most citedInternLM2 Technical Report

29 citations · 58 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV20241 cited

OmniCorpus: A Unified Multimodal Corpus of 10 Billion-Level Images Interleaved with Text

Qingyun Li, Zhe Chen, Weiyun Wang +37

Image-text interleaved data, consisting of multiple images and texts arranged in a natural document format, aligns with the presentation paradigm of internet data and closely resem…

cs.DL20244 cited

OpenDataLab: Empowering General Artificial Intelligence with Open Datasets

Conghui He, Wei Li, Zhenjiang Jin +3

The advancement of artificial intelligence (AI) hinges on the quality and accessibility of data, yet the current fragmentation and variability of data sources hinder efficient data…

cs.CV202416 cited

How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites

Zhe Chen, Weiyun Wang, Hao Tian +32

In this report, we introduce InternVL 1.5, an open-source multimodal large language model (MLLM) to bridge the capability gap between open-source and proprietary commercial models…

cs.CL202429 cited

InternLM2 Technical Report

Zheng Cai, Maosong Cao, Haojiong Chen +97

The evolution of Large Language Models (LLMs) like ChatGPT and GPT-4 has sparked discussions on the advent of Artificial General Intelligence (AGI). However, replicating such advan…

cs.CL2024

WanJuan-CC: A Safe and High-Quality Open-sourced English Webtext Dataset

Jiantao Qiu, Haijun Lv, Zhenjiang Jin +23

This paper presents WanJuan-CC, a safe and high-quality open-sourced English webtext dataset derived from Common Crawl data. The study addresses the challenges of constructing larg…

cs.CL20238 cited

WanJuan: A Comprehensive Multimodal Dataset for Advancing English and Chinese Large Models

Conghui He, Zhenjiang Jin, Chao Xu +6

The rise in popularity of ChatGPT and GPT-4 has significantly accelerated the development of large models, leading to the creation of numerous impressive large language models(LLMs…