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library_name: transformers
base_model: duttaprat/HViLM-base
datasets:
- duttaprat/HVUE-v2
pipeline_tag: text-classification
tags:
- genomics
- virology
- dna
- virus
- pathogenicity
- hvue-v2
license: apache-2.0
---
# HViLM-Patho
**HViLM-Patho** is the official HViLM model for binary virus pathogenicity classification.
- **Fine-tuned from:** [duttaprat/HViLM-base](https://huggingface.co/duttaprat/HViLM-base)
- **Benchmark:** [duttaprat/HVUE-v2](https://huggingface.co/datasets/duttaprat/HVUE-v2)
- **HVUE v2 configuration:** `Pathogenicity/standard_capped_1000bp`
- **Checkpoint selection:** best validation F1 (`checkpoint-3000`)
- **Input:** virus nucleotide sequence
- **Output:** non-pathogenic vs. pathogenic
This repository contains a **standalone full fine-tuned checkpoint**, so users can load `duttaprat/HViLM-Patho` directly without separately loading `HViLM-base`.
## Label Mapping
| ID | Label |
|---:|---|
| 0 | `NON_PATHOGENIC` |
| 1 | `PATHOGENIC` |
## Performance
Held-out HVUE v2 test set, standard 1000-nt configuration:
| Metric | Score |
|---|---:|
| Accuracy | 92.39 |
| F1 | 91.32 |
| MCC | 83.10 |
| Precision | 93.03 |
| Recall | 90.12 |
## Training Details
- **Fine-tuning method:** LoRA
- **LoRA rank:** 8
- **LoRA alpha:** 16
- **Target modules:** query and value projections across all 12 transformer layers
- **Approximate trainable LoRA parameters:** ~0.3M
- **Learning rate:** 3e-5
- **Maximum input length:** 250 BPE tokens (approximately 1000 nt)
- **Early stopping:** patience 3, monitored using validation F1
- **Hardware:** NVIDIA A40 GPU
The released repository contains the full task-specific model weights rather than only the LoRA adapter.
## Usage
```python
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_id = "duttaprat/HViLM-Patho"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForSequenceClassification.from_pretrained(
model_id,
trust_remote_code=True,
)
sequence = "ATGCGTACGTTAGCCGATCGATTACGCGTACGTAGCTAGC"
inputs = tokenizer(
sequence,
return_tensors="pt",
truncation=True,
max_length=250,
)
with torch.no_grad():
logits = model(**inputs).logits
prediction_id = logits.argmax(dim=-1).item()
print(model.config.id2label[prediction_id])
```
Possible outputs are `NON_PATHOGENIC` and `PATHOGENIC`.
## Intended Use
HViLM-Patho is intended for research on virus sequence representation and computational pathogenicity classification. Predictions should be interpreted as model outputs rather than experimental or clinical evidence.
## Related Resources
- [HViLM-base](https://huggingface.co/duttaprat/HViLM-base)
- [HViLM-R0](https://huggingface.co/duttaprat/HViLM-R0)
- [HViLM-Tropism](https://huggingface.co/duttaprat/HViLM-Tropism)
- [HVUE-v2](https://huggingface.co/datasets/duttaprat/HVUE-v2)
- [HViLM GitHub repository](https://github.com/duttaprat/HViLM)
## Citation
```bibtex
@article{dutta2026hvilm,
title={HViLM: A foundation model for viral genomics enables multi-task prediction of pathogenicity, transmissibility, and host tropism},
author={Dutta, Pratik and Vaska, Jack and Surana, Pallavi and Sathian, Rekha and Chao, Max and Zhou, Zhihan and Liu, Han and Davuluri, Ramana V},
journal={bioRxiv},
pages={2026--03},
year={2026},
publisher={Cold Spring Harbor Laboratory}
}
```
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