Large-scale Self-supervised Pre-training Across Tasks, Languages, and Modalities
-
Updated
Nov 9, 2024 - Python
Large-scale Self-supervised Pre-training Across Tasks, Languages, and Modalities
Mobile-Agent: The Powerful Mobile Device Operation Assistant Family
InternLM-XComposer-2.5: A Versatile Large Vision Language Model Supporting Long-Contextual Input and Output
Reasoning in Large Language Models: Papers and Resources, including Chain-of-Thought and OpenAI o1 🍓
Cambrian-1 is a family of multimodal LLMs with a vision-centric design.
mPLUG-DocOwl: Modularized Multimodal Large Language Model for Document Understanding
[CVPR2024] The code for "Osprey: Pixel Understanding with Visual Instruction Tuning"
✨✨Woodpecker: Hallucination Correction for Multimodal Large Language Models. The first work to correct hallucinations in MLLMs.
Agent S: an open agentic framework that uses computers like a human
[ECCV2024] Grounded Multimodal Large Language Model with Localized Visual Tokenization
This project is the official implementation of 'LLMGA: Multimodal Large Language Model based Generation Assistant', ECCV2024 Oral
NeurIPS 2024 Paper: A Unified Pixel-level Vision LLM for Understanding, Generating, Segmenting, Editing
Personal Project: MPP-Qwen14B & MPP-Qwen-Next(Multimodal Pipeline Parallel based on Qwen-LM). Support [video/image/multi-image] {sft/conversations}. Don't let the poverty limit your imagination! Train your own 8B/14B LLaVA-training-like MLLM on RTX3090/4090 24GB.
🔥🔥🔥 A curated list of papers on LLMs-based multimodal generation (image, video, 3D and audio).
Youku-mPLUG: A 10 Million Large-scale Chinese Video-Language Pre-training Dataset and Benchmarks
Awesome_Multimodel is a curated GitHub repository that provides a comprehensive collection of resources for Multimodal Large Language Models (MLLM). It covers datasets, tuning techniques, in-context learning, visual reasoning, foundational models, and more. Stay updated with the latest advancement.
[NeurIPS'24 Spotlight] EVE: Encoder-Free Vision-Language Models
Add a description, image, and links to the mllm topic page so that developers can more easily learn about it.
To associate your repository with the mllm topic, visit your repo's landing page and select "manage topics."