unsloth_finetune_mistral_public.ipynb

{
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      "cell_type": "markdown",
      "metadata": {
        "id": "e4727adb-7646-4397-aaf8-f1d8a702b540"
      },
      "source": [
        "# Fine-tuning Mistral with Unsloth"
      ],
      "id": "e4727adb-7646-4397-aaf8-f1d8a702b540"
    },
    {
      "cell_type": "markdown",
      "source": [
        "**If installation doesn't work refer to the official Unsloth repo for the latest installation instructions in their fine-tuning Colab notebooks: https://github.com/unslothai/unsloth**"
      ],
      "metadata": {
        "id": "F5OU9IaY1zXq"
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      "metadata": {
        "id": "yg3aXa0mYp6Q"
      },
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      "source": [
        "%%capture\n",
        "!pip install unsloth\n",
        "# Also get the latest nightly Unsloth!\n",
        "!pip uninstall unsloth -y && pip install --upgrade --no-cache-dir \"unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git\""
      ],
      "id": "yg3aXa0mYp6Q"
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        "outputId": "5b54319d-971d-4561-f101-c99aa3f15c49"
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              "\n",
              "        window._wandbApiKey = new Promise((resolve, reject) => {\n",
              "            function loadScript(url) {\n",
              "            return new Promise(function(resolve, reject) {\n",
              "                let newScript = document.createElement(\"script\");\n",
              "                newScript.onerror = reject;\n",
              "                newScript.onload = resolve;\n",
              "                document.body.appendChild(newScript);\n",
              "                newScript.src = url;\n",
              "            });\n",
              "            }\n",
              "            loadScript(\"https://cdn.jsdelivr.net/npm/postmate/build/postmate.min.js\").then(() => {\n",
              "            const iframe = document.createElement('iframe')\n",
              "            iframe.style.cssText = \"width:0;height:0;border:none\"\n",
              "            document.body.appendChild(iframe)\n",
              "            const handshake = new Postmate({\n",
              "                container: iframe,\n",
              "                url: 'https://wandb.ai/authorize'\n",
              "            });\n",
              "            const timeout = setTimeout(() => reject(\"Couldn't auto authenticate\"), 5000)\n",
              "            handshake.then(function(child) {\n",
              "                child.on('authorize', data => {\n",
              "                    clearTimeout(timeout)\n",
              "                    resolve(data)\n",
              "                });\n",
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              "    "
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          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"
          ]
        }
      ],
      "source": [
        "import wandb, os\n",
        "wandb.login()\n",
        "\n",
        "wandb_project = \"your-wandb-project\"\n",
        "if len(wandb_project) > 0:\n",
        "    os.environ[\"WANDB_PROJECT\"] = wandb_project"
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      "outputs": [
        {
          "output_type": "display_data",
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            "text/plain": [
              "config.json:   0%|          | 0.00/596 [00:00<?, ?B/s]"
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          "text": [
            "==((====))==  Unsloth: Fast Mistral patching release 2024.3\n",
            "   \\\\   /|    GPU: NVIDIA A100-SXM4-40GB. Max memory: 39.564 GB. Platform = Linux.\n",
            "O^O/ \\_/ \\    Pytorch: 2.2.1+cu121. CUDA = 8.0. CUDA Toolkit = 12.1.\n",
            "\\        /    Bfloat16 = TRUE. Xformers = 0.0.24. FA = True.\n",
            " \"-____-\"     Free Apache license: http://github.com/unslothai/unsloth\n"
          ]
        },
        {
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          "data": {
            "text/plain": [
              "model.safetensors.index.json:   0%|          | 0.00/25.1k [00:00<?, ?B/s]"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading shards:   0%|          | 0/3 [00:00<?, ?it/s]"
            ],
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              "version_major": 2,
              "version_minor": 0,
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          "data": {
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              "model-00001-of-00003.safetensors:   0%|          | 0.00/4.94G [00:00<?, ?B/s]"
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              "version_major": 2,
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              "model_id": "071949fe58d74d6d9da5d45852008312"
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          "metadata": {}
        },
        {
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          "data": {
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        {
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          "data": {
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/3 [00:00<?, ?it/s]"
            ],
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "7df8b42ffa434ee6b8ab8b1ec785f9e9"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "generation_config.json:   0%|          | 0.00/111 [00:00<?, ?B/s]"
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              "model_id": "8de870cb48c04b9ea79fe70ef7758fd8"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/1.46k [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "version_minor": 0,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ccb5a150265d4d19b7990e4efaad9f24"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/72.0 [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
