Showing 4 open source projects for "shellnewhandler windows 10"

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    Ornith-1.0

    Ornith-1.0

    Ornith-1.0 is a self-improving open-source models for agentic coding

    Ornith-1 is an open-source family of agentic coding models from DeepReinforce AI. It is designed for coding agents that need to solve software engineering tasks through iterative tool use and solution rollouts. The project presents 9B dense, 31B dense, 35B mixture-of-experts, and 397B mixture-of-experts variants. These models are post-trained on top of Gemma 4 and Qwen 3.5 foundations. Its training approach uses reinforcement learning to optimize both the solution and the scaffold that...
    Downloads: 5 This Week
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  • 2
    PyGPT

    PyGPT

    Open source personal AI Assistant for Linux, Windows and Mac

    PyGPT is a desktop application that allows you to talk to OpenAI's LLM models such as GPT4 and GPT3 using your own computer and OpenAI API. It allows you to talk in chat mode and in completion mode, as well as generate images using DALL-E 2. PyGPT also adds access to the Internet for GPT via Google Custom Search API and Wikipedia API and includes voice synthesis using Microsoft Azure Text-to-Speech API. Moreover, the application has implemented context memory support, context storage,...
    Downloads: 6 This Week
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  • 3
    MiniMax-M2

    MiniMax-M2

    MiniMax-M2, a model built for Max coding & agentic workflows

    MiniMax-M2 is an open-weight large language model designed specifically for high-end coding and agentic workflows while staying compact and efficient. It uses a Mixture-of-Experts (MoE) architecture with 230 billion total parameters but only 10 billion activated per token, giving it the behavior of a very large model at a fraction of the runtime cost. The model is tuned for end-to-end developer flows such as multi-file edits, compile–run–fix loops, and test-validated repairs across real...
    Downloads: 0 This Week
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  • 4
    MiniMax-M2.5

    MiniMax-M2.5

    State of the art LLM and coding model

    MiniMax-M2.5 is a state-of-the-art foundation model extensively trained with reinforcement learning across hundreds of thousands of real-world environments. It delivers leading performance in coding, agentic tool use, search, and complex office workflows, achieving top benchmark scores such as 80.2% on SWE-Bench Verified and 76.3% on BrowseComp. Designed to reason efficiently and decompose tasks like an experienced architect, M2.5 plans features, structure, and system design before...
    Downloads: 0 This Week
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