How to fine-tune an LLM on Modal

This post details how to fine-tune a Large Language Model (LLM) on Modal. It covers preparing datasets, setting up containerized environments with necessary dependencies (e.g., PyTorch, Hugging Face Transformers), utilizing Modal's `modal.Image` for reproducible environments, and configuring training hyperparameters. The post demonstrates using Modal's `modal.Stub` and `@stub.function` decorators to orchestrate the fine-tuning process, including data loading, model training, and saving checkpoints to Modal's object storage. It also touches on serving the fine-tuned model.