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 هو محرك تدريب شامل للنماذج اللغوية الكبيرة، يقدم مجموعة أدوات للتدريب المسبق، والضبط الدقيق الخاضع للإشراف، وتحسين النماذج متعددة الوسائط للرؤية واللغة. يعمل كمسرع تدريب موزع وإطار عمل متخصص لتوسيع نطاق نماذج خليط الخبراء (Mixture-of-Experts) ومواءمة سلوك النموذج من خلال التعلم التعزيزي من التغذية الراجعة البشرية. يتميز المشروع بتحسينات متقدمة للذاكرة والحوسبة، مثل توازي التسلسل لنوافذ السياق الطويلة جداً وتوازي خط الأنابيب المتداخل لتقليل وقت خمول GPU. يوفر مجموعة مخصصة لتحسين التفضيلات، وتنفيذ تقنيات مثل تحسين السياسة النسبية للمجموعة وتحسين التفضيل المباشر لتحسين سياسات النموذج وأنظمة المكافآت. تغطي مجالات الإمكانيات الواسعة تدريب النماذج الموزعة عبر عقد متعددة، وإعداد مجموعات البيانات متعددة الوسائط، وإدارة الضبط الدقيق القائم على المحولات. يتضمن المحرك أيضاً أدوات لتقييم النموذج، ودمج الأوزان، وتصدير المعلمات المدربة إلى محركات الاستنتاج. تتم إدارة التدريب عبر ملفات تكوين موحدة ومشغلات موزعة لضمان نتائج متسقة عبر مجموعات الحوسبة.

warp-ctc is a high-performance library for calculating connectionist temporal classification loss to train sequence-to-sequence deep learning models. It provides a numerical stability layer using log-space computation to prevent underflow and precision errors during probability calculations for long sequences. The library utilizes hardware-accelerated kernels to compute loss in parallel across CPU and GPU architectures. It focuses on increasing training throughput by optimizing the dynamic programming steps of the CTC algorithm. These capabilities support the training of models for speech re

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