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
```

Improve language tag

by lbourdois - opened Apr 28, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+56

-42

Files changed (1) hide show

README.md +56 -42

README.md CHANGED Viewed

@@ -1,43 +1,57 @@
----
-base_model: Qwen/Qwen2.5-32B-Instruct
-library_name: transformers
-model_name: step-conditional-control
-tags:
-- generated_from_trainer
-- trl
-- sft
-license: apache-2.0
----
-# Model Summary
-- **Repository:** [simplescaling/s1](https://github.com/simplescaling/s1)
-- **Paper:** https://arxiv.org/abs/2501.19393
-# Use
-This is the token-conditional control model for our paper. You can evaluate using the information [here](https://github.com/simplescaling/s1?tab=readme-ov-file#evaluation).
-# Training information
-[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/hashimoto-group/o1/runs/i3e03g4y)
-- TRL: 0.13.0
-- Transformers: 4.48.0
-- Pytorch: 2.3.1
-- Datasets: 3.0.1
-- Tokenizers: 0.21.0
-# 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},
-}
 ```

+---
+base_model: Qwen/Qwen2.5-32B-Instruct
+library_name: transformers
+model_name: step-conditional-control
+tags:
+- generated_from_trainer
+- trl
+- sft
+license: apache-2.0
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+---
+# Model Summary
+- **Repository:** [simplescaling/s1](https://github.com/simplescaling/s1)
+- **Paper:** https://arxiv.org/abs/2501.19393
+# Use
+This is the token-conditional control model for our paper. You can evaluate using the information [here](https://github.com/simplescaling/s1?tab=readme-ov-file#evaluation).
+# Training information
+[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/hashimoto-group/o1/runs/i3e03g4y)
+- TRL: 0.13.0
+- Transformers: 4.48.0
+- Pytorch: 2.3.1
+- Datasets: 3.0.1
+- Tokenizers: 0.21.0
+# 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},
+}
 ```