state-spaces/mamba

Health v2 · maintenance only

Adoption not part of the score

18783 stars · 1806 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

Mamba is a PyTorch library implementing the selective state space model (SSM) architecture for linear-time sequence modeling, with hardware-aware CUDA kernels. It serves as an efficient alternative to Transformers for language modeling and other information-dense sequence tasks.

Use cases

• train a state space model for language modeling
• replace transformers with linear-time sequence models
• run efficient long-context sequence inference
• implement Mamba SSM layers in a PyTorch model
• experiment with subquadratic sequence modeling architectures

When to choose

• you need linear-time scaling for very long sequences
• you want a Transformer alternative with competitive language modeling performance
• you have a CUDA GPU and want hardware-aware efficient SSM kernels

When to avoid

• you need the broad ecosystem and tooling support of standard Transformers
• you have no GPU and cannot compile CUDA extensions
• you need off-the-shelf pretrained models for a quick application build

Facets

library · maturity active

machine-learning deep-learning llm-training llm-inference deep-learning large-language-models machine-learning python cross-platform state-space-models sequence-modeling transformer-alternative cuda-kernels pytorch natural-language-processing gpu linux

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markdown · JSON · MCP: product_card(name="state-spaces/mamba")