Showing 384 open source projects for "gnu/linux"

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  • 1
    Ministral 3 14B Reasoning 2512

    Ministral 3 14B Reasoning 2512

    High-precision 14B multimodal model built for advanced reasoning tasks

    Ministral 3 14B Reasoning 2512 is the largest model in the Ministral 3 series, delivering frontier-level performance with capabilities comparable to the Mistral Small 3.2 24B model. It pairs a 13.5B-parameter language model with a 0.4B vision encoder, enabling strong multimodal reasoning across both text and images. This version is specifically post-trained for reasoning tasks, making it highly effective for math, coding, STEM workloads, and complex multi-step problem-solving. Despite its...
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  • 2
    Ministral 3 3B Instruct 2512

    Ministral 3 3B Instruct 2512

    Ultra-efficient 3B multimodal instruct model built for edge deployment

    Ministral 3 3B Instruct 2512 is the smallest model in the Ministral 3 family, offering a lightweight yet capable multimodal architecture designed for edge and low-resource deployments. It includes a 3.4B-parameter language model paired with a 0.4B vision encoder, enabling it to understand both text and visual inputs. As an FP8 instruct-fine-tuned model, it is optimized for chat, instruction following, and compact agentic tasks while maintaining strong adherence to system prompts. Despite its...
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  • 3
    Ministral 3 14B Instruct 2512

    Ministral 3 14B Instruct 2512

    Efficient 14B multimodal instruct model with edge deployment and FP8

    Ministral 3 14B Instruct 2512 is the largest model in the Ministral 3 family, delivering frontier performance comparable to much larger systems while remaining optimized for edge-level deployment. It combines a 13.5B-parameter language model with a 0.4B-parameter vision encoder, enabling strong multimodal understanding in both text and image tasks. This FP8 instruct-tuned variant is designed specifically for chat, instruction following, and agentic workflows with robust system-prompt...
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  • 4
    NoobAI XL 1.1

    NoobAI XL 1.1

    Open, non-commercial SDXL model for quality image generation

    NoobAI XL 1.1 is a diffusion-based text-to-image generative model developed by Laxhar Dream Lab, fine-tuned from NoobAI XL 1.0 and built upon Illustrious-xl. It leverages the latest Danbooru and e621 datasets, using native tag captions to enhance visual fidelity, style accuracy, and prompt responsiveness. The model introduces refined quality tagging, ranking images by percentile to ensure results reflect modern aesthetic preferences. It supports a range of recommended resolutions around...
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  • 5
    VaultGemma

    VaultGemma

    VaultGemma: 1B DP-trained Gemma variant for private NLP tasks

    VaultGemma is a sub-1B parameter variant of Google’s Gemma family that is pre-trained from scratch with Differential Privacy (DP), providing mathematically backed guarantees that its outputs do not reveal information about any single training example. Using DP-SGD with a privacy budget across a large English-language corpus (web documents, code, mathematics), it prioritizes privacy over raw utility. The model follows a Gemma-2–style architecture, outputs text from up to 1,024 input tokens,...
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  • 6
    Mistral Large 3 675B Instruct 2512

    Mistral Large 3 675B Instruct 2512

    Frontier-scale 675B multimodal instruct MoE model for enterprise AIMis

    Mistral Large 3 675B Instruct 2512 is a state-of-the-art multimodal granular Mixture-of-Experts model featuring 675B total parameters and 41B active parameters, trained from scratch on 3,000 H200 GPUs. As the instruct-tuned FP8 variant, it is optimized for reliable instruction following, agentic workflows, production-grade assistants, and long-context enterprise tasks. It incorporates a massive 673B-parameter language MoE backbone and a 2.5B-parameter vision encoder, enabling rich multimodal...
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  • 7
    Ministral 3 3B Reasoning 2512

    Ministral 3 3B Reasoning 2512

    Compact 3B-param multimodal model for efficient on-device reasoning

    Ministral 3 3B Reasoning 2512 is the smallest reasoning-capable model in the Ministal-3 family, yet delivers a surprisingly capable multimodal and multilingual base for lightweight AI applications. It pairs a 3.4B-parameter language model with a 0.4B-parameter vision encoder, enabling it to understand both text and image inputs. This reasoning-tuned variant is optimized for tasks like math, coding, and other STEM-related problem solving, making it suitable for applications that require...
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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...
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  • 9
    Ministral 3 14B Base 2512

    Ministral 3 14B Base 2512

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

    Ministral 3 14B Base 2512 is the largest model in the Ministral 3 line, offering state-of-the-art language and vision capabilities in a dense, base-pretrained form. 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...
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