Witness the aha moment of VLM with less than $3.

veRL: Volcano Engine Reinforcement Learning for LLM

DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understanding

A generative world for general-purpose robotics & embodied AI learning.

Collection of awesome medical dataset resources.

[CVPR 2024 Oral] InternVL Family: A Pioneering Open-Source Alternative to GPT-4o. 接近GPT-4o表现的开源多模态对话模型

Qwen2.5-VL is the multimodal large language model series developed by Qwen team, Alibaba Cloud.

Segment Anything in Medical Images

Grok open release

The official repo of Qwen-VL (通义千问-VL) chat & pretrained large vision language model proposed by Alibaba Cloud.

BiomedGPT: A Generalist Vision-Language Foundation Model for Diverse Biomedical Tasks

Controllable video and image Generation, SVD, Animate Anyone, ControlNet, ControlNeXt, LoRA

[ICLR 2025] This is the official repository of our paper "MedTrinity-25M: A Large-scale Multimodal Dataset with Multigranular Annotations for Medicine“

王孟源的博客镜像【可搜索】，每 6 小时更新

[NeurIPS 2024 Best Paper][GPT beats diffusion🔥] [scaling laws in visual generation📈] Official impl. of "Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction". An *ult…

[ECCV2024 Oral🔥] Official Implementation of "GiT: Towards Generalist Vision Transformer through Universal Language Interface"

[CVPR 2024 & NeurIPS 2024] EmbodiedScan: A Holistic Multi-Modal 3D Perception Suite Towards Embodied AI

Welcome to the Llama Cookbook! This is your go to guide for Building with Llama: Getting started with Inference, Fine-Tuning, RAG. We also show you how to solve end to end problems using Llama mode…

Inference Llama 2 in one file of pure C

Inference code for Llama models

Empowers LLMs with the ability to see and draw.

A programming framework for agentic AI 🤖 PyPi: autogen-agentchat Discord: https://aka.ms/autogen-discord Office Hour: https://aka.ms/autogen-officehour

Representation Engineering: A Top-Down Approach to AI Transparency

活动资讯小帮手（

Official implementation for paper "Shifting More Attention to Breast Lesion Segmentation in Ultrasound Videos"