Instructions to use Huggggooo/ProtoCycle-7B-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Huggggooo/ProtoCycle-7B-SFT with Transformers:

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

pipe = pipeline("text-generation", model="Huggggooo/ProtoCycle-7B-SFT")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Huggggooo/ProtoCycle-7B-SFT")
model = AutoModelForCausalLM.from_pretrained("Huggggooo/ProtoCycle-7B-SFT", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Huggggooo/ProtoCycle-7B-SFT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Huggggooo/ProtoCycle-7B-SFT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Huggggooo/ProtoCycle-7B-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Huggggooo/ProtoCycle-7B-SFT

SGLang

How to use Huggggooo/ProtoCycle-7B-SFT 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 "Huggggooo/ProtoCycle-7B-SFT" \
    --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": "Huggggooo/ProtoCycle-7B-SFT",
		"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 "Huggggooo/ProtoCycle-7B-SFT" \
        --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": "Huggggooo/ProtoCycle-7B-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Huggggooo/ProtoCycle-7B-SFT with Docker Model Runner:
```
docker model run hf.co/Huggggooo/ProtoCycle-7B-SFT
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

ProtoCycle-7B-SFT

Cold-start SFT checkpoint for ProtoCycle — an agentic protein design model trained to invoke biology tools (scaffold retrieval, constraint building, ESM inpainting, ProTrek scoring) via a <think> / <plan> / <tool_call> / <answer> protocol.

This checkpoint is the SFT stage initialised from Qwen/Qwen2.5-7B-Instruct and is the starting point for the subsequent RL stage (Huggggooo/ProtoCycle-7B).

Base model: Qwen/Qwen2.5-7B-Instruct
Training framework: VeRL / Open-AgentRL
Stage: multi-turn SFT on agentic tool-use trajectories
Epochs: 5
Sequence length: 32k (with Ulysses SP=4)

Training Data

2,000 agentic multi-turn trajectories for protein design, available at Huggggooo/ProtoCycle-Data (sft/ subset).

How to Use

See the ProtoCycle repository: ProtoCycle repo.

Agent Protocol

<think>  ... reasoning ...  </think>
<plan>   ... stage plan ...  </plan>
<tool_call>{"name": "...", "arguments": {...}}</tool_call>
...
<answer>MAEGEITPLKTF...</answer>

Training Data

Agentic multi-turn trajectories for protein design (not released here).

License

Apache-2.0, consistent with the upstream VeRL / Open-AgentRL projects and the underlying Qwen2.5 license.

Citation

If you find this checkpoint useful, please cite the ProtoCycle paper (forthcoming) and the upstream frameworks it builds on: VeRL, Open-AgentRL, ProTrek and ESM.

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Safetensors

Model size

8B params

Tensor type

BF16

Model tree for Huggggooo/ProtoCycle-7B-SFT

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Qwen/Qwen2.5-7B

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Finetunes

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