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ZheJiang University
- Hang Zhou, Zhe Jiang
Stars
Reverse Engineering Gemma 3n: Google's New Edge-Optimized Language Model
Awesome RL Reasoning Recipes ("Triple R")
This repository provides valuable reference for researchers in the field of multimodality, please start your exploratory travel in RL-based Reasoning MLLMs!
Reading notes about Multimodal Large Language Models, Large Language Models, and Diffusion Models
A Survey of Direct Preference Optimization (DPO)
verl: Volcano Engine Reinforcement Learning for LLMs
An Easy-to-use, Scalable and High-performance RLHF Framework based on Ray (PPO & GRPO & REINFORCE++ & vLLM & Ray & Dynamic Sampling & Async Agent RL)
A collection of LLM papers, blogs, and projects, with a focus on OpenAI o1 🍓 and reasoning techniques.
Official Repo for Open-Reasoner-Zero
Fully open reproduction of DeepSeek-R1
This is the official implementation of VideoMaker: Zero-shot Customized Video Generation with the Inherent Force of Video Diffusion Models
Wan: Open and Advanced Large-Scale Video Generative Models
Official repository of In-Context LoRA for Diffusion Transformers
(ෆ`꒳´ෆ) A Survey on Text-to-Image Generation/Synthesis.
A high-throughput and memory-efficient inference and serving engine for LLMs
Ongoing research training transformer models at scale
A library for accelerating Transformer models on NVIDIA GPUs, including using 8-bit floating point (FP8) precision on Hopper, Ada and Blackwell GPUs, to provide better performance with lower memory…
An efficient, flexible and full-featured toolkit for fine-tuning LLM (InternLM2, Llama3, Phi3, Qwen, Mistral, ...)
Pandora: Towards General World Model with Natural Language Actions and Video States
SEED-Voken: A Series of Powerful Visual Tokenizers
A curated list of awesome Multimodal studies.
Qwen3 is the large language model series developed by Qwen team, Alibaba Cloud.
Lumina-T2X is a unified framework for Text to Any Modality Generation
🔥🔥🔥 A curated list of papers on LLMs-based multimodal generation (image, video, 3D and audio).
A collection of awesome video generation studies.