Showing 5 open source projects for "file system"

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  • 1
    KoboldCpp

    KoboldCpp

    Run GGUF models easily with a UI or API. One File. Zero Install.

    KoboldCpp is an easy-to-use AI text-generation software for GGML and GGUF models, inspired by the original KoboldAI. It's a single self-contained distributable that builds off llama.cpp and adds many additional powerful features.
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    Downloads: 475 This Week
    Last Update:
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  • 2
    ChatGPT Desktop Application

    ChatGPT Desktop Application

    🔮 ChatGPT Desktop Application (Mac, Windows and Linux)

    ChatGPT Desktop Application (Mac, Windows and Linux)
    Downloads: 103 This Week
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  • 3
    scraper-with-chatgpt
    It is a powerful data scraping tool that helps you extract information from various online sources. Easily collect data from Google SERP, Maps, Shopify, Zillow, and more. With a user-friendly interface, you can scrape and save data in JSON or Excel formats. Unlock insights from the web effortlessly with scrape-it.cloud API.
    Downloads: 0 This Week
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  • 4
    Alpaca.cpp

    Alpaca.cpp

    Locally run an Instruction-Tuned Chat-Style LLM

    ...This combines the LLaMA foundation model with an open reproduction of Stanford Alpaca a fine-tuning of the base model to obey instructions (akin to the RLHF used to train ChatGPT) and a set of modifications to llama.cpp to add a chat interface. Download the zip file corresponding to your operating system from the latest release. The weights are based on the published fine-tunes from alpaca-lora, converted back into a PyTorch checkpoint with a modified script and then quantized with llama.cpp the regular way.
    Downloads: 9 This Week
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    Go From AI Idea to AI App Fast

    One platform to build, fine-tune, and deploy ML models. No MLOps team required.

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  • 5
    StudioGAN

    StudioGAN

    StudioGAN is a Pytorch library providing implementations of networks

    ...StudioGAN is a self-contained library that provides 7 GAN architectures, 9 conditioning methods, 4 adversarial losses, 13 regularization modules, 6 augmentation modules, 8 evaluation metrics, and 5 evaluation backbones. Among these configurations, we formulate 30 GANs as representatives. Each modularized option is managed through a configuration system that works through a YAML file.
    Downloads: 0 This Week
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