Palmyra-Med-70B
Maintainer: Writer
67
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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
The Palmyra-Med-70B is a powerful large language model (LLM) developed by Writer specifically for the healthcare industry. It is the leading LLM on biomedical benchmarks, outperforming larger models like GPT-4, Claude Opus, Gemini, and Med-PaLM-2 with an average score of 85.87%. The model has been meticulously designed to meet the unique linguistic and knowledge demands of the medical and life sciences sectors, with a focus on precision and accuracy.
Similar models include the Palmyra-Med-70B-32K, which builds upon the Palmyra-Med-70B with an extended context length, and the Palmyra-Fin-70B-32K, a finance-focused model developed by the same team.
Model inputs and outputs
Inputs
- Text data: The
Palmyra-Med-70Bmodel takes text-only data as input, which can include a wide range of biomedical and healthcare-related content, such as clinical notes, research papers, and reference materials.
Outputs
- Generated text: The model generates fluent, domain-specific text as output. This can include summarized information, answers to questions, and other types of natural language generation relevant to the healthcare and life sciences domains.
Capabilities
The Palmyra-Med-70B excels at analyzing and summarizing complex clinical data, extracting key information to generate concise, structured summaries. It can also enhance clinical decision-making by performing advanced entity recognition, identifying important medical concepts such as diseases, symptoms, medications, and anatomical structures.
By leveraging its deep understanding of medical terminology, the model can support applications like clinical decision support, pharmacovigilance, and medical research, enhancing information retrieval, data analysis, and knowledge discovery from electronic health records, research articles, and other biomedical sources.
What can I use it for?
The Palmyra-Med-70B and its derivatives, such as the Palmyra-Med-70B-32K, are intended for non-commercial and research use in the English language. Potential use cases include:
- Assisting with medical research and literature review
- Enhancing clinical decision support systems
- Automating the generation of medical reports and summaries
- Improving information extraction from electronic health records
- Providing domain-specific language understanding for healthcare chatbots and virtual assistants
Things to try
One interesting aspect of the Palmyra-Med-70B is its ability to leverage its extensive biomedical knowledge to provide detailed explanations and insights, going beyond simple question answering. For example, you could prompt the model to explain the mechanisms behind a specific disease or the rationale for a particular medical treatment, drawing upon its deep understanding of relevant anatomical structures, physiological processes, and clinical best practices.
Another interesting approach would be to explore the model's performance on longer, more complex biomedical tasks, such as generating comprehensive literature reviews or research proposals. The extended context length of the Palmyra-Med-70B-32K variant may be particularly well-suited for these types of applications.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
Related Models
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Palmyra-Med-70B-32K
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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.
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Palmyra-Fin-70B-32K
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The Palmyra-Fin-70B-32K is a powerful LLM designed specifically for the finance industry. Developed by Writer, this model outperforms other large language models on various financial tasks and evaluations. It has been fine-tuned on an extensive collection of high-quality financial data, enabling it to comprehend and generate text with precise domain-specific accuracy and fluency. Compared to similar financial language models like pythia-70m, Llama3-OpenBioLLM-70B, and meditron-70b, the Palmyra-Fin-70B-32K stands out for its specialized focus on the finance domain and its robust performance on financial tasks. Model Inputs and Outputs Inputs Text data**: The Palmyra-Fin-70B-32K model takes text-based inputs and generates text outputs. It can handle a context window of up to 32,768 tokens. Outputs Text data**: The model generates natural language text outputs in response to the provided inputs. This can include financial analysis, market predictions, risk assessments, and other finance-related content. Capabilities The Palmyra-Fin-70B-32K model is adept at handling a variety of financial tasks and applications. It excels at answering questions from long financial documents, making it ideal for in-depth financial research and analysis. The model can also be used for tasks like financial report generation, automated financial advice, and market trend prediction. What Can I Use It For? The Palmyra-Fin-70B-32K model is well-suited for a range of finance-related projects and applications. Some potential use cases include: Financial analysis and research**: Leverage the model's understanding of financial concepts and data to generate detailed reports, summaries, and insights from complex financial documents. Market prediction and forecasting**: Use the model to analyze market trends and generate predictions about future performance. Automated financial advice**: Integrate the model into financial advisory systems to provide personalized recommendations and guidance to clients. Compliance and risk assessment**: Utilize the model's capabilities to evaluate financial data and identify potential risks or compliance issues. Things to Try One interesting aspect of the Palmyra-Fin-70B-32K model is its ability to provide detailed, domain-specific explanations and insights. Try prompting the model with complex financial questions or scenarios and observe how it leverages its deep understanding of the subject matter to generate comprehensive, well-reasoned responses. Additionally, you could experiment with using the model for tasks like financial report generation or automated investment advice. By fine-tuning the model on your own financial data and use cases, you may be able to unlock even more specialized capabilities.
