Palmyra-Med-70B-32K
Maintainer: Writer
95
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model Overview
Palmyra-Med-70B-32K is a powerful large language model (LLM) designed specifically for the healthcare and medical domain. Developed by Writer, it builds upon the foundation of Palmyra-Med-70B and offers an extended context length to better meet the unique needs of the healthcare industry.
Compared to other large models like GPT-4, Claude Opus, Gemini, and Med-PaLM-2, Palmyra-Med-70B-32K is the leading LLM on biomedical benchmarks, with an average score of 85.87%, outperforming even a medically trained human test-taker. This specialization is achieved through fine-tuning on an extensive collection of high-quality biomedical data, including the DPO dataset and a custom diverse medical instruction dataset.
Similar models developed by Writer include the Palmyra-Fin-70B-32K, a finance-focused LLM, and the Llama3-OpenBioLLM-70B and Llama3-OpenBioLLM-8B models created by Aaditya Ura of Saama AI Labs.
Model Inputs and Outputs
Inputs
- Text data in English
Outputs
- Generated text in English
Capabilities
Palmyra-Med-70B-32K excels at understanding and generating text with precise domain-specific accuracy and fluency for the medical and life sciences sectors. It can effectively handle tasks like answering medical questions, summarizing clinical notes, and extracting key information from biomedical research articles.
What Can I Use It For?
Palmyra-Med-70B-32K is intended for non-commercial and research use in English. Some potential use cases include:
- Assisting with medical research and analysis by generating summaries of complex biomedical literature
- Enhancing clinical decision support by providing information about diseases, symptoms, treatments, and medical concepts
- Automating the extraction of relevant information from electronic health records and other medical data sources
Things to Try
One interesting aspect of Palmyra-Med-70B-32K is its ability to leverage its deep understanding of medical terminology and context to perform advanced clinical entity recognition. By identifying and categorizing key concepts like diseases, symptoms, and medications, the model can enable more efficient information retrieval and knowledge discovery from unstructured biomedical text.
Developers could explore fine-tuning the model further for specialized medical tasks or incorporating it into applications that assist healthcare professionals in their day-to-day workflows.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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