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danieldk 

posted an update about 1 month ago

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1689

We have been working on a project called kernels. kernels makes it possible to load compute kernels directly from the Hub! 🚀

We plan to give kernels a more proper introduction soon. But for those who have been following along, we are happy to announce a new release:

- New layer API with torch.compile support.
- Experimental support for loading Apple Silicon Metal 🤘 Kernels.
- Generate wheels from Hub kernels for legacy deployments.

Full release notes here: https://github.com/huggingface/kernels/releases/tag/v0.6.0

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betodepaola 

in meta-llama/Llama-Guard-4-12B 2 months ago

Removing new lines

#9 opened 2 months ago by

betodepaola

Updating chat template

1

#8 opened 2 months ago by

betodepaola

Delete chat_template.json

#6 opened 2 months ago by

pcuenq

Updating chat template

#7 opened 2 months ago by

betodepaola

betodepaola 

updated a model 2 months ago

meta-llama/Llama-Prompt-Guard-2-86M

Text Classification • 0.3B • Updated Apr 29 • 99.3k • • 45

pcuenq 

in meta-llama/Llama-Guard-4-12B 2 months ago

Delete chat_template.json

#6 opened 2 months ago by

pcuenq

betodepaola 

updated a model 2 months ago

meta-llama/Llama-Guard-4-12B

Image-Text-to-Text • 12B • Updated Apr 29 • 14.2k • • 45

betodepaola 

in meta-llama/Llama-Guard-4-12B 2 months ago

Adding images

#5 opened 2 months ago by

betodepaola

Adding readme, license and use policy

#4 opened 2 months ago by

betodepaola

Adding readme, license and use policy

#3 opened 2 months ago by

betodepaola

Adding readme, license and use policy

#2 opened 2 months ago by

betodepaola

betodepaola 

in meta-llama/Llama-Prompt-Guard-2-86M 2 months ago

Adding Llama Prompt Guard 2 86M to repo

#1 opened 2 months ago by

betodepaola

pcuenq 

authored a paper 3 months ago

SmolVLM: Redefining small and efficient multimodal models

Paper • 2504.05299 • Published Apr 7 • 192

lysandre 

posted an update 4 months ago

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7301

SmolVLM-2 and SigLIP-2 are now part of transformers in dedicated releases!

They're added on top of the v4.49.0 release, and can be installed from the following tags: v4.49.0-SmolVLM-2 and v4.49.0-SigLIP-2.

This marks a new beginning for the release process of transformers. For the past five years, we've been doing monthly releases featuring many models (v4.49.0, the latest release, features 9 new architectures).

Starting with SmolVLM-2 & SigLIP2, we'll now additionally release tags supporting new models on a stable branch. These models are therefore directly available for use by installing from the tag itself. These tags will continue to be updated with fixes applied to these models.

Going forward, continue expecting software releases following semantic versioning: v4.50.0 will have ~10 new architectures compared to v4.49.0, as well as a myriad of new features, improvements and bug fixes. Accompanying these software releases, we'll release tags offering brand new models as fast as possible, to make them accessible to all immediately.

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florentgbelidji 

posted an update 6 months ago

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𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 𝗬𝗼𝘂𝗿 𝗡𝗲𝘅𝘁 𝗦𝗸𝗶 𝗔𝗱𝘃𝗲𝗻𝘁𝘂𝗿𝗲 𝗝𝘂𝘀𝘁 𝗚𝗼𝘁 𝗦𝗺𝗮𝗿𝘁𝗲𝗿: 𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝗶𝗻𝗴 𝗔𝗹𝗽𝗶𝗻𝗲 𝗔𝗴𝗲𝗻𝘁!🏔️⛷️

With the big hype around AI agents these days, I couldn’t stop thinking about how AI agents could truly enhance real-world activities.
What sort of applications could we build with those AI agents: agentic RAG? self-correcting text-to-sql? Nah, boring…

Passionate about outdoors, I’ve always dreamed of a tool that could simplify planning mountain trips while accounting for all potential risks. That’s why I built 𝗔𝗹𝗽𝗶𝗻𝗲 𝗔𝗴𝗲𝗻𝘁, a smart assistant designed to help you plan safe and enjoyable itineraries in the French Alps and Pyrenees.

