How can I fine-tune a GPT-3.5-turbo model without including a 'system' message in the training data?

Below is some guidance on how to approach your fine-tuning dataset:

Can You Omit the "system" Message?

Yes, you can create your fine-tuning training dataset without including the "system" role. If your API application already provides the system instructions at runtime, you can exclude the "system" messages from your training examples. This approach can help reduce the number of tokens processed during fine-tuning and avoid conflicts with the system instructions you set in your application.

Considerations

• Consistency at Inference:
  Ensure that the behavior you expect (the assistant's style and response tone) remains consistent when the model is applied. The fine-tuned examples should align well with the external system prompt you provide.

• Model Training Context:
  By omitting the "system" messages, the fine-tuning examples focus solely on the back-and-forth between user and assistant. This is valid as long as your deployment request consistently sets the system prompt. The model will format responses based on the additional system instruction provided at runtime.

• Token Savings:
  Removing the "system" messages in your fine-tuning dataset reduces token usage during training, which can be beneficial, but make sure that the model sees enough examples of the interaction style you desire.

Example

Default Dataset (with system message):

JSONCopied
{
  "messages": [
    {"role": "system", "content": "You are a nutritionist providing guidance on healthy eating habits."},
    {"role": "user", "content": "Can you recommend a balanced diet for weight management?"},
    {"role": "assistant", "content": "A balanced diet for weight management includes a variety of fruits, vegetables... (etc)"}
  ]
}

Modified Dataset (without system message):

JSONCopied
{
  "messages": [
    {"role": "user", "content": "Can you recommend a balanced diet for weight management?"},
    {"role": "assistant", "content": "A balanced diet for weight management includes a variety of fruits, vegetables... (etc)"}
  ]
}

In your application, you would provide the system instructions in the API call separately, ensuring that the assistant has the context needed.

Final Note

When fine-tuning, make sure your examples are well aligned with the overall interaction pattern intended during inference. Testing the fine-tuned model in production scenarios can confirm that the removed "system" messages do not adversely impact the model’s responses.

This approach aligns with optimizing token usage while maintaining the overall application behavior.