Awesome GitHub RepositoriesModel Training Toolkits

LLaMA-Factory is a comprehensive suite for dataset preparation, model fine-tuning, memory optimization, and standardized API deployment. It provides a unified platform for the supervised and reward-based fine-tuning of large language models and vision-language models. The framework includes a specialized toolkit for training vision-language models and a model serving interface that deploys trained models through high-performance APIs. It utilizes precision tuning and quantization techniques to reduce the hardware requirements and memory footprint of large models. The system covers data pipel

This project is a comprehensive framework for the entire lifecycle of transformer-based language models, supporting everything from foundational pretraining to specialized deployment. It provides a modular toolkit for defining neural network architectures, managing data preparation pipelines, and executing training routines across various scales. The framework is designed to handle the full model development process, including supervised fine-tuning, behavioral alignment, and the integration of agentic capabilities. What distinguishes this framework is its focus on efficient training and adva

Fastai is a high-level deep learning library built on PyTorch that provides a unified interface for managing the entire machine learning lifecycle. It functions as a comprehensive training toolkit, abstracting hardware management and automating complex training loops to simplify the construction and execution of neural network models. The framework is distinguished by its notebook-centric development environment and a type-dispatching data pipeline that automatically applies transformations based on input data formats. It emphasizes transfer learning through discriminative layer-wise optimiza

This repository serves as a comprehensive collection of reference implementations for the PyTorch machine learning library. It provides practical examples for building, training, and deploying deep learning models, functioning as a toolkit for developers to explore neural network architectures and training workflows. The project distinguishes itself by offering concrete demonstrations of complex machine learning operations, ranging from computer vision tasks like object detection and depth estimation to the training of large-scale transformer models. These examples illustrate how to implement

This project is a distributed training infrastructure designed for aligning large language models through reinforcement learning. It functions as an end-to-end engine for complex alignment tasks, including proximal policy optimization, direct preference optimization, and iterative self-play. By providing a unified framework for multi-turn interactions and tool-use scenarios, it enables the development of models capable of reasoning and external environment engagement. The framework distinguishes itself through a decoupled architecture that separates model training from sample generation. This

This library provides a comprehensive framework for fine-tuning, aligning, and distilling transformer-based language models. It serves as a toolkit for adapting models to specialized domains through supervised learning, while offering advanced methodologies to improve output quality and reasoning capabilities. The project distinguishes itself through specialized alignment and optimization techniques, including direct preference optimization and reinforcement learning, which allow models to be tuned against human preferences without complex reward modeling. It further supports training efficie

Swift is a toolkit for the full-parameter and parameter-efficient fine-tuning of large language and multimodal models. It functions as a multimodal model trainer for text, image, video, and audio data, and includes specialized tools for model compression and reinforcement learning from human feedback. The framework provides an alignment toolkit for optimizing model behavior using preference learning algorithms and reinforcement learning. It integrates parameter-efficient fine-tuning methods to adapt models with minimal memory and compute requirements, alongside utilities for reducing hardware

This project is an automated machine learning framework and toolkit designed for training and tuning custom models for classification, regression, and recommendations. It functions as a multimodal machine learning toolkit capable of processing and training models using a combination of text, image, audio, and sensor data. The framework distinguishes itself as a multimodal data processor that can handle and visualize large datasets on a single machine using column-oriented disk storage. It includes a core machine learning model generator that converts trained models into formats compatible wit

OpenChatKit is a training and inference toolkit for large language models. It provides a comprehensive set of tools for managing the model lifecycle, including a fine-tuning pipeline, a model weight converter, and a command-line interface for interacting with conversational agents. The toolkit features a framework for retrieval augmented generation, allowing models to incorporate relevant context from external vector indices. It also includes utilities for converting trained model checkpoints into formats compatible with standard inference libraries. The project covers conversational AI trai

ImageAI is a Python computer vision library providing a suite of tools for image classification, object detection, and video analytics. It functions as an integrated framework for locating and labeling objects in static images and video streams, utilizing deep learning models for identification and categorization. The project includes a model training toolkit that allows for the creation of custom classifiers and detectors through scratch training or transfer learning. It features a GPU-accelerated inference engine to increase processing speed for vision tasks and includes specialized utiliti

