Instructions to use Sepideh2027/PathogenAgentAI-BioGPT-LoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Sepideh2027/PathogenAgentAI-BioGPT-LoRA with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("microsoft/biogpt") model = PeftModel.from_pretrained(base_model, "Sepideh2027/PathogenAgentAI-BioGPT-LoRA") - Notebooks
- Google Colab
- Kaggle
File size: 2,373 Bytes
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license: mit
language:
- en
tags:
- peft
- lora
- biogpt
- biomedical
- clinvar
base_model: microsoft/biogpt
---
**Year:** 2024–2025
**License:** MIT
**Author:** Sepideh Moafi
# PathogenAgentAI-BioGPT LoRA Adapter
## Model Description
A parameter-efficient LoRA adapter for BioGPT, developed as part of the PathogenAgentAI research software project. Fine-tuned on ClinVar-derived instruction data for biomedical language-model applications.
## Model Details
- Model type: LoRA adapter (PEFT-compatible)
- Base model: microsoft/biogpt
- Framework: PEFT 0.19.1
- Adapter size: ~12.6 MB
- Precision: BF16
- Format: Safetensors
- License: MIT
## LoRA Configuration
- Rank (r): 16
- LoRA alpha: 32
- LoRA dropout: 0.05
- Target modules: q_proj, k_proj, v_proj, o_proj
- Task type: CAUSAL_LM
## Intended Use
- Parameter-efficient adaptation of BioGPT for biomedical text generation
- Research on biomedical language models
- Experimentation with ClinVar-derived variant information
- Reproducible research and model adaptation experiments
Not intended for clinical diagnosis, treatment decisions, or direct clinical use.
## How to Get Started
```python
from peft import PeftModel
from transformers import AutoModelForCausalLM
base_model = AutoModelForCausalLM.from_pretrained("microsoft/biogpt")
model = PeftModel.from_pretrained(base_model, "Sepideh2027/PathogenAgentAI-BioGPT-LoRA")
Training Details
· Training data: ~20,000 ClinVar-derived instruction-style examples (16k/2k/2k train/validation/test split)
· Training procedure: LoRA, BF16 mixed precision
· Hardware: NVIDIA T4 GPU
Limitations
· Inherits all limitations of the base BioGPT model.
· Not validated for clinical use.
· Generated outputs should be independently verified against authoritative biomedical sources.
Related Resources
· Fine-tuned model: Sepideh2027/biogpt-clinvar-finetuned
· Instruction-tuning dataset: Sepideh2027/Agent
Citation
@software{Moafi2025pathogenagent,
author = {Moafi, Sepideh},
title = {PathogenAgentAI-BioGPT LoRA Adapter},
year = {2025},
url = {https://huggingface.co/Sepideh2027/PathogenAgentAI-BioGPT-LoRA}
}
BioGPT-ClinVar: Parameter-Efficient Fine-Tuning of a Biomedical Foundation Model. Research Square, 2026. DOI: 10.21203/rs.3.rs-10196893/v1
Contact
Sepideh Moafi
GitHub: AIResearcher20
Hugging Face: Sepideh2027
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