Tag: Fine-tuning
All the articles with the tag "Fine-tuning".
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Communication-Efficient Wireless Federated Fine-Tuning for Large-Scale AI Models
本文提出了一种无线联邦LoRA微调框架,通过Sparsified Orthogonal Fine-Tuning (SOFT) 和Two Stage Federated Algorithm (TSFA) 优化参数稀疏化和动态资源分配,提高了通信效率和学习性能。
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Quantum-Enhanced LLM Efficient Fine Tuning
本文提出量子张量混合适配(QTHA)方法,通过整合量子神经网络和张量网络,实现LLM的参数高效微调,显著减少参数量并提升性能,为量子增强人工智能奠定基础。
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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.