BackgroundMattingV2 is a deep learning background matting tool and real-time image segmentation framework. It provides a system for isolating foreground subjects from high-resolution images and video feeds in real time. The project includes a deep learning model trainer for optimizing matting models through base convergence and end-to-end refinement. It also functions as a cross-runtime model exporter, converting trained neural networks into interchangeable formats for deployment across different software environments and hardware runtimes. The framework supports streaming processed webcam f

Firefly is a training framework and inference engine for large language models. It functions as a toolkit for pre-training and fine-tuning various open-weight architectures, providing a system for model alignment and parameter-efficient fine-tuning. The project includes utilities for merging adapter weights back into base models to create standalone files. It also provides a model alignment toolkit to format training data according to specific prompt templates, ensuring conversational consistency across different models. The framework supports distributed model training and preference-based

Dieses Projekt ist eine Bibliothek zur Bewertung der Videoqualität und eine Tool-Suite zur Quantifizierung von Videodegradation und zur Identifizierung von Banding-Artefakten. Es bietet eine wahrnehmungsbasierte Videoqualitätsmetrik, die verzerrte Videostreams mit einer hochwertigen Referenz vergleicht, um die menschliche visuelle Wahrnehmung zu schätzen. Das Toolkit enthält ein spezialisiertes System zum Training und zur Validierung benutzerdefinierter wahrnehmungsbasierter Qualitätsmodelle unter Verwendung spezifischer Datensätze. Es bietet zudem einen kontrastbewussten Multiskalen-Index speziell zur Erkennung von Konturierungs- und Banding-Artefakten in Videostreams. Die Bibliothek deckt die Berechnung objektiver Videometriken und die Analyse der Videokompression ab und nutzt dabei sowohl traditionelle mathematische Maße als auch fusionsbasierte Algorithmen. Diese Funktionen ermöglichen die Bewertung verschiedener Encoder oder Bitraten sowie die Identifizierung spezifischer visueller Verzerrungen. Die Implementierung besteht aus einer Kernbibliothek in C mit einem Python-Binding-Wrapper für High-Level-Analysen und Modelltraining.

Dies ist ein chinesisches NLP-Toolkit (Natural Language Processing), das eine Suite von Tools für Wortsegmentierung, Part-of-Speech-Tagging und Named-Entity-Recognition bereitstellt. Es enthält einen neuronalen Dependency-Parser zur Analyse syntaktischer und semantischer Beziehungen zwischen Wörtern sowie eine Machine-Learning-Trainingssuite zur Erstellung benutzerdefinierter linguistischer Modelle mittels annotierter Datensätze. Das Toolkit zeichnet sich durch seine Flexibilität bei der Bereitstellung aus und bietet einen dockerisierten Server sowie ein Web-Service-Interface, das Verarbeitungsfunktionen via API zugänglich macht. Es unterstützt die Verwendung vortrainierter Modelle und erlaubt die Integration externer Lexika und Wörterbucherweiterungen, um die Analysegenauigkeit zu verbessern. Das Projekt deckt eine vollständige Pipeline linguistischer Aufgaben ab, einschließlich Satzsegmentierung, syntaktischem Dependency-Mapping und Semantic-Role-Labeling. Diese Funktionen sind über ein Command-Line-Interface, eigenständige Module oder integrierte Analyse-Pipelines verfügbar. Die Kernlogik ist in C++ implementiert, mit offiziellen Sprach-Bindings für Python und Java.

This project is an LLM research orchestrator and autonomous AI agent framework designed to automate the scientific lifecycle. It functions as an end-to-end research pipeline and model training toolkit, managing everything from initial literature reviews and hypothesis testing to the final drafting of academic papers. The system is distinguished by its ability to convert unstructured academic PDFs into machine-executable knowledge layers, allowing agents to reproduce and extend research findings. It employs a two-loop orchestration architecture and a specialized research engineering skill libr

This project is a research framework and toolkit designed for training large-scale vision transformers and multimodal language models. It provides a comprehensive suite for vision-language pretraining, enabling the development of models that map images and text into shared latent spaces. The framework is distinguished by its capabilities in high-fidelity image generation and multimodal research, utilizing normalizing flows and variational autoencoders to produce images from text prompts or class labels. It supports the development of both generative and contrastive models, allowing for a wide