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deployment scripts

  • Trinity-RFT

    An open-source retrieval-augmented fine-tuning framework that boosts text, image, and video model performance with scalable retrieval.
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    What is Trinity-RFT?
    Trinity-RFT (Retrieval Fine-Tuning) is a unified open-source framework designed to enhance model accuracy and efficiency by combining retrieval and fine-tuning workflows. Users can prepare a corpus, build a retrieval index, and plug the retrieved context directly into training loops. It supports multi-modal retrieval for text, images, and video, integrates with popular vector stores, and offers evaluation metrics and deployment scripts for rapid prototyping and production deployment.
    Trinity-RFT Core Features
    • Multi-modal retrieval index construction
    • Retrieval-augmented fine-tuning pipeline
    • Integration with FAISS and other vector stores
    • Configurable retriever and encoder modules
    • Built-in evaluation and analysis tools
    • Deployment scripts for ModelScope platform
    Trinity-RFT Pro & Cons

    The Cons

    Currently under active development, which might limit stability and production readiness.
    Requires significant computational resources (Python >=3.10, CUDA >=12.4, and at least 2 GPUs).
    Installation and setup process might be complex for users unfamiliar with reinforcement learning frameworks and distributed system management.

    The Pros

    Supports unified and flexible reinforcement fine-tuning modes including on-policy, off-policy, synchronous, asynchronous, and hybrid training.
    Designed with decoupled architecture separating explorer and trainer for scalable distributed deployments.
    Robust agent-environment interaction handling delayed rewards, failures, and long latencies.
    Optimized systematic data processing pipelines for diverse and messy data.
    Supports human-in-the-loop training and integration with major datasets and models from Huggingface and ModelScope.
    Open-source with active development and comprehensive documentation.
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