This blog post provides a comprehensive guide on fine-tuning large language models (LLMs) such as Llama-3, Mistral, and Mixtral using Anyscale. It covers the entire process from preparing input data to launching the fine-tuning job and monitoring the process. The article also discusses serving your model with Anyscale's ray-llm library, including how to serve both LoRA and full-parameter fine-tuned models. Additionally, it offers tips on optimizing for compute cost and monitoring the training progress.