Built using Hugging Face's 𝘀𝗺𝗼𝗹𝗮𝗴𝗲𝗻𝘁𝘀 library, Alpine Agent combines the power of AI with trusted resources like 𝘚𝘬𝘪𝘵𝘰𝘶𝘳.𝘧𝘳 (https://skitour.fr/) and METEO FRANCE. Whether it’s suggesting a route with moderate difficulty or analyzing avalanche risks and weather conditions, this agent dynamically integrates data to deliver personalized recommendations.

In my latest blog post, I share how I developed this project—from defining tools and integrating APIs to selecting the best LLMs like 𝘘𝘸𝘦𝘯2.5-𝘊𝘰𝘥𝘦𝘳-32𝘉-𝘐𝘯𝘴𝘵𝘳𝘶𝘤𝘵, 𝘓𝘭𝘢𝘮𝘢-3.3-70𝘉-𝘐𝘯𝘴𝘵𝘳𝘶𝘤𝘵, or 𝘎𝘗𝘛-4.

⛷️ Curious how AI can enhance adventure planning?
Try the app and share your thoughts: florentgbelidji/alpine-agent

👉 Want to build your own agents? Whether for cooking, sports training, or other passions, the possibilities are endless. Check out the blog post to learn more: https://huggingface.co/blog/florentgbelidji/alpine-agent

Many thanks to @m-ric for helping on building this tool with smolagents!

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andrewrreed 

posted an update 6 months ago

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🚀 Supercharge your LLM apps with Langfuse on Hugging Face Spaces!

Langfuse brings end-to-end observability and tooling to accelerate your dev workflow from experiments through production

Now available as a Docker Space directly on the HF Hub! 🤗

🔍 Trace everything: monitor LLM calls, retrieval, and agent actions with popular frameworks
1⃣ One-click deployment: on Spaces with persistent storage and integrated OAuth
🛠 Simple Prompt Management: Version, edit, and update without redeployment
✅ Intuitive Evals: Collect user feedback, run model/prompt evaluations, and improve quality
📊 Dataset Creation: Build datasets directly from production data to enhance future performance

Kudos to the Langfuse team for this collab and the awesome, open-first product they’re building! 👏 @marcklingen @Clemo @MJannik

🔗 Space: langfuse/langfuse-template-space
🔗 Docs: https://huggingface.co/docs/hub/spaces-sdks-docker-langfuse

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andrewrreed 

posted an update 8 months ago

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Trace LLM calls with Arize AI's Phoenix observability dashboards on Hugging Face Spaces! 🚀

✨ I just added a new recipe to the Open-Source AI Cookbook that shows you how to:
1️⃣ Deploy Phoenix on HF Spaces with persistent storage in a few clicks
2️⃣ Configure LLM tracing with the 𝗦𝗲𝗿𝘃𝗲𝗿𝗹𝗲𝘀𝘀 𝗜𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗔𝗣𝗜
3️⃣ Observe multi-agent application runs with the CrewAI integration

𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗶𝘀 𝗰𝗿𝘂𝗰𝗶𝗮𝗹 for building robust LLM apps.

Phoenix makes it easy to visualize trace data, evaluate performance, and track down issues. Give it a try!

🔗 Cookbook recipe: https://huggingface.co/learn/cookbook/en/phoenix_observability_on_hf_spaces
🔗 Phoenix docs: https://docs.arize.com/phoenix

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SaylorTwift 

posted an update 8 months ago

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How do I test an LLM for my unique needs?
If you work in finance, law, or medicine, generic benchmarks are not enough.
This blog post uses Argilla, Distilllabel and 🌤️Lighteval to generate evaluation dataset and evaluate models.

https://github.com/argilla-io/argilla-cookbook/blob/main/domain-eval/README.md

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ekurtic 

authored a paper 8 months ago

Error Feedback Can Accurately Compress Preconditioners

Paper • 2306.06098 • Published Jun 9, 2023