SQLAssist_Training.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/SupriyaUpadhyaya/HCNLP-Text2Sql-Project/blob/main/SQLAssist_Training.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dependencies"
      ],
      "metadata": {
        "id": "gzVd3IltzQHr"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2eSvM9zX_2d3"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "# Installs Unsloth, Xformers (Flash Attention) and all other packages!\n",
        "!pip install \"unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git\"\n",
        "!pip install --no-deps \"xformers<0.0.26\" trl peft accelerate bitsandbytes\n",
        "!pip install huggingface_hub[hf_transfer]"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Imports"
      ],
      "metadata": {
        "id": "ikLao4ABzLp8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from unsloth import FastLanguageModel\n",
        "from datasets import load_dataset, Dataset\n",
        "from trl import SFTTrainer\n",
        "from transformers import TrainingArguments"
      ],
      "metadata": {
        "id": "V7vguKqrW805"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Load the base model"
      ],
      "metadata": {
        "id": "CsjUk96tyrCh"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QmUBVEnvCDJv"
      },
      "outputs": [],
      "source": [
        "max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!\n",
        "dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+\n",
        "load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.\n",
        "\n",
        "# 4bit pre quantized models we support for 4x faster downloading + no OOMs.\n",
        "fourbit_models = [\n",
        "    \"unsloth/mistral-7b-bnb-4bit\",\n",
        "    \"unsloth/mistral-7b-instruct-v0.2-bnb-4bit\",\n",
        "    \"unsloth/llama-2-7b-bnb-4bit\",\n",
        "    \"unsloth/gemma-7b-bnb-4bit\",\n",
        "    \"unsloth/gemma-7b-it-bnb-4bit\", # Instruct version of Gemma 7b\n",
        "    \"unsloth/gemma-2b-bnb-4bit\",\n",
        "    \"unsloth/gemma-2b-it-bnb-4bit\", # Instruct version of Gemma 2b\n",
        "    \"unsloth/llama-3-8b-bnb-4bit\", # [NEW] 15 Trillion token Llama-3\n",
        "] # More models at https://huggingface.co/unsloth\n",
        "\n",
        "model, tokenizer = FastLanguageModel.from_pretrained(\n",
        "    model_name = \"unsloth/llama-3-8b-bnb-4bit\",\n",
        "    max_seq_length = max_seq_length,\n",
        "    dtype = dtype,\n",
        "    load_in_4bit = load_in_4bit,\n",
        "    # token = \"hf_...\", # use one if using gated models like meta-llama/Llama-2-7b-hf\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We now add LoRA adapters so we only need to update 1 to 10% of all parameters!"
      ],
      "metadata": {
        "id": "SXd9bTZd1aaL"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6bZsfBuZDeCL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9fcdadbc-f5fb-4e78-db20-9b00ceb53f57"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Unsloth 2024.4 patched 32 layers with 32 QKV layers, 32 O layers and 32 MLP layers.\n"
          ]
        }
      ],
      "source": [
        "model = FastLanguageModel.get_peft_model(\n",
        "    model,\n",
        "    r = 16, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128\n",
        "    target_modules = [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\n",
        "                      \"gate_proj\", \"up_proj\", \"down_proj\",],\n",
        "    lora_alpha = 16,\n",
        "    lora_dropout = 0, # Supports any, but = 0 is optimized\n",
        "    bias = \"none\",    # Supports any, but = \"none\" is optimized\n",
        "    # [NEW] \"unsloth\" uses 30% less VRAM, fits 2x larger batch sizes!\n",
        "    use_gradient_checkpointing = \"unsloth\", # True or \"unsloth\" for very long context\n",
        "    random_state = 3407,\n",
        "    use_rslora = False,  # We support rank stabilized LoRA\n",
        "    loftq_config = None, # And LoftQ\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<a name=\"Data\"></a>\n",
        "### Data Prep\n"
      ],
      "metadata": {
        "id": "vITh0KVJ10qX"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LjY75GoYUCB8"
      },
      "outputs": [],
      "source": [
        "text_to_sql_tmpl_str = \"\"\"\\\n",
        "### Instruction:\\n{system_message}{user_message}\\n\\n### Response:\\n{response}\"\"\"\n",
        "\n",
        "text_to_sql_inference_tmpl_str = \"\"\"\\\n",
        "### Instruction:\\n{system_message}{user_message}\\n\\n### Response:\\n\"\"\"\n",
        "\n",
        "\n",
        "def _generate_prompt_sql(input, context, dialect=\"sqlite\", output=\"\"):\n",
        "    system_message = f\"\"\"You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question and context regarding one or more tables.\n",
        "\n",
        "You must output the SQL query that answers the question.\n",
        "\n",
        "    \"\"\"\n",
        "    user_message = f\"\"\"### Dialect:\n",
        "{dialect}\n",
        "\n",
        "### Input:\n",
        "{input}\n",
        "\n",
        "### Context:\n",
        "{context}\n",
        "\n",
        "### Response:\n",
        "\"\"\"\n",
        "    if output:\n",
        "        return text_to_sql_tmpl_str.format(\n",
        "            system_message=system_message,\n",
        "            user_message=user_message,\n",
        "            response=output,\n",
        "        )\n",
        "    else:\n",
        "        return text_to_sql_inference_tmpl_str.format(\n",
        "            system_message=system_message, user_message=user_message\n",
        "        )\n",
        "\n",
        "\n",
        "def generate_prompt(data_point):\n",
