SpikingBrain-7B/
├── hf_7B_model/ # HuggingFace version
├── run_model/   # Model run examples
├── vllm_hymeta/ # vLLM plugins and inference support
├── W8ASpike/    # Quantized inference version
├── setup.py
├── requirements.txt 
└── README.md 

sudo docker run -itd 
    --entrypoint /bin/bash 
    --network host 
    --name hymeta-bench 
    --shm-size 160g 
    --gpus all 
    --privileged 
    -v /host_path:/container_path 
    docker.1ms.run/vllm/vllm-openai:v0.10.0

git clone https://github.com/BICLab/SpikingBrain-7B.git
cd SpikingBrain-7B
pip install .

decorator
pyyaml
scipy
setuptools
setuptools-scm
flash_attn==2.7.3
flash-linear-attention==0.1
vllm==0.10.0
torch==2.7.1

W8ASpike

W8ASpike is the quantized inference version of SpikingBrain-7B, aiming to reduce inference cost under low-precision settings and explore the potential of Spiking Neural Networks (SNNs).

The current implementation adopts pseudo-spiking, where activations are approximated as spike-like signals at the tensor level, rather than true asynchronous event-driven spiking on neuromorphic hardware.

• Pseudo-spiking: Efficient approximation at the tensor level, suitable for prototyping and research.

• True-spiking: Requires asynchronous hardware and event-driven operator support, which is beyond the scope of this repository.

The activation spike encoding process here is inspired by the pseudo-spiking interfaces from BICLab/Int2Spike. For additional PyTorch-based spiking interfaces, please refer to the Int2Spike library.

Available Models

The model weights are hosted on ModelScope. Please select the appropriate version based on your needs:

• Pre-trained model (7B): https://www.modelscope.cn/models/Panyuqi/V1-7B-base
• Chat model (7B-SFT): https://www.modelscope.cn/models/Panyuqi/V1-7B-sft-s3-reasoning
• Quantized weights (7B-W8ASpike): https://www.modelscope.cn/models/Abel2076/SpikingBrain-7B-W8ASpike

Usage

Example scripts are provided in run_model/ for running the model with the released checkpoints.

• Hugging Face
  Load with AutoModelForCausalLM and use as a standard CausalLM (forward or generation); see run_model/run_model_hf.py.
  For the SFT model, a chat template is used; see run_model/run_model_hf_chat_template.py.

• vLLM
  Perform inference using the provided vLLM Hymeta plugin; see run_model/run_model_vllm.py and the vLLM Hymeta section.

Performance Evaluation

Table 1: Performance evaluation of the SpikingBrain-7B pre-trained model. All models are tested with the HuggingFace framework and evaluated using a perplexity-based method. Except for Qwen2.5, the other baselines are trained on limited Chinese data, resulting in clear disadvantages on CMMLU and C-Eval.

Table 2: Performance evaluation of the SpikingBrain-76B pre-trained model. All models are tested with the vLLM framework and evaluated using a perplexity-based method. Except for Qwen2.5, the other baselines are trained on limited Chinese data, resulting in clear disadvantages on CMMLU and C-Eval.

Citation

If you find our work useful, please consider citing SpikingBrain:

@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}
}