Showing 29 open source projects for "cross-platform"

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  • 1
    Style Aligned

    Style Aligned

    Official code for Style Aligned Image Generation via Shared Attention

    StyleAligned is a diffusion-model editing technique and codebase that preserves the visual “style” of an original image while applying new semantic edits driven by text. Instead of fully re-generating an image—and risking changes to lighting, texture, or rendering choices—the method aligns internal features across denoising steps so the target edit inherits the source style. This alignment acts like a constraint on the model’s evolution, steering composition, palette, and brushwork even as...
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  • 2
    GPT Neo

    GPT Neo

    An implementation of model parallel GPT-2 and GPT-3-style models

    An implementation of model & data parallel GPT3-like models using the mesh-tensorflow library. If you're just here to play with our pre-trained models, we strongly recommend you try out the HuggingFace Transformer integration. Training and inference is officially supported on TPU and should work on GPU as well. This repository will be (mostly) archived as we move focus to our GPU-specific repo, GPT-NeoX. NB, while neo can technically run a training step at 200B+ parameters, it is very...
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  • 3
    MUSE

    MUSE

    A library for Multilingual Unsupervised or Supervised word Embeddings

    ...By mapping languages into a common vector space, MUSE makes it straightforward to build cross-lingual applications where resources are scarce for some languages. The training and evaluation pipeline is lightweight and fast, so experimenting with different languages or initialization strategies is easy. Beyond dictionary induction, the learned embeddings are often used as building blocks for downstream tasks like classification, retrieval, or machine translation.
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  • 4
    Hunyuan-MT-7B

    Hunyuan-MT-7B

    Tencent’s 36-language state-of-the-art translation model

    ...It forms part of the Hunyuan Translation Model family, alongside Hunyuan-MT-Chimera, which ensembles outputs for even higher accuracy. Trained with a comprehensive framework spanning pretraining, cross-lingual pretraining, supervised fine-tuning, enhancement, and ensemble refinement, the model achieves competitive results against systems of similar or larger scale. At WMT25, Hunyuan-MT secured first place in 30 of the 31 translation categories it entered, highlighting its reliability in real-world multilingual use. The model offers flexible deployment options with variants such as FP8 quantization and ensemble configurations. ...
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