Showing 8 open source projects for "note"

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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.
    Downloads: 400 This Week
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  • 2
    GPT-Code UI

    GPT-Code UI

    An open source implementation of OpenAI's ChatGPT Code interpreter

    ...You can put a .env in the working directory to load the OPENAI_API_KEY environment variable. For Azure OpenAI Services, there are also other configurable variables like deployment name. See .env.azure-example for more information. Note that model selection on the UI is currently not supported for Azure OpenAI Services.
    Downloads: 0 This Week
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  • 3
    audio-diffusion-pytorch

    audio-diffusion-pytorch

    Audio generation using diffusion models, in PyTorch

    ...The provided models are waveform-based, however, the U-Net (built using a-unet), DiffusionModel, diffusion method, and diffusion samplers are both generic to any dimension and highly customizable to work on other formats. Note: no pre-trained models are provided here, this library is meant for research purposes.
    Downloads: 2 This Week
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  • 4
    Minimal text diffusion

    Minimal text diffusion

    A minimal implementation of diffusion models for text generation

    ...To train a model, run scripts/train.sh. By default, this will train a model on the simple corpus. However, you can change this to any text file using the --train_data argument. Note that you may have to increase the sequence length (--seq_len) if your corpus is longer than the simple corpus. The other default arguments are set to match the best setting I found for the simple corpus.
    Downloads: 0 This Week
    Last Update:
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  • 5
    AnimeGAN

    AnimeGAN

    A simple PyTorch Implementation of Generative Adversarial Networks

    ...Anime-style images of 126 tags are collected from danbooru.donmai.us using the crawler tool gallery-dl. The images are then processed by an anime face detector python-anime face. The resulting dataset contains ~143,000 anime faces. Note that some of the tags may no longer be meaningful after cropping, i.e. the cropped face images under the 'uniform' tag may not contain visible parts of uniforms.
    Downloads: 0 This Week
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  • 6
    CIPS-3D

    CIPS-3D

    3D-aware GANs based on NeRF (arXiv)

    ...The problem of mirror symmetry refers to the sudden change of the direction of the bangs near the yaw angle of pi/2. We propose to use an auxiliary discriminator to solve this problem. Note that in the initial stage of training, the auxiliary discriminator must dominate the generator more than the main discriminator does. Otherwise, if the main discriminator dominates the generator, the mirror symmetry problem will still occur. In practice, progressive training is able to guarantee this. We have trained many times from scratch. ...
    Downloads: 0 This Week
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  • 7
    gpt-2-simple

    gpt-2-simple

    Python package to easily retrain OpenAI's GPT-2 text-generating model

    ...(as the GPT-2 model is hosted on GCP) You can use gpt-2-simple to retrain a model using a GPU for free in this Colaboratory notebook, which also demos additional features of the package. Note: Development on gpt-2-simple has mostly been superceded by aitextgen, which has similar AI text generation capabilities with more efficient training time.
    Downloads: 0 This Week
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  • 8
    onnxt5

    onnxt5

    Summarization, translation, sentiment-analysis, text-generation, etc.

    ...This package is still in the alpha stage, therefore some functionalities such as beam searches are still in development. The simplest way to get started for generation is to use the default pre-trained version of T5 on ONNX included in the package. Please note that the first time you call get_encoder_decoder_tokenizer, the models are being downloaded which might take a minute or two. Other tasks just require to change the prefix in your prompt, for instance for summarization. Run any of the T5 trained tasks in a line (translation, summarization, sentiment analysis, completion, generation) Export your own T5 models to ONNX easily. ...
    Downloads: 0 This Week
    Last Update:
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