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license: apache-2.0
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---
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-
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To use the model with `transformer` package, see the example below:
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```python
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outputs = llm.generate([chat_prompt], sampling_params)
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response_text = outputs[0].outputs[0].text
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print(response_text)
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```
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---
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license: apache-2.0
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language:
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- en
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base_model:
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- aaditya/Llama3-OpenBioLLM-8B
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---
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# OpenBioLLM-Text2Graph-8B
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This model is a biomedical annotation model designed to generate named entity annotations from unlabeled biomedical text.
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It was introduced in the paper [GLiNER-BioMed: A Suite of Efficient Models for Open Biomedical Named Entity Recognition](https://arxiv.org/abs/2504.00676).
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This model enables **high-throughput, cost-efficient synthetic biomedical NER data generation**, serving as the synthetic annotation backbone for [GLiNER-BioMed models](https://huggingface.co/collections/knowledgator/gliner-biomed-67ecf1b7cc62e673dbc8b57f).
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## Usage
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To use the model with `transformer` package, see the example below:
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```python
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outputs = llm.generate([chat_prompt], sampling_params)
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response_text = outputs[0].outputs[0].text
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print(response_text)
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```
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## Citation
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If you use this model, please cite:
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```bibtex
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@misc{yazdani2025glinerbiomedsuiteefficientmodels,
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title={GLiNER-BioMed: A Suite of Efficient Models for Open Biomedical Named Entity Recognition},
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author={Anthony Yazdani and Ihor Stepanov and Douglas Teodoro},
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year={2025},
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eprint={2504.00676},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2504.00676},
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}
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```
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