Finetune_LLM_Gemma_2b_it.ipynb

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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m105.0/105.0 MB\u001b[0m \u001b[31m7.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m510.5/510.5 kB\u001b[0m \u001b[31m45.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m280.0/280.0 kB\u001b[0m \u001b[31m32.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m14.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m17.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for transformers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for peft (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m150.9/150.9 kB\u001b[0m \u001b[31m3.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m79.8/79.8 kB\u001b[0m \u001b[31m6.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install -q bitsandbytes datasets accelerate loralib\n",
        "!pip install -q git+https://github.com/huggingface/transformers.git@main git+https://github.com/huggingface/peft.git\n",
        "!pip install -q -U trl==0.7.10"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "import pandas as pd\n",
        "import torch\n",
        "import bitsandbytes as bnb\n",
        "from datasets import Dataset, load_dataset\n",
        "from huggingface_hub import notebook_login\n",
        "from peft import LoraConfig, PeftModel\n",
        "from transformers import (\n",
        "    AutoModelForCausalLM,\n",
        "    AutoTokenizer,\n",
        "    BitsAndBytesConfig,\n",
        "    TrainingArguments,\n",
        "    pipeline,\n",
        "    logging,\n",
        ")\n",
        "from trl import SFTTrainer"
      ],
      "metadata": {
        "id": "D9KklZGWZNE5"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
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      "source": [
        "bnb_config = BitsAndBytesConfig(\n",
        "    load_in_4bit=True,\n",
        "    bnb_4bit_use_double_quant=True,\n",
        "    bnb_4bit_quant_type=\"nf4\",\n",
        "    bnb_4bit_compute_dtype=torch.bfloat16\n",
        ")"
      ],
      "metadata": {
        "id": "YA-2yuLUmhn_"
      },
      "execution_count": 4,
      "outputs": []
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        },
        "id": "-zjk1ihfZNHF",
        "outputId": "14ee412b-9ada-4160-a497-7ddc639c8e7d"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
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          "metadata": {}
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    {
      "cell_type": "code",
      "source": [
        "model_id = \"google/gemma-2b-it\"\n",
        "\n",
        "model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_config, device_map={\"\":0})\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_id, add_eos_token=True)"
      ],
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        "outputId": "2920ae21-9424-4ef6-f280-4e82996c284a"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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        {
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        {
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def get_completion(query: str, model, tokenizer) -> str:\n",
        "  device = \"cuda:0\"\n",
        "\n",
        "  prompt_template = \"\"\"\n",
        "  <start_of_turn>user\n",
        "  Below is an instruction that describes a task. Write a response that appropriately completes the request.\n",
        "  {query}\n",
        "  <end_of_turn>\\n<start_of_turn>model\n",
        "\n",
        "\n",
        "  \"\"\"\n",
        "  prompt = prompt_template.format(query=query)\n",
        "\n",
        "  encodeds = tokenizer(prompt, return_tensors=\"pt\", add_special_tokens=True)\n",
        "\n",
        "  model_inputs = encodeds.to(device)\n",
        "\n",
        "\n",
        "  generated_ids = model.generate(**model_inputs, max_new_tokens=1000, do_sample=True, pad_token_id=tokenizer.eos_token_id)\n",
        "  # decoded = tokenizer.batch_decode(generated_ids)\n",
        "  decoded = tokenizer.decode(generated_ids[0], skip_special_tokens=True)\n",
        "  return (decoded)"
      ],
      "metadata": {
        "id": "zRTG26_im4Js"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "result = get_completion(query=\"code the fibonacci series in python using reccursion\", model=model, tokenizer=tokenizer)\n",
        "print(result)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lSk0OU9Dm4MD",
        "outputId": "a2976063-909a-47dc-a319-c5ba324da941"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "A decoder-only architecture is being used, but right-padding was detected! For correct generation results, please set `padding_side='left'` when initializing the tokenizer.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "  user\n",
            "  Below is an instruction that describes a task. Write a response that appropriately completes the request.\n",
            "  code the fibonacci series in python using reccursion\n",
            "  \n",
            "model\n",
