Awesome GitHub RepositoriesTraining Throughput Optimization

Axolotl is a distributed training orchestrator and fine-tuning framework for large language models, multimodal systems, and quantized models. It provides a structured environment for specializing pre-trained models through full parameter updates or low-rank adaptation, as well as aligning model outputs with human expectations via preference tuning pipelines and reward modeling. The system distinguishes itself through a configuration-driven pipeline that manages preprocessing and training workflows via a single file for reproducibility. It implements high-throughput optimizations such as multi

Apex is a high-performance toolkit for PyTorch designed to coordinate distributed training, execute fused GPU kernels, manage mixed precision, and implement optimized distributed optimizers. It provides specialized tools for scaling model training across multiple GPUs and nodes to increase processing speed and throughput. The library features high-performance implementations of Adam and LAMB optimizers to reduce synchronization overhead and memory bottlenecks. It utilizes fused CUDA kernels to combine neural network operations, reducing memory overhead and increasing execution speed. The too

ERNIE is a development toolkit for training, fine-tuning, and deploying large language models built on the PaddlePaddle deep learning platform. It provides a comprehensive suite of core components, including an inference server for vision and language models, a training and fine-tuning toolkit, and a framework for building retrieval-augmented generation systems using private knowledge bases. The project features multimodal AI models capable of reasoning across text, images, and video to perform complex visual understanding and information extraction. It distinguishes itself through specialize

LARK is a development toolkit for training, fine-tuning, and deploying large language models and multimodal models based on PaddlePaddle. It functions as a comprehensive framework that includes an LLM training orchestrator, an inference server, and a multimodal model framework for processing text, image, and video inputs. The project features a retrieval-augmented generation system for building conversational applications that integrate web search and private knowledge bases. It provides specific capabilities for multimodal reasoning and complex logic, enabling the extraction of structured da

gpt-neox is a distributed training system and framework for building large-scale autoregressive language models. It implements the transformer architecture and provides a toolkit for training models with billions of parameters by distributing weights across compute clusters. The framework distinguishes itself through extensive support for distributed model parallelism, including pipeline and sequence parallelism, to overcome single-device memory limits. It further supports sparse model architectures using a mixture of experts system with Sinkhorn-based routing. The project covers a broad ran

xtuner 是一个用于大语言模型的综合训练引擎，提供用于预训练、监督微调以及视觉-语言多模态模型优化的工具包。它作为一个分布式训练加速器和专门的框架，用于扩展专家混合（MoE）模型，并通过人类反馈强化学习（RLHF）来对齐模型行为。 该项目的特色在于先进的内存和计算优化，例如用于超长上下文窗口的序列并行，以及用于减少 GPU 空闲时间的交错流水线并行。它提供了一套专门的偏好优化套件，实现了如组相对策略优化（GRPO）和直接偏好优化（DPO）等技术，以优化模型策略和奖励系统。 广泛的功能领域涵盖跨多节点的分布式模型训练、多模态数据集准备以及基于适配器（Adapter）的微调管理。该引擎还包括用于模型评估、权重合并以及将训练参数导出到推理引擎的工具。 训练通过标准化的配置文件和分布式启动器进行管理，以确保跨计算集群的一致结果。

warp-ctc 是一个高性能库，用于计算连接时序分类（CTC）损失，以训练序列到序列（seq2seq）深度学习模型。它通过对数空间计算提供数值稳定性层，防止长序列概率计算过程中的下溢和精度误差。 该库利用硬件加速内核在 CPU 和 GPU 架构上并行计算损失，通过优化 CTC 算法的动态规划步骤来提高训练吞吐量。 这些功能支持语音识别、手写体光学字符识别（OCR）以及通用序列到序列映射模型的训练。该项目还包含在 TensorFlow 中计算损失和进行对齐无关训练的集成方案。

Tinker Cookbook is an open-source framework for fine-tuning large language models, supporting supervised learning, reinforcement learning, and parameter-efficient techniques like LoRA adapters. It provides a complete pipeline for aligning models with human preferences through multi-stage RLHF workflows, from supervised fine-tuning through preference optimization to reinforcement learning. The framework distinguishes itself through recipe-based training orchestration, where fine-tuning workflows are defined as composable recipe files that chain data loading, model configuration, and training l

RLinf is a distributed reinforcement learning orchestrator and embodied AI training framework. It provides the infrastructure to train vision-language-action models and robotic policies using a combination of reinforcement learning and supervised fine-tuning. The system is designed for scaling workloads across GPU clusters, managing the placement of actors, rollout workers, and environment components. It features a specialized robotics data collection pipeline for gathering teleoperated demonstrations and simulation trajectories into standardized replay buffers, alongside a hardware interface