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)
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Updated
Dec 3, 2025 - HTML
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)
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Low-code framework for building custom LLMs, neural networks, and other AI models
Run, manage, and scale AI workloads on any AI infrastructure. Use one system to access & manage all AI compute (Kubernetes, 20+ clouds, or on-prem).
H2O LLM Studio - a framework and no-code GUI for fine-tuning LLMs. Documentation: https://docs.h2o.ai/h2o-llmstudio/
Efficient Triton Kernels for LLM Training
DLRover: An Automatic Distributed Deep Learning System
Collection of best practices, reference architectures, model training examples and utilities to train large models on AWS.
LLM (Large Language Model) FineTuning
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Nvidia GPU exporter for prometheus using nvidia-smi binary
MoBA: Mixture of Block Attention for Long-Context LLMs
LLM-PowerHouse: Unleash LLMs' potential through curated tutorials, best practices, and ready-to-use code for custom training and inferencing.
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"
USP: Unified (a.k.a. Hybrid, 2D) Sequence Parallel Attention for Long Context Transformers Model Training and Inference
InternEvo is an open-sourced lightweight training framework aims to support model pre-training without the need for extensive dependencies.
Adan: Adaptive Nesterov Momentum Algorithm for Faster Optimizing Deep Models
“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。
Byted PyTorch Distributed for Hyperscale Training of LLMs and RLs
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