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              "model_id": "32f6def7f288413cbeec1b4bde3c5803"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from unsloth import FastLanguageModel\n",
        "import torch\n",
        "\n",
        "max_seq_length = 8192 # Choose any! We auto support RoPE Scaling internally!\n",
        "dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+\n",
        "\n",
        "model, tokenizer = FastLanguageModel.from_pretrained(\n",
        "    model_name = \"mistralai/Mistral-7B-Instruct-v0.2\",\n",
        "    max_seq_length = max_seq_length,\n",
        "    dtype = dtype,\n",
        "    load_in_4bit = False,\n",
        "    # max_seq_length = max_seq_length,\n",
        "    trust_remote_code=True\n",
        ")"
      ],
      "id": "HZrz9QDsYwuB"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "be378315-c391-4f7f-8db3-b1442387cc62"
      },
      "source": [
        "## 3. Load Dataset"
      ],
      "id": "be378315-c391-4f7f-8db3-b1442387cc62"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Scraping PR data with file diffs\n",
        "\n"
      ],
      "metadata": {
        "id": "y-zYEgJt5Six"
      },
      "id": "y-zYEgJt5Six"
    },
    {
      "cell_type": "code",
      "source": [
        "import requests\n",
        "import csv\n",
        "import time\n",
        "\n",
        "GITHUB_TOKEN = \"token\"  # Replace with your GitHub token\n",
        "REPO_OWNER = 'octocat'\n",
        "REPO_NAME = 'Hello-World'\n",
        "API_URL = f'https://api.github.com/repos/{REPO_OWNER}/{REPO_NAME}'\n",
        "HEADERS = {\n",
        "    'Authorization': f'token {GITHUB_TOKEN}',\n",
        "    'Accept': 'application/vnd.github.v3+json'\n",
        "}\n",
        "TEXT_FILE = 'pr_data_100.txt'\n",
        "\n",
        "TOTAL_PRS = 5  # Total number of PRs to fetch\n",
        "PER_PAGE = 100  # Maximum items per page, I think max is 100\n",
        "\n",
        "def get_closed_pull_requests(api_url, headers, page, per_page):\n",
        "    \"\"\"Fetches closed pull requests for a specific page.\"\"\"\n",
        "    prs_url = f'{api_url}/pulls?state=closed&sort=updated&direction=desc&per_page={per_page}&page={page}'\n",
        "    print(f\"Fetching PRs from: {prs_url}\")\n",
        "    response = requests.get(prs_url, headers=headers)\n",
        "    if response.status_code == 200:\n",
        "        print(f\"Successfully fetched PRs for page {page}\")\n",
        "        return response.json()\n",
        "    else:\n",
        "        print(f'Failed to fetch PRs for page {page}: {response.status_code}')\n",
        "        return []\n",
        "\n",
        "def get_pr_comments(api_url, pr_number, headers):\n",
        "    \"\"\"Fetches comments for a specific pull request.\"\"\"\n",
        "    comments_url = f'{api_url}/issues/{pr_number}/comments'\n",
        "    print(f\"Fetching comments for PR #{pr_number}\")\n",
        "    response = requests.get(comments_url, headers=headers)\n",
        "    if response.status_code == 200:\n",
        "        print(f\"Successfully fetched comments for PR #{pr_number}\")\n",
        "        return response.json()\n",
        "    else:\n",
        "        print(f'Failed to fetch comments for PR {pr_number}: {response.status_code}')\n",
        "        return []\n",
        "\n",
        "def get_pr_files(api_url, pr_number, headers):\n",
        "    \"\"\"Fetches file changes for a specific pull request.\"\"\"\n",
        "    files_url = f'{api_url}/pulls/{pr_number}/files'\n",
        "    print(f\"Fetching files for PR #{pr_number}\")\n",
        "    response = requests.get(files_url, headers=headers)\n",
        "    if response.status_code == 200:\n",
        "        print(f\"Successfully fetched files for PR #{pr_number}\")\n",
        "        return response.json()\n",
        "    else:\n",
        "        print(f'Failed to fetch files for PR {pr_number}: {response.status_code}')\n",
        "        return []\n",
        "\n",
        "def get():\n",
        "    with open(TEXT_FILE, 'w') as txt_file:\n",
        "        pr_count = 0\n",
        "        page = 1\n",
        "        while pr_count < TOTAL_PRS:\n",
        "            print(f\"Processing page {page}\")\n",
        "            pull_requests = get_closed_pull_requests(API_URL, HEADERS, page, PER_PAGE)\n",
        "\n",
        "            if not pull_requests:  # If no PRs are returned\n",
        "                print(f\"No more PRs found at page {page}. Stopping.\")\n",
        "                break\n",
        "\n",
        "            print(f\"Fetched {len(pull_requests)} PRs from page {page}.\")\n",
        "            for pr in pull_requests:\n",
        "                if pr_count >= TOTAL_PRS:\n",
        "                    print(f\"Reached total PRs count: {TOTAL_PRS}\")\n",
        "                    break\n",
        "                pr_number = pr['number']\n",
        "                print(f\"Processing PR #{pr_number}\")\n",
        "\n",
        "                pr_number = pr['number']\n",
        "                pr_data = f'PR #{pr_number}: {pr[\"title\"]}\\n'\n",
        "                pr_data += f'URL: {pr[\"html_url\"]}\\n'\n",
        "                pr_data += f'Description: {pr[\"body\"]}\\n'\n",
        "                pr_data += f'Created at: {pr[\"created_at\"]}\\n'\n",
        "                pr_data += f'Merged at: {pr[\"merged_at\"]}\\n'\n",
        "\n",
        "                comments = get_pr_comments(API_URL, pr_number, HEADERS)\n",
        "                for comment in comments:\n",
