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: 1,470 This Week
    Last Update:
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  • 2
    dashAI

    dashAI

    dashAI: an interactive platform for training, evaluating and deploying

    dashAI is an open-source, No-code workbench for Exploratory Data Analysis and classical ML. Visual data preparation, multi-model experiments, XAI explainability, and a plugin-based extensible catalog. The platform guides users through a complete, traceable workflow — data ingestion → visual exploration → preprocessing → model training → evaluation → explainability — without writing a single line of code. Each step is explicit and reversible, keeping the user in control rather than...
    Downloads: 26 This Week
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  • 3
    ChatGPT Desktop Application

    ChatGPT Desktop Application

    🔮 ChatGPT Desktop Application (Mac, Windows and Linux)

    ChatGPT Desktop Application (Mac, Windows and Linux)
    Downloads: 230 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: 1 This Week
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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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