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Model merging in llms, mllms, and beyond: Methods, theories, applications and opportunities
Model merging is an efficient empowerment technique in the machine learning community
that does not require the collection of raw training data and does not require expensive …
that does not require the collection of raw training data and does not require expensive …
Lines: Post-training layer scaling prevents forgetting and enhances model merging
Large pre-trained models exhibit impressive zero-shot performance across diverse tasks,
but fine-tuning often leads to catastrophic forgetting, where improvements on a target …
but fine-tuning often leads to catastrophic forgetting, where improvements on a target …
RandLoRA: Full-rank parameter-efficient fine-tuning of large models
Low-Rank Adaptation (LoRA) and its variants have shown impressive results in reducing the
number of trainable parameters and memory requirements of large transformer networks …
number of trainable parameters and memory requirements of large transformer networks …
I-Lora: Iterative Merging of Routing-Tuned Low-Rank Adapters for Multi-task Learning
G Zhao, Q Zhang, S Zhai, D Shen, Y Qiao, T Xu - openreview.net
The advancement of vision-language models has significantly boosted the performance of
embodied and game AI, endowing them with more robust general visual understanding …
embodied and game AI, endowing them with more robust general visual understanding …