Showing 8 open source projects for "m1"

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  • 1
    MiniMax-M1

    MiniMax-M1

    Open-weight, large-scale hybrid-attention reasoning model

    ...Architecturally, it combines Mixture-of-Experts layers with lightning attention, enabling the model to support a native context length of 1 million tokens while using far fewer FLOPs than comparable reasoning models for very long generations. The team emphasizes efficient scaling of test-time compute: at 100K-token generation lengths, M1 reportedly uses only about 25 percent of the FLOPs of some competing models, making extended “think step” traces more feasible. M1 is further trained with large-scale reinforcement learning over diverse tasks.
    Downloads: 0 This Week
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  • 2
    Phi-3-MLX

    Phi-3-MLX

    Phi-3.5 for Mac: Locally-run Vision and Language Models

    Phi-3-Vision-MLX is an Apple MLX (machine learning on Apple silicon) implementation of Phi-3 Vision, a lightweight multi-modal model designed for vision and language tasks. It focuses on running vision-language AI efficiently on Apple hardware like M1 and M2 chips.
    Downloads: 1 This Week
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  • 3
    PDFium Library

    PDFium Library

    Project to compile PDFium library to multiple platforms

    Project to compile PDFium library to multiple platforms. PDFium project is from Google and I only patch it to compile to all platforms.
    Downloads: 12 This Week
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  • 4
    Diffusers

    Diffusers

    State-of-the-art diffusion models for image and audio generation

    Diffusers is the go-to library for state-of-the-art pretrained diffusion models for generating images, audio, and even 3D structures of molecules. Whether you're looking for a simple inference solution or training your own diffusion models, Diffusers is a modular toolbox that supports both. Our library is designed with a focus on usability over performance, simple over easy, and customizability over abstractions. State-of-the-art diffusion pipelines that can be run in inference with just a...
    Downloads: 1 This Week
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  • 5
    OculiX

    OculiX

    Visual Automation IDE — automate anything you see on screen

    ...Key features: - Guided step-by-step recorder with live code preview - Image recognition via OpenCV 4.10 - Dual OCR: Tesseract (built-in) + PaddleOCR (neural, high precision) - Local and remote automation via integrated VNC - SSH tunnels via embedded JSch - Cross-platform: Windows, macOS (Apple Silicon M1-M4), Linux - Scripting: Jython, JRuby, Java, PowerShell, AppleScript - Java 17 recommended (Java 8+ supported) - Full CI/CD with automated builds for all platforms Used worldwide for test automation, RPA, and visual regression testing. MIT License. Maintained by oculix-org.
    Downloads: 132 This Week
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  • 6
    odd-collector

    odd-collector

    Open-source metadata collector based on ODD Specification

    ODD Collector is a lightweight service that gathers metadata from all your data sources. Push-client is a provider which sends information directly to the central repository of the Platform. ODDRN (Open Data Discovery Resource Name) is a unique resource name that identifies entities such as data sources, data entities, dataset fields etc. It is used to build lineage and update metadata.
    Downloads: 0 This Week
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  • 7
    Mocking Bird

    Mocking Bird

    Clone a voice in 5 seconds to generate arbitrary speech in real-time

    MockingBird is an open-source voice cloning and real-time speech generation toolkit that lets you clone a speaker’s voice from a short audio sample (reportedly as little as 5 seconds) and then synthesize arbitrary speech in that voice. It builds on deep-learning based TTS / voice-cloning technology (in the lineage of projects such as Real-Time-Voice-Cloning), but extends it with support for Mandarin Chinese and multiple Chinese speech datasets — broadening its applicability beyond English....
    Downloads: 0 This Week
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  • 8
    Keras TCN

    Keras TCN

    Keras Temporal Convolutional Network

    TCNs exhibit longer memory than recurrent architectures with the same capacity. Performs better than LSTM/GRU on a vast range of tasks (Seq. MNIST, Adding Problem, Copy Memory, Word-level PTB...). Parallelism (convolutional layers), flexible receptive field size (possible to specify how far the model can see), stable gradients (backpropagation through time, vanishing gradients). The usual way is to import the TCN layer and use it inside a Keras model. The receptive field is defined as the...
    Downloads: 0 This Week
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