Showing 4 open source projects for "network monitoring tools"

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  • 1
    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: 2 This Week
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  • 2
    SlowFast

    SlowFast

    Video understanding codebase from FAIR for reproducing video models

    SlowFast is a video understanding framework that captures both spatial semantics and temporal dynamics efficiently by processing video frames at two different temporal resolutions. The slow pathway encodes semantic context by sampling frames sparsely, while the fast pathway captures motion and fine temporal cues by operating on densely sampled frames with fewer channels. Together, these two pathways complement each other, allowing the network to model both appearance and motion without...
    Downloads: 0 This Week
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  • 3
    ChatGLM Efficient Tuning

    ChatGLM Efficient Tuning

    Fine-tuning ChatGLM-6B with PEFT

    ...It includes examples for instruction tuning and dialogue datasets, making it straightforward to stand up a task-specific assistant. Because the code leans on widely used libraries, you can bring your own datasets and monitoring tools with minimal glue. For builders who want results fast, it’s a pragmatic way to specialize ChatGLM while controlling costs and turnaround time.
    Downloads: 0 This Week
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  • 4
    NeuralTalk2

    NeuralTalk2

    Efficient Image Captioning code in Torch, runs on GPU

    ...It improves on the original NeuralTalk implementation through batching, GPU acceleration, and a more efficient training pipeline. The model combines convolutional neural network image features with a recurrent neural network language model. It supports fine-tuning the underlying CNN instead of relying only on fixed visual features. Training and evaluation tools are included for datasets such as MS COCO. The repository also contains visualization utilities, checkpoint conversion tools, and experimental video-captioning code. ...
    Downloads: 0 This Week
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