openai-cookbook/examples/third_party_examples/GPT_finetuning_with_wandb.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vi2LlIiMU2dp"
      },
      "source": [
        "<img src=\"https://wandb.me/logo-im-png\" width=\"400\" alt=\"Weights & Biases\" />\n",
        "<!--- @wandbcode{openai-finetune-gpt35} -->\n",
        "\n",
        "# Fine-tune ChatGPT-3.5 and GPT-4 with Weights & Biases\n",
        "\n",
        "<a href=\"https://colab.research.google.com/github/wandb/examples/blob/master/colabs/openai/Fine_tune_GPT_3_with_Weights_&_Biases.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
        "\n",
        "**Note:** you will need an [OpenAI API key](https://platform.openai.com/account/api-keys) to run this colab."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OKB1NaA3U2dp"
      },
      "source": [
        "If you use OpenAI's API to [fine-tune ChatGPT-3.5](https://platform.openai.com/docs/guides/fine-tuning), you can now use the W&B integration to track experiments, models, and datasets in your central dashboard.\n",
        "\n",
        "All it takes is one line: `openai wandb sync`\n",
        "\n",
        "See the [OpenAI section](https://wandb.me/openai-docs) in the Weights & Biases documentation for full details of the integration"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AebZsbWrU2dp"
      },
      "outputs": [],
      "source": [
        "!pip install -Uq openai tiktoken datasets tenacity wandb"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "L_qbldX6U2dq"
      },
      "outputs": [],
      "source": [
        "# Remove once this PR is merged: https://github.com/openai/openai-python/pull/590 and openai release is made\n",
        "!pip uninstall -y openai -qq \\\n",
        "&& pip install git+https://github.com/morganmcg1/openai-python.git@update_wandb_logger -qqq"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "STVFg2SMU2dq"
      },
      "source": [
        "## Optional: Fine-tune ChatGPT-3.5\n",
        "\n",
        "It's always more fun to experiment with your own projects so if you have already used the openai API to fine-tune an OpenAI model, just skip this section.\n",
        "\n",
        "Otherwise let's fine-tune ChatGPT-3.5 on a legal dataset!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0G1keRuTU2dq"
      },
      "source": [
        "### Imports and initial set-up"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TpRQxo1QU2dq"
      },
      "outputs": [],
      "source": [
        "import openai\n",
        "import wandb\n",
        "\n",
        "import os\n",
        "import json\n",
        "import random\n",
        "import tiktoken\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from pathlib import Path\n",
        "from tqdm.auto import tqdm\n",
        "from collections import defaultdict\n",
        "from tenacity import retry, stop_after_attempt, wait_fixed"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X7h1WmYRU2dr"
      },
      "source": [
        "Start your Weigths & Biases run. If you don't have an account you can sign up for one for free at www.wandb.ai"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "p5fPfbPCU2dr"
      },
      "outputs": [],
      "source": [
        "WANDB_PROJECT = \"OpenAI-Fine-Tune\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AKc9uwv_U2dr"
      },
      "source": [
        "### Set up your API key"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JDs2cWLfU2dr"
      },
      "outputs": [],
      "source": [
        "# # Enter credentials\n",
        "openai_key = \"YOUR_API_KEY\"\n",
        "\n",
        "openai.api_key = openai_key"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YQhM025QU2dr"
      },
      "source": [
        "### Dataset Preparation\n",
        "\n",
        "We download a dataset from [LegalBench](https://hazyresearch.stanford.edu/legalbench/), a project to curate tasks for evaluating legal reasoning, specifically the [Contract NLI Explicit Identification task](https://github.com/HazyResearch/legalbench/tree/main/tasks/contract_nli_explicit_identification).\n",
        "\n",
        "This comprises of a total of 117 examples, from which we will create our own train and test datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "THyWwbMyU2ds"
      },
      "outputs": [],
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "# Download the data, merge into a single dataset and shuffle\n",
        "dataset = load_dataset(\"nguha/legalbench\", \"contract_nli_explicit_identification\")\n",
        "\n",
        "data = []\n",
        "for d in dataset[\"train\"]:\n",
        "  data.append(d)\n",
        "\n",
        "for d in dataset[\"test\"]:\n",
        "  data.append(d)\n",
        "\n",
        "random.shuffle(data)\n",
        "\n",
        "for idx, d in enumerate(data):\n",
        "  d[\"new_index\"] = idx"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JPDSyx9AU2ds"
      },
      "source": [
        "Let's look at a few samples."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "l6NZj5SZU2ds",
        "outputId": "527c1836-cb2b-474d-c0c6-d646e4fd087b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(117,\n",
              " [{'answer': 'No',\n",
              "   'index': '94',\n",
              "   'text': 'Recipient shall use the Confidential Information exclusively for HySafe purposes, especially to advice the Governing Board of HySafe. ',\n",
              "   'document_name': 'NDA_V3.pdf',\n",
              "   'new_index': 0},\n",
              "  {'answer': 'No',\n",
              "   'index': '53',\n",
              "   'text': '3. In consideration of each and every disclosure of CONFIDENTIAL INFORMATION, the Parties agree to: (c) make no disclosures of any CONFIDENTIAL INFORMATION to any party other than officers and employees of a Party to this IRA; (d) limit access to CONFIDENTIAL INFORMATION to those officers and employees having a reasonable need for such INFORMATION and being boUnd by a written obligation to maintain the confidentiality of such INFORMATION; and ',\n",
              "   'document_name': '1084000_0001144204-06-046785_v056501_ex10-16.txt',\n",
              "   'new_index': 1}])"
            ]
          },
          "execution_count": null,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(data), data[0:2]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VSUkLGT0U2ds"
      },
      "source": [
        "### Format our Data for Chat Completion Models\n",