        "                    pr_data += f'Comment: {comment[\"body\"]}\\n'\n",
        "\n",
        "                files = get_pr_files(API_URL, pr_number, HEADERS)\n",
        "\n",
        "                for file in files:\n",
        "                    if file[\"filename\"] != \"yarn.lock\":\n",
        "                        pr_data += f'File: {file[\"filename\"]}, Status: {file[\"status\"]}\\n'\n",
        "\n",
        "                        if \"patch\" in file:\n",
        "                            pr_data += f'Changes: {file[\"patch\"]}\\n'  # This will print the diff of the file\n",
        "\n",
        "                pr_data += '\\n\\n'\n",
        "                txt_file.write(pr_data)\n",
        "\n",
        "                pr_count += 1\n",
        "\n",
        "            if len(pull_requests) < PER_PAGE:  # Break if the last page has been reached\n",
        "                print(\"Reached the last page of PRs.\")\n",
        "                break\n",
        "\n",
        "            page += 1\n",
        "            print(f\"Moving to page {page}\")\n",
        "\n",
        "get()"
      ],
      "metadata": {
        "id": "kssax8bg5OWS"
      },
      "id": "kssax8bg5OWS",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eWE0W7YSVTmx"
      },
      "source": [
        "### Raw text data"
      ],
      "id": "eWE0W7YSVTmx"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OMz8mMjEDVnY"
      },
      "outputs": [],
      "source": [
        "from datasets import Dataset\n",
        "\n",
        "def create_datasets(chunks, eval_split=0.1):\n",
        "    \"\"\"Splits the chunks into training and evaluation datasets.\"\"\"\n",
        "    total_chunks = len(chunks)\n",
        "    eval_start = int(total_chunks * (1 - eval_split))\n",
        "\n",
        "    train_chunks = chunks\n",
        "    eval_chunks = chunks[eval_start:]\n",
        "\n",
        "    train_dataset = Dataset.from_dict({'text': train_chunks})\n",
        "    eval_dataset = Dataset.from_dict({'text': eval_chunks})\n",
        "\n",
        "    return train_dataset, eval_dataset"
      ],
      "id": "OMz8mMjEDVnY"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_hQAvU8XC4Po"
      },
      "source": [
        "#### Wiki"
      ],
      "id": "_hQAvU8XC4Po"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BMjH573ZDYLt"
      },
      "outputs": [],
      "source": [
        "def read_and_chunk_file(file_path, chunk_marker='§'):\n",
        "    with open(file_path, 'r', encoding='utf-8') as file:\n",
        "        text = file.read()\n",
        "\n",
        "    sections = text.split(chunk_marker)[1:]\n",
        "    return sections\n",
        "\n",
        "file_path = '/content/wiki.txt'\n",
        "wiki_chunks = read_and_chunk_file(file_path)"
      ],
      "id": "BMjH573ZDYLt"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fr2rDCYGDhFy"
      },
      "source": [
        "#### Code and PR's"
      ],
      "id": "Fr2rDCYGDhFy"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_4hiN8trDjX3"
      },
      "outputs": [],
      "source": [
        "def chunk_text_by_file_and_pr(filepath):\n",
        "    with open(filepath, 'r') as file:\n",
        "      content = file.read()\n",
        "\n",
        "    lines = content.split('\\n')\n",
        "\n",
        "    chunks = []\n",
        "    current_chunk = []\n",
        "\n",
        "    # Helper function to add non-empty chunks\n",
        "    def add_chunk(c_chunk):\n",
        "        if c_chunk:\n",
        "            chunks.append('\\n'.join(c_chunk))\n",
        "\n",
        "    for line in lines:\n",
        "        # Check for file or PR beginning\n",
        "        if line.startswith('- File:') or line.strip().startswith('- Title:') or line.strip().startswith('PR #'):\n",
        "            # If there's an existing chunk, add it to the chunks list\n",
        "            add_chunk(current_chunk)\n",
        "            current_chunk = [line]  # Start a new chunk\n",
        "        else:\n",
        "            current_chunk.append(line)\n",
        "\n",
        "    # Add the last chunk if it's not empty\n",
        "    add_chunk(current_chunk)\n",
        "\n",
        "    return chunks\n",
        "\n",
        "file_path = '/content/code-and-PRs.txt'\n",
        "\n",
        "code_and_pr_chunks = chunk_text_by_file_and_pr(file_path)"
      ],
      "id": "_4hiN8trDjX3"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lCA5JbABDj0c"
      },
      "source": [
        "#### PR's with file diffs"
      ],
      "id": "lCA5JbABDj0c"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oWfs8X84S8_3"
      },
      "outputs": [],
      "source": [
        "def chunk_text_by_pr_diff(filepath):\n",
        "    with open(filepath, 'r') as file:\n",
        "        content = file.read()\n",
        "\n",
        "    lines = content.split('\\n')\n",
        "\n",
        "    chunks = []  # This will hold the final chunks of text\n",
        "    current_chunk = []  # Temporarily holds lines for the current chunk\n",
        "\n",
        "    def add_chunk(c_chunk):\n",
        "        if c_chunk:\n",
        "            chunks.append('\\n'.join(c_chunk))\n",
        "\n",
        "    for line in lines:\n",
        "        # Check for file or PR beginning\n",
        "        if line.startswith('PR #'):\n",
        "            # If there's an existing chunk, add it to the chunks list\n",
        "            add_chunk(current_chunk)\n",
        "            current_chunk = [line]  # Start a new chunk\n",
        "        else:\n",
        "            current_chunk.append(line)\n",
        "\n",
        "    # Add the last chunk if it's not empty\n",
        "    add_chunk(current_chunk)\n",
