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]:]))

Inference
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
```

Huggggooo commited on Apr 18

Commit

94447ba

verified ·

1 Parent(s): f1006cd

Update README.md

Browse files

Files changed (1) hide show

README.md +2 -25

README.md CHANGED Viewed

@@ -33,15 +33,6 @@ Reward) stage**, initialised from the SFT checkpoint
 - Reward manager: `protein` (see
   [ProtoCycle/verl/workers/reward_manager/protein.py](https://github.com/huggggoooooo/ProtoCycle/blob/main/verl/workers/reward_manager/protein.py))
-## Reward Signal
-The GRPO-TCR reward combines:
-1. **Protocol compliance** — `<think>` / `<plan>` / `<tool_call>` / `<answer>`
-   format.
-2. **Tool-call quality** — per-step ProTrek score of the best sequence after
-   each Stage-1 / Stage-3 tool call.
-3. **Outcome signal** — final (global-best) ProTrek score vs the requirement.
-4. **Efficiency** — penalty for excessively long rollouts.
 See
 [`recipe/protein/reward.py`](https://github.com/huggggoooooo/ProtoCycle/blob/main/recipe/protein/reward.py)
@@ -57,25 +48,11 @@ for the exact formulation.
 <answer>MAEGEITPLKTF...</answer>
 ```
-Registered tools (10):
-- **Stage 1:** `function2seq`, `pathway2seq`, `domain2seq`, `go2seq`,
-  `dna_binding2seq`
-- **Stage 2:** `cofactor2constraints`, `motif2constraints`,
-  `signal2constraints`
-- **Stage 3:** `esm_inpaint`, `get_score`
 ## How to Use
-Inference requires the biology tools shipped in the
-[ProtoCycle](https://github.com/huggggoooooo/ProtoCycle) repo:
-```bash
-export MODEL_DIR=/path/to/ProtoCycle-7B       # this checkpoint
-export MODEL_NAME=ProtoCycle-7B
-export CONDA_ROOT=/path/to/miniconda3
-# Also export PROTREK_*_DIR, ESM_MODEL_PATH, etc. (see ProtoCycle README)
-bash infer_tools.sh
-```
 ## License

 - 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)
 <answer>MAEGEITPLKTF...</answer>
 ```
 ## How to Use
+See the ProtoCycle repository:
+[ProtoCycle](https://github.com/huggggoooooo/ProtoCycle) repo.
 ## License