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)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Huggggooo/ProtoCycle-7B-SFT")
model = AutoModelForCausalLM.from_pretrained("Huggggooo/ProtoCycle-7B-SFT")
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=40)
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
```

ProtoCycle-7B-SFT / README.md

Huggggooo

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	---
	license: apache-2.0
	library_name: transformers
	pipeline_tag: text-generation
	base_model: Qwen/Qwen2.5-7B-Instruct
	tags:
	- protein-design
	- agentic
	- tool-use
	- qwen2.5
	- sft
	language:
	- en
	---

	# 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`](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct)
	and is the starting point for the subsequent RL stage
	([`Huggggooo/ProtoCycle-7B`](https://huggingface.co/Huggggooo/ProtoCycle-7B)).

	- Base model: `Qwen/Qwen2.5-7B-Instruct`
	- Training framework: [VeRL](https://github.com/volcengine/verl) /
	[Open-AgentRL](https://github.com/Gen-Verse/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](https://huggingface.co/datasets/Huggggooo/ProtoCycle-Data) (`sft/` subset).

	## How to Use

	See the ProtoCycle repository:
	[ProtoCycle](https://github.com/huggggoooooo/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](https://github.com/volcengine/verl) /
	[Open-AgentRL](https://github.com/Gen-Verse/Open-AgentRL) projects and the
	underlying [Qwen2.5](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) 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.