4 projects for "lightweight scripting language" with 2 filters applied:

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  • 1
    ggml

    ggml

    Tensor library for machine learning

    ggml is an open-source tensor library designed for efficient machine learning computation with a focus on running models locally and with minimal dependencies. Written primarily in C and C++, the library provides low-level tensor operations and automatic differentiation that allow developers to implement machine learning algorithms and neural networks efficiently. The project emphasizes portability and performance, enabling machine learning inference across a wide range of hardware...
    Downloads: 4 This Week
    Last Update:
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  • 2
    shimmy

    shimmy

    Python-free Rust inference server

    The shimmy project is a lightweight local inference server designed to run large language models with minimal overhead. Written primarily in Rust, the tool provides a small standalone binary that exposes an API compatible with the OpenAI interface, allowing existing applications to interact with local models without significant code changes. This compatibility enables developers to replace remote AI services with locally hosted models while keeping their existing software architecture intact. ...
    Downloads: 1 This Week
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  • 3
    picoGPT

    picoGPT

    An unnecessarily tiny implementation of GPT-2 in NumPy

    ...The project uses a small amount of code to illustrate the essential mathematical operations involved in training and running a transformer-based neural network. Because the code is intentionally lightweight, it is often used as a teaching resource for students learning about natural language processing and deep learning architectures. Developers can explore the repository to understand how language models generate text and how transformer components interact within the architecture.
    Downloads: 0 This Week
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  • 4
    Machine Learning Beginner

    Machine Learning Beginner

    Machine Learning Beginner Public Account Works

    Machine Learning Beginner targets newcomers who are just getting started with machine learning and need a gentle, guided path. It introduces the core vocabulary and the mental map of supervised and unsupervised learning before moving into simple algorithms. The materials prioritize conceptual clarity, then progressively add code to solidify understanding. Step-by-step examples help learners see how data preparation, model training, evaluation, and iteration fit together. Because the scope is...
    Downloads: 0 This Week
    Last Update:
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