How to fine-tune an LLM on Modal

What this post added

This post provides a step-by-step tutorial on fine-tuning a Large Language Model (LLM) on Modal. It details the process of preparing a dataset, setting up the Modal environment with necessary dependencies (e.g., PyTorch, Transformers), writing a training script that utilizes Modal's distributed compute capabilities, and configuring hyperparameters. The post also covers how to upload model weights to cloud storage and deploy the fine-tuned model as a Modal Function for inference. It highlights the use of Modal's GPU support and efficient resource management for LLM training.