Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion
Paper โข 2607.25572 โข Published โข 5
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cedricbonhommeย
authored a
paper 2 days ago
cedricbonhommeย
submitted a
paper to Daily Papers 7 days ago
Post
3300
Sharing how I built the LongCat-Video-Avatar 1.5 Space (+500k views on X) in one agent session. Gave a coding agent its own AI lab on ZeroGPU, framed the goal, walked away. It designed, deployed, tested against the live API, fixed, shipped.
Full recipe with the copy-paste prompt: https://huggingface.co/blog/victor/building-zerogpu-spaces-autonomously
Full recipe with the copy-paste prompt: https://huggingface.co/blog/victor/building-zerogpu-spaces-autonomously
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#1 opened 2 months ago
by 
stephenb1334
cedricbonhommeย
authored a
paper 4 months ago
cedricbonhommeย
submitted a
paper to Daily Papers 4 months ago
Post
6238
Want to share my enthusiasm for zai-org/GLM-5.1 here too ๐ฅ
I think we have it: our open source Claude Code = GLM-5.1 + Pi (https://pi.dev/) - Built a Three.js racing game to eval and it's extremely impressive. Thoughts:
- One-shot car physics with real drift mechanics (this is hard)
- My fav part: Awesome at self iterating (with no vision!) created 20+ Bun.WebView debugging tools to drive the car programmatically and read game state. Proved a winding bug with vector math without ever seeing the screen
- 531-line racing AI in a single write: 4 personalities, curvature map, racing lines, tactical drifting. Built telemetry tools to compare player vs AI speed curves and data-tuned parameters
- All assets from scratch: 3D models, procedural textures, sky shader, engine sounds, spatial AI audio!
- Can do hard math: proved road normals pointed DOWN via vector cross products, computed track curvature normalized by arc length to tune AI cornering speed
You are going to hear about this model a lot in the next months - open source let's go - and thanks z-ai๐๐
I think we have it: our open source Claude Code = GLM-5.1 + Pi (https://pi.dev/) - Built a Three.js racing game to eval and it's extremely impressive. Thoughts:
- One-shot car physics with real drift mechanics (this is hard)
- My fav part: Awesome at self iterating (with no vision!) created 20+ Bun.WebView debugging tools to drive the car programmatically and read game state. Proved a winding bug with vector math without ever seeing the screen
- 531-line racing AI in a single write: 4 personalities, curvature map, racing lines, tactical drifting. Built telemetry tools to compare player vs AI speed curves and data-tuned parameters
- All assets from scratch: 3D models, procedural textures, sky shader, engine sounds, spatial AI audio!
- Can do hard math: proved road normals pointed DOWN via vector cross products, computed track curvature normalized by arc length to tune AI cornering speed
You are going to hear about this model a lot in the next months - open source let's go - and thanks z-ai๐๐
- 5 replies
victorย
submitted a
paper to Daily Papers 5 months ago
Post
2858
Interesting article: use Claude Code to help open models write CUDA kernels (for eg) by turning CC traces into Skills. They made a library out of it ๐
https://huggingface.co/blog/upskill
https://huggingface.co/blog/upskill
cedricbonhommeย
posted an update 7 months ago
Post
2846
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 agentsโcombining models, tools, and orchestrationโhas 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 madeโensuring that, by default, all reasoning and processing stay local on your computer.
VLAI: A RoBERTa-Based Model for Automated Vulnerability Severity Classification (2507.03607)
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 agentsโcombining models, tools, and orchestrationโhas 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 madeโensuring that, by default, all reasoning and processing stay local on your computer.
VLAI: A RoBERTa-Based Model for Automated Vulnerability Severity Classification (2507.03607)
Post
3595
Nvidia is on a roll lately. Nemotron 3 Nano is my new fav local model, but here's the real flex: they published the entire evaluation setup. Configs, prompts, logs, all of it. This is how you do open models ๐ฅ
https://huggingface.co/blog/nvidia/nemotron-3-nano-evaluation-recipe
https://huggingface.co/blog/nvidia/nemotron-3-nano-evaluation-recipe
ehristoforuย
posted an update 11 months ago
Post
2702
๐Hello from the Project Fluently team!
โจ We are happy to share with you our new universal LLM models based on Qwen3 1.7B and 4B โ powerful, multilingual and ready to solve a wide range of problems!
๐ ๏ธ We have conducted additional training and carefully merged them to achieve even better results and maximize the potential of the models.
๐ And most importantly โ the models are completely open and free under the Apache-2.0 license!
๐ Links to repositories:
- FluentlyQwen3-4B: fluently/FluentlyQwen3-4B
- FluentlyQwen3-1.7B: fluently/FluentlyQwen3-1.7B
๐ We will be very glad to hear your feedback and impressions! Your opinion is very important to us!
โจ We are happy to share with you our new universal LLM models based on Qwen3 1.7B and 4B โ powerful, multilingual and ready to solve a wide range of problems!
