Showing 4 open source projects for "support vector machine"

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  • 1
    Langtrace

    Langtrace

    Open Telemetry based end-to-end observability tool for LLM apps

    Langtrace is an open-source, Open Telemetry based end-to-end observability tool for LLM applications, providing real-time tracing, evaluations, and metrics for popular LLMs, LLM frameworks, vectors, and more.. Integrate using Typescript, and Python. Langtrace is an open-source observability tool that collects and analyzes traces and metrics to help you improve your LLM apps.
    Downloads: 6 This Week
    Last Update:
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  • 2
    node-llama-cpp

    node-llama-cpp

    Run AI models locally on your machine with node.js bindings for llama

    node-llama-cpp is a JavaScript and Node.js binding that allows developers to run large language models locally using the high-performance inference engine provided by llama.cpp. The library enables applications built with Node.js to interact directly with local LLM models without requiring a remote API or external service. By using native bindings and optimized model execution, the framework allows developers to integrate advanced language model capabilities into desktop applications, server...
    Downloads: 6 This Week
    Last Update:
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  • 3
    Grounded Docs

    Grounded Docs

    Open-Source Alternative to Context7, Nia, and Ref.Tools

    Grounded Docs is an open-source implementation of a Model Context Protocol server designed to expose documentation and structured information as tools that AI agents can query. The project allows language models and agent frameworks to retrieve and interact with documentation through standardized MCP interfaces. By acting as an intermediary layer between documentation sources and AI tools, the server enables models to access structured documentation in a consistent and machine-readable...
    Downloads: 8 This Week
    Last Update:
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  • 4
    dLLM

    dLLM

    dLLM: Simple Diffusion Language Modeling

    dLLM is an open-source framework designed to simplify the development, training, and evaluation of diffusion-based large language models. Unlike traditional autoregressive models that generate text sequentially token by token, diffusion language models generate text through an iterative denoising process that refines masked tokens over multiple steps. This approach allows models to reason over the entire sequence simultaneously and potentially produce more coherent outputs with bidirectional...
    Downloads: 0 This Week
    Last Update:
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