The LLM Reasoning Model 32 B marks a transformative leap in AI-driven clinical support by focusing on clinical reasoning over mere knowledge recall. Unlike traditional models that serve primarily as reference tools, this advanced model functions as a cognitive assistant, designed to aid healthcare professionals in making intricate diagnostic and treatment decisions. It meticulously processes patient symptoms, test results, and medical histories, employing structured reasoning patterns to recommend subsequent actions aligned with clinical guidelines. Key benefits include: - Transparent Decision Pathways: Provides clear and comprehensible explanations of how conclusions are reached, enhancing trust and reliability. - Consideration of Alternatives: Evaluates multiple hypotheses to ensure thorough analysis and diagnostic accuracy. - Uncertainty Acknowledgment: Recognizes and communicates the inherent uncertainties in medical diagnosis, which is crucial for risk management and decision-making. - Medical Knowledge Integration: Seamlessly incorporates vast medical knowledge, ensuring that all recommendations are up-to-date and evidence-based. - Structured Reasoning Patterns: Uses established clinical reasoning frameworks to simulate the thought processes of seasoned clinicians. The Medical LLM Reasoner 32B outperforms other leading models across most categories, with particularly strong performance in clinical knowledge, professional medicine, and medical education domains. Our benchmarking shows that the 32B model achieves 95-97% of the reasoning performance of larger models while generating tokens at approximately half the computational cost. This model represents a significant step forward in equipping healthcare professionals with a tool that supports complex decision-making with precision and depth, mirroring a clinician's approach to patient care. Key Metrics on Medical Knowledge and Reasoning Tasks Clinical Knowledge Application: Clinical Knowledge benchmark: 86.66% Professional Medicine: 89.24% Medical Genetics: 93.0% Diagnostic Reasoning Performance: MedQA (4 options) :73.67% MedMCQA : 67.92% Cross-Domain Reasoning College Biology: 93% Anatomy: 80.21% College Medicine: 81.19% Research Comprehension and Evidence Evaluation: PubMedQA: 78.9% Recommended Instance for running this model is Standard_NC96ads_A100_v4