HyperAI

1. Tutorial Introduction

"SpikingBrain-1.0" is a native domestically produced, independently controllable brain-like pulse model released in September 2025 by the Institute of Automation, Chinese Academy of Sciences, the National Key Laboratory of Brain Cognition and Brain-like Intelligence, Muxi Integrated Circuit Co., Ltd. and other institutions. Inspired by the brain mechanism, SpikingBrain integrates a hybrid efficient attention mechanism, MoE module and pulse coding into its architecture, and is supported by a universal conversion pipeline compatible with the open source model ecosystem. This enables continuous pre-training with less than 2% of data while achieving performance comparable to mainstream open source models. SpikingBrain achieved more than 100 times acceleration in TTFT of 4M-token sequences, while pulse coding provided sparsity of more than 69% at the micro level. Combined with the MoE sparsity at the macro level, these advances provide valuable guidance for the design of next-generation neuromorphic chips. The relevant paper results are "SpikingBrain Technical Report: Spiking Brain-inspired Large Models".

This tutorial uses a single A6000 GPU as the computing resource. The model deployed in this tutorial is SpikingBrain's V1-7B-sft-s3-reasoning.

2. Effect display

3. Operation steps

1. Start the container

2. Usage steps

If "Model" is not displayed, it means the model is being initialized. Since the model is large, please wait about 2-3 minutes and refresh the page.

4. Discussion

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Citation Information

Thanks to Github user SuperYang Deployment of this tutorial. The reference information of this project is as follows:

@article{pan2025spikingbrain,
  title={SpikingBrain Technical Report: Spiking Brain-inspired Large Models},
  author={Pan, Yuqi and Feng, Yupeng and Zhuang, Jinghao and Ding, Siyu and Liu, Zehao and Sun, Bohan and Chou, Yuhong and Xu, Han and Qiu, Xuerui and Deng, Anlin and others},
  journal={arXiv preprint arXiv:2509.05276},
  year={2025}
}