Showing 7 open source projects for "replace"

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  • 1
    Xorbits Inference

    Xorbits Inference

    Replace OpenAI GPT with another LLM in your app

    Replace OpenAI GPT with another LLM in your app by changing a single line of code. Xinference gives you the freedom to use any LLM you need. With Xinference, you're empowered to run inference with any open-source language models, speech recognition models, and multimodal models, whether in the cloud, on-premises, or even on your laptop.
    Downloads: 3 This Week
    Last Update:
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  • 2
    Paper2Slides

    Paper2Slides

    From Paper to Presentation in One Click

    Paper2Slides is an automation tool that converts research papers, reports, and other documents into polished slide decks and posters with minimal manual effort. It is designed to replace the repetitive work of turning dense technical documents into presentation-friendly structure by extracting key points, figures, and data into a coherent visual narrative. The system supports multiple input formats, so you can process PDFs and common office documents rather than being locked to a single file type. It uses an extraction approach intended to capture critical insights comprehensively, including important visuals and data points that often get missed in naive summarization. ...
    Downloads: 2 This Week
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  • 3
    ClaraVerse

    ClaraVerse

    Claraverse is a opesource privacy focused ecosystem to replace ChatGPT

    ClaraVerse is an open-source private AI workspace designed to give users a unified environment for interacting with large language models, building automations, and managing AI-driven tasks in a self-hosted environment. The platform combines chat interfaces, workflow automation, and long-running task management into a single application that can connect to both local and cloud-based AI models. Users can integrate models from multiple providers such as OpenAI, Anthropic, Google, or locally...
    Downloads: 1 This Week
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  • 4
    MatMul-Free LM

    MatMul-Free LM

    Implementation for MatMul-free LM

    ...Since matrix multiplication is one of the most computationally expensive components of modern language models, the project explores alternative computational strategies that reduce hardware requirements while maintaining comparable performance. The architecture relies on quantization-aware training and lightweight operations to replace conventional dense matrix multiplications with more efficient alternatives. These optimizations can significantly reduce memory consumption and potentially improve computational efficiency during both training and inference. The repository provides implementations of models at several parameter scales and includes tools for experimenting with the architecture using modern machine learning frameworks.
    Downloads: 0 This Week
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  • 5
    Bard API

    Bard API

    The unofficial python package that returns response of Google Bard

    ...It has been designed with a lightweight structure that can easily adapt to the emergence of an official API. Therefore, I strongly discourage using it for any other purposes. If you have access to official PaLM-2 API, replace the provided response with the corresponding official code.
    Downloads: 0 This Week
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  • 6
    Doctor Dignity

    Doctor Dignity

    Doctor Dignity is an LLM that can pass the US Medical Licensing Exam

    ...The code is designed to be hackable rather than production-grade, giving learners a chance to experiment with NLP flows and lightweight back-end components. It also highlights privacy-aware patterns and cautions that this kind of software must not replace licensed medical advice. As a teaching and ideation vehicle, the project invites contributors to iterate on intent classification, response templates, and safe-use boundaries.
    Downloads: 0 This Week
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  • 7
    Petals

    Petals

    Run 100B+ language models at home, BitTorrent-style

    ...You get the comforts of an API with the flexibility of PyTorch. You can also host BLOOMZ, a version of BLOOM fine-tuned to follow human instructions in the zero-shot regime — just replace bloom-petals with bloomz-petals. Petals runs large language models like BLOOM-176B collaboratively — you load a small part of the model, then team up with people serving the other parts to run inference or fine-tuning.
    Downloads: 2 This Week
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