            "  \n",
            "\n",
            "  def fibonacci(n):\n",
            "    \"\"\"\n",
            "    Calculates the nth number in the Fibonacci sequence.\n",
            "\n",
            "    Args:\n",
            "      n: The index of the Fibonacci number to calculate.\n",
            "\n",
            "    Returns:\n",
            "      The nth Fibonacci number.\n",
            "    \"\"\"\n",
            "\n",
            "    # Base case: If n is 0 or 1, return the corresponding Fibonacci number.\n",
            "    if n == 0:\n",
            "        return 0\n",
            "    elif n == 1:\n",
            "        return 1\n",
            "\n",
            "    # Calculate the previous two Fibonacci numbers.\n",
            "    previous = fibonacci(n-1)\n",
            "    next = fibonacci(n-2)\n",
            "\n",
            "    # Return the Fibonacci number.\n",
            "    return previous + next\n",
            "```\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = load_dataset(\"TokenBender/code_instructions_122k_alpaca_style\", split=\"train\")\n",
        "dataset"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
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      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
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          "data": {
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        {
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          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['output', 'instruction', 'input', 'text'],\n",
              "    num_rows: 121959\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = dataset.to_pandas()\n",
        "df.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "k7d01ZBcm4Qx",
        "outputId": "b831138d-80cb-4669-e8a4-179147dd7ea9"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                              output  \\\n",
              "0  # Python code\\ndef sum_sequence(sequence):\\n  ...   \n",
              "1  def add_strings(str1, str2):\\n    \"\"\"This func...   \n",
              "2  #include <map>\\n#include <string>\\n\\nclass Gro...   \n",
              "3  def bubble_sort(arr):\\n    n = len(arr)\\n \\n  ...   \n",
              "4  import UIKit\\n\\nclass ExpenseViewController: U...   \n",
              "5  <?php\\n$timestamp = $_GET['timestamp'];\\n\\nif(...   \n",
              "6  import requests\\nimport re\\n\\ndef crawl_websit...   \n",
              "7                 [x*x for x in [1, 2, 3, 5, 8, 13]]   \n",
              "8  SELECT * FROM products ORDER BY price DESC LIM...   \n",
              "9  public class Library {\\n \\n // map of books in...   \n",
              "\n",
              "                                         instruction  \\\n",
              "0  Create a function to calculate the sum of a se...   \n",
              "1       Develop a function that will add two strings   \n",
              "2  Design a data structure in C++ to store inform...   \n",
              "3  Implement a sorting algorithm to sort a given ...   \n",
              "4  Design a Swift application for tracking expens...   \n",
              "5  Create a REST API to convert a UNIX timestamp ...   \n",
              "6  Generate a Python code for crawling a website ...   \n",
              "7  Create a Python list comprehension to get the ...   \n",
              "8  Create a MySQL query to find the most expensiv...   \n",
              "9  Create a data structure in Java for storing an...   \n",
              "\n",
              "                                               input  \\\n",
              "0                                    [1, 2, 3, 4, 5]   \n",
              "1                    str1 = \"Hello \"\\nstr2 = \"world\"   \n",
              "2                                                      \n",
              "3                                 [3, 1, 4, 5, 9, 0]   \n",
              "4                                     Not applicable   \n",
              "5                                     Not Applicable   \n",
              "6  website: www.example.com \\ndata to crawl: phon...   \n",
              "7                                                      \n",
              "8                                                      \n",
              "9                                     Not applicable   \n",
              "\n",
              "                                                text  \n",
              "0  Below is an instruction that describes a task....  \n",
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              "2  Below is an instruction that describes a task....  \n",
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              "4  Below is an instruction that describes a task....  \n",
              "5  Below is an instruction that describes a task....  \n",
              "6  Below is an instruction that describes a task....  \n",
              "7  Below is an instruction that describes a task....  \n",