        "\n",
        "    return chunks\n",
        "\n",
        "file_path = '/content/pr_data_250.txt'\n",
        "\n",
        "pr_diff_chunks = chunk_text_by_pr_diff(file_path)"
      ],
      "id": "oWfs8X84S8_3"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qlK3s_xCFgsV"
      },
      "source": [
        "#### Further chunk sequences that exceed 8192 tokens"
      ],
      "id": "qlK3s_xCFgsV"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mUC6oTN1DS50",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7bf97504-805c-4d14-c778-07b76d7da21e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "chunk exceeds token limit, chunking further...\n",
            "chunk exceeds token limit, chunking further...\n",
            "chunk exceeds token limit, chunking further...\n",
            "chunk exceeds token limit, chunking further...\n",
            "chunk exceeds token limit, chunking further...\n",
            "chunk exceeds token limit, chunking further...\n",
            "chunk exceeds token limit, chunking further...\n",
            "chunk exceeds token limit, chunking further...\n",
            "chunk exceeds token limit, chunking further...\n",
            "chunk exceeds token limit, chunking further...\n",
            "chunk exceeds token limit, chunking further...\n",
            "chunk exceeds token limit, chunking further...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Token indices sequence length is longer than the specified maximum sequence length for this model (32918 > 32768). Running this sequence through the model will result in indexing errors\n"
          ]
        }
      ],
      "source": [
        "def chunk_data_points(dataset, max_tokens=8192):\n",
        "    \"\"\"\n",
        "    Chunk data points in the dataset into parts of up to max_tokens size.\n",
        "\n",
        "    :param dataset: List of strings, each representing a data point.\n",
        "    :param max_tokens: Maximum number of tokens allowed per chunk.\n",
        "    :return: A new list where each data point is <= max_tokens size.\n",
        "    \"\"\"\n",
        "    chunked_dataset = []\n",
        "    for data_point in dataset:\n",
        "        tokens = tokenizer.tokenize(data_point)\n",
        "        num_tokens = len(tokens)\n",
        "        if num_tokens <= max_tokens:\n",
        "            chunked_dataset.append(data_point)\n",
        "        else:\n",
        "            print(\"chunk exceeds token limit, chunking further...\")\n",
        "            # Since direct token counts are used, need to manage chunks based on tokens directly.\n",
        "            start_index = 0\n",
        "            while start_index < num_tokens:\n",
        "                # The end index is calculated based on the max_tokens, adjusting for the start_index.\n",
        "                end_index = min(start_index + max_tokens, num_tokens)\n",
        "                # Convert tokens back to text. Might need adjustment based on tokenizer specifics.\n",
        "                chunk_text = tokenizer.convert_tokens_to_string(tokens[start_index:end_index])\n",
        "                chunked_dataset.append(chunk_text)\n",
        "                start_index = end_index\n",
        "    return chunked_dataset\n",
        "\n",
        "def chunk_data_points_for_pr_diffs(dataset, max_tokens=8192):\n",
        "    \"\"\"\n",
        "    If a PR's content exceeds the max_tokens size, we just skip. This seemed logical for me, rather than letting the model start predicting random diffed code without previous PR context.\n",
        "\n",
        "    :param dataset: List of strings, each representing a data point.\n",
        "    :param max_tokens: Maximum number of tokens allowed per chunk.\n",
        "    :return: A new list where each data point is <= max_tokens size.\n",
        "    \"\"\"\n",
        "    chunked_dataset = []\n",
        "    for data_point in dataset:\n",
        "        tokens = tokenizer.tokenize(data_point)\n",
        "        if len(tokens) <= max_tokens:\n",
        "            chunked_dataset.append(data_point)\n",
        "    return chunked_dataset\n",
        "\n",
        "chunks_1 = chunk_data_points(wiki_chunks + code_and_pr_chunks)\n",
        "chunks_2 = chunk_data_points_for_pr_diffs(pr_diff_chunks)\n",
        "chunks = chunks_1 + chunks_2\n",
        "\n",
        "final_chunks = []\n",
        "\n",
        "for chunk in chunks:\n",
        "  # Add in the beginning and end of sentence tokens to each text sequence\n",
        "  text = tokenizer.bos_token + chunk + tokenizer.eos_token\n",
        "\n",
        "  final_chunks.append({\"text\": text})"
      ],
      "id": "mUC6oTN1DS50"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kzv7NhLnH501"
      },
      "source": [
        "What a sample of our raw text data looks like before being tokenized and packing is applied:"
      ],
      "id": "Kzv7NhLnH501"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GP73fI4g-ZVT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d3bbb0da-c1c3-400c-99cc-948d56b46f0f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'text': \"<s>- File: Lines.tsx\\n  Content:\\nimport type { FC } from 'react';\\n\\ninterface LinesProps {\\n\\tn: number;\\n\\tmargin?: string;\\n}\\n\\nexport const Lines: FC<LinesProps> = (props) => {\\n\\tconst { n, margin } = props;\\n\\tconst thickness = 1;\\n\\tconst distance = 4;\\n\\tconst height = n * distance;\\n\\treturn (\\n\\t\\t<>\\n\\t\\t\\t<hr\\n\\t\\t\\t\\tcss={{\\n\\t\\t\\t\\t\\tbackgroundImage: `repeating-linear-gradient(to bottom, #dcdcdc, #dcdcdc ${thickness}px, transparent ${thickness}px, transparent ${distance}px)`,\\n\\t\\t\\t\\t\\tbackgroundRepeat: 'repeat',\\n\\t\\t\\t\\t\\tbackgroundPosition: 'top',\\n\\t\\t\\t\\t\\theight: `${height}px`,\\n\\t\\t\\t\\t\\tborder: 0,\\n\\t\\t\\t\\t\\tmargin: margin ? margin : '12px auto 6px',\\n\\t\\t\\t\\t}}\\n\\t\\t\\t/>\\n\\t\\t</>\\n\\t);\\n};\\n\\n</s>\"}"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "final_chunks[500]"
