con-cord/Mod2-no-ref
con-cord/Mod2-no-ref is a 4.3 billion parameter medical LLM-as-a-Judge model, based on the Gemma-3-4B architecture. Developed by con-cord, it is specifically fine-tuned for evaluating generated medical responses without relying on reference answers. This model excels at assessing medical content against predefined clinical criteria, making it suitable for automated quality assurance in medical AI applications.
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Overview
con-cord/Mod2-no-ref is a 4.3 billion parameter medical LLM-as-a-Judge model, built upon the Gemma-3-4B base architecture. This particular version, no-ref, is uniquely designed to evaluate generated medical answers without the need for expert reference answers. It leverages frameworks like Transformers, PEFT/LoRA, and TRL for its fine-tuning process.
Key Capabilities
- Medical Response Evaluation: Specialized in assessing the quality and accuracy of AI-generated medical text.
- Reference-Free Assessment: Operates effectively by evaluating against predefined clinical criteria, eliminating the dependency on ground truth answers.
- Gemma-3-4B Base: Benefits from the foundational capabilities of the Gemma-3-4B model.
Good For
- Automated quality control of medical large language models.
- Scenarios where expert reference answers are unavailable or difficult to obtain.
- Evaluating adherence to clinical guidelines in AI-generated medical content.