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Block Transformer: Enhancing Inference Efficiency in Large Language Models Through Hierarchical Global-to-Local Modeling

Block Transformer: Enhancing Inference Efficiency in Large Language Models Through Hierarchical Global-to-Local Modeling

Block Transformer: Enhancing Inference Efficiency in Large Language Models

Practical Solutions and Value Highlights:

– Large language models face computational challenges due to self-attention mechanism.
– Block Transformer architecture optimizes inference by combining global and local modeling.
– Achieves 10-20x gains in throughput compared to traditional transformers.
– Reduces KV cache memory, enabling larger batch sizes and lower latency.
– Maintains high throughput with longer prompts and large contexts.
– Shows 25x increase in throughput under different scenarios compared to vanilla models.
– Enhances local computational capacity, leading to 1.5x throughput increase over MEGABYTE model.
– Aligns with KV cache compression algorithms for improved performance.
– Offers significant inference-time advantages and throughput improvements.
– Strategic design enhances performance of language models across various domains.

For more information, refer to the Paper and GitHub.

List of Useful Links:

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