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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

vLLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Huggggooo/ProtoCycle-7B"
# 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",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

Docker Model Runner
How to use Huggggooo/ProtoCycle-7B with Docker Model Runner:
```
docker model run hf.co/Huggggooo/ProtoCycle-7B
```

ProtoCycle-7B / README.md

Huggggooo

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	---
	license: apache-2.0
	library_name: transformers
	pipeline_tag: text-generation
	base_model: Huggggooo/ProtoCycle-7B-SFT
	tags:
	- protein-design
	- agentic
	- tool-use
	- qwen2.5
	- reinforcement-learning
	- grpo
	language:
	- en
	---

	# ProtoCycle-7B

	RL checkpoint for ProtoCycle — an agentic protein design model that
	performs multi-step, tool-augmented sequence design.

	This is the **GRPO-TCR (Group Relative Policy Optimization with Tool-Call
	Reward) stage**, initialised from the SFT checkpoint
	[`Huggggooo/ProtoCycle-7B-SFT`](https://huggingface.co/Huggggooo/ProtoCycle-7B-SFT).

	- Base model: `Huggggooo/ProtoCycle-7B-SFT`
	(itself fine-tuned from `Qwen/Qwen2.5-7B-Instruct`)
	- Training framework: [VeRL](https://github.com/volcengine/verl) /
	[Open-AgentRL](https://github.com/Gen-Verse/Open-AgentRL)
	- Stage: agentic RL with GRPO-TCR
	- Rollouts per prompt: 8, max turns: 16
	- Max prompt / response: 8k / 20k tokens
	- Reward manager: `protein` (see
	[ProtoCycle/verl/workers/reward_manager/protein.py](https://github.com/huggggoooooo/ProtoCycle/blob/main/verl/workers/reward_manager/protein.py))


	See
	[`recipe/protein/reward.py`](https://github.com/huggggoooooo/ProtoCycle/blob/main/recipe/protein/reward.py)
	for the exact formulation.

	## Training Data

	10,000 RL prompts for GRPO-TCR training, available at
	[Huggggooo/ProtoCycle-Data](https://huggingface.co/datasets/Huggggooo/ProtoCycle-Data) (`rl/` subset).}

	## Agent Protocol

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

	## How to Use

	See the ProtoCycle repository:
	[ProtoCycle](https://github.com/huggggoooooo/ProtoCycle) repo.


	## License

	Apache-2.0.

	## Citation

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