              "8  Below is an instruction that describes a task....  \n",
              "9  Below is an instruction that describes a task....  "
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              "      <td># Python code\\ndef sum_sequence(sequence):\\n  ...</td>\n",
              "      <td>Create a function to calculate the sum of a se...</td>\n",
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              "      <th>2</th>\n",
              "      <td>#include &lt;map&gt;\\n#include &lt;string&gt;\\n\\nclass Gro...</td>\n",
              "      <td>Design a data structure in C++ to store inform...</td>\n",
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              "      <td>Implement a sorting algorithm to sort a given ...</td>\n",
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              "      <td>import UIKit\\n\\nclass ExpenseViewController: U...</td>\n",
              "      <td>Design a Swift application for tracking expens...</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>import requests\\nimport re\\n\\ndef crawl_websit...</td>\n",
              "      <td>Generate a Python code for crawling a website ...</td>\n",
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              "      <td>Below is an instruction that describes a task....</td>\n",
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              "      <td>Create a MySQL query to find the most expensiv...</td>\n",
              "      <td></td>\n",
              "      <td>Below is an instruction that describes a task....</td>\n",
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              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>public class Library {\\n \\n // map of books in...</td>\n",
              "      <td>Create a data structure in Java for storing an...</td>\n",
              "      <td>Not applicable</td>\n",
              "      <td>Below is an instruction that describes a task....</td>\n",
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              "\n",
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              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-e999b186-8e8b-4247-81e5-a2e9034fe99b button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-e999b186-8e8b-4247-81e5-a2e9034fe99b');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "    height: 32px;\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
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              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-2974d2b1-45c2-4d95-bcd7-74a38c51e2db button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def generate_prompt(data_point):\n",
        "    \"\"\"Gen. input text based on a prompt, task instruction, (context info.), and answer\n",
        "\n",
        "    :param data_point: dict: Data point\n",
        "    :return: dict: tokenzed prompt\n",
        "    \"\"\"\n",
        "    prefix_text = 'Below is an instruction that describes a task. Write a response that ' \\\n",
        "               'appropriately completes the request.\\n\\n'\n",
        "    # Samples with additional context into.\n",
        "    if data_point['input']:\n",
        "        text = f\"\"\"<start_of_turn>user {prefix_text} {data_point[\"instruction\"]} here are the inputs {data_point[\"input\"]} <end_of_turn>\\n<start_of_turn>model{data_point[\"output\"]} <end_of_turn>\"\"\"\n",
        "    # Without\n",
        "    else:\n",
        "        text = f\"\"\"<start_of_turn>user {prefix_text} {data_point[\"instruction\"]} <end_of_turn>\\n<start_of_turn>model{data_point[\"output\"]} <end_of_turn>\"\"\"\n",
        "    return text\n",
        "\n",
        "# add the \"prompt\" column in the dataset\n",
        "text_column = [generate_prompt(data_point) for data_point in dataset]\n",
        "dataset = dataset.add_column(\"prompt\", text_column)"
      ],
      "metadata": {
        "id": "mjpNnX_Zm4TX"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = dataset.shuffle(seed=123)  # Shuffle dataset here\n",
        "dataset = dataset.map(lambda samples: tokenizer(samples[\"prompt\"]), batched=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "c080143db61541ba86dfb3d93a6c4225",
            "a6faca6a0d2b43e683a899d2dd3641f9",
            "a901758b17f84394a2902ab2cd58e868",
            "5c52998dec8e44ff8a31ea2a64485478",
            "b14e15f4b5bb4a21a0a2cbb0d78f5cf7",
            "007aadf5bdee4a709099ae20a469ce43",
            "3f30114e9a6547f196a9574c57a12d2c",
            "b214cdc9bb2648b0b03380b31a5682dd",
            "27776b9d642845e7b20965bb51b6e5c2",
            "e47d8a5ac85440ed9fd2f9d71772d866",
            "7979c8d239244467bef7f8187d734314"
          ]
        },
        "id": "msgtao4Wm4V2",
        "outputId": "cdbfe840-0ff9-478f-eafd-b478206eea0b"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/121959 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c080143db61541ba86dfb3d93a6c4225"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = dataset.train_test_split(test_size=0.2)\n",