๐ ๏ธ We have conducted additional training and carefully merged them to achieve even better results and maximize the potential of the models.
๐ And most importantly โ the models are completely open and free under the Apache-2.0 license!
๐ Links to repositories:
- FluentlyQwen3-4B: fluently/FluentlyQwen3-4B
- FluentlyQwen3-1.7B: fluently/FluentlyQwen3-1.7B
๐ We will be very glad to hear your feedback and impressions! Your opinion is very important to us!
cedricbonhommeย
authored a
paper about 1 year ago
Post
7667
Open Source Avengers, Assemble! Ask an expert AI agent team to solve complex problems together ๐ฅ
Consilium brings together multiple agents that debate and use live research (web, arXiv, SEC) to reach a consensus. You set the strategy, they find the answer.
Credit to @azettl for this awesome demo: Agents-MCP-Hackathon/consilium_mcp
Consilium brings together multiple agents that debate and use live research (web, arXiv, SEC) to reach a consensus. You set the strategy, they find the answer.
Credit to @azettl for this awesome demo: Agents-MCP-Hackathon/consilium_mcp
- 2 replies
Post
2310
Where gone streamlit in huggingface?
- 3 replies
asa1108ย
published a
model about 1 year ago
asa1108ย
updated a
model about 1 year ago
Post
5216
- 1 reply
Post
8578
๐AraClip is now fully integrated with Hugging Face ๐ค
AraClip is a specialized CLIP model that was created by @pain and optimized for Arabic text-image retrieval tasks๐ฅ
๐ Try it out ๐
๐ค model: Arabic-Clip/araclip
๐งฉ Gradio demo: Arabic-Clip/Araclip-Simplified
๐ website: https://arabic-clip.github.io/Arabic-CLIP/
AraClip is a specialized CLIP model that was created by @pain and optimized for Arabic text-image retrieval tasks๐ฅ
๐ Try it out ๐
๐ค model: Arabic-Clip/araclip
๐งฉ Gradio demo: Arabic-Clip/Araclip-Simplified
๐ website: https://arabic-clip.github.io/Arabic-CLIP/
- 2 replies
ehristoforuย
posted an update over 1 year ago
Post
4489
Introducing our first standalone model โ FluentlyLM Prinum
Introducing the first standalone model from Project Fluently LM! We worked on it for several months, used different approaches and eventually found the optimal one.
General characteristics:
- Model type: Causal language models (QwenForCausalLM, LM Transformer)
- Number of parameters: 32.5B
- Number of parameters (not embedded): 31.0B
- Number of layers: 64
- Context: 131,072 tokens
- Language(s) (NLP): English, French, Spanish, Russian, Chinese, Japanese, Persian (officially supported)
- License: MIT
Creation strategy:
The basis of the strategy is shown in Pic. 2.
We used Axolotl & Unsloth for SFT-finetuning with PEFT LoRA (rank=64, alpha=64) and Mergekit for SLERP and TIES mergers.
Evolution:
๐ 12th place in the Open LLM Leaderboard ( open-llm-leaderboard/open_llm_leaderboard) (21.02.2025)
Detailed results and comparisons are presented in Pic. 3.
Links:
- Model: https://huggingface.co/fluently-lm/FluentlyLM-Prinum
- GGUF version: mradermacher/FluentlyLM-Prinum-GGUF
- Demo on ZeroGPU: ehristoforu/FluentlyLM-Prinum-demo
Introducing the first standalone model from Project Fluently LM! We worked on it for several months, used different approaches and eventually found the optimal one.
General characteristics:
- Model type: Causal language models (QwenForCausalLM, LM Transformer)
- Number of parameters: 32.5B
- Number of parameters (not embedded): 31.0B
- Number of layers: 64
- Context: 131,072 tokens
- Language(s) (NLP): English, French, Spanish, Russian, Chinese, Japanese, Persian (officially supported)
- License: MIT
Creation strategy:
The basis of the strategy is shown in Pic. 2.
We used Axolotl & Unsloth for SFT-finetuning with PEFT LoRA (rank=64, alpha=64) and Mergekit for SLERP and TIES mergers.
Evolution:
๐ 12th place in the Open LLM Leaderboard ( open-llm-leaderboard/open_llm_leaderboard) (21.02.2025)
Detailed results and comparisons are presented in Pic. 3.
Links:
- Model: https://huggingface.co/fluently-lm/FluentlyLM-Prinum
- GGUF version: mradermacher/FluentlyLM-Prinum-GGUF
- Demo on ZeroGPU: ehristoforu/FluentlyLM-Prinum-demo
- 7 replies