Compensate Quantization Errors+: Quantized Models Are Inquisitive Learners

• URL: http://arxiv.org/abs/2407.15508v1
• Date: Mon, 22 Jul 2024 09:45:16 GMT
• Title: Compensate Quantization Errors+: Quantized Models Are Inquisitive Learners
• Authors: Yifei Gao, Jie Ou, Lei Wang, Fanhua Shang, Jaji Wu, Jun Cheng,
• Abstract summary: Large Language Models (LLMs) showcase remarkable performance and robust deductive capabilities, yet their expansive size complicates deployment and raises environmental concerns due to substantial resource consumption. We have developed innovative methods that enhance the performance of quantized LLMs, particularly in low-bit settings. Our methods consistently deliver state-of-the-art results across various quantization scenarios and offer deep theoretical insights into the quantization process, elucidating the potential of quantized models for widespread application.
• Score: 17.43650511873449
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) showcase remarkable performance and robust deductive capabilities, yet their expansive size complicates deployment and raises environmental concerns due to substantial resource consumption. The recent development of a quantization technique known as Learnable Singular-value Increment (LSI) has addressed some of these quantization challenges. Leveraging insights from LSI and our extensive research, we have developed innovative methods that enhance the performance of quantized LLMs, particularly in low-bit settings. Our methods consistently deliver state-of-the-art results across various quantization scenarios and offer deep theoretical insights into the quantization process, elucidating the potential of quantized models for widespread application.

Related papers

• A Comprehensive Study on Quantization Techniques for Large Language Models [0.0]
  Large Language Models (LLMs) have been extensively researched and used in both academia and industry. LLMs present significant challenges for deployment on resource-constrained IoT devices and embedded systems. Quantization, a technique that reduces the precision of model values to a smaller set of discrete values, offers a promising solution.
  arXiv  Detail & Related papers  (2024-10-30T04:55:26Z)
• Art and Science of Quantizing Large-Scale Models: A Comprehensive Overview [4.166341398835636]
  We discuss the necessity and impact of model size growth, highlighting the performance benefits as well as the computational challenges and environmental considerations. We delve into various quantization techniques, including both post-training quantization (PTQ) and quantization-aware training (QAT) We examine how these methods address issues like outliers, importance weighting, and activation quantization, ultimately contributing to more sustainable and accessible deployment of large-scale models.
  arXiv  Detail & Related papers  (2024-09-18T02:35:00Z)
• Advancing Multimodal Large Language Models with Quantization-Aware Scale Learning for Efficient Adaptation [70.22782550540714]
  Quantization-aware Scale LeArning method based on multimodal Warmup, termed QSLAW. We introduce a Quantization-aware Scale LeArning method based on multimodal Warmup, termed QSLAW.
  arXiv  Detail & Related papers  (2024-08-07T12:42:09Z)
• Investigating the Impact of Quantization on Adversarial Robustness [22.637585106574722]
  Quantization is a technique for reducing the bit-width of deep models to improve their runtime performance and storage efficiency. In real-world scenarios, quantized models are often faced with adversarial attacks which cause the model to make incorrect inferences. We conduct a first-time analysis of the impact of the quantization pipeline components that can incorporate robust optimization.
  arXiv  Detail & Related papers  (2024-04-08T16:20:15Z)
• What Makes Quantization for Large Language Models Hard? An Empirical Study from the Lens of Perturbation [55.153595212571375]
  Quantization is a technique for improving the memory and computational efficiency of large language models (LLMs) We propose a new perspective on quantization, viewing it as perturbations added to the weights and activations of LLMs. We conduct experiments with various artificial perturbations to explore their impact on LLM performance.
  arXiv  Detail & Related papers  (2024-03-11T03:42:51Z)
• WKVQuant: Quantizing Weight and Key/Value Cache for Large Language Models Gains More [55.0856305773081]
  Large Language Models (LLMs) face significant deployment challenges due to their substantial memory requirements and the computational demands of auto-regressive text generation process. This paper addresses these challenges by focusing on the quantization of LLMs, a technique that reduces memory consumption by converting model parameters and activations into low-bit integers.
  arXiv  Detail & Related papers  (2024-02-19T11:33:21Z)
• Retrieval-based Knowledge Transfer: An Effective Approach for Extreme Large Language Model Compression [64.07696663255155]
  Large-scale pre-trained language models (LLMs) have demonstrated exceptional performance in various natural language processing (NLP) tasks. However, the massive size of these models poses huge challenges for their deployment in real-world applications. We introduce a novel compression paradigm called Retrieval-based Knowledge Transfer (RetriKT) which effectively transfers the knowledge of LLMs to extremely small-scale models.
  arXiv  Detail & Related papers  (2023-10-24T07:58:20Z)
• PB-LLM: Partially Binarized Large Language Models [14.244537605866864]
  This paper explores network binarization, compressing model weights to a single bit, specifically for Large Language Models (LLMs) compression. We propose a novel approach, Partially-Binarized LLM (PB-LLM), which can achieve extreme low-bit quantization while maintaining the linguistic reasoning capacity of quantized LLMs.
  arXiv  Detail & Related papers  (2023-09-29T14:35:27Z)
• Do Emergent Abilities Exist in Quantized Large Language Models: An Empirical Study [90.34226812493083]
  This work aims to investigate the impact of quantization on emphemergent abilities, which are important characteristics that distinguish LLMs from small language models. Our empirical experiments show that these emergent abilities still exist in 4-bit quantization models, while 2-bit models encounter severe performance degradation. To improve the performance of low-bit models, we conduct two special experiments: (1) fine-gained impact analysis that studies which components (or substructures) are more sensitive to quantization, and (2) performance compensation through model fine-tuning.
  arXiv  Detail & Related papers  (2023-07-16T15:11:01Z)
• PreQuant: A Task-agnostic Quantization Approach for Pre-trained Language Models [52.09865918265002]
  We propose a novel quantize before fine-tuning'' framework, PreQuant. PreQuant is compatible with various quantization strategies, with outlier-aware fine-tuning incorporated to correct the induced quantization error. We demonstrate the effectiveness of PreQuant on the GLUE benchmark using BERT, RoBERTa, and T5.
  arXiv  Detail & Related papers  (2023-05-30T08:41:33Z)

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.