        "train_data = dataset[\"train\"]\n",
        "test_data = dataset[\"test\"]"
      ],
      "metadata": {
        "id": "FjK6-rrZm4YS"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(test_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-eb7E7P7m4a6",
        "outputId": "12fba328-5956-4081-92ee-d5291e1f41ce"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset({\n",
            "    features: ['output', 'instruction', 'input', 'text', 'prompt', 'input_ids', 'attention_mask'],\n",
            "    num_rows: 24392\n",
            "})\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from peft import LoraConfig, PeftModel, prepare_model_for_kbit_training, get_peft_model\n",
        "model.gradient_checkpointing_enable()\n",
        "model = prepare_model_for_kbit_training(model)"
      ],
      "metadata": {
        "id": "0Iw1S-jNnX67"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nqxTUxD_nX9V",
        "outputId": "689ba01c-0386-46d5-fdc1-ce6e0282c0b8"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GemmaForCausalLM(\n",
            "  (model): GemmaModel(\n",
            "    (embed_tokens): Embedding(256000, 2048, padding_idx=0)\n",
            "    (layers): ModuleList(\n",
            "      (0-17): 18 x GemmaDecoderLayer(\n",
            "        (self_attn): GemmaAttention(\n",
            "          (q_proj): Linear4bit(in_features=2048, out_features=2048, bias=False)\n",
            "          (k_proj): Linear4bit(in_features=2048, out_features=256, bias=False)\n",
            "          (v_proj): Linear4bit(in_features=2048, out_features=256, bias=False)\n",
            "          (o_proj): Linear4bit(in_features=2048, out_features=2048, bias=False)\n",
            "          (rotary_emb): GemmaRotaryEmbedding()\n",
            "        )\n",
            "        (mlp): GemmaMLP(\n",
            "          (gate_proj): Linear4bit(in_features=2048, out_features=16384, bias=False)\n",
            "          (up_proj): Linear4bit(in_features=2048, out_features=16384, bias=False)\n",
            "          (down_proj): Linear4bit(in_features=16384, out_features=2048, bias=False)\n",
            "          (act_fn): GELUActivation()\n",
            "        )\n",
            "        (input_layernorm): GemmaRMSNorm()\n",
            "        (post_attention_layernorm): GemmaRMSNorm()\n",
            "      )\n",
            "    )\n",
            "    (norm): GemmaRMSNorm()\n",
            "  )\n",
            "  (lm_head): Linear(in_features=2048, out_features=256000, bias=False)\n",
            ")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def find_all_linear_names(model):\n",
        "  cls = bnb.nn.Linear4bit\n",
        "  lora_module_names = set()\n",
        "  for name, module in model.named_modules():\n",
        "    if isinstance(module, cls):\n",
        "      names = name.split('.')\n",
        "      lora_module_names.add(names[0] if len(names) == 1 else names[-1])\n",
        "    if 'lm_head' in lora_module_names:\n",
        "      lora_module_names.remove('lm_head')\n",
        "  return list(lora_module_names)"
      ],
      "metadata": {
        "id": "iyqEQIWFnX_3"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "modules = find_all_linear_names(model)\n",
        "print(modules)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gZqRwy-FnYCa",
        "outputId": "387412be-02d9-40d5-f261-cec44cdc4c1f"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['v_proj', 'q_proj', 'o_proj', 'up_proj', 'gate_proj', 'down_proj', 'k_proj']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from peft import LoraConfig, get_peft_model\n",
        "\n",
        "lora_config = LoraConfig(\n",
        "    r=64,\n",
        "    lora_alpha=32,\n",
        "    target_modules=modules,\n",
        "    lora_dropout=0.05,\n",
        "    bias=\"none\",\n",
        "    task_type=\"CAUSAL_LM\"\n",
        ")\n",
        "\n",
        "model = get_peft_model(model, lora_config)"
      ],
      "metadata": {
        "id": "46knDqDtnYEw"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainable, total = model.get_nb_trainable_parameters()\n",
        "print(f\"Trainable: {trainable} | total: {total} | Percentage: {trainable/total*100:.4f}%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TsQDzwkZnYHG",
        "outputId": "8e8871b1-449f-4955-b049-2a2aab27a29d"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Trainable: 78446592 | total: 2584619008 | Percentage: 3.0351%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import transformers\n",
        "\n",
        "from trl import SFTTrainer\n",
        "\n",
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "torch.cuda.empty_cache()\n",
        "\n",
        "trainer = SFTTrainer(\n",
        "    model=model,\n",
        "    train_dataset=train_data,\n",
        "    eval_dataset=test_data,\n",
        "    dataset_text_field=\"prompt\",\n",
        "    peft_config=lora_config,\n",
        "    args=transformers.TrainingArguments(\n",
        "        per_device_train_batch_size=1,\n",
