Tag: Hugging Face

  • Hugging Face Says Its New Robotics Model Runs Efficiently On A MacBook

    Hugging Face Says Its New Robotics Model Runs Efficiently On A MacBook

    Building advanced robotics projects at home is gradually getting simpler. This week, Hugging Face, an AI development platform, introduced SmolVLA, an open-source robotics model.
    Image Credits: Pixabay

    Building advanced robotics projects at home is gradually getting simpler. This week, Hugging Face, an AI development platform, introduced SmolVLA, an open-source robotics model.

    According to the company, SmolVLA—trained on community-contributed datasets with compatible licenses—surpasses much larger robotics models in both simulated and real-world settings.

    In a blog post, Hugging Face states that “SmolVLA is designed to make vision-language-action (VLA) models more accessible and to drive progress in general-purpose robotics.” The company describes it not only as a compact yet powerful model but also as a framework for training and evaluating generalist robotics systems.

    Building an Affordable Robotics Ecosystem

    SmolVLA is part of Hugging Face’s growing initiative to build a low-cost robotics ecosystem. Last year, it introduced LeRobot, a suite of robotics-specific models, datasets, and tools. More recently, the company acquired French startup Pollen Robotics and launched several affordable robotics platforms, including humanoid robots.

    Hugging Face trained SmolVLA, a model with 450 million parameters, using data from the LeRobot Community Datasets—robotics-specific datasets shared on its AI development platform. Parameters, also known as “weights,” are the internal elements of a model that influence how it functions.

    Hugging Face says SmolVLA runs on a single consumer GPU—or even a MacBook—and lets users test and deploy it on low-cost hardware, including the company’s own robotics platforms.

    Faster Robot Responses Through Asynchronous Processing

    In a notable feature, SmolVLA includes an “asynchronous inference stack,” which, according to Hugging Face, enables the model to handle a robot’s actions separately from its sensory input—what it sees and hears. This design, the company explains in a blog post, allows robots to react more swiftly in dynamic, rapidly changing environments.

    It’s important to point out that Hugging Face isn’t the only contender in the emerging open robotics space.

    Nvidia offers its own suite of open robotics tools, while startup K-Scale Labs is developing components for what it describes as “open-source humanoids.” Other notable players in the field include Dyna Robotics, Physical Intelligence (backed by Jeff Bezos), and RLWRLD.


    Read the original article on: TechCrunch

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  • Hugging Face Unveils a Free AI Tool with Operator-Like Agent Capabilities

    Hugging Face Unveils a Free AI Tool with Operator-Like Agent Capabilities

    A team at Hugging Face has launched a free, cloud-based AI "agent" capable of operating a computer — though it's relatively slow and sometimes prone to errors.
    Image Credits:Hugging Face

    A team at Hugging Face has launched a free, cloud-based AI “agent” capable of operating a computer — though it’s relatively slow and sometimes prone to errors.

    A Web-Based Linux Tool for Automated Task Execution

    Known as the Open Computer Agent, the tool is available through a web interface and runs on a Linux virtual machine equipped with various apps, such as Firefox. Much like OpenAI’s Operator, users can instruct it to perform tasks — for example, “Find Hugging Face HQ in Paris using Google Maps” — and the agent will autonomously open applications and carry out the needed steps.

    Hugging Face didn’t set out to create the most advanced AI agent; instead, the goal was to show how open-source AI models are growing in capability and becoming more affordable to run in the cloud.

    Vision Models Gain Grounding Capabilities for Interactive Virtual Tasks

    As vision models improve, they’re increasingly able to handle complex, agent-like tasks,” said Aymeric Roucher from Hugging Face’s agents team on X. “Some can now perform grounding — identifying the exact location of elements in an image — allowing them to interact with virtual environments by clicking items.”

    Although still imperfect, agentic AI is gaining momentum, with many businesses exploring its use to enhance productivity. A recent KPMG survey found that 65% of companies are experimenting with AI agents, and Markets and Markets forecasts the sector will grow from $7.84 billion in 2025 to $52.62 billion by 2030.

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    Read the original article on: TechCrunch

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