Tag: Fine-tuning
All the articles with the tag "Fine-tuning".
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Block Circulant Adapter for Large Language Models
本文提出块循环适配器方法,通过利用块循环矩阵和FFT优化LLM的微调过程,显著降低存储和计算成本,同时通过学习率调整确保训练稳定。
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MegaScale-Infer: Serving Mixture-of-Experts at Scale with Disaggregated Expert Parallelism
本文提出MegaScale-Infer系统,通过分离注意力模块和FFN模块的并行策略以及高效M2N通信库,优化大规模MoE模型的推理效率,实现高达1.90倍的吞吐量提升。
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VLM Q-Learning: Aligning Vision-Language Models for Interactive Decision-Making
This paper introduces VLM Q-Learning, an offline-to-online reinforcement learning method that fine-tunes Vision-Language Models for interactive decision-making by filtering suboptimal actions with a critic head, achieving significant performance improvements over supervised fine-tuning across multiple multimodal agent tasks.
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Hierarchical Attention Generates Better Proofs
本文提出层次注意力正则化方法,通过引导大型语言模型的注意力机制与数学推理的五级层次结构对齐,在 miniF2F 和 ProofNet 基准上分别提升证明成功率 2.05% 和 1.69%,并显著降低证明复杂度。
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Small or Large? Zero-Shot or Finetuned? Guiding Language Model Choice for Specialized Applications in Healthcare
本文通过实证实验指导在医疗专业应用中语言模型的选择,强调微调小语言模型和领域特定预训练的显著优势,使其在特定任务上超越零-shot 大语言模型。