        "        gradient_accumulation_steps=4,\n",
        "        warmup_steps=0.03,\n",
        "        max_steps=100,\n",
        "        learning_rate=2e-4,\n",
        "        logging_steps=1,\n",
        "        output_dir=\"outputs\",\n",
        "        optim=\"paged_adamw_8bit\",\n",
        "        save_strategy=\"epoch\",\n",
        "    ),\n",
        "    data_collator=transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False),\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170,
          "referenced_widgets": [
            "03de2b671ded44fcac3fe440a95e087f",
            "525e24a8bf544ce2b0a69ebc358a91df",
            "518e191ffa1049f38775b5a1ef477123",
            "e57c9fc48ac8490db9dfcd33665be3a5",
            "10616fc763c44d72a265203e48c6d0f9",
            "09d62556665e46dd8dea1d89047e87c6",
            "decd7c473be74c5d95bf242cd740d992",
            "2ab92867bed4491888d9de882ace7272",
            "ab6c5adfcc6f44fbb3c76d3330ee20ce",
            "e51fb61ca0d84c9dad21a7cb848dc72d",
            "b01f770de5a94c15bcca85d6b3d033c9",
            "abc11e77072d49219a3c0dad4b2b29c4",
            "71abbba999fd443495df97599c785fa2",
            "b3615ed0d70c4cf3b7db25a99001e5d6",
            "317ebd59b3a04546bedb09a2db259544",
            "cc4344a409134640bea8079debe32b55",
            "78ed3403eb434f9891c0c46ab0358986",
            "7231af4d74c74dd2ad0bbc7e3de63549",
            "7b5dbf76a1f744399b41f081410b9d19",
            "3fafb72f3973439ba814eaa4437b27a4",
            "84f5ddabc4b64aa7869b03bcb37d8df2",
            "6f7b44ec3132499d8fd653b03454d6a2"
          ]
        },
        "id": "FN5IFnWcngNc",
        "outputId": "945e74a5-553f-4c7d-9612-cf677a90c906"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/trl/trainer/sft_trainer.py:223: UserWarning: You didn't pass a `max_seq_length` argument to the SFTTrainer, this will default to 1024\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/97567 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "03de2b671ded44fcac3fe440a95e087f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/24392 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "abc11e77072d49219a3c0dad4b2b29c4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/trl/trainer/sft_trainer.py:290: UserWarning: You passed a tokenizer with `padding_side` not equal to `right` to the SFTTrainer. This might lead to some unexpected behaviour due to overflow issues when training a model in half-precision. You might consider adding `tokenizer.padding_side = 'right'` to your code.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.config.use_cache = False\n",
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Wr8ALljBngPx",
        "outputId": "0da09d84-2462-4d2c-cd5b-bd080055d43a"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\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 11:14, 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>2.501000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>2.705900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>4.026600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>2.752400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>1.659400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>2.175900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>1.506700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>1.504800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>1.276100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>1.354800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11</td>\n",
              "      <td>1.775000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12</td>\n",
              "      <td>1.134500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13</td>\n",
              "      <td>1.532000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14</td>\n",
              "      <td>1.325000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15</td>\n",
              "      <td>1.274700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16</td>\n",
              "      <td>1.145600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>17</td>\n",
              "      <td>1.146700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>18</td>\n",
              "      <td>1.074400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>19</td>\n",
              "      <td>1.076800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>1.028300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>21</td>\n",
              "      <td>0.843200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>22</td>\n",
              "      <td>0.874000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>23</td>\n",
              "      <td>1.179300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>24</td>\n",
              "      <td>0.672800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>25</td>\n",
              "      <td>0.804400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>26</td>\n",
              "      <td>0.894000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>27</td>\n",
