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Nicolai Rosenberg
SanRosenberg
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I am thrilled to announce Gemma, new 2B and 7B models from Google, based on the same research and technology used to train the Gemini models! These models achieve state-of-the-art performance for their size, and are launched across Transformers, Google Cloud, and many other surfaces worldwide starting today.
Get started using and adapting Gemma in the model Collection: google/gemma-release-65d5efbccdbb8c4202ec078b
These launches are the product of an outstanding collaboration between the Google DeepMind and Hugging Face teams over the last few months -- very proud of the work both teams have done, from integration with Vertex AI to optimization across the stack. Read more about the partnership in the main launch by @philschmid @osanseviero @pcuenq on the launch blog: https://huggingface.co/blog/gemma
More information below if you are curious about training details, eval results, and safety characteristics!
Gemma Tech Report: https://goo.gle/GemmaReport
Launch announcement: https://blog.google/technology/developers/gemma-open-models/
Get started using and adapting Gemma in the model Collection: google/gemma-release-65d5efbccdbb8c4202ec078b
These launches are the product of an outstanding collaboration between the Google DeepMind and Hugging Face teams over the last few months -- very proud of the work both teams have done, from integration with Vertex AI to optimization across the stack. Read more about the partnership in the main launch by @philschmid @osanseviero @pcuenq on the launch blog: https://huggingface.co/blog/gemma
More information below if you are curious about training details, eval results, and safety characteristics!
Gemma Tech Report: https://goo.gle/GemmaReport
Launch announcement: https://blog.google/technology/developers/gemma-open-models/
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Neural Network Diffusion
Neural Network Diffusion (2402.13144)
Diffusion models have achieved remarkable success in image and video generation. In this work, we demonstrate that diffusion models can also generate high-performing neural network parameters. Our approach is simple, utilizing an autoencoder and a standard latent diffusion model. The autoencoder extracts latent representations of a subset of the trained network parameters. A diffusion model is then trained to synthesize these latent parameter representations from random noise. It then generates new representations that are passed through the autoencoder's decoder, whose outputs are ready to use as new subsets of network parameters. Across various architectures and datasets, our diffusion process consistently generates models of comparable or improved performance over trained networks, with minimal additional cost. Notably, we empirically find that the generated models perform differently with the trained networks. Our results encourage more exploration on the versatile use of diffusion models.
Neural Network Diffusion (2402.13144)
Diffusion models have achieved remarkable success in image and video generation. In this work, we demonstrate that diffusion models can also generate high-performing neural network parameters. Our approach is simple, utilizing an autoencoder and a standard latent diffusion model. The autoencoder extracts latent representations of a subset of the trained network parameters. A diffusion model is then trained to synthesize these latent parameter representations from random noise. It then generates new representations that are passed through the autoencoder's decoder, whose outputs are ready to use as new subsets of network parameters. Across various architectures and datasets, our diffusion process consistently generates models of comparable or improved performance over trained networks, with minimal additional cost. Notably, we empirically find that the generated models perform differently with the trained networks. Our results encourage more exploration on the versatile use of diffusion models.
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