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This post discusses the process of fine-tuning LLMs, particularly focusing on budget instruction fine-tuning of Llama 3 8B Instruct on medical data. It covers different approaches to fine-tuning, such as supervised fine-tuning, few-shot learning, full transfer learning, and domain-specific fine-tuning. The post also introduces LoRA and QLoRA, which are enhanced fine-tuning approaches. It provides a guide for preparing data and fine-tuning Llama 3 8B Instruct using tools and platforms like Hugging Face, Unsloth, and Google Colab.

Budget Instruction Fine-tuning of Llama 3 8B Instruct(on Medical Data) with Hugging Face, Google Colab and Unsloth

Preparing instruction data for Llama 3 8B Instruct (Optional)