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Welcome to the SCAI Hugging Face Space! 🎓🤖 Join us in our mission to advance interdisciplinary research and education in AI, fostering collaboration between researchers, students, and industry partners. Together, we’re shaping the future of artificial intelligence! 🚀🔬🌟 🔍 Vision: Dive into image recognition and perception, driving advancements in mathematics, computer science, and robotics. 🧠 Explanation/Explicability: Enhance the transparency of complex systems, with a focus on health and medicine. 🌍 Ethics: Develop ethical AI solutions for climate, environment, and the universe, ensuring responsible and sustainable practices. 📚 Digital Humanities: Discover how AI transforms our understanding of history, literature, and social sciences. #SorbonneAI #Innovation #EthicalAI #DigitalHumanities
PhysiQuanty 
posted an update 4 days ago
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4368
🌐 We crawled the entirety of Hugging Face to help the community! Huge thanks to the Hugging Face API 🌐
🤖 2.91M model repos (file names included), 📚 1.02M dataset repos, 🚀 1.31M Space repos
🤗 617,501 committers (datasets and models), we’ll share Hugging Face statistics with you in the coming days..

We also identified 61,398 users with “AI/ML Interests”, and NOW we can find each other through our “AI/ML Interests”🤗
HF-Collab-Center/Searching-For-HuggingFace-Users
HF-Collab-Center/All-Model-Repos
HF-Collab-Center/All-Dataset-Repos
HF-Collab-Center/All-Space-Repos

HF-Collab-Center/HF-Users
HF-Collab-Center/HF-Users-with-last-seen
HF-Collab-Center/HF-Users-With-AI-ML-Interests-Only

Made By @QuantaSparkLabs and @PhysiQuanty
C'est français, bon.. en anglais.. mais c'est français ;)
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PhysiQuanty 
posted an update 9 days ago
PhysiQuanty 
posted an update 16 days ago
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5138
❗ Dating apps do not allow us to control the profiles suggested to us based on our mutual search criteria ❗
🧬 If you want to see if your soulmate has already existed, I have published a dataset of 59k anonymized public profiles

SpiceeChat/OkCupid-59k-Anonymized-Profiles

Are you looking for a female ML engineer who is looking for a male ML engineer and you can't find it on the apps ?
You need to look for her, but more importantly, she needs to look for you.
Personally, I'm looking for a physicist I'm encountering the same problem. I can't find it
My answer : Paradox of choice of dating apps solved by patent ⚡ WO2026082672 ⚡
https://patentscope.wipo.int/search/en/detail.jsf?docId=WO2026082672

J'ai du breveté pour te trouver et on se trouvera bientôt !
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PhysiQuanty 
posted an update about 2 months ago
PhysiQuanty 
posted an update 2 months ago
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3149
🧬 Can an LLM speak in binary ?
✅ YES ... RADIX 2 / VOCAB 4
PhysiQuanty/Binary-LLM-POC

🤖 >_ Can an LLM execute logic gates and boolean arithmetic ?

We need to create datasets :
- Neural Arithmetic and Logic Unit (NALU) 32 bits
- Neural Application Binary Interface (NABI) 32 bits

🎯 Optimal Instruction Set = RV32IMAF

This opens the way for code writing and execution by the LLMs themselves without an external CLI.

The more of us who want it, the more possible it will become ...

PhysiQuanty/Binary-Addition-LLM-POC
(10-bits binary addition : binary carry propagation, sampling no longer has any effect on the logits due to the fact that it is deterministic next token.)

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VictorMorand 
authored a paper 3 months ago
zineddine 
authored 2 papers 5 months ago
abidlabs 
authored 3 papers 7 months ago
abidlabs 
posted an update 7 months ago
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11237
Why I think local, open-source models will eventually win.

The most useful AI applications are moving toward multi-turn agentic behavior: systems that take hundreds or even thousands of iterative steps to complete a task, e.g. Claude Code, computer-control agents that click, type, and test repeatedly.

In these cases, the power of the model is not how smart it is per token, but in how quickly it can interact with its environment and tools across many steps. In that regime, model quality becomes secondary to latency.

An open-source model that can call tools quickly, check that the right thing was clicked, or verify that a code change actually passes tests can easily outperform a slightly “smarter” closed model that has to make remote API calls for every move.

