
        "\n",
        "outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)\n",
        "tokenizer.batch_decode(outputs)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "upcOlWe7A1vc",
        "outputId": "9d4a2e07-72f9-44db-a429-160419f40c70"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "('lora_model/tokenizer_config.json',\n",
              " 'lora_model/special_tokens_map.json',\n",
              " 'lora_model/tokenizer.model',\n",
              " 'lora_model/added_tokens.json',\n",
              " 'lora_model/tokenizer.json')"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "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": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "f8b7b32766314e96a758cf4f145cdea8",
            "932f663d06bb4578b56104d6d985f855",
            "02ca8f66af5641fe8814e874aba86ab2",
            "bd46a7dc2ba8460992e732c84618f4e4",
            "8b3e0c0b7d8741eb9b5b0d7a6c0a34f9",
            "aa6c42694a144e60b6a44596aee73fe5",
            "481a74bb83704a469152edfff619ba24",
            "c669ce471c974262a1294dd75ab07f52",
            "781a67ef3e914879bc96effcd8b41697",
            "c2bd4556856d468e815c9ad12283e5e1",
            "9e3bdb36d2234935a05f06909e7c70cc",
            "f61ac1152f6d411980bfc12e944b7d79",
            "98051bd6e5ff45638a2e265f6158cc82",
            "82cc5d76f5b54e9abefe1ad48518b8c9",
            "deb14741904c405a95075171d0e4fab1",
            "98e7ad48d0b04236a17f41d21bcb8fda",
            "b01a4056819641b58c9a7a9b2438fb07",
            "1a1fc8509bbc425abd23b118ccc4d8e8",
            "0944ff904bed41c1b3c8951327073fbf",
            "80be92ccc68f4aa58dbc5a6c3cb6c307",
            "251fe4a4727d452aa51651f4b5a09010",
            "2fd937ed61c84c4e8fe8f50f07b1b0d4",
            "be39eff76599457d9c593fdce251e0f4",
            "22649e48d0bb4a6cbb3af69721713518",
            "3c9203dd50444b09925a44f7401706e1",
            "98fa156bb57d4094b54c11a74c206a3b",
            "3867c9d7eff34ed6afd8dd9b0142f1aa",
            "a171da9a74a448a699cb8107fd26ed9e",
            "f35f11dc7cd34a25850d6606e48a8cf8",
            "ae6a7e18501f42808e1e5d0aed95f713",
            "3d5bfd8c487b4a63b7daec6aea3d3f11",
            "76b03b919b67422baa3e361860525592"
          ]
        },
        "id": "fcJeUBxka83E",
        "outputId": "a41aa2f3-952b-483d-9663-9bc0d53448f3"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f8b7b32766314e96a758cf4f145cdea8",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226,
          "referenced_widgets": [
            "b11ac89ae0ad4c928d15ab99ecad591b",
            "95fd2f1454004fdf91e6a642095477ae",
            "0276aa8fa428434182b0af069e59d4ff",
            "b7adc3b9b62c42a0bd8a4dbf13e4ecfa",
            "d65a36e7e2d940c6ad988ebd0903863c",
            "694a62e2ca40461eba0175de68c0a579",
            "dc76013314ed4243a0516f600a64a09f",
            "a586eaf6669c4487b78f8f1ca1a90257",
            "7feda2cea5ca432cad06a13f48f39000",
            "06c4a0603ace4a1f9733f7aef969ca74",
            "17a908f13a524164bd25d04fa947fc6c",
            "51759205e9364d099979747351179e84",
            "dcd0406128ea4e4d8dcb76c1c153eaa9",
            "1b7bb118d9384b4ca3d420b70adc62cc",
            "943f564f8cce4a87b87de5f4bcfe2398",
            "7434ee2eba9f425b9a2c94e41bd0f8e5",
            "91d102a1e7c44b8a9a9c367983af3d73",
            "a57127f941034eafb418e5b0aac2efd8",
            "66215e5fd4a1487da28bc70e5097f9e1",
            "aa9d328f9c164c77b7eb2f3a14f50061",
            "750bf0da5e6a4d43848ac553dc95d01f",
