50 citations · 97 across the 13 of their papers we have counts for
4 papers · 1 filter
DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence
DeepSeek-AI, Anyi Xu, Bangcai Lin +315
We present a preview version of DeepSeek-V4 series, including two strong Mixture-of-Experts (MoE) language models -- DeepSeek-V4-Pro with 1.6T parameters (49B activated) and DeepSe…
ScalingFilter: Assessing Data Quality through Inverse Utilization of Scaling Laws
Ruihang Li, Yixuan Wei, Miaosen Zhang +3
High-quality data is crucial for the pre-training performance of large language models. Unfortunately, existing quality filtering methods rely on a known high-quality dataset as re…
Xwin-LM: Strong and Scalable Alignment Practice for LLMs
Bolin Ni, JingCheng Hu, Yixuan Wei +4
In this work, we present Xwin-LM, a comprehensive suite of alignment methodologies for large language models (LLMs). This suite encompasses several key techniques, including superv…
Common 7B Language Models Already Possess Strong Math Capabilities
Chen Li, Weiqi Wang, Jingcheng Hu +5
Mathematical capabilities were previously believed to emerge in common language models only at a very large scale or require extensive math-related pre-training. This paper shows t…