view post Post 2625 We've kept pushing our Open-R1 project, an open initiative to replicate and extend the techniques behind DeepSeek-R1.And even we were mind-blown by the results we got with this latest model we're releasing: ⚡️OlympicCoder ( open-r1/OlympicCoder-7B and open-r1/OlympicCoder-32B)It's beating Claude 3.7 on (competitive) programming –a domain Anthropic has been historically really strong at– 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-3Datasets are are releasing:- open-r1/codeforces- open-r1/codeforces-cots- open-r1/ioi- open-r1/ioi-test-cases- open-r1/ioi-sample-solutions- open-r1/ioi-cots- open-r1/ioi-2024-model-solutions See translation 🔥 9 9 🚀 6 6 ❤️ 1 1 + Reply
Sparse Autoencoders Collection SAEs are tools for understanding the internal representations of neural networks. These can be loaded using https://github.com/EleutherAI/sae • 9 items • Updated 30 days ago • 3
Sparse Autoencoders Collection SAEs are tools for understanding the internal representations of neural networks. These can be loaded using https://github.com/EleutherAI/sae • 9 items • Updated 30 days ago • 3
Sparse Autoencoders Collection SAEs are tools for understanding the internal representations of neural networks. These can be loaded using https://github.com/EleutherAI/sae • 9 items • Updated 30 days ago • 3