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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metadata

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.

Base model: Huggggooo/ProtoCycle-7B-SFT (itself fine-tuned from Qwen/Qwen2.5-7B-Instruct)
Training framework: VeRL / 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)

See recipe/protein/reward.py for the exact formulation.

Training Data

10,000 RL prompts for GRPO-TCR training, available at 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 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.