Papers by Haihao Shen
Optimize Weight Rounding via Signed Gradient Descent for the Quantization of LLMs (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have demonstrated exceptional proficiency in language-related tasks, but their deployment poses significant memory and storage requirements. |
| Approach: | They propose a method that optimizes rounding values and weight clipping within 200 steps. |
| Outcome: | The proposed method achieves exceptional results across 2 to 4 bits while maintaining low tuning costs and avoiding additional inference overhead. |