7 projects for "multi language" with 2 filters applied:

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  • 1
    AI-Tutorials/Implementations Notebooks

    AI-Tutorials/Implementations Notebooks

    Codes/Notebooks for AI Projects

    ...The repository contains numerous Jupyter notebooks and code samples that demonstrate modern techniques in machine learning, deep learning, data science, and large language model workflows. It includes implementations for a wide range of AI topics such as computer vision, agent systems, federated learning, distributed systems, adversarial attacks, and generative AI. Many of the tutorials focus on building AI agents, multi-agent systems, and workflows that integrate language models with external tools or APIs. ...
    Downloads: 4 This Week
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  • 2
    FlexLLMGen

    FlexLLMGen

    Running large language models on a single GPU

    FlexLLMGen is an open-source inference engine designed to run large language models efficiently on limited hardware resources such as a single GPU. The system focuses on high-throughput generation workloads where large batches of text must be processed quickly, such as large-scale data extraction or document analysis tasks. Instead of requiring expensive multi-GPU systems, the framework uses techniques such as memory offloading, compression, and optimized batching to run large models on commodity hardware. ...
    Downloads: 0 This Week
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  • 3
    UnBBayes

    UnBBayes

    Framework & GUI for Bayes Nets and other probabilistic models.

    UnBBayes is a probabilistic network framework written in Java. It has both a GUI and an API with inference, sampling, learning and evaluation. It supports Bayesian networks, influence diagrams, MSBN, OOBN, HBN, MEBN/PR-OWL, PRM, structure, parameter and incremental learning. Please, visit our wiki (https://sourceforge.net/p/unbbayes/wiki/Home/) for more information. Check out the license section (https://sourceforge.net/p/unbbayes/wiki/License/) for our licensing policy.
    Downloads: 2 This Week
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  • 4
    AminePlatform

    AminePlatform

    Amine is a Multi-Layer Platform for the dev. of Intelligent Systems

    Amine is an Artificial Intelligence Multi-Layer Java Open Source Platform dedicated to the development of various kinds of Intelligent Systems and Agents (Knowledge-Based, Ontology-Based, Conceptual Graph -CG- Based, NLP, Reasoning and Learning, Natural Language Processing, etc.). Ontology, KB can be created and manipulated with various processes. CG theory is used as the main knowledge representation language.
    Downloads: 1 This Week
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  • $300 Free Credits to Build on Google Cloud Icon
    $300 Free Credits to Build on Google Cloud

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  • 5
    d2l-zh

    d2l-zh

    Chinese-language edition of Dive into Deep Learning

    d2l‑zh is the Chinese-language edition of Dive into Deep Learning, an interactive, open‑source deep learning textbook that combines code, math, and explanatory text. It features runnable Jupyter notebooks compatible with multiple frameworks (e.g., PyTorch, MXNet, TensorFlow), comprehensive theoretical analysis, and exercises. Widely adopted in over 70 countries and used by more than 500 universities for teaching deep learning.
    Downloads: 0 This Week
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  • 6
    DialoGPT

    DialoGPT

    Large-scale pretraining for dialogue

    DialoGPT is an open-source conversational language model developed by Microsoft Research for generating natural dialogue responses using large-scale transformer architectures. The system is built on the GPT-2 architecture and is designed specifically for multi-turn conversation tasks, enabling machines to produce coherent responses during interactive dialogue. The model was trained on a massive dataset of approximately 147 million conversational exchanges extracted from Reddit discussion threads, allowing it to learn patterns of natural human conversation. ...
    Downloads: 48 This Week
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  • 7
    CRFSharp

    CRFSharp

    CRFSharp is a .NET(C#) implementation of Conditional Random Field

    ...Currently, when training corpus, compared with CRF++, CRF# can make full use of multi-core CPUs and only uses very low memory, and memory grow is very smoothly and slowly while amount of training corpus, tags increase. with multi-threads process, CRF# is more suitable for large data and tags training than CRF++ now. For example, in machine with 64GB, CRF# encodes model with more than 4.5 hundred million features quickly.
    Downloads: 0 This Week
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