Fine-tuning and Deploying LLM with Unsloth, SGLang and Runpod

A step-by-step workflow for fine-tuning and deploying large language models using cloud GPU infrastructure. The process covers launching a GPU instance on RunPod, loading a 20B parameter model with Unsloth's memory optimizations, applying LoRA adapters for efficient training, running supervised fine-tuning, exporting the merged model checkpoint, serving it with SGLang's OpenAI-compatible API, and making inference requests. The entire pipeline runs on on-demand GPU compute, enabling practical iteration from training to production deployment.