Fine-tuning GPT-4o Mini: A Step-by-Step Guide

This tutorial demonstrates fine-tuning the cost-effective GPT-4o Mini large language model for stress detection in social media text. We'll leverage the OpenAI API and playground for both fine-tuning and evaluation, comparing performance before and after the process.

Introducing GPT-4o Mini:

GPT-4o Mini stands out as a highly affordable general-purpose LLM. Boasting an 82% score on the MMLU benchmark and surpassing Claude 3.5 Sonnet in chat preferences (LMSYS leaderboard), it offers significant cost savings (60% cheaper than GPT-3.5 Turbo) at 15 cents per million input tokens and 60 cents per million output tokens. It accepts text and image inputs, features a 128K token context window, supports up to 16K output tokens, and its knowledge cutoff is October 2023. Its compatibility with non-English text, thanks to the GPT-4o tokenizer, adds to its versatility. For a deeper dive into GPT-4o Mini, explore our blog post: "What Is GPT-4o Mini?"

Setting Up the OpenAI API:

1. Create an OpenAI account. Fine-tuning incurs costs, so ensure a minimum of $10 USD credit before proceeding.
2. Generate an OpenAI API secret key from your dashboard's "API keys" tab.
3. Configure your API key as an environment variable (DataCamp's DataLab is used in this example).
4. Install the OpenAI Python package: %pip install openai
5. Create an OpenAI client and test it with a sample prompt.

New to the OpenAI API? Our "GPT-4o API Tutorial: Getting Started with OpenAI's API" provides a comprehensive introduction.

Fine-tuning GPT-4o Mini for Stress Detection:

We'll fine-tune GPT-4o Mini using a Kaggle dataset of Reddit and Twitter posts labeled as "stress" or "non-stress."

1. Dataset Creation:

• Load and process the dataset (e.g., the top 200 rows of a Reddit post dataset).
• Retain only the 'title' and 'label' columns.
• Map numerical labels (0, 1) to "non-stress" and "stress".
• Split into training and validation sets (80/20 split).
• Save both sets in JSONL format, ensuring each entry includes a system prompt, user query (post title), and the "assistant" response (label).

2. Dataset Upload:

Use the OpenAI client to upload the training and validation JSONL files.

3. Fine-tuning Job Initiation:

Create a fine-tuning job specifying the file IDs, model name (gpt-4o-mini-2024-07-18), and hyperparameters (e.g., 3 epochs, batch size 3, learning rate multiplier 0.3). Monitor the job's status via the dashboard or API.

Accessing the Fine-tuned Model:

Retrieve the fine-tuned model name from the API and use it to generate predictions via the API or the OpenAI playground.

Model Evaluation:

Compare the base and fine-tuned models using accuracy, classification reports, and confusion matrices on the validation set. A custom predict function generates predictions, and an evaluate function provides the performance metrics.

Conclusion:

This tutorial provides a practical guide to fine-tuning GPT-4o Mini, showcasing its effectiveness in improving text classification accuracy. Remember to explore the linked resources for further details and alternative approaches. For a free, open-source alternative, consider our "Fine-tuning Llama 3.2 and Using It Locally" tutorial.

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