most citedImageBind-LLM: Multi-modality Instruction Tuning

25 citations · 58 across the 7 of their papers we have counts for

collaborators

7 papers

cs.LG2024

DCP: Learning Accelerator Dataflow for Neural Network via Propagation

Peng Xu, Wenqi Shao, Mingyu Ding +1

Deep neural network (DNN) hardware (HW) accelerators have achieved great success in improving DNNs' performance and efficiency. One key reason is dataflow in executing a DNN layer,…

cs.LG2024★ 8 cited

EfficientQAT: Efficient Quantization-Aware Training for Large Language Models

Mengzhao Chen, Wenqi Shao, Peng Xu +4

Large language models (LLMs) are crucial in modern natural language processing and artificial intelligence. However, they face challenges in managing their significant memory requi…

cs.CV2024★ 6 cited

MMT-Bench: A Comprehensive Multimodal Benchmark for Evaluating Large Vision-Language Models Towards Multitask AGI

Kaining Ying, Fanqing Meng, Jin Wang +19

Large Vision-Language Models (LVLMs) show significant strides in general-purpose multimodal applications such as visual dialogue and embodied navigation. However, existing multimod…

cs.LG2024★ 3 cited

BESA: Pruning Large Language Models with Blockwise Parameter-Efficient Sparsity Allocation

Peng Xu, Wenqi Shao, Mengzhao Chen +6

Large language models (LLMs) have demonstrated outstanding performance in various tasks, such as text summarization, text question-answering, and etc. While their performance is im…

cs.MM2023★ 25 cited

ImageBind-LLM: Multi-modality Instruction Tuning

Jiaming Han, Renrui Zhang, Wenqi Shao +14

We present ImageBind-LLM, a multi-modality instruction tuning method of large language models (LLMs) via ImageBind. Existing works mainly focus on language and image instruction tu…

cs.LG2023★ 14 cited

OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models

Wenqi Shao, Mengzhao Chen, Zhaoyang Zhang +7

Large language models (LLMs) have revolutionized natural language processing tasks. However, their practical deployment is hindered by their immense memory and computation requirem…

cs.CV2023★ 2 cited

TinyLVLM-eHub: Towards Comprehensive and Efficient Evaluation for Large Vision-Language Models

Wenqi Shao, Meng Lei, Yutao Hu +8

Recent advancements in Large Vision-Language Models (LVLMs) have demonstrated significant progress in tackling complex multimodal tasks. Among these cutting-edge developments, Goog…