24 citations · 26 across the 5 of their papers we have counts for
6 papers
LiquidGEMM: Hardware-Efficient W4A8 GEMM Kernel for High-Performance LLM Serving
Huanqi Hu, Bowen Xiao, Shixuan Sun +8
Quantization is a critical technique for accelerating LLM inference by reducing memory footprint and improving computational efficiency. Among various schemes, 4-bit weight and 8-b…
Seed Diffusion: A Large-Scale Diffusion Language Model with High-Speed Inference
Yuxuan Song, Zheng Zhang, Cheng Luo +19
We present Seed Diffusion Preview, a large-scale language model based on discrete-state diffusion, offering remarkably fast inference speed. Thanks to non-sequential, parallel gene…
Model Merging in Pre-training of Large Language Models
Yunshui Li, Yiyuan Ma, Shen Yan +23
Model merging has emerged as a promising technique for enhancing large language models, though its application in large-scale pre-training remains relatively unexplored. In this pa…
Seed1.5-VL Technical Report
Dong Guo, Faming Wu, Feida Zhu +194
We present Seed1.5-VL, a vision-language foundation model designed to advance general-purpose multimodal understanding and reasoning. Seed1.5-VL is composed with a 532M-parameter v…
Seed1.5-Thinking: Advancing Superb Reasoning Models with Reinforcement Learning
ByteDance Seed, :, Jiaze Chen +267
We introduce Seed1.5-Thinking, capable of reasoning through thinking before responding, resulting in improved performance on a wide range of benchmarks. Seed1.5-Thinking achieves 8…
MegaScale: Scaling Large Language Model Training to More Than 10,000 GPUs
Ziheng Jiang, Haibin Lin, Yinmin Zhong +29
We present the design, implementation and engineering experience in building and deploying MegaScale, a production system for training large language models (LLMs) at the scale of…