BICLab/SpikingBrain-7B

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1369 stars · 189 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

SpikingBrain-7B is a brain-inspired large language model that combines hybrid efficient attention, MoE modules, and spike encoding, with a conversion pipeline for continual pre-training from open-source models. The repo provides HuggingFace weights, vLLM inference plugins, and quantized (W8ASpike) versions, with support for non-NVIDIA MetaX GPU clusters.

Use cases

• run a spiking brain-inspired LLM locally
• efficient long-context LLM inference with vLLM
• continual pre-training of LLMs on limited data
• research neuromorphic and spike-based model architectures
• deploy LLMs on non-NVIDIA GPU clusters
• quantized LLM inference with sparsity

When to choose

• researching brain-inspired or spiking LLM architectures
• needing efficient long-sequence (million-token) inference
• training on MetaX or non-NVIDIA hardware
• wanting pretrained 7B weights with vLLM support

When to avoid

• needing a mainstream production LLM with broad ecosystem support
• requiring NVIDIA-only optimized tooling without plugin setup
• looking for a general-purpose chat model rather than research code

Facets

library · maturity active

llm-inference llm-training machine-learning deep-learning large-language-models deep-learning artificial-intelligence gpu-computing python spiking-neural-networks neuromorphic mixture-of-experts efficient-attention vllm-plugin model-weights quantization metax-gpu gpu linux docker

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markdown · JSON · MCP: product_card(name="BICLab/SpikingBrain-7B")