Showing 4 open source projects for "open squid box"

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  • Application Monitoring That Won't Slow Your App Down Icon
    Application Monitoring That Won't Slow Your App Down

    AppSignal's Rust-based agent is lightweight and stable. Already running in thousands of production apps.

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  • Gemini 3 and 200+ AI Models on One Platform Icon
    Gemini 3 and 200+ AI Models on One Platform

    Access Google's best plus Claude, Llama, and Gemma. Fine-tune and deploy from one console.

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  • 1
    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: 3 This Week
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  • 2
    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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  • 3
    Mistral Large 3 675B Base 2512

    Mistral Large 3 675B Base 2512

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

    ...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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  • 4
    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...
    Downloads: 0 This Week
    Last Update:
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