Showing 3 open source projects for "structure"

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    Darling

    Darling

    Darwin/macOS emulation layer for Linux

    ...It implements macOS libraries and frameworks, enabling macOS binaries to execute on Linux. Darling has support for DPREFIXes, which are very similar to WINEPREFIXes. They are virtual “chroot” environments with an macOS-like filesystem structure, where you can install software safely. The default DPREFIX location is ~/.darling, but this can be changed by exporting an identically named environment variable. A prefix is automatically created and initialized on first use. Please note that we use overlayfs for creating prefixes, and so we cannot support putting prefix on a filesystem like NFS or eCryptfs. ...
    Downloads: 6 This Week
    Last Update:
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  • 2
    Translation Agent

    Translation Agent

    Agentic translation using reflection workflow

    ...Instead of asking a language model for a single direct translation, it first generates a translation, then asks the model to critique it, and finally uses that critique to produce a stronger version. This structure makes the system more steerable than a traditional translation pipeline. Users can adjust prompts to control tone, formality, terminology, idiom handling, and regional language choices. The project includes example scripts and a simple Python interface for translating between source and target languages. It is useful for researchers, developers, and localization teams exploring how LLM-based workflows can produce better, more customizable translations.
    Downloads: 2 This Week
    Last Update:
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  • 3
    CycleGAN and pix2pix in PyTorch

    CycleGAN and pix2pix in PyTorch

    Image-to-Image Translation in PyTorch

    CycleGAN and pix2pix in PyTorch repository is a PyTorch implementation of two influential image-to-image translation frameworks: CycleGAN (for unpaired translation) and pix2pix (for paired translation). This repo gives developers and researchers a convenient, modern (PyTorch-based) platform to train and test these methods — supporting both paired datasets (input to output) and unpaired datasets (domain-to-domain) with minimal changes. The code supports standard training and inference...
    Downloads: 0 This Week
    Last Update:
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