3 citations · 3 across the 5 of their papers we have counts for
5 papers
K2-V2: A 360-Open, Reasoning-Enhanced LLM
K2 Team, Zhengzhong Liu, Liping Tang +36
We introduce K2-V2, a 360-open LLM built from scratch as a superior base for reasoning adaptation, in addition to functions such as conversation and knowledge retrieval from genera…
MegaMath: Pushing the Limits of Open Math Corpora
Fan Zhou, Zengzhi Wang, Nikhil Ranjan +5
Mathematical reasoning is a cornerstone of human intelligence and a key benchmark for advanced capabilities in large language models (LLMs). However, the research community still l…
LLM360 K2: Building a 65B 360-Open-Source Large Language Model from Scratch
Zhengzhong Liu, Bowen Tan, Hongyi Wang +22
We detail the training of the LLM360 K2-65B model, scaling up our 360-degree OPEN SOURCE approach to the largest and most powerful models under project LLM360. While open-source LL…
Unified Generation, Reconstruction, and Representation: Generalized Diffusion with Adaptive Latent Encoding-Decoding
Guangyi Liu, Yu Wang, Zeyu Feng +9
The vast applications of deep generative models are anchored in three core capabilities -- generating new instances, reconstructing inputs, and learning compact representations --…
LLM360: Towards Fully Transparent Open-Source LLMs
Zhengzhong Liu, Aurick Qiao, Willie Neiswanger +25
The recent surge in open-source Large Language Models (LLMs), such as LLaMA, Falcon, and Mistral, provides diverse options for AI practitioners and researchers. However, most LLMs…