Showing 2171 open source projects for "hardware"

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  • 1
    SuperGemma4

    SuperGemma4

    Fast uncensored Gemma model optimized for local chat and coding

    ...Unlike raw base models, it inherits improvements from the SuperGemma Fast line, resulting in better performance in logic, coding, and real-world text workflows. The model is packaged in GGUF format for efficient use with llama.cpp and has been specifically tested on Apple Silicon hardware, delivering high token speeds and smooth local inference. A neutral chat template is embedded to prevent prompt misrouting issues, ensuring consistent responses without unintended shifts into coding or tool-use modes.
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  • 2
    translategemma-4b-it

    translategemma-4b-it

    Lightweight multimodal translation model for 55 languages

    ...With a compact ~5B parameter footprint and BF16 support, the model is designed to run efficiently on laptops, desktops, and private cloud infrastructure, making advanced translation accessible without heavy hardware requirements. TranslateGemma uses a structured chat template that enforces explicit source and target language codes, ensuring consistent, deterministic behavior and reducing ambiguity in multilingual pipelines. It integrates seamlessly with Hugging Face Transformers through pipelines or direct model initialization, supporting GPU acceleration and scalable deployment.
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  • 3
    gpt-oss-20b

    gpt-oss-20b

    OpenAI’s compact 20B open model for fast, agentic, and local use

    ...GPT-OSS-20B is compatible with Transformers, vLLM, Ollama, PyTorch, and other tools. It is ideal for developers building lightweight AI agents or experimenting with fine-tuning on consumer-grade hardware.
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  • 4
    gpt-oss-120b

    gpt-oss-120b

    OpenAI’s open-weight 120B model optimized for reasoning and tooling

    GPT-OSS-120B is a powerful open-weight language model by OpenAI, optimized for high-level reasoning, tool use, and agentic tasks. With 117B total parameters and 5.1B active parameters, it’s designed to fit on a single H100 GPU using native MXFP4 quantization. The model supports fine-tuning, chain-of-thought reasoning, and structured outputs, making it ideal for complex workflows. It operates in OpenAI’s Harmony response format and can be deployed via Transformers, vLLM, Ollama, LM Studio,...
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  • 5

    uAnts4Home

    An advanced home automation system which exchanges only devices states

    The micro-Ants are small, cheap, 8-bit devices which may outperform current home automation systems, thanks to their "swarm intelligence". This project aims at developing a micro-Ant hardware and firmware. Join and develop with us this new technology!
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  • 6
    Rampart

    Rampart

    Lightweight on-device model for private AI text redaction

    Rampart is a lightweight, on-device privacy protection model developed by the National Design Studio to detect and redact personally identifiable information (PII) before text leaves a user's device. Rather than relying on server-side filtering, Rampart performs token-level PII detection locally, enabling privacy-preserving AI interactions with minimal latency and without exposing sensitive information to external services. The released model is a 14.7 MB ONNX artifact based on a fine-tuned...
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  • 7
    ZAYA1-8B

    ZAYA1-8B

    Efficient MoE reasoning model for coding and math workloads

    ZAYA1-8B is a compact Mixture-of-Experts reasoning model developed by Zyphra, designed to deliver unusually high intelligence density with fewer than 1 billion active parameters. The model contains 8.4B total parameters with around 760M active during inference, allowing it to achieve strong reasoning, mathematics, and coding performance while remaining lightweight enough for efficient local or on-device deployment. ZAYA1-8B is optimized for long-form reasoning and test-time compute...
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  • 8

    PLCduino

    Open Source PLC based on the arduino platform

    PLCduino is an open hardware alternative for industrial and home automation. It uses the well known ATmega chips as main processor, the same used on Arduino. This means that PLCduino is code-compatible with all arduino sketches.
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  • 9
    Ministral 3 8B Instruct 2512

    Ministral 3 8B Instruct 2512

    Compact 8B multimodal instruct model optimized for edge deployment

    ...This FP8 instruct-fine-tuned variant is optimized for chat, instruction following, and structured outputs, making it ideal for daily assistant tasks and lightweight agentic workflows. Designed for edge deployment, the model can run on a wide range of hardware and fits locally on a single 12GB GPU, with the option for even smaller quantized configurations. Its multilingual support covers dozens of major languages, allowing it to work across diverse global environments and applications. The model adheres reliably to system prompts, supports native function calling, and outputs clean JSON, giving it strong tool-use behavior.
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  • 10
    GigaChat 3 Ultra

