AI & ML interests

Make all hub models available for conversion to ONNX format.

Recent Activity

louisbrulenaudetΒ 
posted an update about 16 hours ago
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845
Supercharge Apple’s Shortcuts using Cloudflare Workers and Gemini within minutes (and for free, up to 1,500 requests per day) ☁️✨

Hello everyone, last week, while experimenting for fun, I created an API that allows you to easily access AI models (in this case, Google's) from the Shortcut app in order to analyze data from my apps and make the most of it thanks to the generative capabilities of advanced models.

It costs me nothing, and I think it might be good to share it so that others can build on it.

In README.md, you will find everything you need to get started and put your own microservice into production, which you can call from the app’s HTTP request features.

You will simply be asked to have a free Cloudflare account and an API key obtained from Google's AI Studio.

Feel free to take a look and get back to me if you encounter any problems during deployment.

Here is the GitHub repo where you can find all the source code and run it on your own: https://github.com/louisbrulenaudet/genai-api
louisbrulenaudetΒ 
posted an update 1 day ago
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Although more and more code editors are aligning themselves with the AGENTS.md file standard, some still use specific nomenclatures that can make it difficult to maintain different configuration files when several people are working on the same project with different agents.

Bodyboard addresses this by generating canonical instructions for code helpers from a single AGENTS.md file, thereby streamlining the production of adapter outputs for Gemini CLI, Copilot, Cline, Claude, Rules, Windsurf, and OpenAI Codex integrations.

You just have to:
npm install -g bodyboard

Then run, at the root of your project:
bodyboard all

Link to npm: https://www.npmjs.com/package/bodyboard
Link to the GitHub repo: https://github.com/louisbrulenaudet/bodyboard

It's a very simple project, but it addresses certain issues I've encountered, so why not make it available to everyone...

If you have other ideas for adapters to create, feel free to open a PR on the GitHub repo.
NymboΒ 
posted an update 8 days ago
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I built a general use MCP space ~ Fetch webpages, DuckDuckGo search, Python code execution, Kokoro TTS, Image Gen, Video Gen.

# Tools

1. Fetch webpage
2. Web search via DuckDuckGo (very concise, low excess context)
3. Python code executor
4. Kokoro-82M speech generation
5. Image Generation (use any model from HF Inference Providers)
6. Video Generation (use any model from HF Inference Providers)

The first four tools can be used without any API keys whatsoever. DDG search is free and the code execution and speech gen is done on CPU. Having a HF_READ_TOKEN in the env variables will show all tools. If there isn't a key present, The Image/Video Gen tools are hidden.

Nymbo/Tools
NymboΒ 
posted an update 17 days ago
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Anyone using Jan-v1-4B for local MCP-based web search, I highly recommend you try out Intelligent-Internet/II-Search-4B

Very impressed with this lil guy and it deserves more downloads. It's based on the original version of Qwen3-4B but find that it questions reality way less often. Jan-v1 seems to think that everything it sees is synthetic data and constantly gaslights me
IlyasMoutawwakilΒ 
posted an update about 1 month ago
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πŸš€ Optimum: The Last v1 Release πŸš€
Optimum v1.27 marks the final major release in the v1 series. As we close this chapter, we're laying the groundwork for a more modular and community-driven future:
- Optimum v2: A lightweight core package for porting Transformers, Diffusers, or Sentence-Transformers to specialized AI hardware/software/accelerators..
- Optimum‑ONNX: A dedicated package where the ONNX/ONNX Runtime ecosystem lives and evolves, faster-moving and decoupled from the Optimum core.

🎯 Why this matters:
- A clearer governance path for ONNX, fostering stronger community collaboration and improved developer experience..
- Enable innovation at a faster pace in a more modular, open-source environment.

πŸ’‘ What this means:
- More transparency, broader participation, and faster development driven by the community and key actors in the ONNX ecosystem (PyTorch, Microsoft, Joshua Lochner πŸ‘€, ...)
- A cleaner, more maintainable core Optimum, focused on extending HF libraries to special AI hardware/software/accelerators tooling and used by our partners (Intel Corporation, Amazon Web Services (AWS), AMD, NVIDIA, FuriosaAI, ...)

πŸ› οΈ Major updates I worked on in this release:
βœ… Added support for Transformers v4.53 and SmolLM3 in ONNX/ONNXRuntime.
βœ… Solved batched inference/generation for all supported decoder model architectures (LLMs).

✨ Big shoutout to @echarlaix for leading the refactoring work that cleanly separated ONNX exporter logic and enabled the creation of Optimum‑ONNX.

πŸ“ Release Notes: https://lnkd.in/gXtE_qji
πŸ“¦ Optimum : https://lnkd.in/ecAezNT6
🎁 Optimum-ONNX: https://lnkd.in/gzjyAjSi
#Optimum #ONNX #OpenSource #HuggingFace #Transformers #Diffusers
AtAndDevΒ 
posted an update about 1 month ago
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Qwen 3 Coder is a personal attack to k2, and I love it.
It achieves near SOTA on LCB while not having reasoning.
Finally people are understanding that reasoning isnt necessary for high benches...

Qwen ftw!

DECENTRALIZE DECENTRALIZE DECENTRALIZE
MaziyarPanahiΒ 
posted an update about 1 month ago
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8573
🧬 Breaking news in Clinical AI: Introducing the OpenMed NER Model Discovery App on Hugging Face πŸ”¬

OpenMed is back! πŸ”₯ Finding the right biomedical NER model just became as precise as a PCR assay!

I'm thrilled to unveil my comprehensive OpenMed Named Entity Recognition Model Discovery App that puts 384 specialized biomedical AI models at your fingertips.

