fla-org/flash-linear-attention
🚀 Efficient implementations of state-of-the-art linear attention models
This project offers highly optimized building blocks for developing next-generation AI models that can process very long sequences of information efficiently. It provides ready-to-use implementations of advanced 'linear attention' and 'state space' model architectures. AI researchers and machine learning engineers can use these components to create more powerful and scalable models for tasks like natural language understanding or time-series prediction.
4,549 stars. Used by 1 other package. Actively maintained with 29 commits in the last 30 days. Available on PyPI.
Use this if you are a machine learning researcher or engineer building large language models or other sequence models and need highly optimized components to process long data sequences more efficiently.
Not ideal if you are looking for a complete, end-user application or a no-code solution for general-purpose AI tasks.
Stars
4,549
Forks
431
Language
Python
License
MIT
Category
Last pushed
Mar 12, 2026
Commits (30d)
29
Dependencies
2
Reverse dependents
1
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/transformers/fla-org/flash-linear-attention"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Compare
Related models
thu-ml/SageAttention
[ICLR2025, ICML2025, NeurIPS2025 Spotlight] Quantized Attention achieves speedup of 2-5x...
thu-ml/SpargeAttn
[ICML2025] SpargeAttention: A training-free sparse attention that accelerates any model inference.
fla-org/flame
🔥 A minimal training framework for scaling FLA models
foundation-model-stack/fms-fsdp
🚀 Efficiently (pre)training foundation models with native PyTorch features, including FSDP for...
NX-AI/mlstm_kernels
Tiled Flash Linear Attention library for fast and efficient mLSTM Kernels.