Instructions to use silicobio/peleke-phi-4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use silicobio/peleke-phi-4 with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("microsoft/phi-4")
model = PeftModel.from_pretrained(base_model, "silicobio/peleke-phi-4")

Transformers

How to use silicobio/peleke-phi-4 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="silicobio/peleke-phi-4")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("silicobio/peleke-phi-4", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use silicobio/peleke-phi-4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "silicobio/peleke-phi-4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "silicobio/peleke-phi-4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/silicobio/peleke-phi-4

SGLang

How to use silicobio/peleke-phi-4 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "silicobio/peleke-phi-4" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "silicobio/peleke-phi-4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "silicobio/peleke-phi-4" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "silicobio/peleke-phi-4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use silicobio/peleke-phi-4 with Docker Model Runner:
```
docker model run hf.co/silicobio/peleke-phi-4
```

Model Card for peleke-phi-4

This model is a fine-tuned version of 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

Load in the Model

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()

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.

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

Generate an Antibody Sequence

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.

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

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