Showing 3 open source projects for "execute"

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    Superduper

    Superduper

    Superduper: Integrate AI models and machine learning workflows

    ...This allows developers to completely avoid implementing MLOps, ETL pipelines, model deployment, data migration, and synchronization. Using Superduper is simply "CAPE": Connect to your data, apply arbitrary AI to that data, package and reuse the application on arbitrary data, and execute AI-database queries and predictions on the resulting AI outputs and data.
    Downloads: 0 This Week
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  • 2
    Eko

    Eko

    Build Production-ready Agentic Workflow with Natural Language

    ...It provides a unified interface for managing agents, offering features such as web resource access and high task complexity handling. Eko is open-source and can be used to execute tasks like browser automation, system operations, and software testing.
    Downloads: 0 This Week
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  • 3
    Petals

    Petals

    Run 100B+ language models at home, BitTorrent-style

    ...Single-batch inference runs at ≈ 1 sec per step (token) — up to 10x faster than offloading, enough for chatbots and other interactive apps. Parallel inference reaches hundreds of tokens/sec. Beyond classic language model APIs — you can employ any fine-tuning and sampling methods, execute custom paths through the model, or see its hidden states. You get the comforts of an API with the flexibility of PyTorch. You can also host BLOOMZ, a version of BLOOM fine-tuned to follow human instructions in the zero-shot regime — just replace bloom-petals with bloomz-petals. Petals runs large language models like BLOOM-176B collaboratively — you load a small part of the model, then team up with people serving the other parts to run inference or fine-tuning.
    Downloads: 0 This Week
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