from transformers import AutoTokenizer, AutoModelForSequenceClassification
from peft import PeftModel
import torch

model = AutoModelForSequenceClassification.from_pretrained("facebook/esm2_t33_650M_UR50D", num_labels=1)
model = PeftModel.from_pretrained(model, "null-phnix/amp-genpept-esm2-650m-lora")
tokenizer = AutoTokenizer.from_pretrained("facebook/esm2_t33_650M_UR50D")

def predict_amp(sequence: str) -> float:
    """Return AMP probability for a peptide sequence."""
    inputs = tokenizer(sequence, return_tensors="pt", truncation=True, padding="max_length", max_length=200)
    with torch.no_grad(): logits = model(**inputs).logits
    return torch.sigmoid(logits).item()

print(predict_amp("GLFDVIKKVAGALGSLVK"))

Architecture

Base: ESM-2 650M (33 transformer layers, 1280 hidden dim)
Adapter: LoRA r=16, alpha=32, target_modules=["query","value"]
Head: Single sigmoid output for binary classification

Data

Trained on GenPept-Curated-2025, a balanced, leakage-free AMP benchmark.

null-phnix/amp-genpept-esm2-650m-lora

AMP GenPept Binary Classifier (ESM-2 650M + LoRA)

Performance

Usage

Architecture

Data

Links

null-phnix/
amp-genpept-esm2-650m-lora