Showing 4 open source projects for "murgee activation code"

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    Automate contact and company data extraction

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  • 1
    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,...
    Downloads: 1 This Week
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  • 2
    Ling

    Ling

    Ling is a MoE LLM provided and open-sourced by InclusionAI

    ...Model inference and API code (e.g. integration with Transformers). This collaborative approach accelerates development and ensures that the models remain at the forefront of technology, addressing emerging challenges in various fields.
    Downloads: 0 This Week
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  • 3
    Ludwig AI

    Ludwig AI

    Low-code framework for building custom LLMs, neural networks

    Declarative deep learning framework built for scale and efficiency. Ludwig is a low-code framework for building custom AI models like LLMs and other deep neural networks. Declarative YAML configuration file is all you need to train a state-of-the-art LLM on your data. Support for multi-task and multi-modality learning. Comprehensive config validation detects invalid parameter combinations and prevents runtime failures.
    Downloads: 1 This Week
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  • 4
    ConvNeXt

    ConvNeXt

    Code release for ConvNeXt model

    ConvNeXt is a modernized convolutional neural network (CNN) architecture designed to rival Vision Transformers (ViTs) in accuracy and scalability while retaining the simplicity and efficiency of CNNs. It revisits classic ResNet-style backbones through the lens of transformer design trends—large kernel sizes, inverted bottlenecks, layer normalization, and GELU activations—to bridge the performance gap between convolutions and attention-based models. ConvNeXt’s clean, hierarchical structure...
    Downloads: 0 This Week
    Last Update:
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