Showing 4 open source projects for "port"

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  • 1
    llama.cpp

    llama.cpp

    Port of Facebook's LLaMA model in C/C++

    The llama.cpp project enables the inference of Meta's LLaMA model (and other models) in pure C/C++ without requiring a Python runtime. It is designed for efficient and fast model execution, offering easy integration for applications needing LLM-based capabilities. The repository focuses on providing a highly optimized and portable implementation for running large language models directly within C/C++ environments.
    Downloads: 152 This Week
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  • 2
    Make-A-Video - Pytorch (wip)

    Make-A-Video - Pytorch (wip)

    Implementation of Make-A-Video, new SOTA text to video generator

    ...Passing in images (if one were to pretrain on images first), both temporal convolution and attention will be automatically skipped. In other words, you can use this straightforwardly in your 2d Unet and then port it over to a 3d Unet once that phase of the training is done.
    Downloads: 0 This Week
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  • 3
    min(DALL·E)

    min(DALL·E)

    min(DALL·E) is a fast, minimal port of DALL·E Mini to PyTorch

    This is a fast, minimal port of Boris Dayma's DALL·E Mini (with mega weights). It has been stripped down for inference and converted to PyTorch. The only third-party dependencies are numpy, requests, pillow and torch. The required models will be downloaded to models_root if they are not already there. Set the dtype to torch.float16 to save GPU memory. If you have an Ampere architecture GPU you can use torch.bfloat16.
    Downloads: 0 This Week
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  • 4
    FID score for PyTorch

    FID score for PyTorch

    Compute FID scores with PyTorch

    This is a port of the official implementation of Fréchet Inception Distance to PyTorch. FID is a measure of similarity between two datasets of images. It was shown to correlate well with human judgement of visual quality and is most often used to evaluate the quality of samples of Generative Adversarial Networks. FID is calculated by computing the Fréchet distance between two Gaussians fitted to feature representations of the Inception network.
    Downloads: 0 This Week
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