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本项目旨在分享大模型相关技术原理以及实战经验（大模型工程化、大模型应用落地）
Low-code framework for building custom LLMs, neural networks, and other AI models
The AI Compute Platform for frontier teams. SkyPilot turns fragmented AI compute into one AI supercomputer, so frontier AI teams build custom intelligence faster.
Efficient Triton Kernels for LLM Training
Democratizing Reinforcement Learning for LLMs
H2O LLM Studio - a framework and no-code GUI for fine-tuning LLMs. Documentation: https://docs.h2o.ai/h2o-llmstudio/
《深入理解 AI Infra：量化分析与系统设计》（李博杰 著）开源书稿：从硬件约束和模型架构出发，量化推导 LLM 推理与训练系统设计。含全书正文、PDF、配套计算工具与实验
Continual learning infra for self-improving agents
OpenLake is a high performance storage engine for efficient LLM inference and GPU Training
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"
MoBA: Mixture of Block Attention for Long-Context LLMs
DLRover: An Automatic Distributed Deep Learning System
Nvidia GPU exporter for prometheus using nvidia-smi binary OR using NVML
GraphGen: Enhancing Supervised Fine-Tuning for LLMs with Knowledge-Driven Synthetic Data Generation
Byted PyTorch Distributed for Hyperscale Training of LLMs and RLs
“AI-Compass”将为社区指引在 AI 技术海洋中航行的方向，无论你是初学者还是进阶开发者，都能在这里找到通往 AI 各大方向的路径。旨在帮助开发者系统性地了解 AI 的核心概念、主流技术、前沿趋势，并通过实践掌握从理论到落地的全过程。
A curated collection of papers, technical reports, frameworks, and tools for on-policy distillation (OPD) of large language models
Adan: Adaptive Nesterov Momentum Algorithm for Faster Optimizing Deep Models
LLM-PowerHouse: Unleash LLMs' potential through curated tutorials, best practices, and ready-to-use code for custom training and inferencing.
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