This compact yet powerful 7B-parameter medical language model delivers exceptional performance for common clinical tasks while maintaining efficiency in deployment and operation. Trained on comprehensive medical texts, it excels at clinical summarization, standard medical question-answering, and processing routine patient documentation. Its smaller size enables faster inference and reduced computational costs, making it ideal for organizations seeking to balance performance with resource optimization. Perfect for high-throughput environments requiring quick responses, this model maintains high accuracy in core medical tasks while consuming significantly less computing power than larger variants. Like its siblings, it's optimized for Retrieval-Augmented Generation (RAG), seamlessly integrating with healthcare databases and EHR systems. Choose this model when rapid response times and cost-effectiveness are priorities, without compromising on essential medical comprehension capabilities. Accuracy Outperforms Med-PaLM-1 in clinical reasoning (86.81% vs 83.8%); Achieves 71.70% average across OpenMed benchmarks, comparable to larger models; Superior performance in PubMedQA (75.6%) vs similar-sized models; Matches GPT-4's accuracy in medical QA tasks while being 100x smaller; Ideal for cost-efficient clinical deployments with fast inference;