EpistemeAI/Reasoning-Medical-27B
EpistemeAI/Reasoning-Medical-27B is a 27 billion parameter causal language model with a vision encoder, developed by EpistemeAI. Fine-tuned on 370,000 high-quality medical question-and-answer examples, it is designed for advanced medical reasoning across professional medicine, medical genetics, and clinical knowledge. The model natively supports a 262,144 token context length, extensible up to 1,010,000 tokens, and achieves 93.00% MedQA accuracy in a 2-shot setup.
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Overview
EpistemeAI/Reasoning-Medical-27B is a 27 billion parameter causal language model with a vision encoder, developed by EpistemeAI. It is specifically fine-tuned for advanced medical reasoning, leveraging a large-scale dataset of 370,000 high-quality medical question-and-answer examples, incorporating Chain-of-Thought reasoning. The model supports a native context length of 262,144 tokens, which can be extended up to 1,010,000 tokens using YaRN scaling techniques.
Key Capabilities
- Advanced Medical Reasoning: Designed for professional medicine, medical genetics, college biology/medicine, and clinical knowledge.
- Multimodal Input: Supports text, image, and video inputs, enabling comprehensive medical analysis.
- High MedQA Accuracy: Achieves 93.00% MedQA accuracy in a 2-shot setup, outperforming several other models in reported comparisons.
- Agentic Usage: Excels in tool-calling capabilities and is compatible with frameworks like Qwen-Agent.
- Efficient Fine-tuning: Trained using the GRPO trainer with Unsloth optimization.
Good For
- Medical Research and Information: Ideal for tasks requiring deep medical understanding and reasoning.
- Educational Applications: Suitable for college-level biology and medicine studies.
- Multimodal Medical Analysis: Analyzing medical data from various formats, including X-rays and other visual inputs.
- Developing AI Agents: Building medical AI agents that require robust reasoning and tool-use capabilities.