How to format prompt and completion pairs for fine-tuning GPT-3.5 with documentation and books?

Below is some guidance on how to structure your training data for fine-tuning GPT-3.5 using natural language documentation and programming books for code generation.

Key Requirements

• Training Examples: Each training example must include a "prompt" string and a "completion" string.
• JSON Lines Format: Typically you will prepare your data as a JSON Lines (JSONL) file where each line is a JSON object representing one example.
• Consistent Structure: Ensure that every example follows a consistent structure, which helps the model learn the association between the input (prompt) and the desired output (completion).

Constructing Your Prompt and Completion

Since your data comes from natural language sources like documentation and programming books, you can use those texts by breaking them down into smaller, paired pieces. For example:

• Prompt: Could be a question, a descriptive instruction, or a code snippet asking for an explanation, translation, or correction.
• Completion: Could be the corresponding answer, explanation, corrected code, or commentary based on the provided documentation.

Example

Imagine that you want the model to generate a code explanation for a given code snippet. Your JSONL entry might look like this:

JSONCopied
{
  "prompt": "Explain the following Python code snippet:\n\n def hello_world():\n     print('Hello, world!')\n\nExplanation:",
  "completion": "The function 'hello_world' is defined to print the string 'Hello, world!' when it is called. This simple code snippet demonstrates how to define and call a function in Python."
}

In this example:

• The prompt sets up the context by providing the code and asking for an explanation.
• The completion gives the clear, expected response.

Tips for Preparing Your Data

1. Segment Documentation: Break down large documents into smaller, coherent sections. Each section should have a clearly defined input (prompt) and an expected output (completion).

2. Maintain Natural Language: Since your source material is natural language, preserve the original phrasing where possible. This can help the model learn the style and context necessary for high-quality code generation.

3. Add Clear Delimiters: If needed, include newline characters or other delimiters in your strings so that the training examples remain clear. For instance, add a newline at the end of your prompts or completions to indicate where one ends and the other begins.

4. Review and Clean Data: Remove any irrelevant text or formatting issues. The cleaner your training file is, the better the model can learn the desired patterns.

5. Consistent Examples: The more consistent your examples are in style and format, the easier it is for the model to generalize and produce correct results.

Final Steps

• Conversion: Once you have your examples formatted (e.g., as shown above), compile them into one JSONL file.
• Upload and Fine-tune: Follow the OpenAI fine-tuning guide to upload your training file and start the fine-tuning process.

This should give you a clear direction on preparing your data. Let me know if you need further clarification or assistance with the process.