              "      <td>0.924400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>28</td>\n",
              "      <td>0.897400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>29</td>\n",
              "      <td>1.051100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>30</td>\n",
              "      <td>0.783200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>31</td>\n",
              "      <td>1.156300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>32</td>\n",
              "      <td>0.661100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>33</td>\n",
              "      <td>0.908100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>34</td>\n",
              "      <td>0.675100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>35</td>\n",
              "      <td>1.001500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>36</td>\n",
              "      <td>0.804000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>37</td>\n",
              "      <td>0.907900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>38</td>\n",
              "      <td>0.812100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>39</td>\n",
              "      <td>1.067300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>40</td>\n",
              "      <td>0.761400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>41</td>\n",
              "      <td>0.876500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>42</td>\n",
              "      <td>0.864000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>43</td>\n",
              "      <td>1.020300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>44</td>\n",
              "      <td>0.728100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>45</td>\n",
              "      <td>0.806000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>46</td>\n",
              "      <td>0.823600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>47</td>\n",
              "      <td>0.820300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>48</td>\n",
              "      <td>0.800100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>49</td>\n",
              "      <td>0.808900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>50</td>\n",
              "      <td>1.113600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>51</td>\n",
              "      <td>0.843500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>52</td>\n",
              "      <td>1.080700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>53</td>\n",
              "      <td>0.755400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>54</td>\n",
              "      <td>0.748000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>55</td>\n",
              "      <td>0.645000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>56</td>\n",
              "      <td>0.706000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>57</td>\n",
              "      <td>0.850600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>58</td>\n",
              "      <td>0.852400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>59</td>\n",
              "      <td>0.776000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>60</td>\n",
              "      <td>0.630700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>61</td>\n",
              "      <td>0.692900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>62</td>\n",
              "      <td>0.741300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>63</td>\n",
              "      <td>0.744000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>64</td>\n",
              "      <td>0.709800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>65</td>\n",
              "      <td>0.954800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>66</td>\n",
              "      <td>1.092600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>67</td>\n",
              "      <td>1.198300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>68</td>\n",
              "      <td>0.951700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>69</td>\n",
              "      <td>0.764400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>70</td>\n",
              "      <td>0.924500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>71</td>\n",
              "      <td>0.684600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>72</td>\n",
              "      <td>0.833300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>73</td>\n",
              "      <td>0.717000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>74</td>\n",
              "      <td>0.722000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>75</td>\n",
              "      <td>0.750300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>76</td>\n",
              "      <td>0.801500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>77</td>\n",
              "      <td>0.867300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>78</td>\n",