        "We modify the `base_prompt` from the LegalBench task to make it a zero-shot prompt, as we are training the model instead of using few-shot prompting"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WPCxFBbEU2ds"
      },
      "outputs": [],
      "source": [
        "base_prompt_zero_shot = \"Identify if the clause provides that all Confidential Information shall be expressly identified by the Disclosing Party. Answer with only `Yes` or `No`\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jJFhwJjXU2ds"
      },
      "source": [
        "We now split it into training/validation dataset, lets train on 30 samples and test on the remainder\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "H-2oiwxIU2ds"
      },
      "outputs": [],
      "source": [
        "n_train = 30\n",
        "n_test = len(data) - n_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rYhlg7wJU2ds",
        "outputId": "c0121d63-7c1e-48cd-82c7-b9efeb663df5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(30,\n",
              " 87,\n",
              " 87,\n",
              " {'messages': [{'role': 'system',\n",
              "    'content': 'Identify if the clause provides that all Confidential Information shall be expressly identified by the Disclosing Party. Answer with only `Yes` or `No`'},\n",
              "   {'role': 'user',\n",
              "    'content': '2. The Contractor shall not, without the State’s prior written consent, copy, disclose, publish, release, transfer, disseminate, use, or allow access for any purpose or in any form, any Confidential Information except for the sole and exclusive purpose of performing under the Contract.  '},\n",
              "   {'role': 'assistant', 'content': 'No'}]})"
            ]
          },
          "execution_count": null,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_messages = []\n",
        "test_messages = []\n",
        "\n",
        "for d in data:\n",
        "  prompts = []\n",
        "  prompts.append({\"role\": \"system\", \"content\": base_prompt_zero_shot})\n",
        "  prompts.append({\"role\": \"user\", \"content\": d[\"text\"]})\n",
        "  prompts.append({\"role\": \"assistant\", \"content\": d[\"answer\"]})\n",
        "\n",
        "  if int(d[\"new_index\"]) < n_train:\n",
        "    train_messages.append({'messages': prompts})\n",
        "  else:\n",
        "    test_messages.append({'messages': prompts})\n",
        "\n",
        "len(train_messages), len(test_messages), n_test, train_messages[5]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2Ul1lOV8U2dt"
      },
      "source": [
        "### Save the data to Weigths & Biases\n",
        "\n",
        "Save the data in a train and test file first"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "z_kbcPDtU2dt"
      },
      "outputs": [],
      "source": [
        "train_file_path = 'encoded_train_data.jsonl'\n",
        "with open(train_file_path, 'w') as file:\n",
        "    for item in train_messages:\n",
        "        line = json.dumps(item)\n",
        "        file.write(line + '\\n')\n",
        "\n",
        "test_file_path = 'encoded_test_data.jsonl'\n",
        "with open(test_file_path, 'w') as file:\n",
        "    for item in test_messages:\n",
        "        line = json.dumps(item)\n",
        "        file.write(line + '\\n')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C58GZECzU2dt"
      },
      "source": [
        "Next, we validate that our training data is in the correct format using a script from the [OpenAI fine-tuning documentation](https://platform.openai.com/docs/guides/fine-tuning/)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "awSfo7T4U2dt"
      },
      "outputs": [],
      "source": [
        "# Next, we specify the data path and open the JSONL file\n",
        "\n",
        "def openai_validate_data(dataset_path):\n",
        "  data_path = dataset_path\n",
        "\n",
        "  # Load dataset\n",
        "  with open(data_path) as f:\n",
        "      dataset = [json.loads(line) for line in f]\n",
        "\n",
        "  # We can inspect the data quickly by checking the number of examples and the first item\n",
        "\n",
        "  # Initial dataset stats\n",
        "  print(\"Num examples:\", len(dataset))\n",
        "  print(\"First example:\")\n",
        "  for message in dataset[0][\"messages\"]:\n",
        "      print(message)\n",
        "\n",
        "  # Now that we have a sense of the data, we need to go through all the different examples and check to make sure the formatting is correct and matches the Chat completions message structure\n",
        "\n",
        "  # Format error checks\n",
        "  format_errors = defaultdict(int)\n",
        "\n",
        "  for ex in dataset:\n",
        "      if not isinstance(ex, dict):\n",
        "          format_errors[\"data_type\"] += 1\n",
        "          continue\n",
        "\n",
        "      messages = ex.get(\"messages\", None)\n",
        "      if not messages:\n",
        "          format_errors[\"missing_messages_list\"] += 1\n",
        "          continue\n",
        "\n",
        "      for message in messages:\n",
        "          if \"role\" not in message or \"content\" not in message:\n",
        "              format_errors[\"message_missing_key\"] += 1\n",
        "\n",
        "          if any(k not in (\"role\", \"content\", \"name\") for k in message):\n",
        "              format_errors[\"message_unrecognized_key\"] += 1\n",
        "\n",
        "          if message.get(\"role\", None) not in (\"system\", \"user\", \"assistant\"):\n",
        "              format_errors[\"unrecognized_role\"] += 1\n",
        "\n",
        "          content = message.get(\"content\", None)\n",
        "          if not content or not isinstance(content, str):\n",
        "              format_errors[\"missing_content\"] += 1\n",
        "\n",
        "      if not any(message.get(\"role\", None) == \"assistant\" for message in messages):\n",
        "          format_errors[\"example_missing_assistant_message\"] += 1\n",
        "\n",
        "  if format_errors:\n",
        "      print(\"Found errors:\")\n",
        "      for k, v in format_errors.items():\n",
        "          print(f\"{k}: {v}\")\n",
        "  else:\n",
        "      print(\"No errors found\")\n",
        "\n",
        "  # Beyond the structure of the message, we also need to ensure that the length does not exceed the 4096 token limit.\n",
        "\n",
        "  # Token counting functions\n",
        "  encoding = tiktoken.get_encoding(\"cl100k_base\")\n",
        "\n",
        "  # not exact!\n",
