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-…
---
name: mamba-architecture
description: 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.
version: 1.0.0
author: Orchestra Research
license: MIT
tags: [Model Architecture, Mamba, State Space Models, SSM, Linear Complexity, Long Context, Efficient Inference, Hardware-Aware, Alternative To Transformers]
dependencies: [mamba-ssm, torch, transformers, causal-conv1d]
---
# 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
pip install mamba-ssm[causal-conv1d]… load the full skill through Skill Me