5 citations · 6 across the 5 of their papers we have counts for
5 papers
BitDistiller: Unleashing the Potential of Sub-4-Bit LLMs via Self-Distillation
Dayou Du, Yijia Zhang, Shijie Cao +4
The upscaling of Large Language Models (LLMs) has yielded impressive advances in natural language processing, yet it also poses significant deployment challenges. Weight quantizati…
AFPQ: Asymmetric Floating Point Quantization for LLMs
Yijia Zhang, Sicheng Zhang, Shijie Cao +4
Large language models (LLMs) show great performance in various tasks, but face deployment challenges from limited memory capacity and bandwidth. Low-bit weight quantization can sav…
Adam Accumulation to Reduce Memory Footprints of both Activations and Gradients for Large-scale DNN Training
Yijia Zhang, Yibo Han, Shijie Cao +5
Running out of GPU memory has become a main bottleneck for large-scale DNN training. How to reduce the memory footprint during training has received intensive research attention. W…
Accurate and Structured Pruning for Efficient Automatic Speech Recognition
Huiqiang Jiang, Li Lyna Zhang, Yuang Li +7
Automatic Speech Recognition (ASR) has seen remarkable advancements with deep neural networks, such as Transformer and Conformer. However, these models typically have large model s…
Integer or Floating Point? New Outlooks for Low-Bit Quantization on Large Language Models
Yijia Zhang, Lingran Zhao, Shijie Cao +6
Efficient deployment of large language models (LLMs) necessitates low-bit quantization to minimize model size and inference cost. While low-bit integer formats (e.g., INT8/INT4) ha…