Awesome GitHub RepositoriesModel Training Engines

This repository serves as a centralized collection of state-of-the-art deep learning architectures and reference implementations designed for research and application development. It provides a comprehensive toolkit for computer vision and natural language processing, offering pre-built models and training pipelines for tasks ranging from image classification and object detection to complex sequence modeling. The project distinguishes itself by providing a flexible execution harness that manages the entire training lifecycle, including data ingestion and backpropagation. It supports scalable

This project is a fine-tuning framework and training pipeline designed to optimize and adapt large language and vision models. It provides a specialized toolkit for parameter-efficient tuning and supervised learning, serving as both a trainer for multimodal models and a deployment tool for serving fine-tuned models via high-performance inference engines. The framework focuses on reducing memory and compute requirements by updating a small subset of model parameters. It supports a wide range of adaptation strategies, including vision-language model training to align text, image, video, and aud

Unsloth is a high-performance training and inference platform designed to optimize the lifecycle of large language and multimodal models. It provides a comprehensive engine for fine-tuning, executing, and managing models locally, with a focus on reducing memory consumption and increasing compute speed on consumer-grade hardware. The platform distinguishes itself through hand-optimized kernels and automated computational graph techniques that maximize hardware throughput. It supports advanced training methodologies, including reinforcement learning for reasoning and efficient adapter-based fin

nanoGPT is a lightweight engine for training and fine-tuning transformer-based language models from scratch. It provides a minimalist codebase designed for educational exploration and rapid experimentation with neural network architectures, utilizing self-attention and feed-forward layers to process sequences and predict subsequent elements. The project distinguishes itself through a focus on high-speed data ingestion and hardware-accelerated performance. It includes a dedicated pipeline for transforming raw text into memory-mapped binary files, which enables efficient streaming during traini

Faceswap is a comprehensive framework for automated media manipulation and neural face synthesis. It provides a modular pipeline that manages the entire lifecycle of facial feature extraction, deep learning model training, and image conversion. By coordinating complex computer vision workflows, the system enables users to map facial identities between source and destination datasets while maintaining structural alignment and lighting consistency across video frames. The project distinguishes itself through a highly extensible plugin-based architecture that handles hardware-accelerated process

Dive into LLMs is a framework designed for fine-tuning large language models and constructing modular machine learning pipelines. It provides a structured environment for adjusting pre-trained models on custom datasets while optimizing computational efficiency and training time. The project distinguishes itself by offering an interactive web interface that allows for the deployment and publication of trained models directly to a browser. This enables users to test and interact with model results through a standardized web-based environment. The platform supports the creation of flexible work

Qwen is a comprehensive framework for large language model development, serving, and deployment. It provides a complete ecosystem for transformer-based sequence modeling, offering base models alongside specialized tools for instruction-tuned alignment, fine-tuning, and long-context inference. The project is designed to support both research and production environments, enabling users to train, optimize, and host generative models locally or across distributed hardware. The framework distinguishes itself through its focus on high-performance serving and extensibility. It features a high-perfor

Second-Me is a framework for orchestrating local agent tasks and fine-tuning personal language models. It provides a system for training specialized assistants on local datasets to support custom knowledge retrieval and task execution requirements. The project distinguishes itself through a modular architecture that manages the lifecycle of machine learning tasks. It includes a state manager that persists intermediate training progress to local storage, allowing for the interruption and resumption of long-running configuration processes. Furthermore, the system utilizes standardized protocols

RWKV-LM is a framework for training and deploying recurrent language models. It utilizes a linear-time recurrent architecture that enables text generation and sequence processing with constant memory and time complexity, avoiding the quadratic scaling of traditional attention caches. The project implements a parallelizable training mechanism that allows recurrent models to be trained using global operations while maintaining cache-free inference. It includes state-tuning capabilities to optimize the initial hidden state and utilizes adaptive probability-mass sampling to control token diversit

This project is a comprehensive toolkit designed for the full lifecycle management of large language and multimodal models. It functions as a unified orchestrator that handles the entire development process, ranging from dataset preparation and supervised fine-tuning to advanced reinforcement learning alignment and production-ready inference deployment. The platform distinguishes itself through a specialized reinforcement learning library that supports complex optimization algorithms, including group relative policy optimization and leave-one-out techniques, to improve model instruction-follo

ChatGLM3 is a comprehensive framework for deploying, fine-tuning, and serving large language models. It functions as a high-performance inference engine designed to support conversational AI, enabling developers to build interactive agents capable of multi-turn dialogue, autonomous code execution, and structured tool invocation. The project distinguishes itself through its focus on hardware-agnostic deployment and resource optimization. It supports distributed model parallelism across multiple graphics cards, paged key-value caching for concurrent request processing, and weight quantization t

