How Good is Google Bard's Visual Understanding? An Empirical Study on Open Challenges Paper • 2307.15016 • Published Jul 27, 2023
Accurate LoRA-Finetuning Quantization of LLMs via Information Retention Paper • 2402.05445 • Published Feb 8, 2024
DB-LLM: Accurate Dual-Binarization for Efficient LLMs Paper • 2402.11960 • Published Feb 19, 2024 • 2
BinaryDM: Towards Accurate Binarization of Diffusion Model Paper • 2404.05662 • Published Apr 8, 2024
SliM-LLM: Salience-Driven Mixed-Precision Quantization for Large Language Models Paper • 2405.14917 • Published May 23, 2024 • 1
A Survey of Low-bit Large Language Models: Basics, Systems, and Algorithms Paper • 2409.16694 • Published Sep 25, 2024
VideoEspresso: A Large-Scale Chain-of-Thought Dataset for Fine-Grained Video Reasoning via Core Frame Selection Paper • 2411.14794 • Published Nov 22, 2024 • 13
MC-MoE: Mixture Compressor for Mixture-of-Experts LLMs Gains More Paper • 2410.06270 • Published Oct 8, 2024 • 1
DB-LLM: Accurate Dual-Binarization for Efficient LLMs Paper • 2402.11960 • Published Feb 19, 2024 • 2
How Good Are Low-bit Quantized LLaMA3 Models? An Empirical Study Paper • 2404.14047 • Published Apr 22, 2024 • 45
SliM-LLM: Salience-Driven Mixed-Precision Quantization for Large Language Models Paper • 2405.14917 • Published May 23, 2024 • 1
Accurate LoRA-Finetuning Quantization of LLMs via Information Retention Paper • 2402.05445 • Published Feb 8, 2024