Showing 4 open source projects for "network monitoring tool"

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  • 1
    Clay Foundation Model

    Clay Foundation Model

    The Clay Foundation Model - An open source AI model and interface

    The Clay Foundation Model is an open-source AI model and interface designed to provide comprehensive data and insights about Earth. It aims to serve as a foundational tool for environmental monitoring, research, and decision-making by integrating various data sources and offering an accessible platform for analysis.
    Downloads: 1 This Week
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  • 2
    Cactus Needle

    Cactus Needle

    26m function call model that runs on incredibly small devices

    Needle is an experimental 26-million-parameter function-calling model designed to run on extremely small devices such as phones, watches, glasses, and low-power personal AI hardware. It is based on a Simple Attention Network architecture and was distilled from a much larger model to focus on fast, compact tool-use behavior. The project provides open weights, training details, dataset generation resources, and a playground for testing the model with custom tools. Needle is optimized for single-shot function calling rather than broad conversational ability, so its core use case is selecting the right tool and producing structured arguments. ...
    Downloads: 1 This Week
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  • 3
    ControlNet

    ControlNet

    Let us control diffusion models

    ControlNet is a neural network architecture designed to add conditional control to text-to-image diffusion models. Rather than training from scratch, ControlNet “locks” the weights of a pre-trained diffusion model and introduces a parallel trainable branch that learns additional conditions—like edges, depth maps, segmentation, human pose, scribbles, or other guidance signals.
    Downloads: 2 This Week
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  • 4
    MiniMax-M2.7

    MiniMax-M2.7

    Self-evolving AI model for agents, coding, and complex workflows

    MiniMax-M2.7 is a large-scale open-weight language model designed for advanced agent-based workflows, professional software engineering, and complex productivity tasks. With 229B parameters, it introduces a self-evolution framework in which the model actively improves its own capabilities by updating memory, generating skills, and iterating through reinforcement learning experiments. This process enables it to autonomously refine systems, achieving measurable performance gains such as a 30%...
    Downloads: 0 This Week
    Last Update:
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