Attention Gym
RiseAI-SysAttention-Gym is a Triton-based collection of sparse and quantized attention kernel implementations - Flash Attention 2, sliding-tile attention and several SageAttention variants - built for researchers to rapidly implement, test and validate new attention mechanisms.
Openness
5 high confidence- license
- Apache-2.0(OSI)
- source
- public
- core features withheld
- no
Apache-2.0 license body confirmed. The repository is public and unarchived and builds the whole product, and the README describes no paid tier, enterprise edition or license-gated build beside it, so source is public and the core ungated.
- https://api.github.com/repos/RiseAI-Sys/attention-gym recorded 2026-09-02
Repo metadata for attention-gym's canonical repository - private false, archived false, fork false.
- https://github.com/RiseAI-Sys/attention-gym/blob/main/LICENSE recorded 2026-10-07
LICENSE body carries the verbatim Apache License 2.0 text.
- https://github.com/RiseAI-Sys/attention-gym/blob/main/README.md recorded 2026-10-07
README describes Attention-Gym as a flexible framework built on Triton for implementing, testing and validating sparse and quantized attention mechanisms, listing the specific kernels it currently supports, with no paid tier, enterprise edition or license-gated build beside the published source.
Adoption
1 low confidenceGitHub stars are the only adoption signal for Attention-Gym: the `attn-gym` package on PyPI belongs to a different project, PyTorch Labs' flex-attention examples, and this repository publishes no package of its own.
- https://api.github.com/repos/RiseAI-Sys/attention-gym recorded 2026-09-02
Repo metadata - stargazers_count = 43 - for RiseAI-Sys/attention-gym.
Capability
2 medium confidenceAttention-Gym packages a small, fixed set of already-published attention kernels - Flash Attention 2, sliding-tile attention and several SageAttention variants - as a testbed for experimentation rather than an actively extended kernel library or a compiler. That narrow, fixed scope keeps it well below a production engine such as TensorRT.
- https://github.com/RiseAI-Sys/attention-gym/blob/main/README.md recorded 2026-10-07
README describes Attention-Gym as a flexible framework built on Triton for implementing, testing and validating sparse and quantized attention mechanisms, listing the specific kernels it currently supports, with no paid tier, enterprise edition or license-gated build beside the published source.
Verified 2026-09-27