HViLM-Patho

HViLM-Patho is the official HViLM model for binary virus pathogenicity classification.

• Fine-tuned from: duttaprat/HViLM-base
• Benchmark: 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

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
• HViLM-R0
• HViLM-Tropism
• HVUE-v2
• HViLM GitHub repository

Citation

@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}
}