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Llama3-OpenBioLLM-70B
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Llama3-OpenBioLLM-70B is an advanced open-source biomedical large language model developed by Saama AI Labs. It builds upon the powerful foundations of the Meta-Llama-3-70B-Instruct model, incorporating novel training techniques like Direct Preference Optimization to achieve state-of-the-art performance on a wide range of biomedical tasks. Compared to other open-source models like Meditron-70B and proprietary models like GPT-4, it demonstrates superior results on biomedical benchmarks. Model inputs and outputs Inputs Llama3-OpenBioLLM-70B is a text-to-text model, taking in textual inputs only. Outputs The model generates fluent and coherent text responses, suitable for a variety of natural language processing tasks in the biomedical domain. Capabilities Llama3-OpenBioLLM-70B is designed for specialized performance on biomedical tasks. It excels at understanding and generating domain-specific language, allowing for accurate responses to queries about medical conditions, treatments, and research. The model's advanced training techniques enable it to outperform other open-source and proprietary language models on benchmarks evaluating tasks like medical exam question answering, disease information retrieval, and supporting differential diagnosis. What can I use it for? Llama3-OpenBioLLM-70B is well-suited for a variety of biomedical applications, such as powering virtual assistants to enhance clinical decision-making, providing general health information to the public, and supporting research efforts by automating tasks like literature review and hypothesis generation. Its strong performance on biomedical benchmarks suggests it could be a valuable tool for developers and researchers working in the life sciences and healthcare fields. Things to try Developers can explore using Llama3-OpenBioLLM-70B as a foundation for building custom biomedical natural language processing applications. The model's specialized knowledge and capabilities could be leveraged to create chatbots, question-answering systems, and text generation tools tailored to the needs of the medical and life sciences communities. Additionally, the model's performance could be further fine-tuned on domain-specific datasets to optimize it for specific biomedical use cases.
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meditron-70b
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meditron-70b is a 70 billion parameter Large Language Model (LLM) developed by the EPFL LLM Team. It is adapted from the base Llama-2-70B model through continued pretraining on a curated medical corpus, including PubMed articles, abstracts, medical guidelines, and general domain data. This specialized pretraining allows meditron-70b to outperform Llama-2-70B, GPT-3.5, and Flan-PaLM on multiple medical reasoning tasks. Model inputs and outputs meditron-70b is a causal decoder-only transformer language model that takes text-only data as input and generates text as output. The model has a context length of 4,096 tokens. Inputs Text-only data Outputs Generated text Capabilities meditron-70b is designed to encode medical knowledge from high-quality sources. However, the model is not yet adapted to safely deliver this knowledge within professional actionable constraints. Extensive use-case alignment, testing, and validation is recommended before deploying meditron-70b in medical applications. What can I use it for? Potential use cases for meditron-70b may include medical exam question answering and supporting differential diagnosis, though the model should be used with caution. The EPFL LLM Team is making meditron-70b available for further testing and assessment as an AI assistant to enhance clinical decision-making and expand access to LLMs in healthcare. Things to try Researchers and developers are encouraged to experiment with meditron-70b to assess its capabilities and limitations in the medical domain. However, any outputs or applications should be thoroughly reviewed to ensure safety and responsible use of the model.
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