---
base_model: microsoft/phi-4
library_name: peft
model_name: peleke-phi-4
tags:
- base_model:adapter:microsoft/phi-4
- lora
- sft
- transformers
- trl
- chemistry
- biology
- antibody
- antigen
- protein
- amino-acid
- drug-design
licence: gpl-3
pipeline_tag: text-generation
license: gpl-3.0
datasets:
- silicobio/peleke_antibody-antigen_sabdab
---

# Model Card for peleke-phi-4

This model is a fine-tuned version of [microsoft/phi-4](https://huggingface.co/microsoft/phi-4) for antibody sequence generation.
It takes in an antigen sequence, and returns novel Fv portions of heavy and light chain antibody sequences.

## Quick start

1. Load in the Model

```python
model_name = 'silicobio/peleke-phi-4'
config = PeftConfig.from_pretrained(model_name)

tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)

model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, torch_dtype=torch.bfloat16, trust_remote_code=True).cuda()
model.resize_token_embeddings(len(tokenizer))
model = PeftModel.from_pretrained(model, model_name).cuda()
```

2. Format your Input

This model uses `<epi>` and `</epi>` to annotate epitope residues of interest.

It may be easier to use other characters for annotation, such as `[ ]`'s. For example: `...CSFS[S][F][V]L[N]WY...`.
Then, use the following function to properly format the input.

```python
def format_prompt(antigen_sequence):
    epitope_seq = re.sub(r'\[([A-Z])\]', r'<epi>\1</epi>', antigen_sequence)
    formatted_str = f"Antigen: {epitope_seq}<|im_end|>\nAntibody:"
    return formatted_str
```

3. Generate an Antibody Sequence

```python
prompt = format_prompt(antigen)
inputs = tokenizer(prompt, return_tensors="pt")
inputs = {k: v.cuda() for k, v in inputs.items()}

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=1000,
        do_sample=True,
        temperature=0.7,
        pad_token_id=tokenizer.eos_token_id,
        use_cache=False,
    )

full_text = tokenizer.decode(outputs[0], skip_special_tokens=False)
antibody_sequence = full_text.split('<|im_end|>')[1].replace('Antibody: ', '')
print(f"Antigen: {antigen}\nAntibody: {antibody_sequence}\n")
```

This will generate a `|`-delimited output, which is an Fv portion of a heavy and light chain.

```sh
Antigen: NPPTFSPALL...
Antibody: QVQLVQSGGG...|DIQMTQSPSS...
```

## Training procedure


This model was trained with SFT.

### Framework versions

- PEFT 0.17.0
- TRL: 0.19.1
- Transformers: 4.54.0
- Pytorch: 2.7.1
- Datasets: 4.0.0
- Tokenizers: 0.21.2