Post
3308
Should we assemble affordable open-source robots at Hugging Face for the community. Would you buy them? At what price?
- 7 replies
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Post
2297
Nice new space to see how fast your personal or organization followers are growing on HF:
julien-c/follow-history
As you can see, I still have more followers than @julien-c even if he's trying to change this by building such cool spaces 😝😝😝
julien-c/follow-history
As you can see, I still have more followers than @julien-c even if he's trying to change this by building such cool spaces 😝😝😝
Post
4524
We just crossed 1,500,000 public models on Hugging Face (and 500k spaces, 330k datasets, 50k papers). One new repository is created every 15 seconds. Congratulations all!
- 3 replies
edbeeching
authored
a
paper
11 days ago
lewtun
authored
a
paper
11 days ago
Post
2485
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-3
Datasets 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
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-3
Datasets 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
clefourrier
posted
an
update
11 days ago
Post
1809
Gemma3 family is out! Reading the tech report, and this section was really interesting to me from a methods/scientific fairness pov.
Instead of doing over-hyped comparisons, they clearly state that **results are reported in a setup which is advantageous to their models**.
(Which everybody does, but people usually don't say)
For a tech report, it makes a lot of sense to report model performance when used optimally!
On leaderboards on the other hand, comparison will be apples to apples, but in a potentially unoptimal way for a given model family (like some user interact sub-optimally with models)
Also contains a cool section (6) on training data memorization rate too! Important to see if your model will output the training data it has seen as such: always an issue for privacy/copyright/... but also very much for evaluation!
Because if your model knows its evals by heart, you're not testing for generalization.
Instead of doing over-hyped comparisons, they clearly state that **results are reported in a setup which is advantageous to their models**.
(Which everybody does, but people usually don't say)
For a tech report, it makes a lot of sense to report model performance when used optimally!
On leaderboards on the other hand, comparison will be apples to apples, but in a potentially unoptimal way for a given model family (like some user interact sub-optimally with models)
Also contains a cool section (6) on training data memorization rate too! Important to see if your model will output the training data it has seen as such: always an issue for privacy/copyright/... but also very much for evaluation!
Because if your model knows its evals by heart, you're not testing for generalization.
Post
2059
Introducing OlympicCoder: a series of open reasoning models that can solve olympiad-level programming problems 🧑💻
- 7B open-r1/OlympicCoder-7B
- 32B open-r1/OlympicCoder-32B
We find that OlympicCoder models outperform Claude 3.7 Sonnet, as well as others over 100x larger 💪
Together with the models, we are releasing:
📊CodeForces-CoTs: new dataset of code problems from the most popular competitive coding platform, with R1 traces in C++ and Python open-r1/codeforces-cots
🏆 IOI'2024: a new benchmark of VERY hard programming problems where even frontier models struggle to match human performance open-r1/ioi
For links to the models and datasets, check out our latest progress report from Open R1: https://huggingface.co/blog/open-r1/update-3
- 7B open-r1/OlympicCoder-7B
- 32B open-r1/OlympicCoder-32B
We find that OlympicCoder models outperform Claude 3.7 Sonnet, as well as others over 100x larger 💪
Together with the models, we are releasing:
📊CodeForces-CoTs: new dataset of code problems from the most popular competitive coding platform, with R1 traces in C++ and Python open-r1/codeforces-cots
🏆 IOI'2024: a new benchmark of VERY hard programming problems where even frontier models struggle to match human performance open-r1/ioi
For links to the models and datasets, check out our latest progress report from Open R1: https://huggingface.co/blog/open-r1/update-3
- 1 reply
Post
7217
I was chatting with
@peakji
, one of the cofounders of Manu AI, who told me he was on Hugging Face (very cool!).
He shared an interesting insight which is that agentic capabilities might be more of an alignment problem rather than a foundational capability issue. Similar to the difference between GPT-3 and InstructGPT, some open-source foundation models are simply trained to 'answer everything in one response regardless of the complexity of the question' - after all, that's the user preference in chatbot use cases. Just a bit of post-training on agentic trajectories can make an immediate and dramatic difference.
As a thank you to the community, he shared 100 invite code first-come first serve, just use “HUGGINGFACE” to get access!
He shared an interesting insight which is that agentic capabilities might be more of an alignment problem rather than a foundational capability issue. Similar to the difference between GPT-3 and InstructGPT, some open-source foundation models are simply trained to 'answer everything in one response regardless of the complexity of the question' - after all, that's the user preference in chatbot use cases. Just a bit of post-training on agentic trajectories can make an immediate and dramatic difference.
As a thank you to the community, he shared 100 invite code first-come first serve, just use “HUGGINGFACE” to get access!
- 6 replies
Post
4668
10,000+ models based on Deepseek R1 have been publicly shared on Hugging Face! Which ones are your favorite ones: https://huggingface.co/models?sort=trending&search=r1. Truly game-changer!