        "  # simplified from https://github.com/openai/openai-cookbook/blob/main/examples/How_to_count_tokens_with_tiktoken.ipynb\n",
        "  def num_tokens_from_messages(messages, tokens_per_message=3, tokens_per_name=1):\n",
        "      num_tokens = 0\n",
        "      for message in messages:\n",
        "          num_tokens += tokens_per_message\n",
        "          for key, value in message.items():\n",
        "              num_tokens += len(encoding.encode(value))\n",
        "              if key == \"name\":\n",
        "                  num_tokens += tokens_per_name\n",
        "      num_tokens += 3\n",
        "      return num_tokens\n",
        "\n",
        "  def num_assistant_tokens_from_messages(messages):\n",
        "      num_tokens = 0\n",
        "      for message in messages:\n",
        "          if message[\"role\"] == \"assistant\":\n",
        "              num_tokens += len(encoding.encode(message[\"content\"]))\n",
        "      return num_tokens\n",
        "\n",
        "  def print_distribution(values, name):\n",
        "      print(f\"\\n#### Distribution of {name}:\")\n",
        "      print(f\"min / max: {min(values)}, {max(values)}\")\n",
        "      print(f\"mean / median: {np.mean(values)}, {np.median(values)}\")\n",
        "      print(f\"p5 / p95: {np.quantile(values, 0.1)}, {np.quantile(values, 0.9)}\")\n",
        "\n",
        "  # Last, we can look at the results of the different formatting operations before proceeding with creating a fine-tuning job:\n",
        "\n",
        "  # Warnings and tokens counts\n",
        "  n_missing_system = 0\n",
        "  n_missing_user = 0\n",
        "  n_messages = []\n",
        "  convo_lens = []\n",
        "  assistant_message_lens = []\n",
        "\n",
        "  for ex in dataset:\n",
        "      messages = ex[\"messages\"]\n",
        "      if not any(message[\"role\"] == \"system\" for message in messages):\n",
        "          n_missing_system += 1\n",
        "      if not any(message[\"role\"] == \"user\" for message in messages):\n",
        "          n_missing_user += 1\n",
        "      n_messages.append(len(messages))\n",
        "      convo_lens.append(num_tokens_from_messages(messages))\n",
        "      assistant_message_lens.append(num_assistant_tokens_from_messages(messages))\n",
        "\n",
        "  print(\"Num examples missing system message:\", n_missing_system)\n",
        "  print(\"Num examples missing user message:\", n_missing_user)\n",
        "  print_distribution(n_messages, \"num_messages_per_example\")\n",
        "  print_distribution(convo_lens, \"num_total_tokens_per_example\")\n",
        "  print_distribution(assistant_message_lens, \"num_assistant_tokens_per_example\")\n",
        "  n_too_long = sum(l > 4096 for l in convo_lens)\n",
        "  print(f\"\\n{n_too_long} examples may be over the 4096 token limit, they will be truncated during fine-tuning\")\n",
        "\n",
        "  # Pricing and default n_epochs estimate\n",
        "  MAX_TOKENS_PER_EXAMPLE = 4096\n",
        "\n",
        "  MIN_TARGET_EXAMPLES = 100\n",
        "  MAX_TARGET_EXAMPLES = 25000\n",
        "  TARGET_EPOCHS = 3\n",
        "  MIN_EPOCHS = 1\n",
        "  MAX_EPOCHS = 25\n",
        "\n",
        "  n_epochs = TARGET_EPOCHS\n",
        "  n_train_examples = len(dataset)\n",
        "  if n_train_examples * TARGET_EPOCHS < MIN_TARGET_EXAMPLES:\n",
        "      n_epochs = min(MAX_EPOCHS, MIN_TARGET_EXAMPLES // n_train_examples)\n",
        "  elif n_train_examples * TARGET_EPOCHS > MAX_TARGET_EXAMPLES:\n",
        "      n_epochs = max(MIN_EPOCHS, MAX_TARGET_EXAMPLES // n_train_examples)\n",
        "\n",
        "  n_billing_tokens_in_dataset = sum(min(MAX_TOKENS_PER_EXAMPLE, length) for length in convo_lens)\n",
        "  print(f\"Dataset has ~{n_billing_tokens_in_dataset} tokens that will be charged for during training\")\n",
        "  print(f\"By default, you'll train for {n_epochs} epochs on this dataset\")\n",
        "  print(f\"By default, you'll be charged for ~{n_epochs * n_billing_tokens_in_dataset} tokens\")\n",
        "  print(\"See pricing page to estimate total costs\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wmC69bJkU2dt"
      },
      "source": [
        "Validate train data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vej4HKR7U2dt",
        "outputId": "170ed230-8d7c-4e35-94d4-864db843beb5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Num examples: 30\n",
            "First example:\n",
            "{'role': 'system', 'content': 'Identify if the clause provides that all Confidential Information shall be expressly identified by the Disclosing Party. Answer with only `Yes` or `No`'}\n",
            "{'role': 'user', 'content': 'Recipient shall use the Confidential Information exclusively for HySafe purposes, especially to advice the Governing Board of HySafe. '}\n",
            "{'role': 'assistant', 'content': 'No'}\n",
            "No errors found\n",
            "Num examples missing system message: 0\n",
            "Num examples missing user message: 0\n",
            "\n",
            "#### Distribution of num_messages_per_example:\n",
            "min / max: 3, 3\n",
            "mean / median: 3.0, 3.0\n",
            "p5 / p95: 3.0, 3.0\n",
            "\n",
            "#### Distribution of num_total_tokens_per_example:\n",
            "min / max: 69, 319\n",
            "mean / median: 143.46666666666667, 122.0\n",
            "p5 / p95: 82.10000000000001, 235.10000000000002\n",
            "\n",
            "#### Distribution of num_assistant_tokens_per_example:\n",
            "min / max: 1, 1\n",
            "mean / median: 1.0, 1.0\n",
            "p5 / p95: 1.0, 1.0\n",
            "\n",
            "0 examples may be over the 4096 token limit, they will be truncated during fine-tuning\n",
            "Dataset has ~4304 tokens that will be charged for during training\n",
            "By default, you'll train for 3 epochs on this dataset\n",
            "By default, you'll be charged for ~12912 tokens\n",
            "See pricing page to estimate total costs\n"
          ]
        }
      ],
      "source": [
        "openai_validate_data(train_file_path)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C83jxUI9U2dt"
      },
      "source": [
        "Log our data to Weigths & Biases Artifacts for storage and versioning"
      ]
    },
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      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ep-aT9NKU2dt",