    GigaChat 3 Ultra

    High-performance MoE model with MLA, MTP, and multilingual reasoning

    ...This combination significantly boosts reasoning, coding, and multilingual performance across modern benchmarks. Designed for high-performance deployment, GigaChat 3 Ultra supports major inference engines and offers optimized BF16 and FP8 execution paths for cluster-grade hardware.
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  • 11

    KinectStreamer

    Streams data from 3D cameras over a network.

    This is an application that streams data from the Microsoft Kinect or cameras like it over a network. The program is Intended to be used in robotics applications where the controller cannot use such cameras directly due to hardware/software limitations--such as lacking usb ports or appropriate drivers--or in situations where the camera is not in close proximity to the device that needs to access it. Given that the controller can accept data from over the network, another embedded controller that is able to use these kinds of cameras can be loaded with this software to send the image and depth data from the kinect over the network to the other controller. ...
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  • 12

    Bear 100 Race Aid Station Application

    To be used for the Bear 100 Race: see http://www.bear100.com/

    ...The Bear100 is an annual 100 mile, 2 day race on foot through the Rocky Mountains in North America. Will communicate via serial over a 1200 baud AX.25 link to a ARES/Data backend (which we call NetControl). Can initialize some hardware TNCs with parameters optimal for ARES/Data topology. See ARES paper for topology/backend: http://www.tapr.org/pub_cnc07.html All Aid Stations are expected to use same frequency concurrently, and optionally send queries to Net Control. Note backend will support more frequencies if more radios were hooked up. Expected that subset of stations will use software. ...
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  • 13
    Mistral Large 3 675B Instruct 2512 NVFP4

    Mistral Large 3 675B Instruct 2512 NVFP4

    Quantized 675B multimodal instruct model optimized for NVFP4

    Mistral Large 3 675B Instruct 2512 NVFP4 is a frontier-scale multimodal Mixture-of-Experts model featuring 675B total parameters and 41B active parameters, trained from scratch on 3,000 H200 GPUs. This NVFP4 checkpoint is a post-training-activation quantized version of the original instruct model, created through a collaboration between Mistral AI, vLLM, and Red Hat using llm-compressor. It retains the same instruction-tuned behavior as the FP8 model, making it ideal for production...
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  • 14
    Ministral 3 3B Base 2512

    Ministral 3 3B Base 2512

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

    Ministral 3 3B Base 2512 is the smallest model in the Ministral 3 family, offering a compact yet capable multimodal architecture suited for lightweight AI applications. 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....
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  • 15
    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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  • 16
    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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  • 17
    NES Open Switch

    NES Open Switch

    The 'NES Open Switch' network protocol stack.

    ...The supporting protocols for different media are being developed as individual modules in the stack. For example for Ethernet media support protocol modules like STP, CFM and LACP are present in the stack. This stack can be ported on a networking hardware and a fully functional networking device can be built. The NES Open Switch is developed and maintained by an open community of developers. This stack is written completely in C99. The stack can be used on any operating system which supports POSIX 2001 API. The build system is written in Make. The stack is released under the NES RED License, and it is an open-source software. ...
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  • 18
    Mistral Large 3 675B Instruct 2512

    Mistral Large 3 675B Instruct 2512

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

    ...The model supports dozens of languages and maintains strong system-prompt adherence, making it suitable for global and structured enterprise use. Designed for high performance, it runs on a single node of B200 or H200 GPUs in FP8, and can also operate in NVFP4 mode on H100 or A100 hardware. With a 256k context window, it excels at long-document comprehension, deep retrieval workflows, and complex knowledge-intensive tasks.
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  • 19
    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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  • 20
    Ministral 3 8B Base 2512

    Ministral 3 8B Base 2512

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

    ...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. Its multilingual and multimodal pretraining enables broad applicability across languages and tasks — from generation to classification to vision-language tasks.
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  • 21
    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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