Fine Tuning Data Generator

What it does

The Fine-Tuning Data Generator skill creates synthetic data suitable for fine-tuning large language models. It can generate structured datasets based on provided schemas, allowing users to create training examples without needing to manually craft them. This is particularly useful when real-world data is scarce or sensitive.

When to use it

• Limited Training Data: You need to fine-tune a model but have very few existing examples.
• Data Privacy Concerns: You want to train on specific scenarios without exposing actual user data.
• Schema-Driven Tasks: You're building an agent for tasks with well-defined input/output structures (e.g., code generation, document summarization).
• Rapid Prototyping: Quickly generate training data to test and iterate on fine-tuning approaches.

Key capabilities

• Generates synthetic datasets based on provided schemas.
• Supports structured data formats for model training.
• Allows customization of generated data characteristics.

Example prompts

• "Generate 10 examples of customer service dialogues with the schema: { 'customer_query': string, 'agent_response': string }"
• "Create a dataset of 5 code snippets in Python following this format: { 'prompt': string, 'solution': string }"
• "Give me 3 examples of JSON objects representing product reviews, using the schema: { 'product_id': integer, 'rating': integer, 'comment': string }"

Tips & gotchas

The quality of generated data heavily relies on the accuracy and completeness of the provided schemas. Iteratively refine both the schema and the generated output to achieve desired fine-tuning results.