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30 Jul, 4 tweets, 3 min read
The results for the JAX/Flax sprint are in🎉. Here are the top-3 projects picked by our awesome jury @wightmanr @nikiparmar09 @ashVaswani @Thom_Wolf
🥇 DALL.E mini hf.co/spaces/flax-co…
🥈 CLIP+NeRF hf.co/spaces/flax-co…
🥉 CLIP for Satellite images hf.co/spaces/sujitpa…
Starting today all 🤗 Spaces are publicly viewable 🚀 You can find all the amazing demos created as part of the sprint here 👉 huggingface.co/spaces
This has been the largest Hugging Face event, and we're extremely excited by the results. Almost 800 members joined and had almost 100 projects, 170 models & 36 Spaces! 🤯 That is super impressive given the timeframes of the event!
We would like to thank @avitaloliver, @marcvanzee, @walkforhours, Skye Wanderman-Milne, Jonathan Heek, and all the JAX, Flax, and Cloud TPU team for making this event possible 🤗. And thank you to all the participants for making this an awesome event!

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More from @huggingface

Hugging Face Profile picture
11 Feb
We've been getting lots of questions on how to contribute models to🤗Transformers.

Recently we started to publish model-specific recipes on how to do so!

If you want to get better at open-source contributions and want to contribute to🤗Transformers, here is how it works: 👇
1. Watch out for open proposals to add a model here:
github.com/huggingface/tr…
2. Having read the explanation, if this is a project that interests you and that you think you will be able to finish within ~6 weeks - obviously with the help of the Hugging Face team - please send us a message to team@huggingface.co
Read 6 tweets
Hugging Face Profile picture
10 Feb
Blog alert: check out the new guest post by Amog Kamsetty and the @raydistributed team on training a Retrieval Augmented Generation Model with Hugging Face and Ray!

huggingface.co/blog/ray-rag
The RAG model by @olapiktus @PSH_Lewis and @facebookai colleagues leverages external knowledge sources like Wikipedia to have direct and dynamic access to information at inference time

Part of this process relies on training a retriever to learn how to find that information
@raydistributed is a framework-agnostic, flexible implementation for ad-hoc concurrent programming, which makes it ideal for scaling up this training, making retrieval 2x faster and drastically improving the scalability of RAG distributed fine-tuning

Go try it out for yourself!
Read 4 tweets
Hugging Face Profile picture
6 Mar 20
1/4. Four NLP tutorials are now available on
@kaggle
! It's now easier than ever to leverage tokenizers and transformer models like BERT, GPT2, RoBERTa, XLNet, DistilBERT,... for your next competition! 💪💪💪! #NLProc #NLP #DataScience #kaggle
2/4. Tokenizers - Training your own tokenizer kaggle.com/funtowiczmo/hu…
3/4. Transformers - Getting started with transformers kaggle.com/funtowiczmo/hu…
Read 4 tweets

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