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          "name": "stderr",
          "output_type": "stream",
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            "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mcapecape\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
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          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              " View project at <a href='https://wandb.ai/capecape/OpenAI-Fine-Tune' target=\"_blank\">https://wandb.ai/capecape/OpenAI-Fine-Tune</a>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              " View run at <a href='https://wandb.ai/capecape/OpenAI-Fine-Tune/runs/ivu21mjl' target=\"_blank\">https://wandb.ai/capecape/OpenAI-Fine-Tune/runs/ivu21mjl</a>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "wandb: WARNING Source type is set to 'repo' but some required information is missing from the environment. A job will not be created from this run. See https://docs.wandb.ai/guides/launch/create-job\n"
          ]
        },
        {
          "data": {
            "text/html": [
              " View run <strong style=\"color:#cdcd00\">mild-surf-1</strong> at: <a href='https://wandb.ai/capecape/OpenAI-Fine-Tune/runs/ivu21mjl' target=\"_blank\">https://wandb.ai/capecape/OpenAI-Fine-Tune/runs/ivu21mjl</a><br/>Synced 6 W&B file(s), 0 media file(s), 2 artifact file(s) and 1 other file(s)"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "Find logs at: <code>./wandb/run-20230830_113853-ivu21mjl/logs</code>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "wandb.init(\n",
        "    project=WANDB_PROJECT,\n",
        "    # entity=\"prompt-eng\",\n",
        "    job_type=\"log-data\",\n",
        "    config = {'n_train': n_train,\n",
        "              'n_valid': n_test})\n",
        "\n",
        "wandb.log_artifact(train_file_path,\n",
        "                   \"legalbench-contract_nli_explicit_identification-train\",\n",
        "                   type=\"train-data\")\n",
        "\n",
        "wandb.log_artifact(test_file_path,\n",
        "                   \"legalbench-contract_nli_explicit_identification-test\",\n",
        "                   type=\"test-data\")\n",
        "\n",
        "# keep entity (typically your wandb username) for reference of artifact later in this demo\n",
        "entity = wandb.run.entity\n",
        "\n",
        "wandb.finish()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hD2g6q3GU2dt"
      },
      "source": [
        "### Create a fine-tuned model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4MlVEQTaU2du"
      },
      "source": [
        "We'll now use OpenAI API to fine-tune ChatGPT-3.5\n",
        "\n",
        "Let's first download our training & validation files and save them to a folder called `my_data`. We will retrieve the `latest` version of the artifact, but it could also be `v0`, `v1` or any alias we associated with it"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "referenced_widgets": [
            "dabd1490c65148978081fa0aedb0913b"
          ]
        },
        "id": "K54fvZFTU2du",
        "outputId": "adbcc0c2-c149-4dac-e7be-34253e66788e"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "dabd1490c65148978081fa0aedb0913b",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "VBox(children=(Label(value='Waiting for wandb.init()...\\r'), FloatProgress(value=0.016751802766035932, max=1.0…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "Tracking run with wandb version 0.15.9"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "Run data is saved locally in <code>/Users/tcapelle/work/examples/colabs/openai/wandb/run-20230830_113907-1ili9l51</code>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "Syncing run <strong><a href='https://wandb.ai/capecape/OpenAI-Fine-Tune/runs/1ili9l51' target=\"_blank\">jumping-water-2</a></strong> to <a href='https://wandb.ai/capecape/OpenAI-Fine-Tune' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              " View project at <a href='https://wandb.ai/capecape/OpenAI-Fine-Tune' target=\"_blank\">https://wandb.ai/capecape/OpenAI-Fine-Tune</a>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              " View run at <a href='https://wandb.ai/capecape/OpenAI-Fine-Tune/runs/1ili9l51' target=\"_blank\">https://wandb.ai/capecape/OpenAI-Fine-Tune/runs/1ili9l51</a>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "'my_data/encoded_train_data.jsonl'"
            ]
          },
          "execution_count": null,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "wandb.init(project=WANDB_PROJECT,\n",
        "          #  entity=\"prompt-eng\",\n",
        "           job_type=\"finetune\")\n",
        "\n",
        "artifact_train = wandb.use_artifact(\n",
        "    f'{entity}/{WANDB_PROJECT}/legalbench-contract_nli_explicit_identification-train:latest',\n",
        "    type='train-data')\n",
        "train_file = artifact_train.get_path(train_file_path).download(\"my_data\")\n",
        "\n",
        "train_file"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dh86AmJuU2du"
      },
      "source": [
        "Then we upload the training data to OpenAI. OpenAi has to process the data, so this will take a few minutes depending on the size of your dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xaviUxz7U2du",
        "outputId": "bf00b2e6-6045-4fca-be7d-9e3b0dd86421"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<File file id=file-spPASR6VWco54SqfN2yo7T8v> JSON: {\n",
              "  \"object\": \"file\",\n",
              "  \"id\": \"file-spPASR6VWco54SqfN2yo7T8v\",\n",
              "  \"purpose\": \"fine-tune\",\n",
              "  \"filename\": \"file\",\n",
              "  \"bytes\": 24059,\n",
              "  \"created_at\": 1693388388,\n",
              "  \"status\": \"uploaded\",\n",
              "  \"status_details\": null\n",
              "}"
            ]
          },
          "execution_count": null,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "openai_train_file_info = openai.File.create(\n",
        "  file=open(train_file, \"rb\"),\n",
        "  purpose='fine-tune'\n",
        ")\n",
        "\n",
        "# you may need to wait a couple of minutes for OpenAI to process the file\n",
        "openai_train_file_info"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "woiHI6KoU2du"
      },
      "source": [
        "### Time to train the model!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YvP8ZGqWU2du"
      },
      "source": [