            "ae55f1148be643509d969d7ee5431aec",
            "eb7aee61a2d741248d3961c236478f6c",
            "36b921e6e5b34b7cbfa103b635ad9ed7",
            "d4df7204ce064141b485a163da4ff0f4",
            "7c9eb1800d90459081b860fea43053fe",
            "c3293aae83574c9fa3875e6462ad6ba6",
            "97239cf5760c4d73bbb00d4ddf40e5e2",
            "ad0488c5d9084031847d3b972687a814",
            "e58cd8eff5d74f28b00d627133ca9977",
            "495efefe3a9f40c7ac8081325565c934",
            "f019eab0f77d4daaae803232e1769a5e",
            "c01647004f784e0cb067992d962cc0b1",
            "4782422f71494f2d9f709a213198ae19",
            "98cf04b7cbdd47d1b9fe72650aabdf7c",
            "f2285525db0e4118829e40a77b002dab",
            "41aafaf9eb434af393aafa627698f98e",
            "f94ad9b46eb5460fb99d1aca51b02b0c",
            "7086b0862e04420da3bdf00d1b790dee",
            "0e3533ba24514895abc77dad44f6c44a",
            "6b0a74216a8f474e924dd92fe0bfffd6",
            "eb7e4d0320c441e78f5905bf2cb7c2a8",
            "f6e9ff64da5d4f37946d4062b6cba8b8",
            "9d3858615b334aada065606f8ddd65e3",
            "af76f427e1e143bc8c8e9a4ed2ebac14",
            "a100488d26cd4e9fa988cabff48a9b9d",
            "ed21b5d7c257417faf8cd4fc904607b8",
            "5b1070ff71b14e09bb408b9f39bee34e",
            "72df7e1ac8d64ab8965e22fd12e127f0",
            "3cff14935a924b2fa12d1d63ead18935",
            "04e3f3d1c4fe4790955560d690bde40c",
            "ce601558aaf543739bbf8ad2efacee8f",
            "8363758d6c654d5c8e09cbb703edcca8",
            "25e8207bcd7349059d8ea0c26de0b634",
            "693f961cc69348e99791e39c2bd02d4f",
            "ba7039a49b41440683642363b1ec96dd",
            "35dbacdb77a54fd8966d88ce55746f6a",
            "f39eb78383b64bdb9a141bef030e5f96",
            "b8dad62f27544ec891b1f524934ba2aa",
            "020201182fb7460f9e00634d5e3eca01",
            "42388ce4971843a28edf226f20010597",
            "a25aa10cb81145fda86453fe8b6dd8ac",
            "aa5aa9d4079b48928c1b14e4d8937cf7",
            "8578354186754fec9127da1baed24e05",
            "2722c678226c451a8c398078186f4349",
            "6644dbe452e54aa1bafdd030a6c1f4e8"
          ]
        },
        "id": "0qPpzkGUa7bK",
        "outputId": "900fbdcd-cf64-46c1-9f69-c38292fd62a2"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b11ac89ae0ad4c928d15ab99ecad591b",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "51759205e9364d099979747351179e84",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "adapter_model.safetensors:   0%|          | 0.00/83.1M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Saved model to https://huggingface.co/pinoc3157/trip_advisor\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "eb7aee61a2d741248d3961c236478f6c",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "README.md:   0%|          | 0.00/5.18k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4782422f71494f2d9f709a213198ae19",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/2 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "af76f427e1e143bc8c8e9a4ed2ebac14",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/17.5M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ba7039a49b41440683642363b1ec96dd",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer.model:   0%|          | 0.00/4.24M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
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        "model.push_to_hub(\"pinoc3157/trip_advisor\", token = \"...\") # Online saving\n",
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