        "    full_prompt = _generate_prompt_sql(\n",
        "        data_point[\"instruction\"],\n",
        "        data_point[\"input\"],\n",
        "        dialect=\"sqlite\",\n",
        "        output=data_point[\"response\"],\n",
        "    )\n",
        "    EOS_TOKEN = tokenizer.eos_token\n",
        "    return full_prompt + EOS_TOKEN"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the dataset for fine-tuning\n",
        "dataset_id = \"Clinton/Text-to-sql-v1\"\n",
        "\n",
        "data = load_dataset(dataset_id, split=\"train\")\n",
        "df = data.to_pandas()"
      ],
      "metadata": {
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        "outputId": "d6adc237-b4cf-4bc0-918b-1de8ddfcfe2c"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/118 [00:00<?, ?B/s]"
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/635M [00:00<?, ?B/s]"
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/262208 [00:00<?, ? examples/s]"
            ],
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              "version_major": 2,
              "version_minor": 0,
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    },
    {
      "cell_type": "code",
      "source": [
        "# Apply the chat_template_for_training function to each row in the\n",
        "# dataframe and store the result in a new \"text\" column.\n",
        "df[\"text\"] = df.apply(lambda x: generate_prompt(x), axis=1)\n",
        "\n",
        "# Convert the dataframe back to a Dataset object.\n",
        "formatted_data = Dataset.from_pandas(df)"
      ],
      "metadata": {
        "id": "dXu0awmBegx2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"text\"][0]"
      ],
      "metadata": {
        "id": "AXaFsuKGEz0l",
        "outputId": "f8d0ac93-5654-4b9d-dd67-008b0b848bf3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'### Instruction:\\nYou are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question and context regarding one or more tables.\\n\\nYou must output the SQL query that answers the question.\\n\\n    ### Dialect:\\nsqlite\\n\\n### Input:\\nName the home team for carlton away team\\n\\n### Context:\\nCREATE TABLE table_name_77 (\\n    home_team VARCHAR,\\n    away_team VARCHAR\\n)\\n\\n### Response:\\n\\n\\n### Response:\\nSELECT home_team FROM table_name_77 WHERE away_team = \"carlton\"<|end_of_text|>'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<a name=\"Train\"></a>\n",
        "### Train the model"
      ],
      "metadata": {
        "id": "idAEIeSQ3xdS"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "95_Nn-89DhsL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121,
          "referenced_widgets": [
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        },
        "outputId": "2700e704-d800-48b6-c5db-130027c73c11"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/multiprocess/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
            "  self.pid = os.fork()\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map (num_proc=2):   0%|          | 0/262208 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "df333f62ea954d559381fd4ca4146aad"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "max_steps is given, it will override any value given in num_train_epochs\n"
          ]
        }
      ],
      "source": [
        "trainer = SFTTrainer(\n",
        "    model = model,\n",
        "    tokenizer = tokenizer,\n",
        "    train_dataset = formatted_data,\n",
        "    dataset_text_field = \"text\",\n",
        "    max_seq_length = max_seq_length,\n",
        "    dataset_num_proc = 2,\n",
        "    packing = False, # Can make training 5x faster for short sequences.\n",
        "    args = TrainingArguments(\n",
        "        per_device_train_batch_size = 2,\n",
        "        gradient_accumulation_steps = 4,\n",
        "        warmup_steps = 5,\n",
        "        max_steps = 100,\n",
        "        learning_rate = 2e-4,\n",
        "        fp16 = not torch.cuda.is_bf16_supported(),\n",
        "        bf16 = torch.cuda.is_bf16_supported(),\n",
        "        logging_steps = 1,\n",
        "        optim = \"adamw_8bit\",\n",
        "        weight_decay = 0.01,\n",
        "        lr_scheduler_type = \"linear\",\n",
        "        seed = 3407,\n",
        "        output_dir = \"outputs\",\n",
        "    ),\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2ejIt2xSNKKp",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ef1b45c2-0478-48ce-dec1-2e017fd09622"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GPU = Tesla T4. Max memory = 14.748 GB.\n",
            "5.594 GB of memory reserved.\n"
          ]
        }
      ],
      "source": [
        "#@title Show current memory stats\n",
        "gpu_stats = torch.cuda.get_device_properties(0)\n",
        "start_gpu_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n",
        "max_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3)\n",
        "print(f\"GPU = {gpu_stats.name}. Max memory = {max_memory} GB.\")\n",
        "print(f\"{start_gpu_memory} GB of memory reserved.\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yqxqAZ7KJ4oL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "d455aa1b-471c-4221-a075-5c9227cffc59"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "==((====))==  Unsloth - 2x faster free finetuning | Num GPUs = 1\n",
            "   \\\\   /|    Num examples = 262,208 | Num Epochs = 1\n",
            "O^O/ \\_/ \\    Batch size per device = 2 | Gradient Accumulation steps = 4\n",
            "\\        /    Total batch size = 8 | Total steps = 100\n",
