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Jingya  updated a model 7 days ago
optimum/bge-base-en-v1.5-neuronx
badaoui  updated a Space about 2 months ago
optimum/neuron-export
badaoui  updated a Space about 2 months ago
optimum/neuron-export
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pagezyhf 
posted an update 3 days ago
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2565
🚀 Big news for AI builders!

We’re thrilled to announce that the Qwen3-VL family of vision-language models is now available on Azure AI Foundry, thanks to our collaboration with Microsoft.

We bring open-source innovation to enterprise-grade AI infrastructure, making it easier than ever for enterprise to deploy and scale the latest and greatest from models from hugging Face securely within Azure.

🔍 Highlights:

- Deploy Qwen3-VL instantly via managed endpoints
- Built-in governance, telemetry, and lifecycle management
- True multimodal reasoning — vision, language, and code understanding
- State-of-the-art performance, outperforming closed-source models like Gemini 2.5 Pro and GPT-5
- Available in both *Instruct* and *Thinking* modes, across 24 model sizes

👉 Get started today: search for Qwen3-VL in the Hugging Face Collection on Azure AI Foundry.
  • 1 reply
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Jingya 
updated a model 7 days ago
pagezyhf 
posted an update about 1 month ago
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835
What’s your biggest headache deploying Hugging Face models to the cloud—and how can we fix it for you?
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badaoui 
updated a Space about 2 months ago
pagezyhf 
posted an update about 2 months ago
pagezyhf 
posted an update about 2 months ago
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3887
🤝 Collaborating with AMD to ensure Hugging Face Transformers runs smoothly on AMD GPUs!

We run daily CI on AMD MI325 to track the health of the most important model architectures and we’ve just made our internal dashboard public.

By making this easily accessible, we hope to spark community contributions and improve support for everyone!
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badaoui 
posted an update about 2 months ago
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397
🚀 Optimum libraries keep growing, and Optimum v2 is just around the corner!

I recently added ONNX export support for a bunch of new models in the optimum-onnx library, including: DeepSeek-V3, Cohere, Nemotron, Arcee, StableLM … and more!

⚡ With ONNX export, you can run your favorite models faster and more efficiently across different hardware backends, making deployment and experimentation much smoother.

💡 Have a model you’d love to see supported? Contributions are super welcome — let’s make Optimum even better together!

#ONNX #Optimum #HuggingFace #OpenSource #AI
jeffboudier 
posted an update 2 months ago
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2979
Quick 30s demo of the new Hub > Azure AI integration to deploy HF models in your own Azure account. Now with Py and CLI!

GG @alvarobartt @kramp @pagezyhf
pagezyhf 
posted an update 2 months ago
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3211
We've improved the Deploy button on Hugging Face model pages for Microsoft Azure

1/ no more long waits before seeing model support status

2/ ready-to-use CLI and Python snippets

3/ redirection to Azure AI Foundry rather than Azure ML

✋ if you see any bugs or have feedback, open an issue on our repo:
https://github.com/huggingface/Microsoft-Azure
badaoui 
posted an update 3 months ago
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3183
Is there a "one-size-fits-all" recipe for quantizing Large Language Models? 🤔

As part of my ongoing work in mixed-precision quantization, I've been exploring this question by measuring layer-by-layer sensitivity. The goal is to see if we can find universal rules for which layers can be quantized aggressively without impacting performance.The results are fascinating and reveal two key insights:

1️⃣ Sensitivity profiles are like architectural "fingerprints." Models from the same family share strikingly similar sensitivity patterns. As you can see in the charts below for the Gemma and SmolLM families, the ranking and relative sensitivity of the layers remain remarkably consistent. This suggests that the underlying architecture is a primary driver of a model's quantization behavior.

2️⃣ A "universal" mixed-precision quantization strategy is challenging. While models within a family are similar, these "fingerprints" change dramatically when comparing different architectures like LLaMA, Qwen, and StableLM. This highlights the difficulty in creating a generalized mixed-precision configuration that works optimally across all model families.

However, there is one near-universal truth we uncovered: the mlp.down_proj layer consistently emerges as one of the most sensitive components across all models studied.
This finding strongly resonates with the work in "The Super Weight in Large Language Models" (by Mengxia Yu et al.). The paper identifies that functionally critical parameters, or "super weights," are concentrated in these down_proj layers. Our empirical results provide clear validation for this theory, showing these layers are highly intolerant to precision loss.

In short, while every architecture has a unique sensitivity profile, a fingerprint shaped not only by its core design but also by its specific training dataset and optimization approach, some components remain universally critical!
What are your thoughts?
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pagezyhf 
posted an update 3 months ago
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2187
Deploy GPT OSS models with Hugging Face on Azure AI!

