Papers by Rezaul Karim
ECHO-LLaMA: Efficient Caching for High-Performance LLaMA Training (2025.emnlp-industry)
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Maryam Dialameh, Rezaul Karim, Hossein Rajabzadeh, Omar Mohamed Awad, Boxing Chen, Hyock Ju Kwon, Walid Ahmed, Yang Liu
| Challenge: | ECHO-LLaMA transforms LLa MA models into shared KV caching across certain layers, significantly reducing KV computational complexity while maintaining or improving language performance. |
| Approach: | They propose an efficient LLaMA architecture that transforms LLama models into shared KV caching across certain layers, reducing computational complexity while maintaining or improving language performance. |
| Outcome: | ECHO-LLaMA achieves up to 77% higher token-per-second throughput during training, up to 16% higher Model FLOPs Utilization (MFU) and up to 14% lower loss when trained on an equal number of tokens. |