        "Let's define our ChatGPT-3.5 fine-tuning hyper-parameters."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "V4sV1wohU2du"
      },
      "outputs": [],
      "source": [
        "model = 'gpt-3.5-turbo'\n",
        "n_epochs = 3"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cju9ZZCZU2du",
        "outputId": "75155c4f-f260-4099-dac0-150a98dffc16"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<FineTuningJob fine_tuning.job id=ftjob-x4tl83IlSGolkUF3fCFyZNGs> JSON: {\n",
              "  \"object\": \"fine_tuning.job\",\n",
              "  \"id\": \"ftjob-x4tl83IlSGolkUF3fCFyZNGs\",\n",
              "  \"model\": \"gpt-3.5-turbo-0613\",\n",
              "  \"created_at\": 1693388447,\n",
              "  \"finished_at\": null,\n",
              "  \"fine_tuned_model\": null,\n",
              "  \"organization_id\": \"org-WnF2wEqNkV1Nj65CzDxr6iUm\",\n",
              "  \"result_files\": [],\n",
              "  \"status\": \"created\",\n",
              "  \"validation_file\": null,\n",
              "  \"training_file\": \"file-spPASR6VWco54SqfN2yo7T8v\",\n",
              "  \"hyperparameters\": {\n",
              "    \"n_epochs\": 3\n",
              "  },\n",
              "  \"trained_tokens\": null\n",
              "}"
            ]
          },
          "execution_count": null,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "openai_ft_job_info = openai.FineTuningJob.create(\n",
        "    training_file=openai_train_file_info[\"id\"],\n",
        "    model=model,\n",
        "    hyperparameters={\"n_epochs\": n_epochs}\n",
        ")\n",
        "\n",
        "ft_job_id = openai_ft_job_info[\"id\"]\n",
        "\n",
        "openai_ft_job_info"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dP68_bGSU2du"
      },
      "source": [
        "\n",
        "> this takes around 5 minutes to train, and you get an email from OpenAI when finished."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f-X8R7ILU2du"
      },
      "source": [
        "**Thats it!**\n",
        "\n",
        "Now your model is training on OpenAI's machines. To get the current state of your fine-tuning job, run:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0os7HN9fU2du",
        "outputId": "f67adb43-c91d-4185-8fac-8d1a281a3666"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "('succeeded',\n",
              " 12732,\n",
              " 1693389024,\n",
              " 'ft:gpt-3.5-turbo-0613:weights-biases::7tC85HcX')"
            ]
          },
          "execution_count": null,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "state = openai.FineTuningJob.retrieve(ft_job_id)\n",
        "state[\"status\"], state[\"trained_tokens\"], state[\"finished_at\"], state[\"fine_tuned_model\"]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "59RBnpywU2du"
      },
      "source": [
        "Show recent events for our fine-tuning job"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3X0nX-5HU2du",
        "outputId": "b3a64f02-3fff-4bc1-b166-7783208ca1a3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<OpenAIObject list> JSON: {\n",
              "  \"object\": \"list\",\n",
              "  \"data\": [\n",
              "    {\n",
              "      \"object\": \"fine_tuning.job.event\",\n",
              "      \"id\": \"ftevent-5x9Y6Payk6fIdyJyMRY5um1v\",\n",
              "      \"created_at\": 1693389024,\n",
              "      \"level\": \"info\",\n",
              "      \"message\": \"Fine-tuning job successfully completed\",\n",
              "      \"data\": null,\n",
              "      \"type\": \"message\"\n",
              "    },\n",
              "    {\n",
              "      \"object\": \"fine_tuning.job.event\",\n",
              "      \"id\": \"ftevent-i16NTGNakv9P0RkOtJ7vvvoG\",\n",
              "      \"created_at\": 1693389022,\n",
              "      \"level\": \"info\",\n",
              "      \"message\": \"New fine-tuned model created: ft:gpt-3.5-turbo-0613:weights-biases::7tC85HcX\",\n",
              "      \"data\": null,\n",
              "      \"type\": \"message\"\n",
              "    },\n",
              "    {\n",
              "      \"object\": \"fine_tuning.job.event\",\n",
              "      \"id\": \"ftevent-MkLrJQ8sDgaC67CdmFMwsIjV\",\n",
              "      \"created_at\": 1693389017,\n",
              "      \"level\": \"info\",\n",
              "      \"message\": \"Step 90/90: training loss=0.00\",\n",
              "      \"data\": {\n",
              "        \"step\": 90,\n",
              "        \"train_loss\": 2.5828578600339824e-06,\n",
              "        \"train_mean_token_accuracy\": 1.0\n",
              "      },\n",
              "      \"type\": \"metrics\"\n",
              "    },\n",
              "    {\n",
              "      \"object\": \"fine_tuning.job.event\",\n",
              "      \"id\": \"ftevent-3sRpTRSjK3TfFRZY88HEASpX\",\n",
              "      \"created_at\": 1693389015,\n",
              "      \"level\": \"info\",\n",
              "      \"message\": \"Step 89/90: training loss=0.00\",\n",
              "      \"data\": {\n",
              "        \"step\": 89,\n",
              "        \"train_loss\": 2.5828578600339824e-06,\n",
              "        \"train_mean_token_accuracy\": 1.0\n",
              "      },\n",
              "      \"type\": \"metrics\"\n",
              "    },\n",
              "    {\n",
              "      \"object\": \"fine_tuning.job.event\",\n",
              "      \"id\": \"ftevent-HtS6tJMVPOmazquZ82a1iCdV\",\n",
              "      \"created_at\": 1693389015,\n",
              "      \"level\": \"info\",\n",
              "      \"message\": \"Step 88/90: training loss=0.00\",\n",
              "      \"data\": {\n",
              "        \"step\": 88,\n",
              "        \"train_loss\": 2.5828578600339824e-06,\n",
              "        \"train_mean_token_accuracy\": 1.0\n",
              "      },\n",
              "      \"type\": \"metrics\"\n",
              "    }\n",
              "  ],\n",
              "  \"has_more\": true\n",
              "}"
            ]
          },
          "execution_count": null,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "openai.FineTuningJob.list_events(id=ft_job_id, limit=5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q31LUKh3U2dv"
      },
      "source": [
        "We can run a few different fine-tunes with different parameters or even with different datasets."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hCvmrDWMU2dv"
      },
      "source": [
