Yoga-LLM, Part 2: Instruction Fine-tuning

This is a tutorial on fine-tuning a large language model (LLM) specifically for answering questions on Yoga. The process involves instruction tuning using methods like LoRA for parameter-efficient fine-tuning. The post discusses the selection of tools and frameworks like HuggingFace, Unsloth, and LitGPT for the task. The implementation steps are detailed, including preparation of data and setting training parameters. Inference methods are also covered, using a trained Gemma 2B model for demonstrating the fine-tuning process.