๐ Successfully Fine-Tuned GPT-4O Mini Locally: A Guide for AI Developers
Iโm thrilled to share my journey in locally fine-tuning the GPT-4O Mini model. For those venturing into AI agent development, hereโs a concise breakdown of my process: 1๏ธโฃ Setup: - Installed essential packages: 'openai', 'python-dotenv' - Configured OpenAI API key securely via a '.env' file 2๏ธโฃ Data Preparation: - Curated a JSONL file with 10+ training examples - Each example included: a system message, user input, and the ideal assistant response - For optimal results, aim for 50+ diverse examples 3๏ธโฃ Code Structure: - `config.py`: Handles API keys, model names, and file paths - `tools.py`: Manages data preparation and validation - `app.py`: Executes the fine-tuning process 4๏ธโฃ Fine-Tuning: - Uploaded the training file to OpenAI - Launched the fine-tuning job (completed in ~10-15 minutes for small datasets) - Saved fine-tuned model details post-completion 5๏ธโฃ Testing: - Developed a script to evaluate the fine-tuned model with new prompts Key Takeaways: - Start with a smaller dataset, then scale - The quality of training data is crucial for success - Fine-tuning is iterative, be ready to refine and adjust Next Steps: - Expanding the dataset to enhance model performance - Exploring new use cases and potential specializations Iโd love to hear from others whoโve experimented with fine-tuning models like GPT-4O Mini. Your insights and experiences could be invaluable as we push the boundaries of whatโs possible in AI development. ๐ [Code Link](https://drive.google.com/drive/folders/1CqLIyrPOyVJxAlErsYhn6QvQssTayXVW?usp=sharing) ๐ [OpenAI Documentation] (https://platform.openai.com/docs/guides/fine-tuning) To get more AI development updates, follow me, @Tariq B.. If you found these insights helpful, donโt forget to like and share this post to spread the knowledge among fellow AI developers.