        "## Log OpenAI fine-tune jobs to Weights & Biases"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ehXJW997U2dv"
      },
      "source": [
        "We can log our fine-tunes with a simple command."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "851RDhDBU2dv",
        "outputId": "6512e9ea-f58d-425f-d068-df405cc3ccd7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "usage: openai wandb sync [-h] [-i ID] [-n N_FINE_TUNES] [--project PROJECT]\n",
            "                         [--entity ENTITY] [--force] [--legacy]\n",
            "\n",
            "options:\n",
            "  -h, --help            show this help message and exit\n",
            "  -i ID, --id ID        The id of the fine-tune job (optional)\n",
            "  -n N_FINE_TUNES, --n_fine_tunes N_FINE_TUNES\n",
            "                        Number of most recent fine-tunes to log when an id is\n",
            "                        not provided. By default, every fine-tune is synced.\n",
            "  --project PROJECT     Name of the Weights & Biases project where you're\n",
            "                        sending runs. By default, it is \"OpenAI-Fine-Tune\".\n",
            "  --entity ENTITY       Weights & Biases username or team name where you're\n",
            "                        sending runs. By default, your default entity is used,\n",
            "                        which is usually your username.\n",
            "  --force               Forces logging and overwrite existing wandb run of the\n",
            "                        same fine-tune.\n",
            "  --legacy              Log results from legacy OpenAI /v1/fine-tunes api\n"
          ]
        }
      ],
      "source": [
        "!openai wandb sync --help"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cfnhSQY1U2dv"
      },
      "source": [
        "Calling `openai wandb sync` will log all un-synced fine-tuned jobs to W&B\n",
        "\n",
        "Below we are just logging 1 job, passing:\n",
        "- our OpenAI key as an environment variable\n",
        "- the id of the fine-tune job we'd like to log\n",
        "- the W&B project of where to log it to\n",
        "\n",
        "See the [OpenAI section](https://wandb.me/openai-docs) in the Weights & Biases documentation for full details of the integration"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nl58wxsfU2dv",
        "outputId": "acfe2d6d-3796-4c33-eb9e-7576ea7e69bb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Retrieving fine-tune job...\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mcapecape\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Tracking run with wandb version 0.15.9\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Run data is saved locally in \u001b[35m\u001b[1m/Users/tcapelle/work/examples/colabs/openai/wandb/run-20230830_115915-ftjob-x4tl83IlSGolkUF3fCFyZNGs\u001b[0m\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Run \u001b[1m`wandb offline`\u001b[0m to turn off syncing.\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Syncing run \u001b[33mftjob-x4tl83IlSGolkUF3fCFyZNGs\u001b[0m\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: ⭐️ View project at \u001b[34m\u001b[4mhttps://wandb.ai/capecape/OpenAI-Fine-Tune\u001b[0m\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run at \u001b[34m\u001b[4mhttps://wandb.ai/capecape/OpenAI-Fine-Tune/runs/ftjob-x4tl83IlSGolkUF3fCFyZNGs\u001b[0m\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Waiting for W&B process to finish... \u001b[32m(success).\u001b[0m\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Run history:\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: train_accuracy ▁▁▁▁▁█▁█▁██▁████████████████████████████\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:     train_loss █▇▆▂▂▁▂▁▅▁▁▇▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Run summary:\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: fine_tuned_model ft:gpt-3.5-turbo-061...\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:           status succeeded\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:   train_accuracy 1.0\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:       train_loss 0.0\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run \u001b[33mftjob-x4tl83IlSGolkUF3fCFyZNGs\u001b[0m at: \u001b[34m\u001b[4mhttps://wandb.ai/capecape/OpenAI-Fine-Tune/runs/ftjob-x4tl83IlSGolkUF3fCFyZNGs\u001b[0m\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Synced 6 W&B file(s), 0 media file(s), 1 artifact file(s) and 0 other file(s)\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Find logs at: \u001b[35m\u001b[1m./wandb/run-20230830_115915-ftjob-x4tl83IlSGolkUF3fCFyZNGs/logs\u001b[0m\n",
            "🎉 wandb sync completed successfully\n"
          ]
        }
      ],
      "source": [
        "!OPENAI_API_KEY={openai_key} openai wandb sync --id {ft_job_id} --project {WANDB_PROJECT}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "referenced_widgets": [
            "b21fd890682b4bf786b2346dd18cae9a"
          ]
        },
        "id": "LWHLs2oBU2dv",
        "outputId": "cd141916-2a25-43e7-a66b-3bc9cfe19df2"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b21fd890682b4bf786b2346dd18cae9a",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "VBox(children=(Label(value='0.050 MB of 0.050 MB uploaded (0.000 MB deduped)\\r'), FloatProgress(value=1.0, max…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "wandb: WARNING Source type is set to 'repo' but some required information is missing from the environment. A job will not be created from this run. See https://docs.wandb.ai/guides/launch/create-job\n",
            "upload_file exception https://storage.googleapis.com/wandb-production.appspot.com/capecape/OpenAI-Fine-Tune/1ili9l51/requirements.txt?Expires=1693475972&GoogleAccessId=gorilla-files-url-signer-man%40wandb-production.iam.gserviceaccount.com&Signature=NzF9wj2gS8rMEwRT9wlft2lNubemw67f2qrz9Zy90Bjxg5xCL9pIu%2FRbBGjRwLA2v64PuiP23Au5Dho26Tnw3UjUS1apqTkaOgjWDTlCCiDLzvMUsqHf0lhhWIgGMZcsA4gPpOi%2Bc%2ByJm4z6JE7D6RJ7r8y4fI0Jg6fX9KSWpzh8INiM6fQZiQjUChLVdtNJQZ2gfu7xRc%2BZIUEjgDuUqmS705pIUOgJXA9MS3%2Fhewkc7CxWay4ReMJixBZgaqLIRqHQnyzb38I5nPrRS3JrwrigQyX6tOsK05LDLA0o%2Bs0K11664%2F1ZxO6mSTfOaw7tXUmbUUWFOp33Qq8KXNz9Zg%3D%3D: ('Connection aborted.', RemoteDisconnected('Remote end closed connection without response'))\n",
            "upload_file request headers: {'User-Agent': 'python-requests/2.28.2', 'Accept-Encoding': 'gzip, deflate, br', 'Accept': '*/*', 'Connection': 'keep-alive', 'Content-Length': '4902'}\n",
            "upload_file response body: \n",
