Feature Overview

InstructLab is a platform that allows users to fine-tune models for knowledge and skills. This research is to expand it to fine-tune it for code generation. This works aims to research techniques and best practices for fine-tuning models specifically for code generation using InstructLab.

Goals

• The primary user type for this feature is data scientists and machine learning engineers who want to fine-tune models for code generation tasks.
• This feature will expand the existing functionality of InstructLab by providing more detailed guidance on fine-tuning models for code generation.

Requirements

• Complete documentation on available research techniques for fine-tuning models for code generation that can be used on InstructLab.
• Research a combination of existing and novel techniques for fine-tune a model for code generation with InstructLab
• Step-by-step examples for the proposed research technique.
• Information on best practices for fine-tuning models for code generation, including data preparation, model selection, and evaluation metrics.
• A comparison of different research techniques and their respective pros and cons.

Background

Fine-tuning models for code generation is a complex process that requires a deep understanding of machine learning and programming. InstructLab aims to provide a comprehensive guide to help users navigate this process and achieve the best results when using InstructLab to fine-tune a model for code generation.

Done

• [ ] Documentation on available research techniques for fine-tuning models for code generation is complete.
• [ ] Document the research and proposed pipeline of existing and novel techniques for fine-tune a model for code generation with InstructLab
• [ ] Step-by-step examples for the proposed research technique.
• [ ] Information on best practices for fine-tuning models for code generation, including data preparation, model selection, and evaluation metrics.
• [ ] A comparison of different research techniques and their respective pros and cons.

Questions to Answer

• What are the most effective research techniques for fine-tuning models for code generation?
• How can we ensure that the documentation is comprehensive and easy to understand for users with varying levels of expertise?
• Are there any specific tools or resources that should be included in InstructLab to enhance the user experience?

Out of Scope

• The productization of the research techniques themselves. This feature focuses on providing guidance, best practices, and reference implementations, not the final productized version of it

Customer Considerations

• Users may have varying levels of expertise in machine learning and programming. The documentation should be accessible to users with different backgrounds and skill levels.
• Users may have specific requirements or constraints for their code generation tasks. The documentation should provide guidance on how to adapt the research techniques to meet these requirements.