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

Axolotl is a configuration-driven framework designed for the fine-tuning, evaluation, and quantization of large language models. It functions as a comprehensive orchestrator for distributed training, enabling users to manage complex workflows across multi-node and multi-GPU environments. By utilizing structured configuration files, the platform streamlines the setup of training parameters, dataset paths, and hardware distribution strategies. The project distinguishes itself through its support for diverse training methodologies, including full-parameter tuning, parameter-efficient adaptation,

Ludwig is a declarative machine learning framework designed for training neural networks and large language models using configuration files instead of manual coding. It functions as a multimodal model builder and a low-code tool for supervised fine-tuning, allowing users to build models that process mixed inputs of text, images, audio, and tabular data. The project distinguishes itself through an automated hyperparameter optimizer and a system for large language model fine-tuning using parameter-efficient adapters. It features a multimodal data pipeline and the ability to automatically gener

OpenRLHF is a training framework and alignment library designed for reinforcement learning from human feedback across distributed GPU clusters. It provides tools for aligning large language models and multimodal vision-language models using algorithms such as PPO, GRPO, and DPO. The framework distinguishes itself through a distributed inference engine that overlaps sample rollout with training to increase throughput. It supports scaling to models exceeding 70 billion parameters via parameter sharding and handles long-context sequences through ring-attention sequence parallelism. The project

ANE is an open-source framework for training neural networks directly on Apple's Neural Engine hardware, bypassing Apple's public Core ML toolchain through reverse-engineered private APIs. It provides low-level control over the ANE, enabling developers to compile custom compute graphs into binary kernels, partition transformer model layers into hardware-compatible subgraphs, and share GPU-allocated memory with the Neural Engine via zero-copy IOSurface buffers. The framework distinguishes itself by offering direct access to hardware performance counters and power telemetry for benchmarking thr

xtuner este un motor de antrenare cuprinzător pentru modele de limbaj mari (LLM), oferind un toolkit pentru pre-antrenare, fine-tuning supervizat și optimizarea modelelor multimodale vision-language. Servește ca un accelerator de antrenare distribuită și un framework specializat pentru scalarea modelelor Mixture-of-Experts și alinierea comportamentului modelului prin învățare prin consolidare din feedback uman (RLHF). Proiectul se distinge prin optimizări avansate de memorie și calcul, cum ar fi paralelismul de secvență pentru ferestre de context ultra-lungi și paralelismul de pipeline intercalat pentru a reduce timpul de inactivitate al GPU-ului. Oferă o suită dedicată pentru optimizarea preferințelor, implementând tehnici precum Group Relative Policy Optimization și Direct Preference Optimization pentru a rafina politicile modelului și sistemele de recompensă. Zonele largi de capabilități acoperă antrenarea distribuită a modelelor pe mai multe noduri, pregătirea seturilor de date multimodale și gestionarea fine-tuning-ului bazat pe adaptoare. Motorul include, de asemenea, instrumente pentru evaluarea modelului, fuziunea ponderilor (weight merging) și exportul parametrilor antrenați către motoarele de inferență. Antrenarea este gestionată prin fișiere de configurare standardizate și launchere distribuite pentru a asigura rezultate consistente pe clusterele de calcul.

Acest proiect este un framework de instruction tuning și un generator de date sintetice care utilizează modele teacher de mare capacitate pentru a produce perechi de tip instrucțiune-răspuns pentru antrenarea modelelor student mai mici. Oferă seturi de date și instrumente pentru supervised instruction tuning și învățare prin consolidare din feedback uman (RLHF). Framework-ul este specializat în tuning cross-lingual, oferind exemple de înaltă calitate de urmare a instrucțiunilor în engleză și chineză pentru a îmbunătăți generalizarea modelului în diferite scripturi. Include un instrument de modelare a recompenselor pentru crearea seturilor de date de preferință și evaluări comparative utilizate pentru a antrena sistemele de recompensă. Proiectul acoperă evaluarea modelelor de limbaj prin benchmark-uri de preferință și seturi de date de instrucțiuni nenaturale, precum și vizualizarea antrenamentului pentru a transforma log-urile brute în grafice de performanță.

UltraChat is a collection of large-scale conversational datasets and instruction-tuning data designed for training and evaluating generative AI models. It provides structured JSON data consisting of complex, multi-round dialogue sequences intended to refine the performance of large language models in chat tasks. The project focuses on improving reasoning and response quality through a diverse set of interactions across multiple sectors. These datasets are used for supervised fine-tuning and instruction tuning workflows to improve how models follow complex directions and maintain context acros

ChatGPT-System-Prompts is a curated reference collection of system prompts, prompt templates, and instructions designed to control and customize the behavior of conversational artificial intelligence models. The repository functions as an AI persona prompt collection, providing pre-written natural language instructions that configure language models to adopt specific roles, tones, professional styles, and interactive viewpoints during chat sessions. The content is organized as a flat file content management system utilizing markdown-document-based storage and distributed version control. This