Datasetloom

DatasetLoom  Intelligent Dataset Construction Platform for Multimodal Large Model Training

DatasetLoom is an intelligent platform designed for multimodal large model training. It supports end-to-end workflows including visual question answering, image captioning, DPO dataset generation, AI scoring, and training corpus export. This platform is tailored for AI engineers, researchers, and teams who need to build high-quality multimodal datasets.

Benefits

DatasetLoom offers several key advantages:*End-to-End Workflow: From document parsing and image annotation to model scoring and corpus export, DatasetLoom provides a complete data pipeline.*RAG Capabilities: Enables large models to generate dialogue datasets based on real-world knowledge, creating more professional, accurate, and traceable SFT and DPO training data.*Multimodal Data Support: Supports a wide range of data types including images, PDFs, Word documents, Markdown, and TXT files.*Smart Document Chunking: Automatically chunks documents by paragraph, heading, or semantic boundaries for better data organization.*AI Auto-Scoring System: Leverages LLMs to score output quality and compare multiple models.*Multi-User Collaboration: Supports role-based access control, allowing teams to work together efficiently.*Integration with Multiple Models: Compatible with GPT-4V, LLaVA, Qwen-VL, and other models.*Vector Database Integration: Built-in support for Qdrant vector database for high-performance vector storage and similarity search.

Use Cases

DatasetLoom is versatile and can be used in various scenarios:*AI Training Data Generation: Rapidly build SFT/DPO datasets for fine-tuning LLMs or multimodal models.*Academic & Research Data Curation: Parse papers and textbooks to generate Q&A pairs, summaries, and exercises.*Domain-Specific Knowledge Bases: Structure documents in healthcare, law, finance, and other fields for Q&A generation.*Model Evaluation & Comparison: Compare outputs from different models like GPT-4V, LLaVA, and Qwen-VL.*Team Collaboration & Annotation: Support multi-user workflows with clear permission controls.*Multimodal Content Understanding: Joint image and text processing to generate aligned multimodal data.*RAG-Driven Dialogue Data Generation: Generate professional, accurate, and source-traceable SFT/DPO dialogue datasets from real documents.

Additional Information

DatasetLoom is built on a modern Monorepo architecture using Next.js, NestJS, and Turborepo. It supports frontend-backend decoupling, high maintainability, and flexible scalability. The project is open-source and licensed under the MIT License, permitting free use, modification, and commercial applications. For those interested in contributing, the project welcomes issues and pull requests. DatasetLoom also supports Docker for easy deployment to servers or cloud environments.