      ],
      "id": "GP73fI4g-ZVT"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5GUfBG42VWPc"
      },
      "source": [
        "### Labelled conversational data"
      ],
      "id": "5GUfBG42VWPc"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MMZf7NqmICAP"
      },
      "source": [
        "#### QA data"
      ],
      "id": "MMZf7NqmICAP"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xjTHVcsxIUzc"
      },
      "outputs": [],
      "source": [
        "from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments\n",
        "from datasets import Dataset, load_dataset\n",
        "import pandas as pd\n",
        "import json"
      ],
      "id": "xjTHVcsxIUzc"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jSpOjMopIRWk"
      },
      "outputs": [],
      "source": [
        "chat_data_path = '/content/QA_data.jsonl'\n",
        "qa_texts = []\n",
        "with open(chat_data_path, 'r', encoding='utf-8') as file:\n",
        "    for line in file:\n",
        "        chat = json.loads(line)\n",
        "        text = tokenizer.apply_chat_template(\n",
        "            chat[\"messages\"], tokenize=False, add_generation_prompt=False\n",
        "        )\n",
        "        qa_texts.append(text)"
      ],
      "id": "jSpOjMopIRWk"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M6eKkW3gIVx4"
      },
      "source": [
        "#### Code"
      ],
      "id": "M6eKkW3gIVx4"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gXXNWoDDIa9w"
      },
      "outputs": [],
      "source": [
        "files = []\n",
        "code_texts = []\n",
        "\n",
        "with open(\"/content/code.txt\", 'r', encoding='utf-8') as file:\n",
        "    content = file.read()\n",
        "\n",
        "    # Splitting the content into sections for each file\n",
        "    sections = content.split(\"- File: \")[1:]  # Skip the first split before the first file name\n",
        "\n",
        "    for section in sections:\n",
        "        # Splitting each section into file name and content\n",
        "        parts = section.split(\"Content:\\n\", 1)\n",
        "        file_name = parts[0].strip()\n",
        "        file_content = parts[1].strip() if len(parts) > 1 else \"\"\n",
        "\n",
        "        # Drop any files larger than n tokens\n",
        "        if len(tokenizer.tokenize(file_content)) > 8192:\n",
        "          continue\n",
        "\n",
        "        files.append([file_name, file_content])\n",
        "\n",
        "for i in range(0, len(files), 1):\n",
        "    qa = [{'role': 'user', 'content': f\"What does the {files[i][0]} file look like in `your-repo`\"}, {'role': 'assistant', 'content': f\"As of DATE, {files[i][0]} looks like this: \\n {files[i][1]}\"}]\n",
        "\n",
        "    text = tokenizer.apply_chat_template(qa, tokenize=False, add_generation_prompt=False)\n",
        "    code_texts.append(text)"
      ],
      "id": "gXXNWoDDIa9w"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wHmziSGlIbjr"
      },
      "source": [
        "#### Wiki"
      ],
      "id": "wHmziSGlIbjr"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uFi-boxlIce3"
      },
      "outputs": [],
      "source": [
        "wiki_texts = []\n",
        "\n",
        "def read_and_chunk_file(file_path, chunk_marker='§'):\n",
        "    with open(file_path, 'r', encoding='utf-8') as file:\n",
        "        text = file.read()\n",
        "\n",
        "    sections = text.split(chunk_marker)\n",
        "\n",
        "    qa_pairs = []\n",
        "\n",
        "    for section in sections[1:]:\n",
        "        if section.strip():\n",
        "            if '\\n' in section:\n",
        "                question, answer = section.split('\\n', 1)\n",
        "                qa_pairs.append((question.strip(), answer.strip()))\n",
        "            else:\n",
        "                qa_pairs.append((section.strip(), \"\"))\n",
        "\n",
        "    for pair in qa_pairs:\n",
        "      qa_inner = [{'role': 'user', 'content': pair[0]}, {'role': 'assistant', 'content': pair[1]}]\n",
        "\n",
        "      text = tokenizer.apply_chat_template(qa_inner, tokenize=False, add_generation_prompt=False)\n",
        "      wiki_texts.append(text)\n",
        "\n",
        "file_path = '/content/wiki-QA.txt'\n",
        "read_and_chunk_file(file_path)"
      ],
      "id": "uFi-boxlIce3"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4zTZbcxqha0O"
      },
      "outputs": [],
      "source": [
        "import random\n",
        "\n",
        "texts = qa_texts + code_texts + wiki_texts\n",
        "random.shuffle(texts)\n",
        "\n",
        "# Convert the preprocessed texts into a pandas DataFrame\n",
        "pandas_dataset = pd.DataFrame(texts, columns=[\"text\"])\n",
        "\n",
        "# Convert the pandas DataFrame into a HuggingFace Dataset\n",
        "dataset = Dataset.from_pandas(pandas_dataset)\n",
        "\n",
        "from datasets import DatasetDict\n",
        "\n",
        "datasets = dataset.train_test_split(test_size=0.2)\n",
        "train_dataset_qa = datasets['train']\n",
        "eval_dataset = datasets['test']"
      ],
      "id": "4zTZbcxqha0O"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0sHPblsXIRm1"
      },
      "source": [
        "**Note:** Upon finding the optimal hyperparameters for your model, it can be beneificial to re-run the training using ALL the data available, including 20% of the dataset you'd usually use for evaluation. This is just so we can take advantage of all the data we have available."
