DBellQuant: Breaking the Bell with Double-Bell Transformation for LLMs Post Training Binarization

• URL: http://arxiv.org/abs/2507.01027v1
• Date: Wed, 18 Jun 2025 06:41:03 GMT
• Title: DBellQuant: Breaking the Bell with Double-Bell Transformation for LLMs Post Training Binarization
• Abstract summary: DBellQuant is a post-training quantization framework for large language models.<n>It achieves nearly 1-bit weight compression and 6-bit activation quantization with minimal performance degradation.<n>It sets a new state-of-the-art by preserving superior model performance under aggressive weight and activation quantization.
• Score: 38.333517224831624
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) demonstrate remarkable performance but face substantial computational and memory challenges that limit their practical deployment. Quantization has emerged as a promising solution; however, its effectiveness is often limited by quantization errors arising from weight distributions that are not quantization-friendly and the presence of activation outliers. To address these challenges, we introduce DBellQuant, an innovative post-training quantization (PTQ) framework that achieves nearly 1-bit weight compression and 6-bit activation quantization with minimal performance degradation. DBellQuant uses Learnable Transformation for Dual-Bell (LTDB) algorithm, which transforms single-bell weight distributions into dual-bell forms to reduce binarization errors and applies inverse transformations to smooth activations. DBellQuant sets a new state-of-the-art by preserving superior model performance under aggressive weight and activation quantization. For example, on the Wikitext2 dataset, DBellQuant achieves a perplexity of 14.39 on LLaMA2-13B with 6-bit activation quantization, significantly outperforming BiLLM's 21.35 without activation quantization, underscoring its potential in compressing LLMs for real-world applications.

