Personal Copilot: Fine-tune StarCoder/Code Llama on private code with PEFT (QLoRA) using Hugging Face workflow

AI Impact Summary

The post outlines an end-to-end approach to creating a personalized coding assistant by fine-tuning code-generation models (StarCoder family, Code Llama) on proprietary or public code using PEFT (QLoRA) and, for full fine-tuning, FSDP with Flash Attention V2. It details a data pipeline that collects from GitHub, clones locally to avoid rate limits, filters to code files, and uses Feather for efficient serialization, enabling enterprise-scale customization with predictable resource needs. With explicit GPU memory and cost estimates, it highlights a feasible path for on-prem or cloud training but also notes governance and licensing considerations when using public repositories for training data.