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  • 1
    StableSwarmUI

    StableSwarmUI

    Multi-user UI for managing and running Stable Diffusion workflows tool

    ...It focuses on enabling multiple users to interact with shared resources, making it suitable for collaborative or server-based deployments. It provides a centralized system where users can submit, monitor, and manage generation tasks through a browser interface. It abstracts much of the complexity involved in running diffusion models by offering a structured environment for handling prompts, outputs, and processing queues. StableSwarmUI is built to work alongside backend systems that execute the actual image generation, allowing separation between user interaction and compute workloads. ...
    Downloads: 2 This Week
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  • 2
    Best-of Machine Learning with Python

    Best-of Machine Learning with Python

    A ranked list of awesome machine learning Python libraries

    ...All projects are ranked by a project-quality score, which is calculated based on various metrics automatically collected from GitHub and different package managers. If you like to add or update projects, feel free to open an issue, submit a pull request, or directly edit the projects.yaml. Contributions are very welcome! General-purpose machine learning and deep learning frameworks.
    Downloads: 0 This Week
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  • 3
    Agentless

    Agentless

    An agentless approach to automatically solve software development

    ...In the final stage, the framework validates potential patches by running regression tests and additional reproduction tests to confirm whether the fix resolves the original error. Based on these results, the system ranks the candidate patches and selects the most reliable solution to submit.
    Downloads: 0 This Week
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  • 4
    Hivemind

    Hivemind

    Decentralized deep learning in PyTorch. Built to train models

    ...Train neural networks of arbitrary size: parts of their layers are distributed across the participants with the Decentralized Mixture-of-Experts. If you have succesfully trained a model or created a downstream repository with the help of our library, feel free to submit a pull request that adds your project to the list.
    Downloads: 0 This Week
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  • 5
    High-Level Training Utilities Pytorch

    High-Level Training Utilities Pytorch

    High-level training, data augmentation, and utilities for Pytorch

    ...Comprehensive data augmentation, transforms, sampling, and loading. Utility tensor and variable functions so you don't need numpy as often. Have any feature requests? Submit an issue! I'll make it happen. Specifically, any data augmentation, data loading, or sampling functions. ModuleTrainer. The ModuleTrainer class provides a high-level training interface that abstracts away the training loop while providing callbacks, constraints, initializers, regularizers, and more. You also have access to the standard evaluation and prediction functions. ...
    Downloads: 0 This Week
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  • 6
    TensorFlow Hub

    TensorFlow Hub

    A library for transfer learning by reusing parts of TensorFlow models

    ...These models can be loaded directly into TensorFlow pipelines and fine-tuned for new tasks using transfer learning techniques. The repository supports contributions from the community, allowing developers to submit models that become available for use by other machine learning practitioners. By enabling reusable model modules, TensorFlow Hub significantly reduces development time and computational cost when building machine learning systems.
    Downloads: 0 This Week
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  • 7
    TensorFlow Addons

    TensorFlow Addons

    Useful extra functionality for TensorFlow 2.x maintained by SIG-addons

    ...The maintainers of TensorFlow Addons can be found in the CODEOWNERS file of the repo. This file is parsed and pull requests will automatically tag the owners using a bot. If you would like to maintain something, please feel free to submit a PR. We encourage multiple owners for all submodules. TensorFlow Addons is actively working towards forward compatibility with TensorFlow 2.x.
    Downloads: 1 This Week
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  • 8
    Sockeye

    Sockeye

    Sequence-to-sequence framework, focused on Neural Machine Translation

    ...For a quickstart guide to training a standard NMT model on any size of data, see the WMT 2014 English-German tutorial. If you are interested in collaborating or have any questions, please submit a pull request or issue. You can also send questions to sockeye-dev-at-amazon-dot-com. Developers may be interested in our developer guidelines. Starting with version 3.0.0, Sockeye is also based on PyTorch. We maintain backwards compatibility with MXNet models of version 2.3.x with 3.0.x. If MXNet 2.x is installed, Sockeye can run both with PyTorch or MXNet. ...
    Downloads: 0 This Week
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  • 9
    Finetune Transformer LM

    Finetune Transformer LM

    Code for "Improving Language Understanding by Generative Pre-Training"

    finetune-transformer-lm is a research codebase that accompanies the paper “Improving Language Understanding by Generative Pre-Training,” providing a minimal implementation focused on fine-tuning a transformer language model for evaluation tasks. The repository centers on reproducing the ROCStories Cloze Test result and includes a single-command training workflow to run the experiment end to end. It documents that runs are non-deterministic due to certain GPU operations and reports a median...
    Downloads: 1 This Week
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  • 10
    EvalAI

    EvalAI

    Evaluating state of the art in AI

    ...If the challenge needs extra computational power, challenge organizers can easily add their own cluster of worker nodes to process participant submissions while we take care of hosting the challenge, handling user submissions, and maintaining the leaderboard. EvalAI lets participants submit code for their agent in the form of docker images which are evaluated against test environments on the evaluation server. During the evaluation, the worker fetches the image, test environment, and model snapshot and spins up a new container to perform the evaluation.
    Downloads: 0 This Week
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