HViLM-Tropism

HViLM-Tropism is the official HViLM model for binary host tropism classification: human-tropic versus non-human-tropic viruses.

• Fine-tuned from: duttaprat/HViLM-base
• Benchmark: duttaprat/HVUE-v2
• HVUE v2 configuration: Host_Tropism/standard_95_1000bp
• Checkpoint selection: best validation F1 (checkpoint-14000)
• Input: virus nucleotide sequence
• Output: non-human-tropic vs. human-tropic

This repository contains a standalone full fine-tuned checkpoint, so users can load duttaprat/HViLM-Tropism directly without separately loading HViLM-base.

Label Mapping

ID	Label
0	NON_HUMAN_TROPIC
1	HUMAN_TROPIC

Performance

Held-out HVUE v2 test set, standard 1000-nt configuration:

Metric	Score
Accuracy	96.49
F1	74.49
MCC	48.99
Precision	74.96
Recall	74.04

Class Imbalance

The HVUE v2 Host Tropism benchmark is strongly imbalanced. Accuracy should therefore be interpreted together with F1 and MCC. HViLM retains substantially stronger minority-class discrimination than vanilla DNABERT-2 trained on the same imbalanced setting and avoids majority-class collapse without explicit class balancing.

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-Tropism"

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_HUMAN_TROPIC and HUMAN_TROPIC.

Intended Use

HViLM-Tropism is intended for research and benchmarking of virus sequence-based host association prediction. Host association is biologically context-dependent and may include multi-host, zoonotic, and reverse-zoonotic relationships; predictions should not be interpreted as definitive evidence of host range.

Related Resources

• HViLM-base
• HViLM-Patho
• HViLM-R0
• 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}
}