ApiQ: Finetuning of 2-Bit Quantized Large Language Model

• URL: http://arxiv.org/abs/2402.05147v3
• Date: Fri, 21 Jun 2024 14:03:48 GMT
• Title: ApiQ: Finetuning of 2-Bit Quantized Large Language Model
• Authors: Baohao Liao, Christian Herold, Shahram Khadivi, Christof Monz,
• Abstract summary: ApiQ is designed to restore the lost information from quantization by concurrently initializing the LoRA components and quantizing the weights of LLMs. It consistently achieves superior finetuning results across various bit-widths.
• Score: 12.328293460903911
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Memory-efficient finetuning of large language models (LLMs) has recently attracted huge attention with the increasing size of LLMs, primarily due to the constraints posed by GPU memory limitations and the effectiveness of these methods compared to full finetuning. Despite the advancements, current strategies for memory-efficient finetuning, such as QLoRA, exhibit inconsistent performance across diverse bit-width quantizations and multifaceted tasks. This inconsistency largely stems from the detrimental impact of the quantization process on preserved knowledge, leading to catastrophic forgetting and undermining the utilization of pretrained models for finetuning purposes. In this work, we introduce a novel quantization framework, ApiQ, designed to restore the lost information from quantization by concurrently initializing the LoRA components and quantizing the weights of LLMs. This approach ensures the maintenance of the original LLM's activation precision while mitigating the error propagation from shallower into deeper layers. Through comprehensive evaluations conducted on a spectrum of language tasks with various LLMs, ApiQ demonstrably minimizes activation error during quantization. Consequently, it consistently achieves superior finetuning results across various bit-widths.

Related papers

• Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning [104.27224674122313]
  Fine-tuning MLLM has become a common practice to improve performance on specific downstream tasks. To balance the trade-off between generalization and specialization, we propose measuring the parameter importance for both pre-trained and fine-tuning distributions.
  arXiv  Detail & Related papers  (2024-11-17T01:16:37Z)
• ASER: Activation Smoothing and Error Reconstruction for Large Language Model Quantization [18.017182472532415]
  ASER is an algorithm consisting of low-rank compensation for quantization error with LoRA-style matrices constructed by whitening SVD. ASER is capable of quantizing typical outliers to low-bit ones, particularly preserving accuracy even in W4A8 per-channel setup.
  arXiv  Detail & Related papers  (2024-11-12T12:52:04Z)
• Channel-Wise Mixed-Precision Quantization for Large Language Models [47.00361921910259]
  Large Language Models (LLMs) have demonstrated remarkable success across a wide range of language tasks. Weight-only quantization presents a promising solution to reduce the memory footprint of LLMs. We introduce Channel-Wise Mixed-Precision Quantization (CMPQ), a novel mixed-precision quantization method.
  arXiv  Detail & Related papers  (2024-10-16T21:34:41Z)
• 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)
• QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models [85.02796681773447]
  We propose a quantization-aware low-rank adaptation (QA-LoRA) algorithm. The motivation lies in the imbalanced degrees of freedom of quantization and adaptation. QA-LoRA is easily implemented with a few lines of code.
  arXiv  Detail & Related papers  (2023-09-26T07:22:23Z)
• FineQuant: Unlocking Efficiency with Fine-Grained Weight-Only Quantization for LLMs [9.072821427818557]
  Large Language Models (LLMs) have achieved state-of-the-art performance across various language tasks but pose challenges for practical deployment. We propose an efficient weight-only quantization method that reduces memory consumption and accelerates inference for LLMs. We evaluate our approach on large-scale open source models such as OPT-175B and internal MoE models, showcasing minimal accuracy loss while achieving up to 3.65 times higher throughput.
  arXiv  Detail & Related papers  (2023-08-16T23:57:41Z)
• 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)
• Memory-Efficient Fine-Tuning of Compressed Large Language Models via sub-4-bit Integer Quantization [27.79783067245817]
  Large language models (LLMs) face the challenges in fine-tuning and deployment due to their high memory demands and computational costs. This paper presents Efficient Adaptation and Quantization-aware (PEQA) - a simple yet effective method that combines the advantages of PEFT with quantized LLMs.
  arXiv  Detail & Related papers  (2023-05-23T15:20:01Z)

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.