            "upload_file exception https://storage.googleapis.com/wandb-production.appspot.com/capecape/OpenAI-Fine-Tune/1ili9l51/conda-environment.yaml?Expires=1693475972&GoogleAccessId=gorilla-files-url-signer-man%40wandb-production.iam.gserviceaccount.com&Signature=wKnFdg7z7CiJOMn4WSvt6GSj2hPnMr0Xc4KuwAXa8akLucmw700x%2FWF87jmWaqnp%2FK4%2BF6JTRghQAokXF9jxCcXBSYhgFhCVACrOVyN%2BSTZ4u8tDgD6Dm%2FEFwWObiH%2BALSS1N0FmG7i6kL9Evyng3yPc4noEz%2FkLNIDIascAPgUe9UkPaBCRc9j7OxzYJx07bpeL4HaGe4yaCvk2mSVr4l%2FUfsICBI6E4KKrLDvtZvFFFUB4MgqXp0Sxc0k0pOxaw9zZhiNQQELDnhnuNY4wi78EPiXN1BpU6bTgIYaHe5mkS%2B7M5HiFs83ML98JI2OeRiAjAGtIIETT4xDjTYWVpA%3D%3D: ('Connection aborted.', RemoteDisconnected('Remote end closed connection without response'))\n",
            "upload_file request headers: {'User-Agent': 'python-requests/2.28.2', 'Accept-Encoding': 'gzip, deflate, br', 'Accept': '*/*', 'Connection': 'keep-alive', 'Content-Length': '8450'}\n",
            "upload_file response body: \n"
          ]
        },
        {
          "data": {
            "text/html": [
              " View run <strong style=\"color:#cdcd00\">jumping-water-2</strong> at: <a href='https://wandb.ai/capecape/OpenAI-Fine-Tune/runs/1ili9l51' target=\"_blank\">https://wandb.ai/capecape/OpenAI-Fine-Tune/runs/1ili9l51</a><br/>Synced 7 W&B file(s), 0 media file(s), 0 artifact file(s) and 1 other file(s)"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "Find logs at: <code>./wandb/run-20230830_113907-1ili9l51/logs</code>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "wandb.finish()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eashAVIvU2dv"
      },
      "source": [
        "Our fine-tunes are now successfully synced to Weights & Biases.\n",
        "\n",
        "![image.png](data:image/png;base64,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)\n",
        "\n",
        "Anytime we have new fine-tunes, we can just call `openai wandb sync` to add them to our dashboard."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xfX_af2EU2dz"
      },
      "source": [
        "## Run evalution and log the results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9tvlkGoOU2dz"
      },
      "source": [
        "The best way to evaluate a generative model is to explore sample predictions from your evaluation set.\n",
        "\n",
        "Let's generate a few inference samples and log them to W&B and see how the performance compares to a baseline ChatGPT-3.5 model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SI00xE9nU2d0",
        "outputId": "1f01df3d-0360-49e8-c92e-40ea8df73639"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "Tracking run with wandb version 0.15.9"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "Run data is saved locally in <code>/Users/tcapelle/work/examples/colabs/openai/wandb/run-20230830_115947-iepk19m2</code>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "Syncing run <strong><a href='https://wandb.ai/capecape/OpenAI-Fine-Tune/runs/iepk19m2' target=\"_blank\">ethereal-energy-4</a></strong> to <a href='https://wandb.ai/capecape/OpenAI-Fine-Tune' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              " View project at <a href='https://wandb.ai/capecape/OpenAI-Fine-Tune' target=\"_blank\">https://wandb.ai/capecape/OpenAI-Fine-Tune</a>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              " View run at <a href='https://wandb.ai/capecape/OpenAI-Fine-Tune/runs/iepk19m2' target=\"_blank\">https://wandb.ai/capecape/OpenAI-Fine-Tune/runs/iepk19m2</a>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "There are 87 test examples\n"
          ]
        }
      ],
      "source": [
        "wandb.init(project=WANDB_PROJECT,\n",
        "           job_type='eval')\n",
        "\n",
        "artifact_valid = wandb.use_artifact(\n",
        "    f'{entity}/{WANDB_PROJECT}/legalbench-contract_nli_explicit_identification-test:latest',\n",
        "    type='test-data')\n",
        "test_file = artifact_valid.get_path(test_file_path).download(\"my_data\")\n",
        "\n",
        "with open(test_file) as f:\n",
        "    test_dataset = [json.loads(line) for line in f]\n",
        "\n",
        "print(f\"There are {len(test_dataset)} test examples\")\n",
        "wandb.config.update({\"num_test_samples\":len(test_dataset)})"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZFUUHVMFU2d0"
      },
      "source": [
        "### Run evaluation on the Fine-Tuned Model\n",
        "Set up OpenAI call with retries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FagY6Ev_U2d0"
      },
      "outputs": [],
      "source": [
        "@retry(stop=stop_after_attempt(3), wait=wait_fixed(60))\n",
        "def call_openai(messages=\"\", model=\"gpt-3.5-turbo\"):\n",
        "  return openai.ChatCompletion.create(model=model, messages=messages, max_tokens=10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9PB5SJrJU2d0"
      },
      "source": [
        "Let's get our trained model id"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "O8vNiUlFU2d0",
        "outputId": "d196a4f1-4c47-4977-b2bd-2f0e485075f7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "'ft:gpt-3.5-turbo-0613:weights-biases::7tC85HcX'"
            ]
          },
          "execution_count": null,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "state = openai.FineTuningJob.retrieve(ft_job_id)\n",
        "ft_model_id = state[\"fine_tuned_model\"]\n",
        "ft_model_id"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0ncxuaqBU2d0"
      },
      "source": [
        "Run evaluation and log results to W&B"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "referenced_widgets": [
            "683c7db769d044f79cf9409f00af137d"
          ]
        },
        "id": "6YF8ED9HU2d0",
        "outputId": "c952c07f-d972-4201-8b62-ee1409dd8f9d"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "683c7db769d044f79cf9409f00af137d",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/87 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "prediction_table = wandb.Table(columns=['messages', 'completion', 'target'])\n",
        "\n",
        "eval_data = []\n",
        "\n",
        "for row in tqdm(test_dataset):\n",
        "    messages = row['messages'][:2]\n",
        "    target = row[\"messages\"][2]\n",
        "\n",
        "    # res = call_openai(model=ft_model_id, messages=messages)\n",
        "    res = openai.ChatCompletion.create(model=model, messages=messages, max_tokens=10)\n",
        "    completion = res.choices[0].message.content\n",
        "\n",
