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This paper presents Relaxed Recursive Transformers, a novel approach to parameter sharing in large language models (LLMs) that reduces their size and deployment costs while maintaining performance. By reusing a single block of layers and incorporating low-rank adaptation (LoRA) modules, these models achieve significant improvements in efficiency and throughput, outperforming traditional models of similar size. The authors also introduce Continuous Depth-wise Batching, enhancing inference speed by allowing d

Author

Aman Rogerss

Language

EN