Problem Statement/Motivation: 🎯
Currently, leveraging Large Language Models (LLMs) for code assistance, generation, and understanding often involves sending proprietary code to third-party services. This presents potential privacy and security concerns. Additionally, reliance on external services can introduce latency and lacks offline capabilities.
Our services extensive codebase, contained within our Git repositories, represents a valuable dataset. Fine-tuning a capable open-source model like Devstral specifically on our code could provide a highly contextualized, fast, and private LLM assistant. This would empower developers with tailored code suggestions, explanations, and generation capabilities directly within their local development environment.
Goals/Acceptance Criteria: âś…
Successfully fine-tune a version of the Devstral model on a selected subset of our team's Git repositories.
The fine-tuned model can be run locally using Ollama on a typical developer machine with acceptable performance (e.g., response time for suggestions < 1-2 seconds).
Developers can query the local model from VS Code (e.g., via a compatible extension like Continue or a similar tool) for:
Code completion/suggestion relevant to our codebase.
Explaining snippets of our proprietary code.
Generating boilerplate or utility functions based on our coding patterns.
The entire process maintains data privacy by keeping our codebase and the fine-tuned model strictly within our local/private infrastructure.
Documentation is created for setting up and using the local LLM.
High-Level Tasks: 🛠️
[ ] Research & Planning:
[ ] Identify suitable Git repositories and branches for the training dataset.
[ ] Research best practices for preparing code data for fine-tuning (e.g., filtering, formatting, anonymization if needed).
[ ] Evaluate hardware requirements for fine-tuning and local inference.
[ ] Investigate and select the most appropriate VS Code plugin(s) for Ollama integration.
[ ] Data Preparation:
[ ] Clone selected repositories.
[ ] Implement scripts to clean, preprocess, and format the code data.
[ ] Model Fine-Tuning:
[ ] Set up the fine-tuning environment for Devstral.
[ ] Perform initial fine-tuning runs and iterate on parameters.
[ ] Evaluate the performance and quality of the fine-tuned model.
[ ] Deployment & Integration:
[ ] Package the fine-tuned model for Ollama.
[ ] Develop instructions for team members to install Ollama and the custom model.
[ ] Configure VS Code plugin(s) to connect to the local Ollama instance.
[ ] Testing & Feedback:
[ ] Conduct internal testing with the development team.
[ ] Gather feedback and iterate on the model/setup.
[ ] Documentation:
[ ] Create user guides for setup and usage.
[ ] Document the fine-tuning process for future reference.
Considerations/Potential Challenges: 🤔
Data Volume & Quality: Ensuring enough high-quality, representative code is used for effective fine-tuning.
Computational Resources: Fine-tuning can be resource-intensive. We need to assess if local machines are sufficient or if a dedicated training server is needed.
Model Performance: Achieving a good balance between model size, inference speed, and the quality of assistance.
Maintenance: The model may need to be periodically retrained as the codebase evolves.