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A PyTorch native platform for training generative AI models
A high-performance inference engine for LLMs, optimized for diverse AI accelerators.
VeOmni: Scaling Any Modality Model Training with Model-Centric Distributed Recipe Zoo
Train a 1B LLM with 1T tokens from scratch by personal
Implementation for FP8/INT8 Rollout for RL training without performence drop.
verl-agent is an extension of veRL, designed for training LLM/VLM agents via RL. verl-agent is also the official code for paper "Group-in-Group Policy Optimization for LLM Agent Training"
Fully open reproduction of DeepSeek-R1
An Easy-to-use, Scalable and High-performance RLHF Framework based on Ray (PPO & GRPO & REINFORCE++ & vLLM & Ray & Dynamic Sampling & Async Agentic RL)
verl: Volcano Engine Reinforcement Learning for LLMs
A Next-Generation Training Engine Built for Ultra-Large MoE Models
Use PEFT or Full-parameter to CPT/SFT/DPO/GRPO 500+ LLMs (Qwen3, Qwen3-MoE, Llama4, GLM4.5, InternLM3, DeepSeek-R1, ...) and 200+ MLLMs (Qwen3-VL, Qwen3-Omni, InternVL3.5, Ovis2.5, Llava, GLM4v, Ph…
Data processing for and with foundation models! 🍎 🍋 🌽 ➡️ ➡️🍸 🍹 🍷
Creation of annotated datasets from scratch using Generative AI and Foundation Computer Vision models
DataDreamer: Prompt. Generate Synthetic Data. Train & Align Models. 🤖💤
mllm-npu: training multimodal large language models on Ascend NPUs
unify-easy-llm（ULM）旨在打造一个简易的一键式大模型训练工具，支持Nvidia GPU、Ascend NPU等不同硬件以及常用的大模型。
Fine-tuning & Reinforcement Learning for LLMs. 🦥 Train OpenAI gpt-oss, DeepSeek-R1, Qwen3, Gemma 3, TTS 2x faster with 70% less VRAM.
DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
EasyNLP: A Comprehensive and Easy-to-use NLP Toolkit
🤖 Assemble, configure, and deploy autonomous AI Agents in your browser.
Train transformer language models with reinforcement learning.
整理开源的中文大语言模型，以规模较小、可私有化部署、训练成本较低的模型为主，包括底座模型，垂直领域微调及应用，数据集与教程等。
Pytorch implementation of CVPR2020 paper “VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation”