README.md

---
license: mit
language:
- en
base_model:
- facebook/esm2_t12_35M_UR50D
tags:
- Protein
- Langeuage
---


# TransHLA2.0-IM

A minimal Hugging Face-compatible PyTorch model for peptide–HLA binding classification using ESM with optional LoRA and cross-attention. There is no custom predict API; inference follows the training path: tokenize peptide and HLA pseudosequence with the ESM tokenizer, pad or truncate to fixed lengths (default peptide=16, HLA=36), run a forward pass as `logits, features = model(epitope_ids, hla_ids)`, then apply softmax to get the binding probability.

## Quick Start

Requirements:
- Python >= 3.8
- torch >= 2.0
- transformers >= 4.40
- peft (only if you use LoRA/PEFT adapters)

## Install:
```bash
pip install torch transformers peft
```
## Usage (Transformers)
```python
import torch
import torch.nn.functional as F
from transformers import AutoModel, AutoTokenizer
model_id = "SkywalkerLu/TransHLA2.0-IM"
model = AutoModel.from_pretrained(model_id, trust_remote_code=True).to(device).eval()
```
## How to use TransHLA2.0-IM
```python
import torch
import torch.nn.functional as F
from transformers import AutoModel, AutoTokenizer

# Device
device = "cuda" if torch.cuda.is_available() else "cpu"

# Load model (replace with your model id if different)
model_id = "SkywalkerLu/TransHLA2.0-IM"
model = AutoModel.from_pretrained(model_id, trust_remote_code=True).to(device).eval()

# Load tokenizer used in training (ESM2 650M)
tok = AutoTokenizer.from_pretrained("facebook/esm2_t33_650M_UR50D")

# Example inputs
peptide = "GILGFVFTL"  # 9-mer example
# Fake placeholder pseudosequence for demo; replace with a real one from your mapping/data
hla_pseudoseq = (
    "YYSEYRNIYAQTDESNLYLSYDYYTWAERAYEWY"
)

# Fixed lengths (must match training)
PEP_LEN = 16
HLA_LEN = 36
PAD_ID = tok.pad_token_id if tok.pad_token_id is not None else 1

def pad_to_len(ids_list, target_len, pad_id):
    return ids_list + [pad_id] * (target_len - len(ids_list)) if len(ids_list) < target_len else ids_list[:target_len]

# Tokenize
pep_ids = tok(peptide, add_special_tokens=True)["input_ids"]
hla_ids = tok(hla_pseudoseq, add_special_tokens=True)["input_ids"]

# Pad/truncate
pep_ids = pad_to_len(pep_ids, PEP_LEN, PAD_ID)
hla_ids = pad_to_len(hla_ids, HLA_LEN, PAD_ID)

# Tensors (batch=1)
pep_tensor = torch.tensor([pep_ids], dtype=torch.long, device=device)
hla_tensor = torch.tensor([hla_ids], dtype=torch.long, device=device)

# Forward + probability
with torch.no_grad():
    logits, features = model(pep_tensor, hla_tensor)
    prob_bind = F.softmax(logits, dim=1)[0, 1].item()
    pred = int(prob_bind >= 0.5)

print({"peptide": peptide, "bind_prob": round(prob_bind, 6), "label": pred})
```


## Batch Inference (Python)

```python
import torch
import torch.nn.functional as F
from transformers import AutoModel, AutoTokenizer

# Device
device = "cuda" if torch.cuda.is_available() else "cpu"

# Load model and tokenizer
model_id = "SkywalkerLu/TransHLA2.0-IM"  # replace with your model id if different
model = AutoModel.from_pretrained(model_id, trust_remote_code=True).to(device).eval()
tok = AutoTokenizer.from_pretrained("facebook/esm2_t33_650M_UR50D")

# Fixed lengths (must match training)
PEP_LEN = 16
HLA_LEN = 36
PAD_ID = tok.pad_token_id if tok.pad_token_id is not None else 1

def pad_to_len(ids_list, target_len, pad_id):
    return ids_list + [pad_id] * (target_len - len(ids_list)) if len(ids_list) < target_len else ids_list[:target_len]

# Example batch (use real HLA pseudosequences in your data)
batch = [
    {"peptide": "GILGFVFTL", "hla_pseudo": "YYSEYRNIYAQTDESNLYLSYDYYTWAERAYEWY"},
    {"peptide": "NLVPMVATV", "hla_pseudo": "YYSEYRNIYAQTDESNLYLSYDYYTWAERAYEWY"},
    {"peptide": "SIINFEKL",  "hla_pseudo": "YYSEYRNIYAQTDESNLYLSYDYYTWAERAYEWY"},
]

# Tokenize and pad/truncate
pep_ids_batch, hla_ids_batch = [], []
for item in batch:
    pep_ids = tok(item["peptide"], add_special_tokens=True)["input_ids"]
    hla_ids = tok(item["hla_pseudo"], add_special_tokens=True)["input_ids"]
    pep_ids_batch.append(pad_to_len(pep_ids, PEP_LEN, PAD_ID))
    hla_ids_batch.append(pad_to_len(hla_ids, HLA_LEN, PAD_ID))

# To tensors
pep_tensor = torch.tensor(pep_ids_batch, dtype=torch.long, device=device)  # [B, PEP_LEN]
hla_tensor = torch.tensor(hla_ids_batch, dtype=torch.long, device=device)  # [B, HLA_LEN]

# Forward
with torch.no_grad():
    logits, _ = model(pep_tensor, hla_tensor)   # logits shape: [B, 2]
    probs = F.softmax(logits, dim=1)[:, 1]      # binding probability for class-1

# Threshold to labels (0/1)
labels = (probs >= 0.5).long().tolist()

# Print results
for i, item in enumerate(batch):
    print({
        "peptide": item["peptide"],
        "bind_prob": float(probs[i].item()),
        "label": labels[i]
    })
```