Fine-Tuning Code Language Models - A Practical Guide

Learn how to fine-tune Code Language Models (LLMs) in this 22-minute tutorial that demonstrates practical implementation using StarCoder. Master the process of creating instruction-based fine-tuning datasets and understand the technical setup requirements including Torch, Transformers, and PEFT libraries. Explore real-world examples of code generation tasks like Python implementations for prime numbers and cosine similarity calculations. Gain insights into GPU memory requirements, environment configuration, and integration with platforms like Hugging Face and Weights & Biases. Discover the inner workings of various code generation models including Microsoft Copilot, Amazon's CodeWhisperer, GitHub's Copilot X, OpenAI's code interpreter, and Google's Palm Coder. Understand how LLMs process both human language and code through transformer architecture, including techniques like causal masking and infilling. Follow along with a hands-on Colab notebook demonstration that shows the complete fine-tuning process for Code LLMs ranging from 2B to 16B model sizes.