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mamba-architecture
State-space model with O(n) complexity vs Transformers' O(n²). 5× faster inference, million-token sequences, no KV cache. Selective SSM with hardware-aware design. Mamba-1 (d_state=16) and Mamba-2 (d_state=128, multi-head). Models 130M-2.8B on HuggingFace.
backend/cli/skills/ml-training/mamba/SKILL.md
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- agent Repository topic
- ai Repository topic
- bun Repository topic
- cli Repository topic
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- ml Repository topic
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- model-architecture Declared skill tag
- mamba Declared skill tag
- state-space-models Declared skill tag
- ssm Declared skill tag
- linear-complexity Declared skill tag
- long-context Declared skill tag
- efficient-inference Declared skill tag
- hardware-aware Declared skill tag
- alternative-to-transformers Declared skill tag
- engineering Derived category
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# Mamba - Selective State Space Models
## Quick start
Mamba is a state-space model architecture achieving O(n) linear complexity for sequence modeling.
**Installation**:
```bash
# Install causal-conv1d (optional, for efficiency)
pip install causal-conv1d>=1.4.0
# Install Mamba
pip install mamba-ssm
# Or both together