Showing 157 open source projects for "point"

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  • 1
    Evidence-based Guideline and Decision Support System. Provides patient specific point of care reminders in order to aid physicians provide high quality care. Input/output in the form of HL7 CDA Level 2 documents. Knowledge is encoded using Arden Syntax.
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  • 2
    MEDDAC is the seeding point for designing a new approach to Operating System design itself. It is an Open Source project with roots in mainframe design and development the author began in 1979. Monitor Editor Disassembler Decompiler Assembler Compiler
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  • 3
    Virtual machine/emulator; "holding pen" for self-replicating programs written in custom RISC assembly-like language, evolving via random point mutations and periodic fitness-based cullings. Inspired (like Avida) by Thomas Ray's alife simulator, Tierra
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  • 4
    Nemotron 3

    Nemotron 3

    Large language model developed and released by NVIDIA

    NVIDIA-Nemotron-3-Nano-30B-A3B-FP8 is a state-of-the-art large language model developed and released by NVIDIA as part of its Nemotron 3 family, optimized for high-efficiency inference and strong reasoning performance in open AI workloads. It is the post-trained and FP8-quantized variant of the Nemotron 3 Nano model, meaning its weights and activations are represented in 8-bit floating point (FP8) to dramatically reduce memory usage and computational cost while retaining high accuracy. The base Nano architecture uses a hybrid Mamba-Transformer Mixture-of-Experts (MoE) design, allowing the model to activate only a small fraction of its 31.6 billion parameters per token, which improves speed and efficiency without sacrificing quality on complex queries. ...
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  • 5
    using simple cuboids and gjk algorithm, this general purpose algorithm gives a ready made code to work with swissranger 3D camera.
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  • 6
    Another point of view to Artificial Neuronal Networks
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  • 7
    Ministral 3 8B Base 2512

    Ministral 3 8B Base 2512

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

    ...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. ...
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