Power Attention is an open-source implementation designed to optimize the core operation of symmetric power transformers, enabling efficient training and inference on long-context sequences. It serves as a drop-in replacement for various attention forms, significantly improving performance metrics like loss-per-FLOP compared to traditional and linear attention models. The architecture’s adjustable hyperparameter allows for better balance between weight and state FLOPs, enhancing scalability and learning efficiency.

The DeepSeek-R1-GGUF model repository on Hugging Face hosts large datasets and model files for text generation tasks, specifically utilizing the DeepSeek architecture. It includes multiple versions of the model, all under an MIT license, and is part of a community-driven project by Unsloth AI.