Showing 8 open source projects for "box"

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  • 1
    MCPB

    MCPB

    One-click local MCP server installation in desktop apps

    ...The repository includes the bundle spec, a CLI to scaffold and pack bundles, and the loading/verification code used by Claude’s desktop apps, including support for auto-updates and a curated directory. It supports multiple implementation styles—Node.js, Python, or native binaries—and provides guidance on bundling dependencies so bundles run out-of-the-box.
    Downloads: 6 This Week
    Last Update:
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  • 2
    CogVLM

    CogVLM

    A state-of-the-art open visual language model

    ...It includes checkpoints for chat, base, and grounding variants, plus recipes for model-parallel inference and LoRA fine-tuning. The documentation covers task prompts for general dialogue, visual grounding (box→caption, caption→box, caption+boxes), and GUI agent workflows that produce structured actions with bounding boxes.
    Downloads: 5 This Week
    Last Update:
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  • 3
    Qwen3-VL-Embedding

    Qwen3-VL-Embedding

    Multimodal embedding and reranking models built on Qwen3-VL

    ...Together, they support advanced information retrieval workflows such as image-text search, visual question answering (VQA), and video-text matching, while providing out-of-the-box support for more than 30 languages.
    Downloads: 0 This Week
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  • 4
    Free AI Watermark Remover - FreeRepair

    Free AI Watermark Remover - FreeRepair

    AI-powered tool to quickly remove watermarks from images flawlessly

    ...As a free, open-source, AI-powered tool, FreeRepair makes it easy to remove watermarks, logos, text or clutter from images, and blurry images can be made clearer or larger. No installation, no internet connection, it works out of the box, safe and secure, unlimited.
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    Downloads: 22 This Week
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  • 5
    Multi-Agent Emergence Environments

    Multi-Agent Emergence Environments

    Environment generation code for the paper "Emergent Tool Use"

    ...Developers can construct custom environments by combining modular components such as Boxes, Ramps, and RandomWalls using a flexible layering approach that reduces code duplication. The framework includes several predefined environments—such as Hide and Seek, Box Locking, Blueprint Construction, and Shelter Construction—that model distinct problem-solving and collaboration scenarios.
    Downloads: 0 This Week
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  • 6
    layoutlm-base-uncased

    layoutlm-base-uncased

    Multimodal Transformer for document image understanding and layout

    layoutlm-base-uncased is a multimodal transformer model developed by Microsoft for document image understanding tasks. It incorporates both text and layout (position) features to effectively process structured documents like forms, invoices, and receipts. This base version has 113 million parameters and is pre-trained on 11 million documents from the IIT-CDIP dataset. LayoutLM enables better performance in tasks where the spatial arrangement of text plays a crucial role. The model uses a...
    Downloads: 0 This Week
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  • 7
    Mistral Large 3 675B Base 2512

    Mistral Large 3 675B Base 2512

    Frontier-scale 675B multimodal base model for custom AI training

    ...The model is engineered for reliability, long-context comprehension, and stable performance across many enterprise, scientific, and knowledge-intensive workloads. Its architecture includes a powerful language MoE and a 2.5B-parameter vision encoder, enabling multimodal understanding out of the box. Mistral Large 3 Base supports deployment on-premises using FP8 or NVFP4 formats, enabling high-performance workflows on B200, H200, H100, or A100 hardware.
    Downloads: 0 This Week
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  • 8
    Ministral 3 8B Base 2512

    Ministral 3 8B Base 2512

    Versatile 8B-base multimodal LLM, flexible foundation for custom AI

    Ministral 3 8B Base 2512 is a mid-sized, dense model in the Ministral 3 series, designed as a general-purpose foundation for text and image tasks. It pairs an 8.4B-parameter language model with a 0.4B-parameter vision encoder, enabling unified multimodal capabilities out of the box. As a “base” model (i.e., not fine-tuned for instruction or reasoning), it offers a flexible starting point for custom downstream tasks or fine-tuning. The model supports a large 256k token context window, making it capable of handling long documents or extended dialogues. Because it comes from the edge-optimized Ministral 3 family, it remains deployable on reasonably powerful hardware while offering a good balance between capability and resource use. ...
    Downloads: 0 This Week
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