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Optimizing Language Models: Decoding Griffin’s Local Attention and Memory Efficiency
- The article discusses the optimization of language models by decoding Griffin's local attention and memory efficiency, focusing on various aspects of model architecture and efficiency.
- Griffin incorporates recurrent blocks and local attention layers in its temporal mixing blocks, showing superior performance over global attention MQA Transformers across different sequence lengths.
- Even with a fixed local attention window size of 1024, Griffin outperforms global attention MQA Transformers, but the performance gap narrows with increasing sequence length.
- Models trained on sequence lengths of 2048, 4096, and 8192 tokens reveal insights into the impact of local attention window sizes on model performance.
- The article also delves into inference speeds, estimating memory-boundedness for components like linear layers and self-attention in recurrent and Transformer models.
- Analysis of cache sizes in recurrent and Transformer models emphasizes the transition from a 'parameter bound' to a 'cache bound' regime with larger sequence lengths.
- Further results on next token prediction with longer contexts and details of tasks like Selective Copying and Induction Heads are also presented in the article.
- The article provides valuable insights into optimizing language models for efficiency and performance, contributing to advancements in the field of natural language processing.
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