Skill · em Dados, IA e pesquisa

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.

Procedência

Antes de instalar

4 arquivos · 29,3 KB · só texto, nenhum script

Instalar na sua CLI

O comando baixa a versão fixada (commit 57f899e) direto da origem, para a pasta que a CLI lê. Precisa de curl (macOS e Linux); no Windows não há comando, porque o Rook Labs é para macOS.

Claude Code

Neste projeto: instala em .claude/skills/mamba-architecture/.

d=".claude/skills/mamba-architecture"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/ai-research/model-architecture-mamba"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/architecture-details.md" "$u/references/architecture-details.md" \
  -o "$d/references/benchmarks.md" "$u/references/benchmarks.md" \
  -o "$d/references/training-guide.md" "$u/references/training-guide.md"

Global: instala em ~/.claude/skills/mamba-architecture/.

d="$HOME/.claude/skills/mamba-architecture"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/ai-research/model-architecture-mamba"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/architecture-details.md" "$u/references/architecture-details.md" \
  -o "$d/references/benchmarks.md" "$u/references/benchmarks.md" \
  -o "$d/references/training-guide.md" "$u/references/training-guide.md"

Codex

Neste projeto: instala em .agents/skills/mamba-architecture/.

d=".agents/skills/mamba-architecture"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/ai-research/model-architecture-mamba"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/architecture-details.md" "$u/references/architecture-details.md" \
  -o "$d/references/benchmarks.md" "$u/references/benchmarks.md" \
  -o "$d/references/training-guide.md" "$u/references/training-guide.md"

Global: instala em ~/.agents/skills/mamba-architecture/.

d="$HOME/.agents/skills/mamba-architecture"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/ai-research/model-architecture-mamba"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/architecture-details.md" "$u/references/architecture-details.md" \
  -o "$d/references/benchmarks.md" "$u/references/benchmarks.md" \
  -o "$d/references/training-guide.md" "$u/references/training-guide.md"

Antigravity

Neste projeto: instala em .agents/skills/mamba-architecture/.

d=".agents/skills/mamba-architecture"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/ai-research/model-architecture-mamba"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/architecture-details.md" "$u/references/architecture-details.md" \
  -o "$d/references/benchmarks.md" "$u/references/benchmarks.md" \
  -o "$d/references/training-guide.md" "$u/references/training-guide.md"

Global: instala em ~/.gemini/antigravity-cli/skills/mamba-architecture/.

d="$HOME/.gemini/antigravity-cli/skills/mamba-architecture"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/ai-research/model-architecture-mamba"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/architecture-details.md" "$u/references/architecture-details.md" \
  -o "$d/references/benchmarks.md" "$u/references/benchmarks.md" \
  -o "$d/references/training-guide.md" "$u/references/training-guide.md"

Peça ao Rook

Já usa o Rook Labs? Cole no chat do Rook: instale a skill https://rooklabs.sh/marketplace/cct.mamba-architecture

Prévia do SKILL.md

---
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]
```

**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
…

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