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.
npx skills add Orchestra-Research/AI-Research-SKILLs --skill mamba --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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] ``` **Prerequisites**: Linux, NVIDIA GPU, PyTorch 1.12+, CUDA 11.6+ **Basic usage** (Mamba block): ```python import torch from mamba_ssm import Mamba batch, length, dim = 2, 64, 16 x = torch.randn(batch, length, dim).to("cuda") model = Mamba( d_model=dim, # Model dimension d_state=16, # SSM state dimension d_conv=4, # Conv1d kernel size expand=2 # Expansion factor ).to("cuda") y = model(x) # O(n) complexity! assert y.shape == x.shape ``` ## Common workflows ### Workflow 1: Language model with Mamba-2 **Complete LM with generation**: ```python from mamba_ssm.models.mixer_seq_simple import MambaLMHeadModel from mamba_ssm.models.config_mamba import MambaConfig import torch # Configure Mamba-2 LM config = MambaConfig( d_model=1024, # Hidden dimension n_layer=24, # Number of layers vocab_size=50277, # Vocabulary size ssm_cfg=dict( layer=
- Quick start
- Common workflows
- Workflow 1: Language model with Mamba-2
- Workflow 2: Use pretrained Mamba models
- Workflow 3: Mamba-1 vs Mamba-2
- Workflow 4: Benchmark vs Transformers
- When to use vs alternatives
- Common issues
- Advanced topics
- Hardware requirements
- Resources
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] Benchmark Mamba python benchmarks/benchmark_generation_mamba_simple.py \ Benchmark Transformer pip install mamba-ssm --no-build-isolation
What does the mamba-architecture skill do?
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.
How do I install it?
Run `npx skills add Orchestra-Research/AI-Research-SKILLs --skill mamba --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From Orchestra-Research/AI-Research-SKILLs, a repository with 11,391 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.