Fine-Tuning Models for Code Completion

Fine-tuning a code completion model on your codebase produces suggestions that match your coding style, use your internal libraries correctly, and follow your team conventions. Here is a practical guide to doing it effectively.

When Fine-Tuning Makes Sense

Fine-tuning is worth the effort when: your codebase uses internal frameworks or DSLs that general models do not know, you have strong coding conventions that general models ignore, or you work in a niche language or domain. If you are writing standard React or Python, general models are already good enough and fine-tuning provides marginal improvement.

Dataset Preparation

The quality of your fine-tuning dataset matters more than its size. Extract functions and methods from your codebase, creating input-output pairs where the input is the function signature and docstring, and the output is the implementation. Filter out generated code, test fixtures, and boilerplate. Aim for 5,000-10,000 high-quality examples rather than 100,000 mediocre ones.

Choosing a Base Model

Start with a model that is already good at code: DeepSeek Coder, CodeLlama, or StarCoder2. The 7B parameter size is the sweet spot for fine-tuning — large enough to be useful, small enough to fine-tune on a single GPU. Use LoRA (Low-Rank Adaptation) to reduce memory requirements and training time by 80% compared to full fine-tuning.

Training Configuration

Use a learning rate of 1e-5 to 5e-5, train for 3-5 epochs, and monitor validation loss for overfitting. With LoRA rank 16 and alpha 32, you can fine-tune a 7B model on a single A100 or H100 GPU in about 4 hours. Use bf16 precision and gradient checkpointing to maximize batch size.

Evaluation

Measure completion quality with three metrics: exact match rate (does the completion match the held-out code), functional correctness (does it pass the associated tests), and human preference (do developers prefer fine-tuned completions over base model completions in blind A/B tests). A well-fine-tuned model should improve all three metrics by 15-30% over the base model on your codebase.