Text Generation
Transformers
Safetensors
reasoning
olympiad
mathematics
science
reinforcement-learning
test-time-scaling
long-context
Instructions to use Simplified-Reasoning/SU-01 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Simplified-Reasoning/SU-01", dtype="auto") - 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/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Simplified-Reasoning/SU-01", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Simplified-Reasoning/SU-01", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Simplified-Reasoning/SU-01", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - 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 (#1)
Browse files- Add pipeline tag and library name to SU-01 metadata (9adcba891c13360fb9d01eda1a5bf04ac7811ac5)
Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>
README.md
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---
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license: apache-2.0
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tags:
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- reasoning
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- olympiad
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A compact 30B-A3B reasoning model for rigorous mathematical and scientific olympiad problem solving.
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<p align="center">
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<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%">
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</p>
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year={2026},
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url={http://arxiv.org/abs/2605.13301}
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}
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```
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---
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license: apache-2.0
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- reasoning
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- olympiad
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A compact 30B-A3B reasoning model for rigorous mathematical and scientific olympiad problem solving.
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The model was presented in the paper [Achieving Gold-Medal-Level Olympiad Reasoning via Simple and Unified Scaling](https://huggingface.co/papers/2605.13301).
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<p align="center">
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<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%">
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</p>
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year={2026},
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url={http://arxiv.org/abs/2605.13301}
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}
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```
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