      ],
      "id": "0sHPblsXIRm1"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MXyR5zE6MqgR"
      },
      "outputs": [],
      "source": [
        "train_dataset = final_chunks + train_dataset_qa.to_list()\n",
        "\n",
        "# To include ALL data:\n",
        "# train_dataset = final_chunks + dataset.to_list()"
      ],
      "id": "MXyR5zE6MqgR"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CFD4maCFM2YL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a3727eba-1e4b-4788-f7fb-d28afab3868c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1837"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "len(train_dataset)"
      ],
      "id": "CFD4maCFM2YL"
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset_qa[3]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sDu17ZImsdVK",
        "outputId": "8e000121-383d-45d2-f683-1a36faec33ae"
      },
      "id": "sDu17ZImsdVK",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'text': \"<s>[INST] Where can the end-to-end tests be found in The Guardian's manage-frontend? [/INST]The end-to-end tests live in `cypress/tests/e2e`.</s>\"}"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9eb4c7fe-c05a-479a-9208-84d86d22c0bf"
      },
      "source": [
        "## 4. Set up LoRA and training environment"
      ],
      "id": "9eb4c7fe-c05a-479a-9208-84d86d22c0bf"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QkFUBWwxfzCv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "32c2f68d-3a7e-4488-c944-f00237f95afd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Unsloth 2024.3 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 = 768, # 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 = 1024,\n",
        "    lora_dropout = 0, # Supports any, but = 0 is optimized\n",
        "    bias = \"none\",    # Supports any, but = \"none\" is optimized\n",
        "    use_gradient_checkpointing = True,\n",
        "    random_state = 3407,\n",
        ")"
      ],
      "id": "QkFUBWwxfzCv"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tnhAq_kK4VAY"
      },
      "outputs": [],
      "source": [
        "project = \"Mistral-Instruct-16bit-8K-Sweep1\"\n",
        "run_name = \"run_1_SS\" # Defining a separate run name in case you want to start another one resuming from a checkpoint\n",
        "project_and_run_name = project + \"-\" + run_name\n",
        "output_dir = \"./\" + project_and_run_name"
      ],
      "id": "tnhAq_kK4VAY"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Callback for uploading checkpoints to Google Drive\n",
        "\n",
        "**Note: ** Checkpoints, especially those with a rank size as high as 768 will take up a lot of storage (10GB). There's [a recent offer](https://blog.google/products/google-one/google-one-gemini-ai-gmail-docs-sheets/) from Google for their Google One AI Premium subscription where you get two months free of 2TB storage in Google Drive amongst some other things like Gemini Ultra access."
      ],
      "metadata": {
        "id": "uBwLhWK85bRL"
      },
      "id": "uBwLhWK85bRL"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pxTuXUb5aAPo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "71b7b072-d51d-48be-d791-7c5186c9ebf9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "id": "pxTuXUb5aAPo"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MLumlAUumaLc"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import shutil\n",
        "from pathlib import Path\n",
        "from transformers import TrainerCallback\n",
        "\n",
        "class UploadCheckpointCallback(TrainerCallback):\n",
        "    def __init__(self, output_dir, google_drive_dir):\n",
        "        super().__init__()\n",
        "        self.output_dir = output_dir\n",
        "        self.google_drive_dir = google_drive_dir\n",
        "\n",
        "    def on_save(self, args, state, control, **kwargs):\n",
        "        list_of_dirs = [d for d in os.listdir(self.output_dir) if os.path.isdir(os.path.join(self.output_dir, d)) and 'checkpoint' in d]\n",
        "        list_of_dirs.sort(key=lambda x: os.path.getmtime(os.path.join(self.output_dir, x)), reverse=True)\n",
        "\n",
        "        if list_of_dirs:\n",
        "            latest_checkpoint_dir = os.path.join(self.output_dir, list_of_dirs[0])\n",
        "            destination_path = os.path.join(self.google_drive_dir, os.path.basename(latest_checkpoint_dir))\n",
        "\n",
        "            # Ensure the destination directory does not exist before copying\n",
        "            if os.path.exists(destination_path):\n",
        "                shutil.rmtree(destination_path)\n",
        "            shutil.copytree(latest_checkpoint_dir, destination_path)\n",
        "            print(f'Uploaded {latest_checkpoint_dir} to Google Drive: {destination_path}')\n",
        "\n",
        "            # Delete the local checkpoint directory after upload to stop disk from filling up\n",
        "            shutil.rmtree(latest_checkpoint_dir)\n",
        "            print(f'Deleted local checkpoint directory: {latest_checkpoint_dir}')\n",
        "\n",
