Model Overview

Description:

Palmyra-Med-70b-32k, created by Writer, builds upon the foundation of Palmyra-Med-70b and offers an extended context length and meets the needs of the healthcare industry. The leading LLM on biomedical benchmarks, with an average score of 85.87%, outperforming GPT-4, Claude Opus, Gemini and Med-PaLM-2 base model and a medically trained human test-taker.

Specialized for Biomedical Applications

Palmyra-Med-70B-32k is meticulously designed to meet the unique linguistic and knowledge demands of the medical and life sciences sectors. It has been fine-tuned on an extensive collection of high-quality biomedical data, ensuring it can comprehend and generate text with precise domain-specific accuracy and fluency.

Intended Use

Intended Use Cases Palmyra-Med-70B-32k is intended for commercial and research use in English. Instruction tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.

Out-of-scope Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Writer Open source License. Use in languages other than English**.

** Developers may fine-tune Palmyra-Med-70b-32k models for languages beyond English provided they comply with the Writer Open source License and the Acceptable Use Policy.

Evaluation Results

Palmyra-Med-70B-32k outperforms larger models like GPT-4, Gemini and Med-PaLM-1 across 9 diverse biomedical datasets, achieving state-of-the-art results with an average score of 85.9% despite having fewer parameters. Its strong performance in tasks like Clinical KG, Medical Genetics, and PubMedQA underscores its effective grasp of biomedical knowledge.

Following its evaluation on needle-in-haystack, the Palmyra-Med-70B-32k model achieved almost perfect scores, highlighting its robust capability in efficiently processing extensive medical documents.

Medical Use Cases

Palmyra-Med-70B-32k excels in analyzing and summarizing complex clinical notes, EHR data, and discharge summaries, extracting key information to generate concise, structured summaries. It can answer a wide range of medical questions and perform advanced clinical entity recognition, identifying key medical concepts such as diseases, symptoms, medications, procedures, and anatomical structures from unstructured text.

By leveraging its deep understanding of medical terminology, the model enhances information retrieval, data analysis, and knowledge discovery from EHRs, research articles, and other biomedical sources. These capabilities support applications like clinical decision support, pharmacovigilance, and medical research.

Bias, Risks, and Limitations

Palmyra-Med-70B-32k, despite leveraging high-quality data, may contain inaccuracies, biases, or misalignments and has not been rigorously evaluated in clinical trials or real-world healthcare settings. It is advised not to use the model for direct patient care, clinical decision support, or professional medical purposes. Instead, its use should be confined to research by qualified individuals who understand its limitations. Palmyra-Med-70B-32k should not replace professional medical judgment, and adapting it for medical use would require extensive additional work, including thorough testing, guideline alignment, bias mitigation, human oversight, and regulatory compliance. Always consult a qualified healthcare provider for personal medical needs.

Third-Party Community Consideration

This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party’s requirements for this application and use case; see link to Writer's Model Card.

Ethical Considerations:

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. Please report security vulnerabilities or NVIDIA AI Concerns here.

License, Acceptable Use, and Research Privacy Policy

By using this model, you are agreeing to the terms and conditions of the
License.

Model Developer: Writer

Model Release Date: June 10th, 2024

Model Architecture

Architecture Type: Transformer
Network Architecture: Llama
Finetuned from model: Palmyra-X-004

Input

Input Type: Text
Input Format: String
Input Parameters: max_tokens, temperature, top_p, stop, frequency_penalty, presence_penalty, seed

Output

Output Type: Text
Output Format: String

Software Integration:

Supported Hardware Platform(s): NVIDIA Hopper
[Preferred/Supported] Operating System(s): Linux

Inference

Engine: TensorRT-LLM

Test Hardware: H100