Showing 1087 open source projects for "foundation"

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  • 1
    Projects of this a foundation, it was born in the "Universidad Distrital" Bogotá, Colombia, and is made up of programmers and designers of the college comunity. Our objetive is create good games using extreme programming techniques and optimization.
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  • 2
    Joy is Web Framework with PHP. Netology Software Foundation Feature List -- Module (View, Layout) Master Page Controls Themes Template Engine Logger Tracer Authentication Authorization MVC CMS Scheduler
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  • 3
    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. ...
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  • 4
    OpenWar is going to be a new open source 3D RTS (Real Time Strategy) Engine for the PC. Our goal is to create a strong, but flexible foundation that allows people to make their own RTS games with relative ease.
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  • 5
    cosmos

    cosmos

    Algorithms that run our universe | Your personal library of every algo

    Cosmos (by OpenGenus Foundation) is your personal offline collection of every algorithm and data structure one will ever encounter and use in a lifetime. This provides solutions in various languages spanning C, C++, Java, JavaScript, Swift, Python, Go and others. This work is maintained by a community of hundreds of people and is a massive collaborative effort to bring the readily available coding knowledge offline.
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  • 6
    Unlimited-OCR

    Unlimited-OCR

    Layout-aware OCR model for multilingual document understanding

    ...It supports multilingual recognition, robust handling of noisy or distorted images, and integration into document AI pipelines. Built for practical deployment, Unlimited-OCR can serve as the foundation for document indexing, retrieval-augmented generation, knowledge extraction, and intelligent document processing.
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  • 7
    Qwen3-Next

    Qwen3-Next

    Qwen3-Next: 80B instruct LLM with ultra-long context up to 1M tokens

    Qwen3-Next-80B-A3B-Instruct is the flagship release in the Qwen3-Next series, designed as a next-generation foundation model for ultra-long context and efficient reasoning. With 80B total parameters and 3B activated at a time, it leverages hybrid attention (Gated DeltaNet + Gated Attention) and a high-sparsity Mixture-of-Experts architecture to achieve exceptional efficiency. The model natively supports a context length of 262K tokens and can be extended up to 1 million tokens using RoPE scaling (YaRN), making it highly capable for processing large documents and extended conversations. ...
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  • 8
    Qwen-Image-Edit

    Qwen-Image-Edit

    An advanced bilingual image editing with semantic control

    ...A standout feature is its bilingual text editing in English and Chinese, which preserves original font, size, and style during modifications. Benchmarks confirm its state-of-the-art performance in image editing, establishing it as a reliable foundation for both artistic and practical tasks. Its applications span IP creation, meme generation, background changes, clothing edits, and fine corrections in artworks or calligraphy.
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  • 9
    granite-timeseries-ttm-r2

    granite-timeseries-ttm-r2

    Tiny pre-trained IBM model for multivariate time series forecasting

    granite-timeseries-ttm-r2 is part of IBM’s TinyTimeMixers (TTM) series—compact, pre-trained models for multivariate time series forecasting. Unlike massive foundation models, TTM models are designed to be lightweight yet powerful, with only ~805K parameters, enabling high performance even on CPU or single-GPU machines. The r2 version is pre-trained on ~700M samples (r2.1 expands to ~1B), delivering up to 15% better accuracy than the r1 version. TTM supports both zero-shot and fine-tuned forecasting, handling minutely, hourly, daily, and weekly resolutions. ...
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  • 10
    Qwable-v1

    Qwable-v1

    Agentic coding model combining Opus reasoning and Fable tools

    Qwable-v1 is an open-weight agentic coding model created through a chained distillation process based on Qwen3.6-35B-A3B. The model combines two distinct training stages: first, it was fine-tuned on reasoning traces derived from Claude Opus 4.7 to improve structured reasoning, and then further trained on Claude Fable-5 agentic tool-use traces to develop autonomous coding and tool-calling behavior. The result is a 35B Mixture-of-Experts model with only 3B active parameters that can switch...
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  • 11
    Ministral 3 3B Base 2512

    Ministral 3 3B Base 2512

    Small 3B-base multimodal model ideal for custom AI on edge hardware

    ...It combines a 3.4B-parameter language model with a 0.4B vision encoder, enabling both text and image understanding in a tiny footprint. As the base pretrained model, it is not fine-tuned for instructions or reasoning, making it the ideal foundation for custom post-training, domain adaptation, or specialized downstream tasks. The model is fully optimized for edge deployment and can run locally on a single GPU, fitting in 16GB VRAM in BF16 or less than 8GB when quantized. It supports dozens of languages, making it practical for multilingual, global, or distributed environments. ...
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  • 12
    Ministral 3 14B Base 2512

    Ministral 3 14B Base 2512

    Powerful 14B-base multimodal model — flexible base for fine-tuning

    ...It combines a 13.5B-parameter language model with a 0.4B-parameter vision encoder, enabling both high-quality text understanding/generation and image-aware tasks. As a “base” model (i.e. not fine-tuned for instruction or reasoning), it provides a flexible foundation ideal for custom fine-tuning or downstream specialization. The model remains efficient enough for on-prem or local deployment — it fits in ~32 GB VRAM in BF16, and requires under ~24 GB when quantized. It supports dozens of languages, making it suitable for multilingual applications around the world. With a large 256 k-token context window, Ministral 3 14B Base 2512 can handle very long inputs, complex documents, or large contexts.
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