39 citations · 47 across the 2 of their papers we have counts for
5 papers
Found in the Middle: How Language Models Use Long Contexts Better via Plug-and-Play Positional Encoding
Zhenyu Zhang, Runjin Chen, Shiwei Liu +5
This paper aims to overcome the "lost-in-the-middle" challenge of large language models (LLMs). While recent advancements have successfully enabled LLMs to perform stable language…
FP6-LLM: Efficiently Serving Large Language Models Through FP6-Centric Algorithm-System Co-Design
Haojun Xia, Zhen Zheng, Xiaoxia Wu +10
Six-bit quantization (FP6) can effectively reduce the size of large language models (LLMs) and preserve the model quality consistently across varied applications. However, existing…
DeepSpeed-Chat: Easy, Fast and Affordable RLHF Training of ChatGPT-like Models at All Scales
Zhewei Yao, Reza Yazdani Aminabadi, Olatunji Ruwase +16
ChatGPT-like models have revolutionized various applications in artificial intelligence, from summarization and coding to translation, matching or even surpassing human performance…
A Hybrid Tensor-Expert-Data Parallelism Approach to Optimize Mixture-of-Experts Training
Siddharth Singh, Olatunji Ruwase, Ammar Ahmad Awan +3
Mixture-of-Experts (MoE) is a neural network architecture that adds sparsely activated expert blocks to a base model, increasing the number of parameters without impacting computat…
DeepSpeed Inference: Enabling Efficient Inference of Transformer Models at Unprecedented Scale
Reza Yazdani Aminabadi, Samyam Rajbhandari, Minjia Zhang +8
The past several years have witnessed the success of transformer-based models, and their scale and application scenarios continue to grow aggressively. The current landscape of tra…