        "    eval_data.append([messages, completion, target])\n",
        "    prediction_table.add_data(messages[1]['content'], completion, target[\"content\"])\n",
        "\n",
        "wandb.log({'predictions': prediction_table})"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2BKojUKMU2d0"
      },
      "source": [
        "Calculate the accuracy of the fine-tuned model and log to W&B\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "n02OqDO9U2d0",
        "outputId": "7b3a9bd1-6af6-4f27-b9e1-186d204cc55f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy is 0.8390804597701149\n"
          ]
        }
      ],
      "source": [
        "correct = 0\n",
        "for e in eval_data:\n",
        "  if e[1].lower() == e[2][\"content\"].lower():\n",
        "    correct+=1\n",
        "\n",
        "accuracy = correct / len(eval_data)\n",
        "\n",
        "print(f\"Accuracy is {accuracy}\")\n",
        "wandb.log({\"eval/accuracy\": accuracy})\n",
        "wandb.summary[\"eval/accuracy\"] = accuracy"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MtlZPVG2U2d0"
      },
      "source": [
        "### Run evaluation on a Baseline model for comparison\n",
        "Lets compare our model to the baseline model, `gpt-3.5-turbo`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "referenced_widgets": [
            "56a6a14b0c1346149c973f6c71d31b6c"
          ]
        },
        "id": "9E0IRSTfU2d0",
        "outputId": "0d016d12-1aa7-40aa-9403-3b0864acbc62"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "56a6a14b0c1346149c973f6c71d31b6c",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/87 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "baseline_prediction_table = wandb.Table(columns=['messages', 'completion', 'target'])\n",
        "baseline_eval_data = []\n",
        "\n",
        "for row in tqdm(test_dataset):\n",
        "    messages = row['messages'][:2]\n",
        "    target = row[\"messages\"][2]\n",
        "\n",
        "    res = call_openai(model=\"gpt-3.5-turbo\", messages=messages)\n",
        "    completion = res.choices[0].message.content\n",
        "\n",
        "    baseline_eval_data.append([messages, completion, target])\n",
        "    baseline_prediction_table.add_data(messages[1]['content'], completion, target[\"content\"])\n",
        "\n",
        "wandb.log({'baseline_predictions': baseline_prediction_table})"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i98CsZJ1U2d1"
      },
      "source": [
        "Calculate the accuracy of the fine-tuned model and log to W&B"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1Sl4zmPwU2d1",
        "outputId": "7fd58a23-d4ee-4f7b-d8dc-03e208e1a5fd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Baseline Accurcy is: 0.7931034482758621\n"
          ]
        }
      ],
      "source": [
        "baseline_correct = 0\n",
        "for e in baseline_eval_data:\n",
        "  if e[1].lower() == e[2][\"content\"].lower():\n",
        "    baseline_correct+=1\n",
        "\n",
        "baseline_accuracy = baseline_correct / len(baseline_eval_data)\n",
        "print(f\"Baseline Accurcy is: {baseline_accuracy}\")\n",
        "wandb.log({\"eval/baseline_accuracy\": baseline_accuracy})\n",
        "wandb.summary[\"eval/baseline_accuracy\"] =  baseline_accuracy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "referenced_widgets": [
            "e7fa6b38a02847758fdd424affbc74c1"
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        },
        "id": "EbeRF7vEU2d1",
        "outputId": "0b679cda-7740-42d9-bbe6-feb11d69ea6f"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"
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              "VBox(children=(Label(value='0.248 MB of 0.248 MB uploaded (0.000 MB deduped)\\r'), FloatProgress(value=1.0, max…"
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          "metadata": {},
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        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "wandb: WARNING Source type is set to 'repo' but some required information is missing from the environment. A job will not be created from this run. See https://docs.wandb.ai/guides/launch/create-job\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<style>\n",
              "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: left ; width: auto;} td:nth-child(2) {text-align: left ; width: 100%}\n",
              "    .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; justify-content: flex-start; width: 100% }\n",
              "    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
              "    </style>\n",
              "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>eval/accuracy</td><td>▁</td></tr><tr><td>eval/baseline_accuracy</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>eval/accuracy</td><td>0.83908</td></tr><tr><td>eval/baseline_accuracy</td><td>0.7931</td></tr></table><br/></div></div>"
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          "data": {
            "text/html": [
              " View run <strong style=\"color:#cdcd00\">ethereal-energy-4</strong> at: <a href='https://wandb.ai/capecape/OpenAI-Fine-Tune/runs/iepk19m2' target=\"_blank\">https://wandb.ai/capecape/OpenAI-Fine-Tune/runs/iepk19m2</a><br/>Synced 7 W&B file(s), 2 media file(s), 2 artifact file(s) and 1 other file(s)"
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              "Find logs at: <code>./wandb/run-20230830_115947-iepk19m2/logs</code>"
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        "wandb.finish()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A9nh4b15U2d1"
      },
      "source": [
        "And thats it! In this example we have prepared our data, logged it to Weights & Biases, fine-tuned an OpenAI model using that data, logged the results to Weights & Biases and then run evaluation on the fine-tuned model.\n",
        "\n",
        "From here you can start to train on larger or more complex tasks, or else explore other ways to modify ChatGPT-3.5 such as giving it a different tone and style or response.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aVtXGnB_U2d1"
      },
      "source": [
        "# Resources\n",
        "\n",
        "* [OpenAI Fine-Tuning Guide](https://platform.openai.com/docs/guides/fine-tuning)\n",
        "* [W&B Integration with OpenAI API Documentation](https://wandb.me/openai-docs)\n",
        "* [W&B Report: GPT-3 exploration & fine-tuning tips](http://wandb.me/openai-report)"
      ]
    }
  ],
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