Showing 3 open source projects for "mimo network coding"

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  • 1
    PyTorch-Tutorial-2nd

    PyTorch-Tutorial-2nd

    CV, NLP, LLM project applications, and advanced engineering deployment

    PyTorch-Tutorial-2nd is an open-source educational repository that provides structured tutorials for learning deep learning with the PyTorch framework. The project serves as a practical companion to a second edition of a PyTorch learning guide and is designed to help learners understand neural network concepts through hands-on coding examples. The repository covers a wide range of topics including tensor operations, neural network construction, model training workflows, and optimization strategies. It also introduces practical machine learning techniques such as convolutional neural networks, recurrent networks, and other architectures commonly used in modern AI applications. ...
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  • 2
    MiMo-V2.5

    MiMo-V2.5

    Omnimodal AI model for agents, coding, and long-context tasks

    ...The model natively processes text, images, video, and audio within a unified system, enabling cross-modal understanding and complex task execution in a single pipeline. With a context window of up to 1 million tokens, it can handle large documents, extended conversations, and multi-step workflows without fragmentation. MiMo-V2.5 delivers near-Pro-level performance in coding, reasoning, and agent tasks while maintaining lower cost and faster inference speeds. It also integrates advanced components such as multi-token prediction modules and specialized vision and audio encoders, making it well-suited for autonomous agents and software development.
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  • 3
    MiMo-V2.5-Pro

    MiMo-V2.5-Pro

    Flagship MoE model for long-context agents and complex coding

    MiMo-V2.5-Pro is Xiaomi’s flagship Mixture-of-Experts (MoE) model built for the most demanding agentic, software engineering, and long-horizon reasoning tasks. It features approximately 1.02 trillion total parameters with 42B activated per inference, balancing extreme capability with efficient execution. The model supports a 1 million token context window, enabling it to maintain coherence across long workflows involving thousands of tool calls and multi-step reasoning chains....
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