Papers with DSQ
Dynamic Stashing Quantization for Efficient Transformer Training (2023.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have demonstrated impressive performance on a range of Natural Language Processing (NLP) tasks. |
| Approach: | They propose a dynamic quantization strategy that reduces the amount of memory operations and reduces arithmetic cost by 20.95 on two translation tasks and three classification tasks. |
| Outcome: | The proposed model reduces the amount of arithmetic operations by 20.95 and the number of DRAM operations by 2.55 on two translation tasks and three classification tasks. |
Q-Mamba: Towards more efficient Mamba models via post-training quantization (2025.findings-acl)
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| Challenge: | Existing studies show that Mamba architectures have room for further optimization in linear projections and state caches. |
| Approach: | They propose a decoupled scale quantization scheme to mitigate outliers in states and channels by applying separate quantization scales. |
| Outcome: | The proposed method reduces memory consumption by 50% across various quantization settings, model sizes, and generation and zero-shot tasks. |