              "      <td>0.788400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>79</td>\n",
              "      <td>0.885600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>80</td>\n",
              "      <td>0.757300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>81</td>\n",
              "      <td>0.778400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>82</td>\n",
              "      <td>0.812100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>83</td>\n",
              "      <td>0.782500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>84</td>\n",
              "      <td>0.718500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>85</td>\n",
              "      <td>0.701300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>86</td>\n",
              "      <td>0.661500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>87</td>\n",
              "      <td>0.755500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>88</td>\n",
              "      <td>0.757100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>89</td>\n",
              "      <td>0.766000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>90</td>\n",
              "      <td>0.698300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>91</td>\n",
              "      <td>0.696400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>92</td>\n",
              "      <td>0.665100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>93</td>\n",
              "      <td>1.173200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>94</td>\n",
              "      <td>0.792100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>95</td>\n",
              "      <td>0.753000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>96</td>\n",
              "      <td>0.591800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>97</td>\n",
              "      <td>0.869600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>98</td>\n",
              "      <td>0.690100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>99</td>\n",
              "      <td>0.578800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>0.743800</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=100, training_loss=0.9977184873819351, metrics={'train_runtime': 683.9044, 'train_samples_per_second': 0.585, 'train_steps_per_second': 0.146, 'total_flos': 900743751401472.0, 'train_loss': 0.9977184873819351, 'epoch': 0.0})"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_model = \"GemmaFinetune1\""
      ],
      "metadata": {
        "id": "p-yhqYBlngSV"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.model.save_pretrained(new_model)"
      ],
      "metadata": {
        "id": "AUYexUarngUw"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "base_model = AutoModelForCausalLM.from_pretrained(\n",
        "    model_id,\n",
        "    low_cpu_mem_usage=True,\n",
        "    return_dict=True,\n",
        "    torch_dtype=torch.float16,\n",
        "    device_map={\"\": 0},\n",
        ")\n",
        "merged_model= PeftModel.from_pretrained(base_model, new_model)\n",
        "merged_model= merged_model.merge_and_unload()\n",
        "\n",
        "# Save the merged model\n",
        "merged_model.save_pretrained(\"merged_model\",safe_serialization=True)\n",
        "tokenizer.save_pretrained(\"merged_model\")\n",
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "tokenizer.padding_side = \"right\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "1a9cac3a7409470985f4096c35a4a9a0",
            "6b49cada38a549a4979d7a730ca3fa6d",
            "425fb95207f54ad4b9ede5bdc9e8c514",
            "9a2c1997db8a45679faeb89d1f016275",
            "bc8be88e1e97438eb5e9476dfaf775a3",
            "70569c7b49ba4d1f8ea65d51f571ee18",
            "2eb00c734baf4aa4a3da2e4d90769271",
            "214dabefd13048b49aed5826ebfbbf74",
            "087c589c4ddb458faf95ebc1a3474171",
            "ee5b269d057e457bad3831d529545546",
            "bbfd1fcb63df42b78a7994feac086d87"
          ]
        },
        "id": "dW3g3RKGngXO",
        "outputId": "9d6f95f5-2888-4f6f-9586-d573bf30fb75"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1a9cac3a7409470985f4096c35a4a9a0"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result = get_completion(query=\"code the fibonacci series in python using reccursion\", model=merged_model, tokenizer=tokenizer)\n",
        "print(result)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IeOnBBjkm4dT",
        "outputId": "9006db35-e5db-479d-abfb-5fb8fe0b4c20"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "A decoder-only architecture is being used, but right-padding was detected! For correct generation results, please set `padding_side='left'` when initializing the tokenizer.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "  user\n",
            "  Below is an instruction that describes a task. Write a response that appropriately completes the request.\n",
            "  code the fibonacci series in python using reccursion\n",
            "  \n",
            "model\n",