            " \"-____-\"     Number of trainable parameters = 41,943,040\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='100' max='100' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [100/100 21:15, Epoch 0/1]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>2.035300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>1.557900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>2.147200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>1.637300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>1.114700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>1.481800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>1.686200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>1.209800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.924600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>0.929900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11</td>\n",
              "      <td>0.811800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12</td>\n",
              "      <td>0.760600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13</td>\n",
              "      <td>0.796700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14</td>\n",
              "      <td>0.765600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15</td>\n",
              "      <td>0.825700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16</td>\n",
              "      <td>0.909900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>17</td>\n",
              "      <td>0.788300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>18</td>\n",
              "      <td>0.699200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>19</td>\n",
              "      <td>0.779800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>0.742500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>21</td>\n",
              "      <td>0.648400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>22</td>\n",
              "      <td>0.736500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>23</td>\n",
              "      <td>0.817000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>24</td>\n",
              "      <td>0.762800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>25</td>\n",
              "      <td>0.528100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>26</td>\n",
              "      <td>0.704900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>27</td>\n",
              "      <td>0.634600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>28</td>\n",
              "      <td>0.766700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>29</td>\n",
              "      <td>0.588900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>30</td>\n",
              "      <td>0.590800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>31</td>\n",
              "      <td>0.539700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>32</td>\n",
              "      <td>0.599700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>33</td>\n",
              "      <td>0.639200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>34</td>\n",
              "      <td>0.682800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>35</td>\n",
              "      <td>0.478800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>36</td>\n",
              "      <td>0.531700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>37</td>\n",
              "      <td>0.503000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>38</td>\n",
              "      <td>0.556800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>39</td>\n",
              "      <td>0.731400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>40</td>\n",
              "      <td>0.612600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>41</td>\n",
              "      <td>0.698400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>42</td>\n",
              "      <td>0.550300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>43</td>\n",
              "      <td>0.583700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>44</td>\n",
              "      <td>0.715200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>45</td>\n",
              "      <td>0.627700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>46</td>\n",
              "      <td>0.580200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>47</td>\n",
              "      <td>0.557100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>48</td>\n",
              "      <td>0.498200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>49</td>\n",
              "      <td>0.463300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>50</td>\n",
              "      <td>0.591300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>51</td>\n",
              "      <td>0.803900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>52</td>\n",
              "      <td>0.602700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>53</td>\n",
              "      <td>0.425200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>54</td>\n",
              "      <td>0.732600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>55</td>\n",
              "      <td>0.615300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>56</td>\n",
              "      <td>0.522800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>57</td>\n",
              "      <td>0.598900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>58</td>\n",
              "      <td>0.462100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>59</td>\n",
              "      <td>0.690700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>60</td>\n",
              "      <td>0.356000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>61</td>\n",
              "      <td>0.666600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>62</td>\n",
              "      <td>0.477800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>63</td>\n",
