Showing 5 open source projects for "x-plane"

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  • 1
    x-unet

    x-unet

    Implementation of a U-net complete with efficient attention

    Implementation of a U-net complete with efficient attention as well as the latest research findings. For 3d (video or CT / MRI scans).
    Downloads: 1 This Week
    Last Update:
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  • 2
    GPTel

    GPTel

    A no-frills ChatGPT client for Emacs

    ...Supports conversations (not just one-off queries) and multiple independent sessions. You can go back and edit your previous prompts, or even ChatGPT’s previous responses when continuing a conversation. These will be fed back to ChatGPT. Run M-x gptel to start or switch to the ChatGPT buffer. It will ask you for the key if you skipped the previous step. Run it with a prefix-arg to start a new session. In the gptel buffer, send your prompt with M-x gptel-send, bound to C-c RET. Set chat parameters (GPT model, directives etc) for the session by calling gptel-send with a prefix argument.
    Downloads: 0 This Week
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  • 3
    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: 347 This Week
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  • 4
    DALL-E 2 - Pytorch

    DALL-E 2 - Pytorch

    Implementation of DALL-E 2, OpenAI's updated text-to-image synthesis

    ...Specifically, this repository will only build out the diffusion prior network, as it is the best performing variant (but which incidentally involves a causal transformer as the denoising network) To train DALLE-2 is a 3 step process, with the training of CLIP being the most important. To train CLIP, you can either use x-clip package, or join the LAION discord, where a lot of replication efforts are already underway. Then, you will need to train the decoder, which learns to generate images based on the image embedding coming from the trained CLIP.
    Downloads: 7 This Week
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  • 5
    TFKit

    TFKit

    Handling multiple nlp task in one pipeline

    ...All data will be in csv format - tfkit will use csv for all task, normally it will have two columns, first columns is the input of models, the second column is the output of models. Plane text with no tokenization - there is no need to tokenize text before training, or do re-calculating for tokenization, tfkit will handle it for you. No header is needed.
    Downloads: 0 This Week
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