llm-training

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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/

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。

Adan: Adaptive Nesterov Momentum Algorithm for Faster Optimizing Deep Models

A curated collection of papers, technical reports, frameworks, and tools for on-policy distillation (OPD) of large language models

LLM-PowerHouse: Unleash LLMs' potential through curated tutorials, best practices, and ready-to-use code for custom training and inferencing.

Continual learning infra for self-improving agents

USP: Unified (a.k.a. Hybrid, 2D) Sequence Parallel Attention for Long Context Transformers Model Training and Inference

历年ICLR论文和开源项目合集，包含ICLR2021、ICLR2022、ICLR2023、ICLR2024、ICLR2025.

LLM (Large Language Model) FineTuning