AI & Machine Learning
Mamba Architecture
Explains how to use Mamba selective state-space models (state-spaces/mamba package, Mamba-1 with d_state=16 and Mamba-2 with multi-head structure and d_state=128) for linear-time sequence modeling. Covers installation, the Mamba block, building a language model with MambaLMHeadModel, loading pretrained state-spaces checkpoints (130M to 2.8B) from HuggingFace, and benchmarking against Transformers. Use when implementing or loading Mamba models, processing very long sequences without a KV cache, building streaming applications, choosing between Mamba-1 and Mamba-2, or fixing install and CUDA memory problems. Requires Linux and an NVIDIA GPU. Do not use for standard Transformer models, or for RWKV, RetNet, or Hyena architectures.
- Publisher / source
- KalarisLabs/research-agent-skills (found on the CSV list)
- Upstream record
- KalarisLabs/research-agent-skills
- Version
- Version 2
- Published
- Publication review
- Approved for publication
- Content fingerprint
177cefd567adda438ccd364a70d3389966939aca54c207b35062fe78ca1939ed