GIFT-SW: Gaussian noise Injected Fine-Tuning of Salient Weights for LLMs

• URL: http://arxiv.org/abs/2408.15300v1
• Date: Tue, 27 Aug 2024 14:41:14 GMT
• Title: GIFT-SW: Gaussian noise Injected Fine-Tuning of Salient Weights for LLMs
• Authors: Maxim Zhelnin, Viktor Moskvoretskii, Egor Shvetsov, Egor Venediktov, Mariya Krylova, Aleksandr Zuev, Evgeny Burnaev,
• Abstract summary: We introduce a novel PEFT method, called Gaussian noise Injected Fine Tuning of Salient Weights (GIFT-SW) Our method updates only salient columns, while injecting Gaussian noise into non-salient ones. Experiments with LLaMA models demonstrate that GIFT-SW outperforms full fine-tuning and modern PEFT methods under the same computational budget.
• Score: 51.02233412547456
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Parameter Efficient Fine-Tuning (PEFT) methods have gained popularity and democratized the usage of Large Language Models (LLMs). Recent studies have shown that a small subset of weights significantly impacts performance. Based on this observation, we introduce a novel PEFT method, called Gaussian noise Injected Fine Tuning of Salient Weights (GIFT-SW). Our method updates only salient columns, while injecting Gaussian noise into non-salient ones. To identify these columns, we developeda generalized sensitivity metric that extends and unifies metrics from previous studies. Experiments with LLaMA models demonstrate that GIFT-SW outperforms full fine-tuning and modern PEFT methods under the same computational budget. Moreover, GIFT-SW offers practical advantages to recover performance of models subjected to mixed-precision quantization with keeping salient weights in full precision.

Related papers

• RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models [95.32315448601241]
  We propose an algorithm named Rotated Straight-Through-Estimator (RoSTE) RoSTE combines quantization-aware supervised fine-tuning (QA-SFT) with an adaptive rotation strategy to reduce activation outliers. Our findings reveal that the prediction error is directly proportional to the quantization error of the converged weights, which can be effectively managed through an optimized rotation configuration.
  arXiv  Detail & Related papers  (2025-02-13T06:44:33Z)
• Unveiling the Mystery of Weight in Large Foundation Models: Gaussian Distribution Never Fades [14.113021234825084]
  This paper presents a pioneering exploration of the mechanisms underlying large foundation models' weights. We find that their weights predominantly follow a Gaussian distribution, with occasional sharp, inverted T-shaped, or linear patterns. We conclude that optimal weights should exhibit zero-mean, symmetry, and sparsity, with the sparse values being a truncated Gaussian distribution and a few outliers.
  arXiv  Detail & Related papers  (2025-01-18T05:43:17Z)
• Taming Sensitive Weights : Noise Perturbation Fine-tuning for Robust LLM Quantization [5.718172547021947]
  We propose Noise Perturbation Fine-tuning (NPFT) to tame the sensitive weights' impact on the quantization error. NPFT identifies outlier weights and add random weight perturbations on the outliers as the model going through a PEFT optimization. When applied to OPT and LLaMA models, our NPFT method achieves stable performance improvements for both uniform and non-uniform quantizers.
  arXiv  Detail & Related papers  (2024-12-08T21:46:22Z)
• IntLoRA: Integral Low-rank Adaptation of Quantized Diffusion Models [68.55148272295916]
  We propose IntLoRA, to push the efficiency limits by using integer type (INT) low-rank parameters to adapt the quantized diffusion models. IntLoRA offers three key advantages: (i) for fine-tuning, the pre-trained weights are quantized, reducing memory usage; (ii) for storage, both pre-trained and low-rank weights are in INT which consumes less disk space; (iii) for inference, IntLoRA weights can be naturally merged into quantized pre-trained weights through efficient integer multiplication or bit-shifting.
  arXiv  Detail & Related papers  (2024-10-29T05:50:17Z)
• TRAWL: Tensor Reduced and Approximated Weights for Large Language Models [11.064868044313855]
  We introduce TRAWL (Tensor Reduced and Approximated Weights for Large Language Models), a technique that applies tensor decomposition across multiple weight matrices to effectively denoise LLMs by capturing global structural patterns. Our experiments show that TRAWL improves model performance by up to 16% over baseline models on benchmark datasets, without requiring additional data, training, or fine-tuning.
  arXiv  Detail & Related papers  (2024-06-25T04:01:32Z)
• Data-freeWeight Compress and Denoise for Large Language Models [101.53420111286952]
  We propose a novel approach termed Data-free Joint Rank-k Approximation for compressing the parameter matrices. We achieve a model pruning of 80% parameters while retaining 93.43% of the original performance without any calibration data.
  arXiv  Detail & Related papers  (2024-02-26T05:51:47Z)
• 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)
• 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)
• Neural networks with late-phase weights [66.72777753269658]
  We show that the solutions found by SGD can be further improved by ensembling a subset of the weights in late stages of learning. At the end of learning, we obtain back a single model by taking a spatial average in weight space.
  arXiv  Detail & Related papers  (2020-07-25T13:23:37Z)

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.