Showing 4 open source projects for "links"

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  • 1
    OpenAI Realtime Embedded

    OpenAI Realtime Embedded

    Instructions on how to use the Realtime API on Microcontrollers

    ...It does not appear to include a full cross-platform embedded SDK in the main branch (the core content is mostly links and minimal README), but acts as a launching point for integrating realtime on microcontrollers.
    Downloads: 0 This Week
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  • 2
    Hy-MT2
    ...They can handle tasks involving terminology, style, personalization, delimiters, structured data, and background-aware translation. The repository also provides model links, inference guidance, deployment examples, training documentation, and quantization resources. Overall, Hy-MT2 is useful for developers and researchers who need flexible, instruction-following machine translation models for production, evaluation, or experimentation.
    Downloads: 0 This Week
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  • 3
    Stable Diffusion Web UI Extensions

    Stable Diffusion Web UI Extensions

    Extension index for stable-diffusion-webui

    ...It aggregates metadata for hundreds of community plugins—image utilities, ControlNet tools, upscalers, prompt helpers, animation suites—so users can browse and add capabilities directly from the UI. The index maintains short descriptions, tags, and repository links, enabling quick filtering by purpose or workflow. It also standardizes submission format so extension authors can contribute entries that the Web UI can parse reliably. For end users, this turns the Web UI into a modular platform where new features appear without manual cloning or guesswork. The project effectively coordinates a thriving plugin ecosystem, keeping discovery and updates lightweight and centralized.
    Downloads: 0 This Week
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  • 4
    CLIP

    CLIP

    CLIP, Predict the most relevant text snippet given an image

    CLIP (Contrastive Language-Image Pretraining) is a neural model that links images and text in a shared embedding space, allowing zero-shot image classification, similarity search, and multimodal alignment. It was trained on large sets of (image, caption) pairs using a contrastive objective: images and their matching text are pulled together in embedding space, while mismatches are pushed apart. Once trained, you can give it any text labels and ask it to pick which label best matches a given image—even without explicit training for that classification task. ...
    Downloads: 0 This Week
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