AttnHut GitHub repo bundles Transformer attention mechanisms for easy swapping
Switch attention mechanisms in your SLM with one line of code
AttnHut is a new open-source repository by developer egmaminta that provides clean, modular implementations of diverse Transformer attention mechanisms. Originally built to enable quick swapping between different attention types during small language model (SLM) experiments and benchmarking, the repo already covers several prominent approaches and includes a specific implementation of MiniMax M3's sparse attention. The code is designed to be framework-agnostic and can be dropped into existing projects with minimal friction.
The repo's applicability extends beyond NLP: the attention implementations can be used in computer vision tasks, to modernize vision encoders, and even in reinforcement learning. Notably, AttnHut can integrate with Andrej Karpathy's autoregressive research framework, making it immediately useful for researchers building custom language models. The project encourages open contributions—developers are invited to submit PRs with attention mechanisms not yet covered, aiming to grow into a comprehensive reference for the community.
- Covers multiple Transformer attention mechanisms for quick benchmarking in SLMs
- Includes MiniMax M3's sparse attention and integrates with Karpathy's research framework
- Modular design works for NLP, computer vision, vision encoders, and reinforcement learning
Why It Matters
One repo to experiment with attention variants—saves researchers and students weeks of implementation work.