🎯 Why This Matters in Healthcare AI:
Traditional clinical text mining required hours of manual model evaluation. My Discovery App instantly connects researchers, clinicians, and data scientists with the exact NER models they need for their biomedical entity extraction tasks.

πŸ”¬ What You Can Discover:
βœ… Pharmacological Models - Extract "chemical compounds", "drug interactions", and "pharmaceutical" entities from clinical notes
βœ… Genomics & Proteomics - Identify "DNA sequences", "RNA transcripts", "gene variants", "protein complexes", and "cell lines"
βœ… Pathology & Disease Detection - Recognize "pathological formations", "cancer types", and "disease entities" in medical literature
βœ… Anatomical Recognition - Map "anatomical systems", "tissue types", "organ structures", and "cellular components"
βœ… Clinical Entity Extraction - Detect "organism species", "amino acids", 'protein families", and "multi-tissue structures"

πŸ’‘ Advanced Features:
πŸ” Intelligent Entity Search - Find models by specific biomedical entities (e.g., "Show me models detecting CHEM + DNA + Protein")
πŸ₯ Domain-Specific Filtering - Browse by Oncology, Pharmacology, Genomics, Pathology, Hematology, and more
πŸ“Š Model Architecture Insights - Compare BERT, RoBERTa, and DeBERTa implementations
⚑ Real-Time Search - Auto-filtering as you type, no search buttons needed
🎨 Clinical-Grade UI - Beautiful, intuitive interface designed for medical professionals

Ready to revolutionize your biomedical NLP pipeline?

πŸ”— Try it now: OpenMed/openmed-ner-models
🧬 Built with: Gradio, Transformers, Advanced Entity Mapping
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louisbrulenaudetΒ 
posted an update about 2 months ago
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2829
Because hackathons are often the starting point for many AI projects, I've created a Python-backend template incorporating my feedback to streamline collaboration and urgent deployments 🏎️

Within a year, I had the opportunity to participate in hackathons organized by Mistral, OpenAI, and DeepMind and this GitHub template is structured around several fundamental building blocks and recommendations I offer developers eager to participate in their first hackathon, whether as part of a team or individually. Its emphasis is on rapid setup and deployment through:
- uv as a package manager, simplifying usage via a series of pre-configured make commands.
- FastAPI for API management, structured in a modular architecture designed to minimize branch conflicts during merges to main branches (using minimal health-check and ping routes to verify Docker’s proper execution and backend accessibility on the local network).
- Pydantic for validation and type handling, which simplifies debugging and enhances understanding of data objects.
- A set of custom instructions tailored for agents (Cline and GitHub Copilot), aimed at improving overall comprehension of the application and optimizing the vibe-coding experience.

This template includes unit tests with a 100% success rate and test coverage, as well as a minimal CI file ensuring that the FastAPI application runs correctly. Thus, merging code that breaks the server into production becomes impossible ⛔️

In general, I would reiterate an essential piece of advice: your two main adversaries are branch conflictsβ€”particularly when the same file is modified concurrently within a brief period, especially if your architecture isn’t built for scalabilityβ€”and deployment issues under urgent circumstances ⏱️

Link to GitHub: https://github.com/louisbrulenaudet/hackathon-backend

Simply issue these commands and you can ship your code at the speed of light:
make init
make dev
NymboΒ 
posted an update 2 months ago
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2820
Anyone know how to reset Claude web's MCP config? I connected mine when the HF MCP first released with just the default example spaces added. I added lots of other MCP spaces but Claude.ai doesn't update the available tools... "Disconnecting" the HF integration does nothing, deleting it and adding it again does nothing.

Refreshing tools works fine in VS Code because I can manually restart it in mcp.json, but claude.ai has no such option. Anyone got any ideas?
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louisbrulenaudetΒ 
posted an update 2 months ago
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🌐 Clinical Trials Dataset now available on Hugging Face! 🧬

I’ve just released a comprehensive, ML-ready dataset featuring 500,000+ clinical trial records sourced directly from ClinicalTrials.gov for biomedical NLP, healthcare analytics, and clinical research applications πŸ€—

I wanted to produce the most complete and up-to-date dump with all raw data partially flattened to simplify extraction, self-querying and processing.

Do you have any ideas about what we can do with it? Using descriptions to enhance specialized embedding models?

louisbrulenaudet/clinical-trials
AtAndDevΒ 
posted an update 3 months ago
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2987
deepseek-ai/DeepSeek-R1-0528

This is the end
  • 1 reply
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Aurelien-MorganΒ 
posted an update 4 months ago
NymboΒ 
posted an update 4 months ago
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4094
Haven't seen this posted anywhere - Llama-3.3-8B-Instruct is available on the new Llama API. Is this a new model or did someone mislabel Llama-3.1-8B?
  • 1 reply
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NymboΒ 
posted an update 4 months ago
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PSA for anyone using Nymbo/Nymbo_Theme or Nymbo/Nymbo_Theme_5 in a Gradio space ~

Both of these themes have been updated to fix some of the long-standing inconsistencies ever since the transition to Gradio v5. Textboxes are no longer bright green and in-line code is readable now! Both themes are now visually identical across versions.

If your space is already using one of these themes, you just need to restart your space to get the latest version. No code changes needed.
Aurelien-MorganΒ 
posted an update 4 months ago
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3145
The Almighty function-caller

How would you like to build smart GenAi infrastructure ?
Give extensive tools memory to your edge agentic system,
And optimize the resources it takes to run yet a high-performance set of agents ?

We came up with a novel approach to function-calling at scale for smart companies and corporate-grade use-cases.

Read our full-fledged blog article on this here on Hugging Face :
https://huggingface.co/blog/Aurelien-Morgan/the-almighty-function-caller