        "google_drive_dir = '/content/drive/My Drive/your_drive_folder/' + project + \"/\" +run_name + \"/\"\n",
        "\n",
        "upload_checkpoint_callback = UploadCheckpointCallback(output_dir, google_drive_dir)"
      ],
      "id": "MLumlAUumaLc"
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Copy a checkpoint from Google Drive if you are resuming from a checkpoint"
      ],
      "metadata": {
        "id": "meu5wTQUSwaG"
      },
      "id": "meu5wTQUSwaG"
    },
    {
      "cell_type": "code",
      "source": [
        "!cp -r \"/content/drive/My Drive/your_drive_folder/Mistral-Instruct-16bit-8K-Sweep1/run_1_SS/checkpoint-1120/\" \"/content/latest_checkpoint/\""
      ],
      "metadata": {
        "id": "TvuAjb4dP7jr"
      },
      "id": "TvuAjb4dP7jr",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iQCx0IC6Qo19"
      },
      "source": [
        "## 5. Training"
      ],
      "id": "iQCx0IC6Qo19"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IpcbWcAZgaq9"
      },
      "outputs": [],
      "source": [
        "from trl import SFTTrainer\n",
        "from transformers import TrainingArguments\n",
        "from datetime import datetime\n",
        "\n",
        "wandbname = project + \"-\" + run_name\n",
        "\n",
        "trainer = SFTTrainer(\n",
        "    model = model,\n",
        "    callbacks=[upload_checkpoint_callback],\n",
        "    train_dataset = train_dataset,\n",
        "    eval_dataset = eval_dataset,\n",
        "    tokenizer = tokenizer,\n",
        "    max_seq_length = max_seq_length,\n",
        "    packing = True,\n",
        "    dataset_text_field=\"text\",\n",
        "    args = TrainingArguments(\n",
        "        per_device_eval_batch_size = 1,\n",
        "        per_device_train_batch_size = 1,\n",
        "        gradient_accumulation_steps = 1,\n",
        "        warmup_ratio = 0,\n",
        "        max_grad_norm = 1.0,\n",
        "        num_train_epochs = 5,\n",
        "        learning_rate = 2e-5,\n",
        "        fp16 = not torch.cuda.is_bf16_supported(),\n",
        "        bf16 = torch.cuda.is_bf16_supported(),\n",
        "        logging_steps = 1,\n",
        "        evaluation_strategy=\"epoch\",\n",
        "        optim = \"adamw_8bit\",\n",
        "        weight_decay = 0.1,\n",
        "        lr_scheduler_type = \"cosine\",\n",
        "        save_strategy=\"epoch\",\n",
        "        seed = 3407,\n",
        "        output_dir = output_dir,\n",
        "        # report_to=\"wandb\",\n",
        "        run_name=f\"{wandbname}-{datetime.now().strftime('%Y-%m-%d-%H-%M')}\"\n",
        "    ),\n",
        ")\n",
        "\n",
        "trainer.train()\n",
        "\n",
        "# If resuming from a checkpoint:\n",
        "# trainer.train(resume_from_checkpoint=\"/content/latest-checkpoint/\")"
      ],
      "id": "IpcbWcAZgaq9"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "24beb8e2-1ea7-4c30-a8cc-37ff6b6e62b0"
      },
      "source": [
        "## 6. Try the Trained Model!\n"
      ],
      "id": "24beb8e2-1ea7-4c30-a8cc-37ff6b6e62b0"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gaDHoXknVfjj"
      },
      "source": [
        "### With chat template\n",
        "\n",
        "The `apply_chat_template` function injects the special [INST] tokens to your prompt"
      ],
      "id": "gaDHoXknVfjj"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xjjOHCfIqB4g",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "75f7dc77-113c-451b-ae6a-6631b53c7070"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[\"<s> [INST] Explain the theory of relativity to me in detail [/INST] The Theory of Relativity is a fundamental concept in physics that was developed by Albert Einstein between 1905 and 1915. It consists of two parts: the Special Theory of Relativity (STR) and the General Theory of Relativity (GTR).\\n\\n1. Special Theory of Relativity (STR, 1905):\\n\\nSTR is based on two postulates:\\n\\na) The laws of physics are the same in all inertial frames of reference. This means that there is no preferred inertial frame of reference in the universe.\\n\\nb) The speed of light in a vacuum is the same for all observers, regardless of their motion or the motion of the source of light. This speed is approximately 299,792,458 meters per second (m/s) or 186,282 miles per second (mi/s).\\n\\nFrom these postulates, several consequences follow:\\n\\n- Time dilation: Moving clocks run slower than stationary clocks. This means that a clock moving relative to an observer will appear to tick more slowly than a clock that is at rest.\\n- Length contraction: Moving objects are shorter in the direction of motion than at rest. This means that a rod that is at rest appears to have a certain length, but if it is moving relative to an observer, it will appear shorter in the direction of motion.\\n- Mass-energy equivalence: Energy (E) and mass (m) are interchangeable. This means that mass can be converted into energy and vice versa, according to the famous equation E = mc², where c is the speed of light.