flash-linear-attention and flame
Flash-linear-attention provides the optimized model implementations that flame is purpose-built to train at scale, making them complements that work together in a stack rather than alternatives.
About flash-linear-attention
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.
About flame
fla-org/flame
🔥 A minimal training framework for scaling FLA models
This project provides a training framework for creating highly efficient large language models, specifically those using Flash Linear Attention (FLA). It takes raw text datasets, like the FineWeb-Edu corpus, and outputs a trained language model ready for use in various applications. It's designed for machine learning researchers and engineers focused on developing custom, performant language models.
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