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badaoui 
posted an update 11 days ago
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Building high-performance, reproducible kernels for AMD ROCm just got a lot easier.

I've put together a guide on building, testing, and sharing ROCm-compatible kernels using the Hugging Face kernel-builder and kernels libraries; so you can focus on optimizing performance rather than spending time on setup.

Learn how to:

- Use Nix for reproducible builds
- Integrate kernels as native PyTorch operators
- Share your kernels on the Hub for anyone to use with kernels.get_kernel()

We use the 🏆 award-winning RadeonFlow GEMM kernel as a practical example.

📜 Check out the full guide here : https://huggingface.co/blog/build-rocm-kernels
pagezyhf 
posted an update 29 days ago
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🚀 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.
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pagezyhf 
posted an update 2 months ago
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What’s your biggest headache deploying Hugging Face models to the cloud—and how can we fix it for you?
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pagezyhf 
posted an update 3 months ago
pagezyhf 
posted an update 3 months ago
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🤝 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 3 months ago
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🚀 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 3 months ago
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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 3 months ago
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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 4 months ago
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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 4 months ago
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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 4 months ago
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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/
pagezyhf 
posted an update 4 months ago
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🟪 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 5 months ago
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🎉 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 5 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