Fine-Tune OpenAI GPT-OSS 20B on the Dermatology Dataset

OpenAI's GPT-OSS 20B, a refined open-source AI model, is particularly adept at instruction-following and can be efficiently fine-tuned for specialized tasks using LoRA (Low-Rank Adaptation), which updates a minimal portion of the model's parameters to enable fine-tuning on consumer-grade hardware. This tutorial provides a step-by-step guide to fine-tuning GPT-OSS 20B into a specialized dermatology assistant using a custom Firecrawl Dermatology Q&A dataset. The process involves setting up a suitable environment, loading the model and tokenizer, and applying the OpenAI Harmony prompt format to ensure structured, role-based training. Fine-tuning with LoRA adapters updates only a small percentage of parameters, making the process resource-efficient, and results in a model that generates concise and clinically relevant responses. The tutorial also highlights the importance of prompt engineering for achieving precise and domain-specific answers during inference, demonstrating how modern web scraping APIs can facilitate the creation of high-quality datasets and accelerate AI development.