\\n\\n2. General Theory of Relativity (GTR, 1915):\\n\\nGTR is a theory of gravitation that extends STR to include acceleration and non-inertial frames of reference. It is based on the following principles:\\n\\n- Gravity is not a force, but a curvature of spacetime caused by mass and energy.\\n- The laws of physics are the same in all coordinate systems, including those that are accelerating or non-inertial.\\n\\nFrom these principles, Einstein derived the field equations of GTR, which describe how matter and energy cause spacetime to curve. These equations have been incredibly successful in explaining various phenomena, such as the bending of light around massive objects, the precession of Mercury's orbit, and the existence of black holes.\\n\\nIn summary, the Theory of Relativity is a revolutionary concept in physics that fundamentally changed our understanding of space, time, and gravity. It has been confirmed by numerous experiments and observations and is a cornerstone of modern physics.</s>\"]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "from unsloth.chat_templates import get_chat_template\n",
        "\n",
        "tokenizer = get_chat_template(\n",
        "    tokenizer,\n",
        "    chat_template = \"mistral\", # Supports zephyr, chatml, mistral, llama, alpaca, vicuna, vicuna_old, unsloth\n",
        "    mapping = {\"role\" : \"from\", \"content\" : \"value\", \"user\" : \"human\", \"assistant\" : \"gpt\"}, # ShareGPT style\n",
        "    map_eos_token = True, # Maps <|im_end|> to </s> instead\n",
        ")\n",
        "\n",
        "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n",
        "\n",
        "messages = [\n",
        "    {\"from\": \"human\", \"value\": \"\"\"Explain the theory of relativity to me in detail\"\"\"},\n",
        "]\n",
        "inputs = tokenizer.apply_chat_template(\n",
        "    messages,\n",
        "    tokenize = True,\n",
        "    add_generation_prompt = True, # Must add for generation\n",
        "    return_tensors = \"pt\",\n",
        ").to(\"cuda\")\n",
        "\n",
        "outputs = model.generate(input_ids = inputs, max_new_tokens = 8192, use_cache = True)\n",
        "tokenizer.batch_decode(outputs)"
      ],
      "id": "xjjOHCfIqB4g"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BL5-ln89VhkI"
      },
      "source": [
        "### Without chat template"
      ],
      "id": "BL5-ln89VhkI"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "D7bV2cWUZXhV"
      },
      "outputs": [],
      "source": [
        "from transformers import TextIteratorStreamer\n",
        "from threading import Thread\n",
        "text_streamer = TextIteratorStreamer(tokenizer)\n",
        "import textwrap\n",
        "max_print_width = 100\n",
        "\n",
        "inputs = tokenizer(\n",
        "[\n",
        "    \"\"\"Explain the theory of relativity to me\"\"\"\n",
        "]*1, return_tensors = \"pt\").to(\"cuda\")\n",
        "\n",
        "generation_kwargs = dict(\n",
        "    inputs,\n",
        "    streamer = text_streamer,\n",
        "    max_new_tokens = 8192,\n",
        "    use_cache = True,\n",
        ")\n",
        "thread = Thread(target = model.generate, kwargs = generation_kwargs)\n",
        "thread.start()\n",
        "\n",
        "length = 0\n",
        "for j, new_text in enumerate(text_streamer):\n",
        "    if j == 0:\n",
        "        wrapped_text = textwrap.wrap(new_text, width = max_print_width)\n",
        "        length = len(wrapped_text[-1])\n",
        "        wrapped_text = \"\\n\".join(wrapped_text)\n",
        "        print(wrapped_text, end = \"\")\n",
        "    else:\n",
        "        length += len(new_text)\n",
        "        if length >= max_print_width:\n",
        "            length = 0\n",
        "            print()\n",
        "        print(new_text, end = \"\")\n",
        "    pass\n",
        "pass"
      ],
      "id": "D7bV2cWUZXhV"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-cC8gXRnrF-O"
      },
      "source": [
        "### 7. Save the model and upload to Google Drive"
      ],
      "id": "-cC8gXRnrF-O"
    },
    {
      "cell_type": "code",
      "source": [
        "model.save_pretrained_merged(\"your-model\", tokenizer, save_method = \"merged_16bit\",)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sto00P5jSm_T",
        "outputId": "101ef28b-5eeb-42ea-d39a-18fd87338549"
      },
      "id": "sto00P5jSm_T",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Unsloth: Merging 4bit and LoRA weights to 16bit...\n",
            "Unsloth: Will use up to 55.8 out of 83.48 RAM for saving.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 32/32 [00:00<00:00, 38.34it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Unsloth: Saving tokenizer... Done.\n",
            "Unsloth: Saving model... This might take 5 minutes for Llama-7b...\n",
            "Done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!cp -r \"/content/your-model/\" \"/content/drive/My Drive/your-model/\""
      ],
      "metadata": {
        "id": "-tP8_-9GSt1V"
      },
      "id": "-tP8_-9GSt1V",
      "execution_count": null,
      "outputs": []
    }
  ],
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