Trends · 研究趋势
数据来自 Hugging Face 论文的 AI 提取关键词,按月统计研究方向的增长与热度。
Yiming Wang, Yu Lin, Xiaodong Zeng +1 authors
MultiLoRA improves multi-task adaptation for large language models by reducing the dominance of top singular vectors in LoRA parameter updates through horizontal scaling and modified initialization.
Han Zhao, Min Zhang, Wei Zhao +3 authors
Cobra, a linear-complexity multimodal large language model, integrates the Mamba language model with visual modality, achieving competitive performance and faster inference speed compared to existing state-of-the-art models.
Kai Lv, Yuqing Yang, Tengxiao Liu +3 authors
A new optimizer reduces memory usage for full parameter fine-tuning of large language models, enabling 65B model training on a single machine with limited GPUs.
Ying Sheng, Shiyi Cao, Dacheng Li +9 authors
S-LoRA is a system that allows for efficient and scalable serving of numerous LoRA adapters using a unified memory pool, tensor parallelism, and custom CUDA kernels.
Clifton Poth, Hannah Sterz, Indraneil Paul +7 authors
Adapters is an open-source library designed for parameter-efficient and modular transfer learning in large language models, offering a unified interface for various adapter methods.
Benjamin Spector, Chris Re
A staged speculative decoding algorithm enhances LLM inference efficiency in small-batch, on-device settings by restructuring speculative batches and adding a second decoding stage.
Yaqi Zhang, Di Huang, Bin Liu +7 authors
MotionGPT generates realistic human motion using multimodal control signals by adapting a small portion of LLM parameters to handle unified input instructions.
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