3 Repos
Awesome GitHub RepositoriesMulti-Modal Data Processing
Tools and interfaces for managing and preprocessing diverse media types such as text, image, audio, and video for AI training.
Distinct from Cross-Modal Context Management: None of the candidates cover the general CLI-based processing of multiple modalities before training; they focus on retrieval, binding, or context management.
Explore 3 awesome GitHub repositories matching artificial intelligence & ml · Multi-Modal Data Processing. Refine with filters or upvote what's useful.
Awesome Multi-Modal Data Processing GitHub Repositories
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- pku-alignment/align-anything
PKU-Alignment/align-anything
4,661Auf GitHub ansehenAlign-anything ist ein Framework für das Alignment von multimodalen Large Language Models, das für das Fine-Tuning von Modellen über Text, Bild, Video und Audio hinweg entwickelt wurde. Es fungiert als Orchestrator für verteiltes Training und als Toolkit zur Implementierung von präferenzbasiertem Lernen, um sicherzustellen, dass das Modellverhalten menschlichen Absichten und Werten entspricht. Das Framework bietet spezialisierte Pipelines für Supervised Fine-Tuning und Direct Preference Optimization. Es enthält einen leistungsstarken Inference-Engine-Wrapper für Actor-Modelle, um die Zeit für die Sequenzgenerierung zu verkürzen, sowie eine dedizierte Trainingsumgebung für die Verfeinerung von Vision-Language-Action-Modellen in der Robotik. Das System verwaltet die multimodale Datenverarbeitung über ein Command-Line-Interface und unterstützt das automatisierte Deployment von Trainings-Workloads auf Hardware-Clustern mit Ressourcenmanagement. Die Funktionen umfassen die Implementierung von Alignment-Algorithmen, multimodales Fine-Tuning und die Optimierung von Hardwareressourcen.
Includes a command-line interface to manage and streamline the processing of diverse media inputs before training.
Python - flagai-open/flagai
FlagAI-Open/FlagAI
3,870Auf GitHub ansehenFlagAI is a distributed deep learning framework and platform designed for the end-to-end lifecycle of large-scale foundation models. It provides a toolkit for training, fine-tuning, and deploying large language models and multi-modal systems across multi-node computing clusters. The project features hardware-agnostic compute abstractions to ensure consistent execution across different accelerators. It includes a dedicated library for parameter-efficient fine-tuning, allowing large neural networks to be adapted to specific tasks with minimal parameter updates and reduced computational overhead
Provides a unified execution interface for processing diverse data types like text and images.
Python - open-mmlab/mmpretrain
open-mmlab/mmpretrain
3,842Auf GitHub ansehenmmpretrain is a modular PyTorch computer vision framework designed for developing, training, and benchmarking deep learning architectures. It serves as a comprehensive toolkit for vision tasks, providing a specialized platform for multimodal machine learning and self-supervised learning. The project features a computer vision model zoo containing architectural definitions and pre-trained weights for backbones such as ViT, ConvNeXt, and Swin Transformer. It distinguishes itself through a dedicated self-supervised learning toolkit that implements algorithms like MAE and DINO to train models wit
Processes diverse media types through specialized encoders and shared embedding spaces for joint image and text analysis.
Pythonbeitclipconstrastive-learning
