yuvalkansal/QwQ-Med-3
QwQ-Med-3 is a 32.8 billion parameter medical reasoning model developed by Yuval Kansal, fine-tuned from Qwen/QwQ-32B. It specializes in multi-hop reasoning paths derived from medical Knowledge Graphs, enabling it to provide structured, evidence-aligned chain-of-thought responses. This model is primarily designed for medical question answering and evaluation on ICD-coded clinical vignettes.
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QwQ-Med-3: Medical Reasoning Model
QwQ-Med-3 is a 32.8 billion parameter language model developed by Yuval Kansal, Bhishma Dedhia, and Niraj K. Jha, specifically engineered for advanced medical reasoning. It is fine-tuned from the Qwen/QwQ-32B base model using a Supervised Fine-Tuning (SFT) pipeline.
Key Capabilities
- Multi-hop Medical Reasoning: The model is trained on question-answer pairs grounded in multi-hop reasoning paths extracted from a medical Knowledge Graph. This process teaches it to generate structured, evidence-based chain-of-thought reasoning.
- Knowledge Graph Alignment: Its training methodology ensures that its reasoning is aligned with and supported by medical knowledge graph evidence, enhancing reliability in medical contexts.
Intended Use Cases
- Medical Question Answering: Excels in scenarios requiring complex, multi-hop reasoning to answer medical queries.
- Clinical Vignette Evaluation: Suitable for evaluating performance on ICD-coded clinical vignettes.
- Research: Valuable for research into knowledge graph-guided language model training and domain-specific AI development.
This model is introduced in the paper "Bottom-up Domain-specific Superintelligence: A Reliable Knowledge Graph is What We Need" (arXiv:2507.13966). The associated code is available at jha-lab/bottom-up-superintelligence, and a dedicated benchmark, yuvalkansal/KG-Med-Bench, is provided for evaluation.