Fine-tuning allows you to customize ChatGPT by training it on your conversational data. Here is how to format and prepare your data:
1. Collect Conversational Data
Gather example dialogues that represent the types of conversations you want your AI to be able to have. Some options:
- Customer support transcripts
- Forum/messaging app exchanges
- Dialogue scripts
- Have humans chat naturally & record exchanges
Aim for a few thousand varied, high-quality conversations.
2. Format as JSONL
Organize data into the required JSONL format. Each line should be a JSON object containing a “messages” list:
{"messages": [
{"role": "system", "content": "Introduction message"},
{"role": "user", "content": "User's question or statement"},
{"role": "assistant", "content": "Assistant's response"}
]}NOTE: If you have your data in CSV format, do not worry. Follow this link. Here is a quick python script to convert your csv files into jsonl format.
The system message introduces the assistant. User messages provide input. Assistant responses contain ideal outputs.
3. Train/Validate/Test Split
Split your formatted data into three sets:
- Training (70-80%): Main data to train the model
- Validation (10-15%): Used to tune hyperparameters
- Test (10-15%): Unseen data to evaluate performance
4. Check Quality & Diversity
Verify your data is high-quality and contains diverse examples covering the full range of desired conversations.
Remove incorrectly formatted data. Check for imbalanced labels if doing classification.
5. Upload to Cloud Storage
Upload JSONL files to cloud storage like GCS, S3, Azure Blob. This allows access during fine-tuning.
6. Start Fine-Tuning Job
Use an API like OpenAI to initiate fine-tuning, pointing to your training data.
Monitor training progress. The model will learn from your conversational data.
7. Evaluate Fine-Tuned Model
Once trained, test your customized model’s performance on the unseen test set.
Iterate if necessary – the more quality data, the better it will perform!
This process allows you to create an AI assistant tailored to your needs. The key is high-quality, representative training conversations in the required JSONL format.