Achieving binary weight and activation for LLMs using Post-Training Quantization
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866913918710972416 |
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| author | Song, Siqing Wang, Chuang Wang, Ruiqi Yang, Yi Zhang, Xu-Yao |
| author_facet | Song, Siqing Wang, Chuang Wang, Ruiqi Yang, Yi Zhang, Xu-Yao |
| contents | Quantizing large language models (LLMs) to 1-bit precision significantly reduces computational costs, but existing quantization techniques suffer from noticeable performance degradation when using weight and activation precisions below 4 bits (W4A4). In this paper, we propose a post-training quantization framework with W(1+1)A(1*4) configuration, where weights are quantized to 1 bit with an additional 1 bit for fine-grain grouping and activations are quantized to 1 bit with a 4-fold increase in the number of channels. For weight quantization, we propose utilizing Hessian-aware fine-grained grouping along with an EM-based quantization scheme. For activation quantization, we decompose INT4-quantized activations into a 4 * INT1 format equivalently and simultaneously smooth the scaling factors based on quantization errors, which further reduces the quantization errors in activations. Our method surpasses state-of-the-art (SOTA) LLM quantization baselines on W2A4 across multiple tasks, pushing the boundaries of existing LLM quantization methods toward fully binarized models. Code is available at https://github.com/JimmyCrave/LLM-PTQ-binarization. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_05352 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Achieving binary weight and activation for LLMs using Post-Training Quantization Song, Siqing Wang, Chuang Wang, Ruiqi Yang, Yi Zhang, Xu-Yao Machine Learning Artificial Intelligence Quantizing large language models (LLMs) to 1-bit precision significantly reduces computational costs, but existing quantization techniques suffer from noticeable performance degradation when using weight and activation precisions below 4 bits (W4A4). In this paper, we propose a post-training quantization framework with W(1+1)A(1*4) configuration, where weights are quantized to 1 bit with an additional 1 bit for fine-grain grouping and activations are quantized to 1 bit with a 4-fold increase in the number of channels. For weight quantization, we propose utilizing Hessian-aware fine-grained grouping along with an EM-based quantization scheme. For activation quantization, we decompose INT4-quantized activations into a 4 * INT1 format equivalently and simultaneously smooth the scaling factors based on quantization errors, which further reduces the quantization errors in activations. Our method surpasses state-of-the-art (SOTA) LLM quantization baselines on W2A4 across multiple tasks, pushing the boundaries of existing LLM quantization methods toward fully binarized models. Code is available at https://github.com/JimmyCrave/LLM-PTQ-binarization. |
| title | Achieving binary weight and activation for LLMs using Post-Training Quantization |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2504.05352 |