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. |
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EfficientQAT: Efficient Quantization-Aware Training for Large Language Models (2025.acl-long)
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| Challenge: | Quantization-aware training (QAT) is a low-bit training solution that requires substantial training resources. |
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