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Co-authored-by: Jiahuan Zhang <[email protected]>

README (1).md

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+---
+license: apache-2.0
+tags:
+- medical
+datasets:
+- biomed
+---
+# BioMedGPT-LM-7B
+
+**BioMedGPT-LM-7B** is the first large generative language model based on Llama2 in the biomedical domain.
+It was fine-tuned from the Llama2-7B-Chat with millions of biomedical papers from the [S2ORC corpus](https://github.com/allenai/s2orc/blob/master/README.md). Through further fine-tuning, BioMedGPT-LM-7B outperforms or is on par with human and significantly larger general-purpose foundation models on several biomedical QA benchmarks.
+
+### Training Details
+
+The model was trained with the following hyperparameters:
+
+* Epochs: 5
+* Batch size: 192
+* Context length: 2048
+* Learning rate: 2e-5
+
+BioMedGPT-LM-7B is finetuned on over 26 billion tokens highly pertinent to the field of biomedicine. The fine-tuning data are extracted from 5.5 million biomedical papers in S2ORC data using PubMed Central (PMC)-ID and PubMed ID as criteria.
+
+### Model Developers
+
+PharMolix  
+
+### How to Use
+
+BioMedGPT-LM-7B is the generative language model of **[BioMedGPT-10B](https://github.com/BioFM/OpenBioMed)**, an open-source version of BioMedGPT.
+BioMedGPT is an open multimodal generative pre-trained transformer (GPT) for biomedicine, which bridges the natural language modality and diverse biomedical data modalities via large generative language models.
+
+More technical details of BioMedGPT-LM-7B, BioMedGPT-10B, and BioMedGPT can be found in the [technical report](https://pan.baidu.com/s/1iAMBkuoZnNAylhopP5OgEg?pwd=7a6b).
+
+![The architecture of BioMedGPT-10B](BioMedGPT-10B.jpeg)
+
+### Technical Report
+
+"BioMedGPT: Open Multimodal Generative Pre-trained Transformer for BioMedicine"
+
+### github
+
+[https://github.com/BioFM/OpenBioMed](https://github.com/BioFM/OpenBioMed)
+
+### Limitations
+
+Large-scale generative language models represent a novel technology, with their generated outputs determined by probabilities, possibly resulting in unforeseen issues, such as generating responses that may contain elements of danger, bias, discrimination, or other harmful content. Up to this point, our experiments focus on data within the field of English biomedical domains, leaving many scenarios unaddressed. Therefore, before using BioMedGPT, developers should conduct safety testing and necessary adjustments. While BioMedGPT has expertise in areas like biomedicine and chemistry, it should not be employed for research that is hazardous or could endanger human life, and it cannot replace medical professionals or provide professional treatment advice. Users need to be cautious and take extra care when using these models.