We are introducing SM70, a 70 billion-parameter Large Language Model that is specifically designed for SpassMed's medical devices under the brand name 'JEE1' (pronounced as G1 and means 'Life'). This large language model provides more accurate and safe responses to medical-domain questions. To fine-tune SM70, we used around 800K data entries from the publicly available dataset MedAlpaca. The Llama2 70B open-sourced model served as the foundation for SM70, and we employed the QLoRA technique for fine-tuning. The evaluation is conducted across three benchmark datasets - MEDQA - USMLE, PUBMEDQA, and USMLE - each representing a unique aspect of medical knowledge and reasoning. The performance of SM70 is contrasted with other notable LLMs, including Llama2 70B, Clinical Camel 70 (CC70), GPT 3.5, GPT 4, and Med-Palm, to provide a comparative understanding of its capabilities within the medical domain. Our results indicate that SM70 outperforms several established models in these datasets, showcasing its proficiency in handling a range of medical queries, from fact-based questions derived from PubMed abstracts to complex clinical decision-making scenarios. The robust performance of SM70, particularly in the USMLE and PUBMEDQA datasets, suggests its potential as an effective tool in clinical decision support and medical information retrieval. Despite its promising results, the paper also acknowledges the areas where SM70 lags behind the most advanced model, GPT 4, thereby highlighting the need for further development, especially in tasks demanding extensive medical knowledge and intricate reasoning.

翻译：我们推出SM70——一个专为SpassMed旗下医疗器械品牌“JEE1”（读音为G1，意为“生命”）设计的700亿参数大语言模型。该模型能为医疗领域问题提供更准确、更安全的回答。为微调SM70，我们使用了公开数据集MedAlpaca中约80万条数据条目，以Llama2 70B开源模型为基础，并采用QLoRA技术进行微调。模型评估在三个基准数据集（MEDQA-USMLE、PUBMEDQA和USMLE）上进行，每个数据集分别代表医学知识与推理的不同维度。通过与Llama2 70B、Clinical Camel 70（CC70）、GPT 3.5、GPT 4及Med-Palm等知名大语言模型的对比，我们揭示了SM70在医疗领域的综合能力。结果显示，SM70在多个数据集上优于多款成熟模型，展现出从PubMed摘要事实性问题到复杂临床决策场景的广泛医疗问答处理能力。特别是其在USMLE和PUBMEDQA数据集上的稳健表现，表明其作为临床决策支持与医学信息检索工具的巨大潜力。尽管成果显著，本文亦指出SM70在部分任务（尤其需要广博医学知识与复杂推理的场景）中仍落后于最高水平模型GPT 4，从而凸显了进一步发展的必要性。