How can I fine-tune a model to mimic my unique chat style?

Below is some guidance based on best practices for fine-tuning conversational models:

Use the Chat Format

When using the Chat Completions API, each training example should be structured as a conversation—a list of messages, each with a role (e.g., user, assistant) and content. This means your training data should mirror the format you expect during production.

Include Relevant Context

• Mirroring your use case: If your chatbot will receive and use chat history during interactions, then your training examples should incorporate that context. In other words, if you normally provide previous turns when generating a response, include the relevant conversation history in each example.
• Training the desired behavior: Training examples should capture the ideal responses you want the model to give. This may include previous user queries and your responses to help the model learn to generate similar replies.

Balancing Context Length

• Full context vs. trim: Only include chat history that is relevant for achieving your desired outcome. Too much unrelated context might confuse the model, while too little might not capture the conversational nuances.
• Diverse examples: Create a diverse set of demonstration conversations. Ensure that at least some examples contain multiple turns to show how the conversation flows. In contrast, other examples might be simpler if that reflects a typical interaction.

Example Format

Below is a simplified example following the Chat Completions API format:

JSONCopied
[
  {"role": "system", "content": "You are a helpful assistant that talks like [Your Name]."},
  {"role": "user", "content": "Hey, how's it going?"},
  {"role": "assistant", "content": "I'm doing well, just like always. What can I do for you today?"}
]

If chat context or history is usually provided during interaction, include those turns in your examples. By doing so, the model learns to use conversational context to produce more coherent and personalized responses that align with your communication style.

Summary

• Include conversation history if it’s part of your intended interaction style.
• Format each example as a list of messages with roles (system, user, assistant) as outlined in the Chat Completions API.
• Tailor your examples to provide the specific context and desired responses to better replicate your conversational style.

Following these guides should help ensure that your fine-tuned model performs well in a real-world conversational setting.