              "      <td>0.690000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>64</td>\n",
              "      <td>0.593600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>65</td>\n",
              "      <td>0.380500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>66</td>\n",
              "      <td>0.690400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>67</td>\n",
              "      <td>0.413000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>68</td>\n",
              "      <td>0.587700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>69</td>\n",
              "      <td>0.594300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>70</td>\n",
              "      <td>0.574500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>71</td>\n",
              "      <td>0.668900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>72</td>\n",
              "      <td>0.607600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>73</td>\n",
              "      <td>0.550200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>74</td>\n",
              "      <td>0.446700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>75</td>\n",
              "      <td>0.496000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>76</td>\n",
              "      <td>0.531000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>77</td>\n",
              "      <td>0.584300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>78</td>\n",
              "      <td>0.707500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>79</td>\n",
              "      <td>0.591300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>80</td>\n",
              "      <td>0.365100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>81</td>\n",
              "      <td>0.573800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>82</td>\n",
              "      <td>0.493700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>83</td>\n",
              "      <td>0.575600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>84</td>\n",
              "      <td>0.543500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>85</td>\n",
              "      <td>0.430800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>86</td>\n",
              "      <td>0.538300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>87</td>\n",
              "      <td>0.403300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>88</td>\n",
              "      <td>0.658400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>89</td>\n",
              "      <td>0.472300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>90</td>\n",
              "      <td>0.525600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>91</td>\n",
              "      <td>0.522700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>92</td>\n",
              "      <td>0.568000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>93</td>\n",
              "      <td>0.526600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>94</td>\n",
              "      <td>0.410700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>95</td>\n",
              "      <td>0.528800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>96</td>\n",
              "      <td>0.376600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>97</td>\n",
              "      <td>0.571700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>98</td>\n",
              "      <td>0.664100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>99</td>\n",
              "      <td>0.517800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>0.460700</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "trainer_stats = trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pCqnaKmlO1U9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7dd3e570-be90-4e55-b991-4800284d6910",
        "cellView": "form"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "329.7482 seconds used for training.\n",
            "5.5 minutes used for training.\n",
            "Peak reserved memory = 6.311 GB.\n",
            "Peak reserved memory for training = 0.717 GB.\n",
            "Peak reserved memory % of max memory = 42.792 %.\n",
            "Peak reserved memory for training % of max memory = 4.862 %.\n"
          ]
        }
      ],
      "source": [
        "#@title Show final memory and time stats\n",
        "used_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n",
        "used_memory_for_lora = round(used_memory - start_gpu_memory, 3)\n",
        "used_percentage = round(used_memory         /max_memory*100, 3)\n",
        "lora_percentage = round(used_memory_for_lora/max_memory*100, 3)\n",
        "print(f\"{trainer_stats.metrics['train_runtime']} seconds used for training.\")\n",
        "print(f\"{round(trainer_stats.metrics['train_runtime']/60, 2)} minutes used for training.\")\n",
        "print(f\"Peak reserved memory = {used_memory} GB.\")\n",
        "print(f\"Peak reserved memory for training = {used_memory_for_lora} GB.\")\n",
        "print(f\"Peak reserved memory % of max memory = {used_percentage} %.\")\n",
        "print(f\"Peak reserved memory for training % of max memory = {lora_percentage} %.\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<a name=\"Save\"></a>\n",
        "### Saving finetuned models\n"
      ],
      "metadata": {
        "id": "uMuVrWbjAzhc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.save_pretrained(\"lora_model\") # Local saving\n",
        "tokenizer.save_pretrained(\"lora_model\")"
      ],
      "metadata": {
        "id": "C9xUTDYVghYk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7f27ed44-066a-43ff-e1f3-595b0c56df79"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('lora_model/tokenizer_config.json',\n",
