Task Vector Quantization for Memory-Efficient Model Merging

• URL: http://arxiv.org/abs/2503.06921v1
• Date: Mon, 10 Mar 2025 05:00:24 GMT
• Title: Task Vector Quantization for Memory-Efficient Model Merging
• Abstract summary: We propose quantizing task vectors instead of quantizing fine-tuned checkpoints.<n>Our method maintains or improves model merging performance while using only 8% of the memory required for full-precision checkpoints.
• Score: 11.777982281994193
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Model merging enables efficient multi-task models by combining task-specific fine-tuned checkpoints. However, storing multiple task-specific checkpoints requires significant memory, limiting scalability and restricting model merging to larger models and diverse tasks. In this paper, we propose quantizing task vectors (i.e., the difference between pre-trained and fine-tuned checkpoints) instead of quantizing fine-tuned checkpoints. We observe that task vectors exhibit a narrow weight range, enabling low precision quantization (up to 4 bit) within existing task vector merging frameworks. To further mitigate quantization errors within ultra-low bit precision (e.g., 2 bit), we introduce Residual Task Vector Quantization, which decomposes the task vector into a base vector and offset component. We allocate bits based on quantization sensitivity, ensuring precision while minimizing error within a memory budget. Experiments on image classification and dense prediction show our method maintains or improves model merging performance while using only 8% of the memory required for full-precision checkpoints.

Related papers

• PACT: Preserving Anchored Cores in Task-vectors for Model Merging [68.52455853496585]
  Model merging aims to combine multiple task-specific fine-tuned models into a single multi-task model.<n>Most existing model merging approaches follow the Task Arithmetic paradigm.<n>We propose PACT, which preserves the anchored task-specific cores (i.e., LBW dimensions) within task vectors by aligning their complements with the subspace of the pre-trained weights.
  arXiv  Detail & Related papers  (2026-06-17T02:48:35Z)
• Decomposing Task Vectors for Refined Model Editing [21.799465464971092]
  We propose a principled decomposition method that separates each task vector into two components.<n>By identifying invariant subspaces across projections, our approach enables more precise control over concept manipulation.
  arXiv  Detail & Related papers  (2025-12-27T07:53:44Z)
• Learning Grouped Lattice Vector Quantizers for Low-Bit LLM Compression [57.54335545892155]
  We introduce a Grouped Lattice Vector Quantization (GLVQ) framework that assigns each group of weights a customized lattice codebook.<n>Our approach achieves a better trade-off between model size and accuracy compared to existing post-training quantization baselines.
  arXiv  Detail & Related papers  (2025-10-23T20:19:48Z)
• Purifying Task Vectors in Knowledge-Aware Subspace for Model Merging [83.5273168208788]
  Model merging aims to integrate task-specific abilities from individually fine-tuned models into a single model without extra training.<n>The merged model often suffers from notable performance degradation due to the conflicts caused by task-irrelevant redundancy in task vectors.<n>We propose Purifying TAsk Vectors (PAVE) in knowledge-aware subspace to overcome these challenges.
  arXiv  Detail & Related papers  (2025-10-16T14:02:57Z)
• Multi-Task Model Merging via Adaptive Weight Disentanglement [69.7292615212444]
  We introduce an Adaptive Weight Disentanglement method for model merging.<n>We successfully extract redundant vectors, and after their subtraction, the task vectors retain robust performance.
  arXiv  Detail & Related papers  (2024-11-27T20:08:55Z)
• NegMerge: Sign-Consensual Weight Merging for Machine Unlearning [21.081262106431506]
  Machine unlearning aims to selectively remove specific knowledge from a trained model.<n>Existing approaches, such as Task Arithmetic, fine-tune the model on the forget set to create a task vector for subtraction from the original model's weight.<n>We propose a novel method that utilizes all fine-tuned models trained with varying hyper parameters instead of a single selection.
  arXiv  Detail & Related papers  (2024-10-08T00:50:54Z)
• Localizing Task Information for Improved Model Merging and Compression [61.16012721460561]
  We show that the information required to solve each task is still preserved after merging as different tasks mostly use non-overlapping sets of weights. We propose Consensus Merging, an algorithm that eliminates such weights and improves the general performance of existing model merging approaches.
  arXiv  Detail & Related papers  (2024-05-13T14:54:37Z)
• Editing Models with Task Arithmetic [69.97273155842966]
  Changing how pre-trained models behave is a common practice when developing machine learning systems. We build task vectors by subtracting the weights of a pre-trained model from the weights of the same model after fine-tuning on a task. We show that these task vectors can be modified and combined together through arithmetic operations such as negation and addition.
  arXiv  Detail & Related papers  (2022-12-08T05:50:53Z)
• Patching open-vocabulary models by interpolating weights [85.12977566514984]
  Open-vocabulary models like CLIP achieve high accuracy across many image classification tasks. We study model patching, where the goal is to improve accuracy on specific tasks without degrading accuracy on tasks where performance is already adequate. Our findings demonstrate that it is possible to expand the set of tasks on which open-vocabulary models achieve high accuracy without re-training them from scratch.
  arXiv  Detail & Related papers  (2022-08-10T23:47:43Z)
• n-hot: Efficient bit-level sparsity for powers-of-two neural network quantization [0.0]
  Powers-of-two (PoT) quantization reduces the number of bit operations of deep neural networks on resource-constrained hardware. PoT quantization triggers a severe accuracy drop because of its limited representation ability. We propose an efficient PoT quantization scheme that balances accuracy and costs in a memory-efficient way.
  arXiv  Detail & Related papers  (2021-03-22T10:13:12Z)
• VS-Quant: Per-vector Scaled Quantization for Accurate Low-Precision Neural Network Inference [7.886868529510128]
  Quantization maps floating-point weights and activations in a trained model to low-bitwidth integer values using scale factors. Excessive quantization, reducing precision too aggressively, results in accuracy degradation. Per-vector scale factors can be implemented with low-bitwidth integers when using a two-level quantization scheme.
  arXiv  Detail & Related papers  (2021-02-08T19:56:04Z)
• Parameter-Efficient Transfer Learning with Diff Pruning [108.03864629388404]
  diff pruning is a simple approach to enable parameter-efficient transfer learning within the pretrain-finetune framework. We find that models finetuned with diff pruning can match the performance of fully finetuned baselines on the GLUE benchmark.
  arXiv  Detail & Related papers  (2020-12-14T12:34:01Z)
• Leveraging Automated Mixed-Low-Precision Quantization for tiny edge microcontrollers [76.30674794049293]
  This paper presents an automated mixed-precision quantization flow based on the HAQ framework but tailored for the memory and computational characteristics of MCU devices. Specifically, a Reinforcement Learning agent searches for the best uniform quantization levels, among 2, 4, 8 bits, of individual weight and activation tensors. Given an MCU-class memory bound to 2MB for weight-only quantization, the compressed models produced by the mixed-precision engine result as accurate as the state-of-the-art solutions.
  arXiv  Detail & Related papers  (2020-08-12T06:09:58Z)
• Post-training Quantization with Multiple Points: Mixed Precision without Mixed Precision [20.081543082708688]
  We propose multipoint quantization, a method that approximates a full-precision weight vector using a linear combination of multiple vectors of low-bit numbers. We show that our method outperforms a range of state-of-the-art methods on ImageNet classification and it can be generalized to more challenging tasks like PASCAL VOC object detection.
  arXiv  Detail & Related papers  (2020-02-20T22:37:45Z)

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.