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Mamba State-Space Model Guide

Guides an agent to build, load and benchmark Mamba (state-space) language models with linear-time sequence processing.

Source Download skill
Repository
Orchestra-Research/AI-Research-SKILLs
Stars
14,195
Licence
MIT
Files
4 files

What it does

This skill teaches an AI agent how to work with Mamba, a neural network design that processes long sequences with cost that grows linearly rather than quadratically, unlike the Transformer models behind most chatbots. Once installed, the agent can write code that sets up Mamba-1 or Mamba-2 blocks, load pretrained Mamba models from HuggingFace, generate text, and compare speed against Transformers. It also gives guidance on installation, common errors and hardware memory needs.

Great for

  • Machine learning engineers exploring alternatives to Transformer architectures
  • Researchers benchmarking long-context or streaming inference on NVIDIA GPUs
  • Developers who want working starter code for loading small pretrained Mamba models

Skip it if

  • Users without an NVIDIA GPU or on Windows/macOS, since the documented prerequisites are Linux, NVIDIA GPU and CUDA 11.6+ (the skill cannot make these run elsewhere)
  • Anyone expecting the claimed 5x speedup or million-token results to hold in every setting; the skill states these as general figures without per-task evidence in the shown file
  • Non-technical users who just want to chat with an AI, since this is a coding guide for building model code

Try asking

  • “Write a PyTorch script that runs a Mamba-2 block on a batch of random sequences and checks the output shape”
  • “Load state-spaces/mamba-130m from HuggingFace and generate a short continuation of my prompt”
  • “Compare generation speed of Mamba-2.8B against Pythia-2.8B using the benchmark commands in the guide”
  • “Help me fix a CUDA out-of-memory error when training a Mamba model”
Step-by-step guide: how to use Mamba State-Space Model GuideInstall, first run, examples, tips and FAQs

Install in 3 steps

  1. 1

    Download the skill

    The ZIP contains the 4 original files, the MIT licence and an attribution note.

    mamba.zip
  2. 2

    Add it to your agent

    Claude Code (all projects)

    mkdir -p ~/.claude/skills && unzip mamba.zip -d ~/.claude/skills/

    Claude Code / Agent SDK (this project only)

    mkdir -p .claude/skills && unzip mamba.zip -d .claude/skills/

    Claude apps (web/desktop): Settings → Capabilities → Skills → Upload skill, and choose the ZIP.

  3. 3

    Ask for the task

    No special command needed — the agent reads the skill's description and loads it when your request matches. Try one of the prompts above.

Get better results

  • Verify the installed mamba-ssm and causal-conv1d versions match your CUDA and PyTorch before running; install binary wheels to avoid long source builds.
  • Treat the speed and memory figures in the skill as claims to measure on your own hardware, and ask the agent to run the benchmark script yourself.
  • Review any pip install or model download commands the agent proposes, since they run code and fetch files from the internet.

What's inside

  • references/architecture-details.md
  • references/benchmarks.md
  • references/training-guide.md
  • SKILL.md

Redistributed unmodified from Orchestra-Research/AI-Research-SKILLs under the MIT licence. The assessment was drafted with AI from the skill's files on 2026-10-29. Skills can instruct your agent to run code — read SKILL.md and any scripts before installing.