vmal/med-advisor-conversation-4B

TEXT GENERATIONPricing:Input $0.4 / Cached $0.08 / Output $0.8Concurrent Unit Cost:1Model Size:4BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Oct 1, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold

vmal/med-advisor-conversation-4B is a 4 billion parameter Qwen3-based language model, fully fine-tuned on medical conversations. It is designed to act as a medical explainer, adjusting its responses based on the audience (patient, caregiver, science-literate adult, medical student, or healthcare worker). The model supports multi-turn conversations and features both 'thinking' and 'non-thinking' modes, excelling at providing nuanced medical explanations and safety escalations.

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Med Advisor Conversation 4B

Med Advisor Conversation 4B is a 4 billion parameter medical explainer model, built on Qwen3-4B and extensively fine-tuned using reinforcement learning (RL100 checkpoint) on medical conversations. Its core capability is to adapt its explanations to different audiences, including patients, caregivers, science-literate adults, medical students, and healthcare workers.

Key Capabilities

  • Audience-Adjusted Explanations: Provides medical information tailored to five distinct user types, modifying depth and vocabulary accordingly.
  • Multi-Turn Conversations: Capable of engaging in extended dialogues, updating explanations based on user corrections or follow-up information.
  • Thinking and Non-thinking Modes: Features an enable_thinking switch, allowing the model to reason internally before generating a visible answer, which significantly improves performance (e.g., 59.9% in thinking mode vs. 54.4% non-thinking on HealthBench Consensus).
  • Broad Medical Scope: Covers 16 medical domains and 6 types of requests, including explanations, lab interpretations, medication reasoning, self-care advice, and safety escalation.
  • Safety-Oriented: Trained to prioritize emergency escalation and to decline requests for diagnosis, prescribing, or personal medical advice.

Good For

  • Educational Tools: Ideal for applications requiring clear, evidence-aware medical and scientific explanations.
  • Research: Serves as a valuable artifact for studying conversation-level reinforcement learning in specialized domains.
  • Information Dissemination: Useful for explaining medical terms, mechanisms, evidence strength, and guiding users on when to seek clinical advice.
  • Simulated Medical Scenarios: Can be used in training environments for medical students or healthcare workers to practice communication and information retrieval.