Instructions to use simplescaling/step-conditional-control with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use simplescaling/step-conditional-control with Transformers:

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

pipe = pipeline("text-generation", model="simplescaling/step-conditional-control")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("simplescaling/step-conditional-control")
model = AutoModelForCausalLM.from_pretrained("simplescaling/step-conditional-control")
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 simplescaling/step-conditional-control with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/simplescaling/step-conditional-control

SGLang

How to use simplescaling/step-conditional-control 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 "simplescaling/step-conditional-control" \
    --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": "simplescaling/step-conditional-control",
		"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 "simplescaling/step-conditional-control" \
        --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": "simplescaling/step-conditional-control",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use simplescaling/step-conditional-control with Docker Model Runner:
```
docker model run hf.co/simplescaling/step-conditional-control
```

Muennighoff commited on Feb 3, 2025

Commit

924f311

verified ·

1 Parent(s): 4ed9a37

Update README.md

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@@ -12,7 +12,7 @@ license: apache-2.0
 # Model Summary
 - **Repository:** [simplescaling/s1](https://github.com/simplescaling/s1)
-- **Paper:** TODO
 # Use
@@ -31,5 +31,13 @@ This is the token-conditional control model for our paper. You can evaluate usin
 # Citation
 ```bibtex
-TODO
 ```

 # Model Summary
 - **Repository:** [simplescaling/s1](https://github.com/simplescaling/s1)
+- **Paper:** https://arxiv.org/abs/2501.19393
 # Use
 # Citation
 ```bibtex
+@misc{muennighoff2025s1simpletesttimescaling,
+      title={s1: Simple test-time scaling},
+      author={Niklas Muennighoff and Zitong Yang and Weijia Shi and Xiang Lisa Li and Li Fei-Fei and Hannaneh Hajishirzi and Luke Zettlemoyer and Percy Liang and Emmanuel Candès and Tatsunori Hashimoto},
+      year={2025},
+      eprint={2501.19393},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2501.19393},
+}
 ```