🚀 Successfully Fine-Tuned GPT-4O Mini Locally: A Guide for AI Developers

I’m thrilled to share my journey in locally fine-tuning the GPT-4O Mini model. For those venturing into AI agent development, here’s a concise breakdown of my process:

1️⃣ Setup:

- Installed essential packages: 'openai', 'python-dotenv'

- Configured OpenAI API key securely via a '.env' file

2️⃣ 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

3️⃣ 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

4️⃣ 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

5️⃣ 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

I’d love to hear from others who’ve experimented with fine-tuning models like GPT-4O Mini. Your insights and experiences could be invaluable as we push the boundaries of what’s possible in AI development.

🔗 [Code Link](https://drive.google.com/drive/folders/1CqLIyrPOyVJxAlErsYhn6QvQssTayXVW?usp=sharing)

🔗 [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, don’t forget to like and share this post to spread the knowledge among fellow AI developers.

P.S. Grateful for the OpenAI community’s support and documentation throughout this process!

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