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Comprehensive pipeline de machine learning Tools for Every Need

Get access to pipeline de machine learning solutions that address multiple requirements. One-stop resources for streamlined workflows.

pipeline de machine learning

  • 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.

  • ClassiCore-Public

    ClassiCore-Public automates ML classification, offering data preprocessing, model selection, hyperparameter tuning, and scalable API deployment.
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    What is ClassiCore-Public?
    ClassiCore-Public provides a comprehensive environment for building, optimizing, and deploying classification models. It features an intuitive pipeline builder that handles raw data ingestion, cleaning, and feature engineering. The built-in model zoo includes algorithms like Random Forests, SVMs, and deep learning architectures. Automated hyperparameter tuning uses Bayesian optimization to find optimal settings. Trained models can be deployed as RESTful APIs or microservices, with monitoring dashboards tracking performance metrics in real time. Extensible plugins let developers add custom preprocessing, visualization, or new deployment targets, making ClassiCore-Public ideal for industrial-scale classification tasks.
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