Instructions to use Simplified-Reasoning/SU-01 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Simplified-Reasoning/SU-01 with Transformers:

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

pipe = pipeline("text-generation", model="Simplified-Reasoning/SU-01")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Simplified-Reasoning/SU-01")
model = AutoModelForCausalLM.from_pretrained("Simplified-Reasoning/SU-01")
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 Simplified-Reasoning/SU-01 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Simplified-Reasoning/SU-01"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Simplified-Reasoning/SU-01",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Simplified-Reasoning/SU-01

SGLang

How to use Simplified-Reasoning/SU-01 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 "Simplified-Reasoning/SU-01" \
    --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": "Simplified-Reasoning/SU-01",
		"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 "Simplified-Reasoning/SU-01" \
        --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": "Simplified-Reasoning/SU-01",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Simplified-Reasoning/SU-01 with Docker Model Runner:
```
docker model run hf.co/Simplified-Reasoning/SU-01
```

Add pipeline tag and library name to SU-01 metadata

by nielsr HF Staff - opened 10 days ago

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

-1

Files changed (1) hide show

README.md +5 -1

README.md CHANGED Viewed

@@ -1,5 +1,7 @@
 ---
 license: apache-2.0
 tags:
 - reasoning
 - olympiad
@@ -14,6 +16,8 @@ tags:
 A compact 30B-A3B reasoning model for rigorous mathematical and scientific olympiad problem solving.
 <p align="center">
   <img src="https://github.com/Simplified-Reasoning/SU-01/raw/main/page/source_html/simplex-pipeline-hires.png" alt="SU-01 training and inference pipeline" width="100%">
 </p>
@@ -271,4 +275,4 @@ If you find SU-01 useful, please cite the project:
   year={2026},
   url={http://arxiv.org/abs/2605.13301}
 }
-```

 ---
 license: apache-2.0
+pipeline_tag: text-generation
+library_name: transformers
 tags:
 - reasoning
 - olympiad
 A compact 30B-A3B reasoning model for rigorous mathematical and scientific olympiad problem solving.
+The model was presented in the paper [Achieving Gold-Medal-Level Olympiad Reasoning via Simple and Unified Scaling](https://huggingface.co/papers/2605.13301).
 <p align="center">
   <img src="https://github.com/Simplified-Reasoning/SU-01/raw/main/page/source_html/simplex-pipeline-hires.png" alt="SU-01 training and inference pipeline" width="100%">
 </p>
   year={2026},
   url={http://arxiv.org/abs/2605.13301}
 }
+```