This repository contains a PEFT/LoRA adapter for RARE-PHENIX Module 1, the phenotype extraction module of RARE-PHENIX.
What this model does
This adapter is intended to extract rare disease phenotype mentions from clinical text.
Input:
The patient has global developmental delay and hypotonia.
Output:
The patient has <span class="condition">global developmental delay</span> and <span class="condition">hypotonia</span>.
Important scope note
This is Module 1 only.
It does not perform:
HPO standardization
HPO term ranking
disease diagnosis
gene or variant prioritization
The full RARE-PHENIX workflow includes additional downstream modules for HPO standardization and rare disease-relevant HPO prioritization.
Training data
This adapter was fine-tuned on the public RareDis corpus.
It is not the full Undiagnosed Diseases Network (UDN)-trained RARE-PHENIX model described in the manuscript. Controlled-access Undiagnosed Diseases Network (UDN) data and UDN-trained model artifacts are not included.
Base model
This is a LoRA adapter and requires access to the corresponding Meta Llama-2 base model:
meta-llama/Llama-2-13b-chat-hf
Users must separately request and obtain access to the gated Meta Llama-2 model through Hugging Face.
This adapter is intended for research use in rare disease phenotype extraction workflows.
Limitations
This model is not intended for autonomous clinical diagnosis.
Outputs should be reviewed by domain experts.
The public adapter may perform differently from the UDN-trained RARE-PHENIX model described in the manuscript.
The model may incorrectly tag negated findings, family history, or non-patient conditions; downstream post-processing and expert review are recommended.
Do not send protected health information to environments that are not approved for PHI.
Citation
If you use this adapter, please cite:
Shyr, C., Hu, Y., Tinker, R.J., Cassini, T.A., Byram, K.W., Hamid, R., Fabbri, D.V., Wright, A., Peterson, J.F., Bastarache, L., and Xu, H. 2026. An artificial intelligence framework for end-to-end rare disease phenotyping from clinical notes using large language models. arXiv preprint arXiv:2602.20324.