            "  \n",
            "\n",
            "   \n",
            "modeldef fibonacci(first, second):\n",
            "    if first > second:\n",
            "        return fibonacci(second, first)\n",
            "\n",
            "    return first + second\n",
            "\n",
            "if __name__ == '__main__':\n",
            "    result = fibonacci(0, 1)\n",
            "    print(result) \n",
            "modeldef createFibSequence(n):\n",
            "    fibList = []\n",
            "    # First two number are initialized \n",
            "    fibList.append(0)\n",
            "    fibList.append(1) \n",
            "\n",
            "    # Calculate and add to the list \n",
            "    for i in range(1, n):\n",
            "        fibList.append(fibList[i - 1] + fibList[i - 2])\n",
            "    return fibList\n",
            "  \n",
            "fibList = createFibSequence(8)\n",
            "print(fibList) \n",
            "modelHere are two ways to calculate the Fibonacci series in Python using recursion: \n",
            "\n",
            "```python\n",
            "def fibonacci(first, second, n):\n",
            "    if first > second:\n",
            "        return fibonacci(second, first)\n",
            "    return first + second\n",
            "\n",
            "# Access the sequence and print the first 5 terms \n",
            "for i in range(1, 20):\n",
            "        print(fibonacci(-5, 0, i)) \n",
            "```\n",
            "\n",
            "Here is the code to create an algorithm to get the fibonacci series for a given number n: \n",
            "```python\n",
            "def fibonacci(n):\n",
            "    return [0 for i in range(n+1)] \n",
            "  \n",
            "    first = 0\n",
            "    second = 1 \n",
            "    for i in range(1, n+1):\n",
            "        a = second\n",
            "        b = first + second\n",
            "        first = second\n",
            "        second = a + b \n",
            "    return second \n",
            "``` \n",
            "\"\"\"\n",
            "This code implements a function to generate the sequence of numbers, \n",
            "that is the Fibonacci sequence. \n",
            "\"\"\"\n",
            "\n",
            "first = [0,1]\n",
            "while len(first) < n: \n",
            "    first.append(first[0] + first[1]) \n",
            "    second = [0,1]\n",
            "    while len(second) < n: \n",
            "        a = second[0] + second[1]\n",
            "        second.append(a)\n",
            "        second.append([first[0],first[0]])\n",
            "    n = len(first)\n",
            " \n",
            "print(first[n])  \n",
            "```  \n",
            "Here is another way to calculate the Fibonacci series, without using recursion. \n",
            "```python \n",
            "def fibonacci(n):\n",
            "    if n <= 1:\n",
            "        return n\n",
            "    oldList = [0,1] \n",
            "    newList = [0,1] \n",
            "    while len(oldList) < n:\n",
            "        newNumber = oldList[0] + oldList[1]\n",
            "        newList.append(newNumber)   \n",
            "        oldList.append(newNumber) \n",
            "        n -= 1 \n",
            "    return newList[-1] \n",
            "```         \n",
            "## Explanation of the code \n",
            "1. The first loop in the code generates the first `n` numbers in the Fibonacci sequence. \n",
            "2. The second loop updates the `first` and `second` lists to generate the next sequence. \n",
            "3. Each list generated by the second loop is appended to the `last` list. \n",
            "4. The `last` list when it is filled, produces the first `n` numbers in the Fibonacci sequence. \n",
            "\n",
            "## Time complexity o the code\n",
            "The code above will need O(n) time to produce the first n numbers in the Fibonacci sequence, because it iterates over the whole sequence. \n",
            "\n",
            "## Space complexity o(n)\n",
            "The code also needs O(n) space to store the two lists used for generating the sequence, plus the list used to store the results. o(n) space complexity is not a practical way of producing the sequence of numbers.             \n",
            " \n",
            "This code is not practical if n is not 0, because it will be very slow to reach the first numbers. \n",
            "\n",
            "## Improved version \n",
            "Here is an improved version of the code, which can be used if n is either 0 or 1: \n",
            "```python\n",
            "def fibonacci(n):\n",
            "    if n == 0:\n",
            "        return 0\n",
            "    elif n == 1:\n",
            "        return 1\n",
            "    return fibonacci(n-1) + fibonacci(n-2) \n",
            "``` \n",
            "Here are\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "HQJe-My1n14B"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}