Related papers

• BWLA: Breaking the Barrier of W1AX Post-Training Quantization for LLMs [10.07268309735318]
  Large language models (LLMs) have driven major progress in NLP, yet their substantial memory and compute demands still hinder practical deployment.<n>We propose BWLA, the first post-training quantization framework that preserves high accuracy while achieving 1-bit weight quantization.<n>On Qwen3-32B, BWLA reaches a Wikitext2 perplexity of 11.92 under 6-bit activations, improves five zero-shot tasks by more than 70%, and delivers 3.26 times inference speedup.
  arXiv  Detail & Related papers  (2026-05-01T05:42:57Z)
• LBLLM: Lightweight Binarization of Large Language Models via Three-Stage Distillation [29.676785178178104]
  We present LBLLM, a lightweight binarization framework that achieves W(1+1)A4 quantization through a novel three-stage quantization strategy.<n>LBLLM, trained only using 0.016B tokens with a single GPU, surpasses existing state-of-the-art binarization methods on W2A4 quantization settings.
  arXiv  Detail & Related papers  (2026-04-21T07:25:02Z)
• D$^2$Quant: Accurate Low-bit Post-Training Weight Quantization for LLMs [33.883527341335856]
  Weight-only post-training quantization (PTQ) is appealing as it reduces memory usage and enables practical speedup without low-bit operators or specialized hardware.<n> accuracy often degrades significantly in weight-only PTQ at sub-4-bit precision.<n>We propose D$2$Quant, a novel weight-only PTQ framework that improves quantization from both the weight and activation perspectives.
  arXiv  Detail & Related papers  (2026-01-30T05:49:48Z)
• Achieving binary weight and activation for LLMs using Post-Training Quantization [32.247768389896144]
  Quantizing large language models (LLMs) to 1-bit precision significantly reduces computational costs.<n>Existing quantization techniques suffer from noticeable performance degradation when using weight and activation precisions below 4 bits (W4A4)
  arXiv  Detail & Related papers  (2025-04-07T04:50:04Z)
• FlatQuant: Flatness Matters for LLM Quantization [58.28221892035609]
  We propose FlatQuant, a new post-training quantization approach that enhances the flatness of weights and activations.<n>Our approach identifies optimal affine transformations for each linear layer, calibrated in hours via a lightweight objective.<n>It achieves less than 1% accuracy drop for W4A4 quantization on the LLaMA-3-70B model, surpassing SpinQuant by 7.5%.
  arXiv  Detail & Related papers  (2024-10-12T08:10:28Z)
• LRQ: Optimizing Post-Training Quantization for Large Language Models by Learning Low-Rank Weight-Scaling Matrices [41.17378536966264]
  Low-Rank Quantization (LRQ) reconstructs the outputs of an intermediate Transformer block by leveraging low-rank weight-scaling matrices.<n>Thanks to parameter sharing via low-rank structure, LRQ only needs to learn significantly fewer parameters while enabling the individual scaling of weights.<n>We show the superiority of LRQ over prior LLM PTQ works under (i) 8-bit weight and per-tensor activation quantization, (ii) 4-bit weight and 8-bit per-token activation quantization, and (iii) low-bit weight-only quantization schemes.
  arXiv  Detail & Related papers  (2024-07-16T09:32:07Z)
• BiSup: Bidirectional Quantization Error Suppression for Large Language Models [13.042992673384466]
  We introduce BiSup, a Bi-directional quantization error Suppression method. We show that BiSup can improve performance over two state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-05-24T08:39:27Z)
• DB-LLM: Accurate Dual-Binarization for Efficient LLMs [83.70686728471547]
  Large language models (LLMs) have significantly advanced the field of natural language processing. Existing ultra-low-bit quantization always causes severe accuracy drops. We propose a novel Dual-Binarization method for LLMs, namely DB-LLM.
  arXiv  Detail & Related papers  (2024-02-19T09:04:30Z)
• CBQ: Cross-Block Quantization for Large Language Models [66.82132832702895]
  Post-training quantization (PTQ) has played a key role in compressing large language models (LLMs) with ultra-low costs.<n>We propose CBQ, a cross-block reconstruction-based PTQ method for LLMs.<n> CBQ employs a cross-block dependency using a reconstruction scheme, establishing long-range dependencies across multiple blocks to minimize error accumulation.
  arXiv  Detail & Related papers  (2023-12-13T07:56:27Z)
• AWEQ: Post-Training Quantization with Activation-Weight Equalization for Large Language Models [0.18416014644193066]
  AWEQ excels in both ultra-low-bit quantization and 8-bit weight and activation (W8A8) quantization. We have further refined the equalization method to mitigate quantization bias error, ensuring the robustness of the model.
  arXiv  Detail & Related papers  (2023-11-02T15:18:22Z)
• OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models [57.27101446992148]
  Large language models (LLMs) have revolutionized natural language processing tasks. Recent post-training quantization (PTQ) methods are effective in reducing memory footprint and improving the computational efficiency of LLM. We introduce an Omnidirectionally calibrated Quantization technique for LLMs, which achieves good performance in diverse quantization settings.
  arXiv  Detail & Related papers  (2023-08-25T02:28:35Z)
• NoisyQuant: Noisy Bias-Enhanced Post-Training Activation Quantization for Vision Transformers [53.85087932591237]
  NoisyQuant is a quantizer-agnostic enhancement for the post-training activation quantization performance of vision transformers. Building on the theoretical insight, NoisyQuant achieves the first success on actively altering the heavy-tailed activation distribution. NoisyQuant largely improves the post-training quantization performance of vision transformer with minimal computation overhead.
  arXiv  Detail & Related papers  (2022-11-29T10:02:09Z)
• BiTAT: Neural Network Binarization with Task-dependent Aggregated Transformation [116.26521375592759]
  Quantization aims to transform high-precision weights and activations of a given neural network into low-precision weights/activations for reduced memory usage and computation. Extreme quantization (1-bit weight/1-bit activations) of compactly-designed backbone architectures results in severe performance degeneration. This paper proposes a novel Quantization-Aware Training (QAT) method that can effectively alleviate performance degeneration.
  arXiv  Detail & Related papers  (2022-07-04T13:25:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.