Building on HF

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Cédric

cedricbonhomme

https://www.cedricbonhomme.org

AI & ML interests

AI/NLP for Vulnerability Management. Multi-Agent Systems.

Recent Activity

updated a Space about 6 hours ago

CIRCL/README

liked a model 1 day ago

Qwen/Qwen2.5-Coder-7B-Instruct

updated a model 1 day ago

CIRCL/vulnerability-severity-classification-roberta-base

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reacted to julien-c's post with 🤗🔥 15 days ago

Post

8885

BOOOOM: Today I'm dropping TINY AGENTS

the 50 lines of code Agent in Javascript 🔥

I spent the last few weeks working on this, so I hope you will like it.

I've been diving into MCP (Model Context Protocol) to understand what the hype was all about.

It is fairly simple, but still quite powerful: MCP is a standard API to expose sets of Tools that can be hooked to LLMs.

But while doing that, came my second realization:

Once you have a MCP Client, an Agent is literally just a while loop on top of it. 🤯

➡️ read it exclusively on the official HF blog: https://huggingface.co/blog/tiny-agents

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reacted to omarkamali's post with 👍 18 days ago

Post

1570

New year, new dataset 🚀

I just released omarkamali/wikipedia-labels, with all the structural labels and namespace from wikipedia in 300+ languages. A gift for the data preprocessors and cleaners among us.

Happy new year 2026 everyone! 🎆

posted an update 18 days ago

Post

2676

With VLAgentIc, you can now use your local Qwen installation via Ollama and leverage the models CIRCL/vulnerability-severity-classification-roberta-base and CIRCL/cwe-parent-vulnerability-classification-roberta-base.

The project is available here:
https://github.com/vulnerability-lookup/VLAgentIc

The VLAI Severity and CWE classifiers are available on Hugging Face:
- CIRCL/vulnerability-severity-classification-roberta-base
- CIRCL/cwe-parent-vulnerability-classification-roberta-base

The concept of AI agents—combining models, tools, and orchestration—has become fairly standardized during the last year, but VLAgentIc brings something unique:

- Agents communicate over XMPP, enabling concurrent tasks and asynchronous messaging thanks to the SPADE framework.
- Built-in presence and discovery streamline interactions between components.
- Flexible behaviours make orchestrating AI-assisted security workflows seamless for future connections
- Last but not least, the VLAI Severity and VLAI CWE classifiers are now wrapped as LLM Tools and run entirely locally.

New, more comprehensive agent tools will soon be available, leveraging the Vulnerability-Lookup API and supporting the GCVE project.

The Human-in-the-Loop agent tool will be designed to notify you and request authorization whenever a query to an external service is about to be made—ensuring that, by default, all reasoning and processing stay local on your computer.

VLAI: A RoBERTa-Based Model for Automated Vulnerability Severity Classification (2507.03607)

reacted to prithivMLmods's post with 👍🔥 about 1 month ago

Post

5563

Introducing the Z Image Turbo LoRA DLC App, a gallery space for plug-and-play Z-Image-Turbo LoRAs. It features a curated collection of impressive LoRAs for generating high-quality images. By default, it runs on the base model. Simply choose a LoRA, type your prompt, and generate images. You can find the app and more details below. 🤗🧪

● Space [Demo]: prithivMLmods/Z-Image-Turbo-LoRA-DLC
● Collection: https://huggingface.co/collections/prithivMLmods/image-generation-apps-collection
● Check the list of Z-Image LoRA's: https://huggingface.co/models?other=base_model:adapter:Tongyi-MAI/Z-Image-Turbo
● Github: https://github.com/PRITHIVSAKTHIUR/Z-Image-Turbo-LoRA-DLC

Other related image gen spaces:-

● FLUX-LoRA-DLC2: prithivMLmods/FLUX-LoRA-DLC2
● FLUX-LoRA-DLC: prithivMLmods/FLUX-LoRA-DLC
● Qwen-Image-LoRA-DLC: prithivMLmods/Qwen-Image-LoRA-DLC
● Qwen-Image-Edit-2509-LoRAs-Fast: prithivMLmods/Qwen-Image-Edit-2509-LoRAs-Fast
● Qwen-Image-Edit-2509-LoRAs-Fast-Fusion: prithivMLmods/Qwen-Image-Edit-2509-LoRAs-Fast-Fusion

& more...

To know more about it, visit the app page or the respective model page!

2 replies

reacted to m-ric's post with 🚀 11 months ago

Post

4924

We now have a Deep Research for academia: SurveyX automatically writes academic surveys nearly indistinguishable from human-written ones 🔥

Researchers from Beijing and Shanghai just published the first application of a deep research system to academia: their algorithm, given a question, can give you a survey of all papers on the subject.

To make a research survey, you generally follow two steps, preparation (collect and organize papers) and writing (outline creation, writing, polishing). Researchers followed the same two steps and automated them.

🎯 For the preparation part, a key part is find all the important references on the given subject.
Researchers first cast a wide net of all relevant papers. But then finding the really important ones is like distilling knowledge from a haystack of information. To solve this challenge, they built an “AttributeTree” object that structures key information from citations. Ablating these AttributeTrees significantly decreased structure and synthesis scores, so they were really useful!

📝 For the writing part, key was to get a synthesis that's both short and true. This is not easy to get with LLMs! So they used methods like LLM-based deduplication to shorten the too verbose listings made by LLMs, and RAG to grab original quotes instead of made-up ones.

As a result, their system outperforms previous approaches by far!

As assessed by LLM-judges, the quality score os SurveyX even approaches this of human experts, with 4.59/5 vs 4.75/5 🏆

I advise you to read the paper, it's a great overview of the kind of assistants that we'll get in the short future! 👉 SurveyX: Academic Survey Automation via Large Language Models (2502.14776)
Their website shows examples of generated surveys 👉 http://www.surveyx.cn/

reacted to stefan-it's post with 👍 11 months ago

Post

5184

She arrived 😍

[Expect more models soon...]

2 replies

Cédric

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