Instructions to use simplescaling/s1.1-32B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use simplescaling/s1.1-32B with Transformers:

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

pipe = pipeline("text-generation", model="simplescaling/s1.1-32B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("simplescaling/s1.1-32B")
model = AutoModelForCausalLM.from_pretrained("simplescaling/s1.1-32B")
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 simplescaling/s1.1-32B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "simplescaling/s1.1-32B"
# 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/s1.1-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/simplescaling/s1.1-32B

SGLang

How to use simplescaling/s1.1-32B 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/s1.1-32B" \
    --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/s1.1-32B",
		"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/s1.1-32B" \
        --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/s1.1-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use simplescaling/s1.1-32B with Docker Model Runner:
```
docker model run hf.co/simplescaling/s1.1-32B
```

Improve language tag

by lbourdois - opened Apr 28, 2025

base: refs/heads/main

←

from: refs/pr/7

Discussion Files changed

+50

-36

Files changed (1) hide show

README.md +50 -36

README.md CHANGED Viewed

@@ -1,37 +1,51 @@
----
-pipeline_tag: text-generation
-inference: true
-license: apache-2.0
-datasets:
-- simplescaling/s1K-1.1
-base_model:
-- Qwen/Qwen2.5-32B-Instruct
-library_name: transformers
----
-# Model Summary
-> s1.1 is our sucessor of [s1](https://huggingface.co/simplescaling/s1-32B) with better reasoning performance by leveraging reasoning traces from r1 instead of Gemini.
-- **Logs:** https://wandb.ai/hashimoto-group/o1/runs/m1ilia77/overview
-- **Repository:** [simplescaling/s1](https://github.com/simplescaling/s1)
-- **Paper:** https://arxiv.org/abs/2501.19393
-This model is a successor of [s1-32B](https://huggingface.co/simplescaling/s1-32B) with slightly better performance.
-Thanks to [Bespoke Labs](https://huggingface.co/bespokelabs) ([Ryan Marten](https://huggingface.co/ryanmarten)) for helping generate r1 traces for s1K with [Curator](https://github.com/bespokelabsai/curator/).
-# Use
-The model usage is documented [here](https://github.com/simplescaling/s1?tab=readme-ov-file#inference).
-# Evaluation
-| Metric | s1-32B | s1.1-32B | o1-preview | o1 | DeepSeek-R1 | DeepSeek-R1-Distill-Qwen-32B |
-|---|---|---|---|---|---|---|
-| # examples | 1K | 1K | ? | ? | >800K | 800K |
-| AIME2024 | 56.7 | 56.7 | 40.0 | 74.4 | 79.8 | 72.6 |
-| AIME2025 I | 26.7 | 60.0 | 37.5 | ? | 65.0 | 46.1 |
-| MATH500 | 93.0 | 95.4 | 81.4 | 94.8 | 97.3 | 94.3 |
-| GPQA-Diamond | 59.6 | 63.6 | 75.2 | 77.3 | 71.5 | 62.1 |
 Note that s1-32B and s1.1-32B use budget forcing in this table; specifically ignoring end-of-thinking and appending "Wait" up to four times.

+---
+pipeline_tag: text-generation
+inference: true
+license: apache-2.0
+datasets:
+- simplescaling/s1K-1.1
+base_model:
+- Qwen/Qwen2.5-32B-Instruct
+library_name: transformers
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+---
+# Model Summary
+> s1.1 is our sucessor of [s1](https://huggingface.co/simplescaling/s1-32B) with better reasoning performance by leveraging reasoning traces from r1 instead of Gemini.
+- **Logs:** https://wandb.ai/hashimoto-group/o1/runs/m1ilia77/overview
+- **Repository:** [simplescaling/s1](https://github.com/simplescaling/s1)
+- **Paper:** https://arxiv.org/abs/2501.19393
+This model is a successor of [s1-32B](https://huggingface.co/simplescaling/s1-32B) with slightly better performance.
+Thanks to [Bespoke Labs](https://huggingface.co/bespokelabs) ([Ryan Marten](https://huggingface.co/ryanmarten)) for helping generate r1 traces for s1K with [Curator](https://github.com/bespokelabsai/curator/).
+# Use
+The model usage is documented [here](https://github.com/simplescaling/s1?tab=readme-ov-file#inference).
+# Evaluation
+| Metric | s1-32B | s1.1-32B | o1-preview | o1 | DeepSeek-R1 | DeepSeek-R1-Distill-Qwen-32B |
+|---|---|---|---|---|---|---|
+| # examples | 1K | 1K | ? | ? | >800K | 800K |
+| AIME2024 | 56.7 | 56.7 | 40.0 | 74.4 | 79.8 | 72.6 |
+| AIME2025 I | 26.7 | 60.0 | 37.5 | ? | 65.0 | 46.1 |
+| MATH500 | 93.0 | 95.4 | 81.4 | 94.8 | 97.3 | 94.3 |
+| GPQA-Diamond | 59.6 | 63.6 | 75.2 | 77.3 | 71.5 | 62.1 |
 Note that s1-32B and s1.1-32B use budget forcing in this table; specifically ignoring end-of-thinking and appending "Wait" up to four times.