Achieving binary weight and activation for LLMs using Post-Training Quantization (2025.findings-acl)
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| Challenge: | Existing methods for quantizing large language models suffer from performance degradation when weights are quantized to 1 bit. |
| Approach: | They propose a post-training quantization framework with W(1+1)A(14) configuration . they propose utilizing Hessian-aware fine-grained grouping along with an EM-based quantization scheme . |
| Outcome: | The proposed method surpasses state-of-the-art (SOTA) LLM quantization baselines on W2A4 across multiple tasks. |
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