We’re thrilled to enable OpenAI GPT OSS models on Azure AI Model Catalog for Azure users to try the model securely the day of its release.

In our official launch blogpost, there’s a section on how to deploy the model to your Azure AI Hub. Get started today!

https://huggingface.co/blog/welcome-openai-gpt-oss#azure
pagezyhf 
posted an update 3 months ago
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273
We now have the newest Open AI models available on the Dell Enterprise Hub!

We built the Dell Enterprise Hub to provide access to the latest and greatest model from the Hugging Face community to our on-prem customers. We’re happy to give secure access to this amazing contribution from Open AI on the day of its launch!

https://dell.huggingface.co/
IlyasMoutawwakil 
posted an update 3 months ago
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3440
🚀 Optimum: The Last v1 Release 🚀
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.

💡 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 👀, ...)
- 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, ...)

🛠️ Major updates I worked on in this release:
✅ Added support for Transformers v4.53 and SmolLM3 in ONNX/ONNXRuntime.
✅ 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.

📝 Release Notes: https://lnkd.in/gXtE_qji
📦 Optimum : https://lnkd.in/ecAezNT6
🎁 Optimum-ONNX: https://lnkd.in/gzjyAjSi
#Optimum #ONNX #OpenSource #HuggingFace #Transformers #Diffusers
pagezyhf 
posted an update 3 months ago
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356
🟪 Qwen/Qwen3‑235B‑A22B‑Instruct‑2507‑FP8 is now available in Microsoft Azure for one‑click deployment! 🚀

Check out their blogpost: https://qwenlm.github.io/blog/qwen3/

You can now find it in the Hugging Face Collection in Azure ML or Azure AI Foundry, along with 10k other Hugging Face models 🤗🤗
Qwen/Qwen3-235B-A22B-Instruct-2507-FP8

Bear with us for the non‑quantized version.
pagezyhf 
posted an update 4 months ago
pagezyhf 
posted an update 4 months ago
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212
🎉 New in Azure Model Catalog: NVIDIA Parakeet TDT 0.6B V2

We're excited to welcome Parakeet TDT 0.6B V2—a state-of-the-art English speech-to-text model—to the Azure Foundry Model Catalog.

What is it?

A powerful ASR model built on the FastConformer-TDT architecture, offering:
🕒 Word-level timestamps
✍️ Automatic punctuation & capitalization
🔊 Strong performance across noisy and real-world audio

It runs with NeMo, NVIDIA’s optimized inference engine.

Want to give it a try? 🎧 You can test it with your own audio (up to 3 hours) on Hugging Face Spaces before deploying.If it fits your need, deploy easily from the Hugging Face Hub or Azure ML Studio with secure, scalable infrastructure!

📘 Learn more by following this guide written by @alvarobartt

https://huggingface.co/docs/microsoft-azure/azure-ai/examples/deploy-nvidia-parakeet-asr
pagezyhf 
posted an update 4 months ago
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If you want to dive into how the HF team worked with @seungrokj at @AMD
to optimize kernels on MI300, you should give a read to our latest blog!

Such a great educational material for anyone curious about the world of optimizing low level ML.

https://huggingface.co/blog/mi300kernels
badaoui 
authored a paper 4 months ago
pagezyhf 
posted an update 4 months ago
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1639
In case you missed it, Hugging Face expanded its collaboration with Azure a few weeks ago with a curated catalog of 10,000 models, accessible from Azure AI Foundry and Azure ML!

@alvarobartt cooked during these last days to prepare the one and only documentation you need, if you wanted to deploy Hugging Face models on Azure. It comes with an FAQ, great guides and examples on how to deploy VLMs, LLMs, smolagents and more to come very soon.

We need your feedback: come help us and let us know what else you want to see, which model we should add to the collection, which model task we should prioritize adding, what else we should build a tutorial for. You’re just an issue away on our GitHub repo!

https://huggingface.co/docs/microsoft-azure/index
jeffboudier 
posted an update 4 months ago
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AMD summer hackathons are here!
A chance to get hands-on with MI300X GPUs and accelerate models.
🇫🇷 Paris - Station F - July 5-6
🇮🇳 Mumbai - July 12-13
🇮🇳 Bengaluru - July 19-20

Hugging Face and GPU Mode will be on site and on July 6 in Paris @ror will share lessons learned while building new kernels to accelerate Llama 3.1 405B on ROCm

Register to Paris event: https://lu.ma/fmvdjmur?tk=KeAbiP
All dates: https://lu.ma/calendar/cal-3sxhD5FdxWsMDIz