Hawk represents a significant advance in recurrent model architecture, utilizing the RG-LRU layer to effectively address the challenges of scaling and efficiency in training.
The introduction of Griffin, which combines RG-LRU with local attention, shows promising results in improving both training efficiency and inference speed compared to traditional models.
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