The second week of fine-tuning a Llama 3.2 3B model for medical question answering focused on data preparation. Initially, a dataset of USMLE multiple-choice questions was considered, but it was unsuitable for generating clinical explanations. The project then switched to the ChatDoctor HealthCareMagic 100K dataset, which contains real patient questions and doctor responses. A cleaning pipeline was developed to remove platform-specific filler, trailing sign-offs, and input artifacts, while also filtering for quality and content length. AI
IMPACT Fine-tuning open-source models like Llama 3.2 on specialized datasets can lead to more capable and domain-specific AI assistants.
RANK_REASON The cluster describes the process of fine-tuning an open-source LLM on a specific domain dataset, which falls under research.
Read on Medium — fine-tuning tag →
- LLaMA 3.2–1B Instruct
- QLoRA
- lavita/ChatDoctor-HealthCareMagic-100K
- lavita/medical-qa-datasets
- Llama 3.2 3B
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