Showing 4 open source projects for "neuron"

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  • 1
    Neuron AI

    Neuron AI

    The PHP Agentic Framework to build production-ready AI driven apps

    Neuron AI is a PHP agentic framework for building production-ready AI applications that connect models, memory, vector databases, and tools into working agents. It is designed for developers who want to create systems such as RAG pipelines, multi-agent workflows, and business process automations without having to hand-build every integration from scratch.
    Downloads: 2 This Week
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  • 2
    PowerInfer

    PowerInfer

    High-speed Large Language Model Serving for Local Deployment

    ...This hybrid execution strategy significantly reduces memory bottlenecks and improves overall inference speed. PowerInfer incorporates specialized algorithms and sparse operators to manage neuron activation patterns and minimize data transfers between hardware components. As a result, it enables powerful language models to run on consumer hardware while achieving performance comparable to more expensive server-grade systems.
    Downloads: 0 This Week
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  • 3
    BertViz

    BertViz

    BertViz: Visualize Attention in NLP Models (BERT, GPT2, BART, etc.)

    ...The head view visualizes attention for one or more attention heads in the same layer. It is based on the excellent Tensor2Tensor visualization tool. The model view shows a bird's-eye view of attention across all layers and heads. The neuron view visualizes individual neurons in the query and key vectors and shows how they are used to compute attention.
    Downloads: 0 This Week
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  • 4
    Automated Interpretability

    Automated Interpretability

    Code for Language models can explain neurons in language models paper

    The automated-interpretability repository implements tools and pipelines for automatically generating, simulating, and scoring explanations of neuron (or latent feature) behavior in neural networks. Instead of relying purely on manual, ad hoc interpretability probing, this repo aims to scale interpretability by using algorithmic methods that produce candidate explanations and assess their quality. It includes a “neuron explainer” component that, given a target neuron or latent feature, proposes natural language explanations or heuristics (e.g. ...
    Downloads: 0 This Week
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