Eventually, the balance tips: it becomes impractical for an agent to rely on remote inference for every micro-action. Just as no one would tolerate a keyboard that required a network request per keystroke, users won’t accept agent workflows bottlenecked by latency. All devices will ship with local, open-source models that are “good enough” and the expectation will shift toward everything running locally. It’ll happen sooner than most people think.
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benkabongo25 
authored a paper 7 months ago
VictorMorand 
authored a paper 7 months ago
abidlabs 
posted an update 8 months ago
BaggerOfWords 
authored a paper 12 months ago
abidlabs 
posted an update 12 months ago
abidlabs 
posted an update about 1 year ago
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5279
HOW TO ADD MCP SUPPORT TO ANY 🤗 SPACE

Gradio now supports MCP! If you want to convert an existing Space, like this one hexgrad/Kokoro-TTS, so that you can use it with Claude Desktop / Cursor / Cline / TinyAgents / or any LLM that supports MCP, here's all you need to do:

1. Duplicate the Space (in the Settings Tab)
2. Upgrade the Gradio sdk_version to 5.28 (in the README.md)
3. Set mcp_server=True in launch()
4. (Optionally) add docstrings to the function so that the LLM knows how to use it, like this:

def generate(text, speed=1):
    """
    Convert text to speech audio.

    Parameters:
        text (str): The input text to be converted to speech.
        speed (float, optional): Playback speed of the generated speech.


That's it! Now your LLM will be able to talk to you 🤯
abidlabs 
posted an update about 1 year ago
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2850
Hi folks! Excited to share a new feature from the Gradio team along with a tutorial.

If you don't already know, Gradio is an open-source Python library used to build interfaces for machine learning models. Beyond just creating UIs, Gradio also exposes API capabilities and now, Gradio apps can be launched Model Context Protocol (MCP) servers for LLMs.

If you already know how to use Gradio, there are only two additional things you need to do:
* Add standard docstrings to your function (these will be used to generate the descriptions for your tools for the LLM)
* Set mcp_server=True in launch()


Here's a complete example (make sure you already have the latest version of Gradio installed):


import gradio as gr

def letter_counter(word, letter):
    """Count the occurrences of a specific letter in a word.
    
    Args:
        word: The word or phrase to analyze
        letter: The letter to count occurrences of
        
    Returns:
        The number of times the letter appears in the word
    """
    return word.lower().count(letter.lower())

demo = gr.Interface(
    fn=letter_counter,
    inputs=["text", "text"],
    outputs="number",
    title="Letter Counter",
    description="Count how many times a letter appears in a word"
)

demo.launch(mcp_server=True)



This is a very simple example, but you can add the ability to generate Ghibli images or speak emotions to any LLM that supports MCP. Once you have an MCP running locally, you can copy-paste the same app to host it on [Hugging Face Spaces](https://huggingface.co/spaces/) as well.

All free and open-source of course! Full tutorial: https://www.gradio.app/guides/building-mcp-server-with-gradio
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abidlabs 
posted an update about 1 year ago
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JOURNEY TO 1 MILLION DEVELOPERS

5 years ago, we launched Gradio as a simple Python library to let researchers at Stanford easily demo computer vision models with a web interface.

Today, Gradio is used by >1 million developers each month to build and share AI web apps. This includes some of the most popular open-source projects of all time, like Automatic1111, Fooocus, Oobabooga’s Text WebUI, Dall-E Mini, and LLaMA-Factory.

How did we get here? How did Gradio keep growing in the very crowded field of open-source Python libraries? I get this question a lot from folks who are building their own open-source libraries. This post distills some of the lessons that I have learned over the past few years:

1. Invest in good primitives, not high-level abstractions
2. Embed virality directly into your library
3. Focus on a (growing) niche
4. Your only roadmap should be rapid iteration
5. Maximize ways users can consume your library's outputs

1. Invest in good primitives, not high-level abstractions

When we first launched Gradio, we offered only one high-level class (gr.Interface), which created a complete web app from a single Python function. We quickly realized that developers wanted to create other kinds of apps (e.g. multi-step workflows, chatbots, streaming applications), but as we started listing out the apps users wanted to build, we realized what we needed to do:

Read the rest here: https://x.com/abidlabs/status/1907886