              " 'lora_model/special_tokens_map.json',\n",
              " 'lora_model/tokenizer.json')"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.save_pretrained_merged(\"model_16\", tokenizer, save_method = \"merged_16bit\",)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IOxmm0WYcTuQ",
        "outputId": "79b4beb8-64ce-44a4-800f-d4469c8c390d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Unsloth: You have 1 CPUs. Using `safe_serialization` is 10x slower.\n",
            "We shall switch to Pytorch saving, which will take 3 minutes and not 30 minutes.\n",
            "To force `safe_serialization`, set it to `None` instead.\n",
            "Unsloth: Kaggle/Colab has limited disk space. We need to delete the downloaded\n",
            "model which will save 4-16GB of disk space, allowing you to save on Kaggle/Colab.\n",
            "Unsloth: Will remove a cached repo with size 5.7G\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Unsloth: Merging 4bit and LoRA weights to 16bit...\n",
            "Unsloth: Will use up to 3.19 out of 12.67 RAM for saving.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            " 44%|████▍     | 14/32 [00:00<00:00, 20.79it/s]We will save to Disk and not RAM now.\n",
            "100%|██████████| 32/32 [01:22<00:00,  2.58s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Unsloth: Saving tokenizer... Done.\n",
            "Unsloth: Saving model... This might take 5 minutes for Llama-7b...\n",
            "Unsloth: Saving model_16/pytorch_model-00001-of-00004.bin...\n",
            "Unsloth: Saving model_16/pytorch_model-00002-of-00004.bin...\n",
            "Unsloth: Saving model_16/pytorch_model-00003-of-00004.bin...\n",
            "Unsloth: Saving model_16/pytorch_model-00004-of-00004.bin...\n",
            "Done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!huggingface-cli login"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "POMky9m9jpw5",
        "outputId": "69cb0c5e-36e6-4a3a-f209-d2ff42257f32"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "    _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|\n",
            "    _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|\n",
            "    _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|\n",
            "    _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|\n",
            "    _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|\n",
            "\n",
            "    To login, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n",
            "Enter your token (input will not be visible): \n",
            "Add token as git credential? (Y/n) Y\n",
            "Token is valid (permission: write).\n",
            "\u001b[1m\u001b[31mCannot authenticate through git-credential as no helper is defined on your machine.\n",
            "You might have to re-authenticate when pushing to the Hugging Face Hub.\n",
            "Run the following command in your terminal in case you want to set the 'store' credential helper as default.\n",
            "\n",
            "git config --global credential.helper store\n",
            "\n",
            "Read https://git-scm.com/book/en/v2/Git-Tools-Credential-Storage for more details.\u001b[0m\n",
            "Token has not been saved to git credential helper.\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "To speed up the upload, set the following variable"
      ],
      "metadata": {
        "id": "TOt_evFfydvX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%env HF_HUB_ENABLE_HF_TRANSFER=1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UncBlHt2kmDo",
        "outputId": "47aaff12-9f30-4c96-9e67-7aaebf9ef475"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "env: HF_HUB_ENABLE_HF_TRANSFER=1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# model.push_to_hub_merged(\"basavaraj/text2sql-Llama3-8b\", tokenizer, save_method = \"merged_16bit\", token = \"<My_Token>\")\n",
        "!huggingface-cli upload basavaraj/text2sql-Llama3-8b model_16 ."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kjIlN6wPjymi",
        "outputId": "69e36475-2aac-4207-dea9-de72840fc43e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  0% 0/4 [00:00<?, ?it/s]\n",
            "pytorch_model-00001-of-00004.bin:   0% 0.00/4.98G [00:00<?, ?B/s]\u001b[A\n",
            "pytorch_model-00001-of-00004.bin:   0% 16.0M/4.98G [00:12<1:04:16, 1.29MB/s]\u001b[A\n",
            "pytorch_model-00001-of-00004.bin:   1% 32.0M/4.98G [00:14<32:22, 2.55MB/s]  \u001b[A\n",
            "pytorch_model-00001-of-00004.bin:   1% 48.0M/4.98G [00:15<20:00, 4.10MB/s]\u001b[A\n",
            "pytorch_model-00001-of-00004.bin:   1% 64.0M/4.98G [00:15<12:20, 6.63MB/s]\u001b[A\n",
            "pytorch_model-00001-of-00004.bin:   2% 80.0M/4.98G [00:15<08:04, 10.1MB/s]\u001b[A\n",
            "pytorch_model-00001-of-00004.bin:   2% 96.0M/4.98G [00:15<05:39, 14.4MB/s]\u001b[A\n",
            "pytorch_model-00001-of-00004.bin:   3% 128M/4.98G [00:16<03:12, 25.2MB/s] \u001b[A\n",
            "pytorch_model-00001-of-00004.bin:   3% 160M/4.98G [00:16<02:07, 37.7MB/s]\u001b[A\n",
            "pytorch_model-00001-of-00004.bin:   4% 176M/4.98G [00:16<02:10, 36.8MB/s]\u001b[A\n",
            "pytorch_model-00001-of-00004.bin:   5% 224M/4.98G [00:17<01:13, 64.8MB/s]\u001b[A\n",
            "pytorch_model-00001-of-00004.bin:   5% 256M/4.98G [00:17<01:01, 77.0MB/s]\u001b[A\n",
            "pytorch_model-00001-of-00004.bin:   6% 304M/4.98G [00:17<00:43, 108MB/s] \u001b[A\n",
            "pytorch_model-00001-of-00004.bin:   7% 368M/4.98G [00:17<00:27, 168MB/s]\u001b[A\n",
            "pytorch_model-00001-of-00004.bin:   9% 432M/4.98G [00:17<00:19, 230MB/s]\u001b[A\n",
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