Hugging Face Selected for the French Data Protection Agency Enhanced Support Program

This blog post was originally published on LinkedIn on 05/15/2023 We are happy to announce that Hugging Face has been selected by the CNIL (French Data Protection Authority) to benefit from its Enhanced Support program! This new program picked three companies with “strong potential for economic development” out of over 40 candidates, who will receive support in understanding and implementing their duties with respect to data protection – a daunting and necessary endeavor in the context of the rapidly evolving […]

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Hugging Face Collaborates with Microsoft to launch Hugging Face Model Catalog on Azure

Today, we are thrilled to announce that Hugging Face expands its collaboration with Microsoft to bring open-source models from the Hugging Face Hub to Azure Machine Learning. Together we built a new Hugging Face Hub Model Catalog available directly within Azure Machine Learning Studio, filled with thousands of the most popular Transformers models from the Hugging Face Hub. With this new integration, you can now deploy Hugging Face models in just a few clicks on managed endpoints, running onto secure […]

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Making LLMs even more accessible with bitsandbytes, 4-bit quantization and QLoRA

LLMs are known to be large, and running or training them in consumer hardware is a huge challenge for users and accessibility. Our LLM.int8 blogpost showed how the techniques in the LLM.int8 paper were integrated in transformers using the bitsandbytes library. As we strive to make models even more accessible to anyone, we decided to collaborate with bitsandbytes again to allow users to run models in 4-bit precision. This includes a large majority of HF models, in any modality (text, […]

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Optimizing Stable Diffusion for Intel CPUs with NNCF and 🤗 Optimum

Latent Diffusion models are game changers when it comes to solving text-to-image generation problems. Stable Diffusion is one of the most famous examples that got wide adoption in the community and industry. The idea behind the Stable Diffusion model is simple and compelling: you generate an image from a noise vector in multiple small steps refining the noise to a latent image representation. This approach works very well, but it can take a long time to generate an image if […]

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