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It's beating Claude 3.7 on (competitive) programming βa domain Anthropic has been historically really strong atβ and it's getting close to o1-mini/R1 on olympiad level coding with just 7B parameters!
And the best part is that we're open-sourcing all about its training dataset, the new IOI benchmark, and more in our Open-R1 progress report #3: https://huggingface.co/blog/open-r1/update-3
We find that OlympicCoder models outperform Claude 3.7 Sonnet, as well as others over 100x larger πͺ
Together with the models, we are releasing:
πCodeForces-CoTs: new dataset of code problems from the most popular competitive coding platform, with R1 traces in C++ and Python open-r1/codeforces-cots
π IOI'2024: a new benchmark of VERY hard programming problems where even frontier models struggle to match human performance open-r1/ioi
For Inference Providers who have built support for our Billing API (currently: Fal, Novita, HF-Inference β with more coming soon), we've started enabling Pay as you go (=PAYG)
What this means is that you can use those Inference Providers beyond the free included credits, and they're charged to your HF account.
You can see it on this view: any provider that does not have a "Billing disabled" badge, is PAYG-compatible.
An assembly of 18 European companies, labs, and universities have banded together to launch πͺπΊ EuroBERT! It's a state-of-the-art multilingual encoder for 15 European languages, designed to be finetuned for retrieval, classification, etc.
πͺπΊ 15 Languages: English, French, German, Spanish, Chinese, Italian, Russian, Polish, Portuguese, Japanese, Vietnamese, Dutch, Arabic, Turkish, Hindi 3οΈβ£ 3 model sizes: 210M, 610M, and 2.1B parameters - very very useful sizes in my opinion β‘οΈ Sequence length of 8192 tokens! Nice to see these higher sequence lengths for encoders becoming more common. βοΈ Architecture based on Llama, but with bi-directional (non-causal) attention to turn it into an encoder. Flash Attention 2 is supported. π₯ A new Pareto frontier (stronger *and* smaller) for multilingual encoder models π Evaluated against mDeBERTa, mGTE, XLM-RoBERTa for Retrieval, Classification, and Regression (after finetuning for each task separately): EuroBERT punches way above its weight. π Detailed paper with all details, incl. data: FineWeb for English and CulturaX for multilingual data, The Stack v2 and Proof-Pile-2 for code.
The next step is for researchers to build upon the 3 EuroBERT base models and publish strong retrieval, zero-shot classification, etc. models for all to use. I'm very much looking forward to it!