Mix80/ClinicaQwen-MedQA

TEXT GENERATIONConcurrency Cost:1Model Size:1.5BQuant:BF16Ctx Length:32kTool Calling:SupportedPublished:Jun 2, 2026License:apache-2.0Architecture:Transformer Open Weights Cold

ClinicaQwen-MedQA is a 1.5 billion parameter medical question-answering assistant developed by Mix80, built on the Qwen2.5 architecture with a 32768-token context length. It features a dual-stage training pipeline to ensure high clinical accuracy and eliminate hallucinations, specifically optimized for medical terminology comprehension and conversational QA. This ultra-efficient model is designed for deployment on low-VRAM edge hardware, excelling in specialized medical information extraction.

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ClinicaQwen-MedQA: Hyper-Specialized Medical QA

ClinicaQwen-MedQA is a lightweight, 1.5 billion parameter model built upon the Qwen/Qwen2.5-1.5B architecture, specifically designed for medical question-answering. It addresses the critical need for accuracy in medical AI by employing a rigorous dual-stage training pipeline to minimize hallucinations and maximize clinical relevance.

Key Capabilities & Features

  • Dual-Stage Optimization: Undergoes sequential training for deep medical terminology comprehension (Stage 1, using gamino/wiki_medical_terms) and strict conversational QA alignment (Stage 2, using medalpaca/medical_meadow_medical_flashcards).
  • Hallucination Reduction: Fine-tuned with completion-loss masking to prevent common "echo bugs" and ensure factual adherence.
  • Ultra-Efficient Deployment: Optimized to run seamlessly in native bfloat16 precision, making it suitable for consumer-grade or serverless GPU instances with low VRAM requirements.
  • Specialized Accuracy: Achieves low perplexity scores (2.65 in Stage 2) indicating strong performance in its specialized domain.

Ideal Use Cases

  • Medical Information Extraction: Answering specific medical questions with high factual accuracy.
  • Educational Tools: Assisting in learning and understanding medical terminology and concepts.
  • Research Support: Providing quick, reliable answers for medical research queries.

Note: This model is an AI research tool and should not be used as a substitute for professional medical advice or diagnosis.

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