Mistral Large 2 utilizes a sliding window attention (SWA) mechanism, which allows each layer to attend to a fixed number of previous hidden states. This approach is designed to handle longer sequences more effectively by reducing computational complexity and memory requirements. Specifically, each token at layer k attends to tokens within a window of size 4,096 from the previous layer. This mechanism enables higher layers to access information from further in the past than what the attention patterns might initially suggest.

In addition to sliding window attention, Mistral Large 2 also employs Grouped Query Attention (GQA). GQA is a technique that speeds up inference and reduces memory requirements by grouping queries, keys, and values, which allows for more efficient computation.

The sliding window attention mechanism is particularly useful for managing sequences of varying lengths. By using a fixed cache size and a context length of 8K, Mistral Large 2 can maintain coherence and relevance over long conversations or documents, ensuring consistent and meaningful outputs throughout extended interactions.

Overall, the attention mechanism in Mistral Large 2 is designed to balance performance with efficient inference, making it suitable for a wide range of applications, including code generation, mathematics, reasoning, and multilingual support.

For more detailed information, you can refer to the official documentation and research papers on Mistral Large 2.

Would you like more details on any specific aspect of Mistral Large 2's attention mechanism?