Post
5897
Super happy to welcome Nvidia as our latest enterprise hub customer. They have almost 2,000 team members using Hugging Face, and close to 20,000 followers of their org. Can't wait to see what they'll open-source for all of us in the coming months!
Nvidia's org: https://huggingface.co/nvidia
Enterprise hub: https://huggingface.co/enterprise
Nvidia's org: https://huggingface.co/nvidia
Enterprise hub: https://huggingface.co/enterprise
Post
2828
What are the best organizations to follow on
@huggingface
?
On top of my head:
- Deepseek (35,000 followers): https://huggingface.co/deepseek-ai
- Meta Llama (27,000 followers): https://huggingface.co/meta-llama
- Black Forrest Labs (11,000 followers): https://huggingface.co/black-forest-labs
- OpenAI (5,000 followers): https://huggingface.co/openai
- Nvidia (16,000 followers): https://huggingface.co/nvidia
- MIcrosoft (9,000 followers): https://huggingface.co/microsoft
- AllenAI (2,000 followers): https://huggingface.co/allenai
- Mistral (5,000 followers): https://huggingface.co/mistralai
- XAI (600 followers): https://huggingface.co/xai-org
- Stability AI (16,000 followers): https://huggingface.co/stabilityai
- Qwen (16,000 followers): https://huggingface.co/Qwen
- GoogleAI (8,000 followers): https://huggingface.co/google
- Unsloth (3,000 followers): https://huggingface.co/unsloth
- Bria AI (4,000 followers): https://huggingface.co/briaai
- NousResearch (1,300 followers): https://huggingface.co/NousResearch
Bonus, the agent course org with 17,000 followers: https://huggingface.co/agents-course
On top of my head:
- Deepseek (35,000 followers): https://huggingface.co/deepseek-ai
- Meta Llama (27,000 followers): https://huggingface.co/meta-llama
- Black Forrest Labs (11,000 followers): https://huggingface.co/black-forest-labs
- OpenAI (5,000 followers): https://huggingface.co/openai
- Nvidia (16,000 followers): https://huggingface.co/nvidia
- MIcrosoft (9,000 followers): https://huggingface.co/microsoft
- AllenAI (2,000 followers): https://huggingface.co/allenai
- Mistral (5,000 followers): https://huggingface.co/mistralai
- XAI (600 followers): https://huggingface.co/xai-org
- Stability AI (16,000 followers): https://huggingface.co/stabilityai
- Qwen (16,000 followers): https://huggingface.co/Qwen
- GoogleAI (8,000 followers): https://huggingface.co/google
- Unsloth (3,000 followers): https://huggingface.co/unsloth
- Bria AI (4,000 followers): https://huggingface.co/briaai
- NousResearch (1,300 followers): https://huggingface.co/NousResearch
Bonus, the agent course org with 17,000 followers: https://huggingface.co/agents-course
- 1 reply
Post
3488
We crossed 1B+ tokens routed to inference providers partners on HF, that we released just a few days ago.
Just getting started of course but early users seem to like it & always happy to be able to partner with cool startups in the ecosystem.
Have you been using any integration and how can we make it better?
https://huggingface.co/blog/inference-providers
Just getting started of course but early users seem to like it & always happy to be able to partner with cool startups in the ecosystem.
Have you been using any integration and how can we make it better?
https://huggingface.co/blog/inference-providers
Post
4963
Introducing OpenR1-Math-220k!
open-r1/OpenR1-Math-220k
The community has been busy distilling DeepSeek-R1 from inference providers, but we decided to have a go at doing it ourselves from scratch 💪
What’s new compared to existing reasoning datasets?
♾ Based on AI-MO/NuminaMath-1.5: we focus on math reasoning traces and generate answers for problems in NuminaMath 1.5, an improved version of the popular NuminaMath-CoT dataset.
🐳 800k R1 reasoning traces: We generate two answers for 400k problems using DeepSeek R1. The filtered dataset contains 220k problems with correct reasoning traces.
📀 512 H100s running locally: Instead of relying on an API, we leverage vLLM and SGLang to run generations locally on our science cluster, generating 180k reasoning traces per day.
⏳ Automated filtering: We apply Math Verify to only retain problems with at least one correct answer. We also leverage Llama3.3-70B-Instruct as a judge to retrieve more correct examples (e.g for cases with malformed answers that can’t be verified with a rules-based parser)
📊 We match the performance of DeepSeek-Distill-Qwen-7B by finetuning Qwen-7B-Math-Instruct on our dataset.
🔎 Read our blog post for all the nitty gritty details: https://huggingface.co/blog/open-r1/update-2
open-r1/OpenR1-Math-220k
The community has been busy distilling DeepSeek-R1 from inference providers, but we decided to have a go at doing it ourselves from scratch 💪
What’s new compared to existing reasoning datasets?
♾ Based on AI-MO/NuminaMath-1.5: we focus on math reasoning traces and generate answers for problems in NuminaMath 1.5, an improved version of the popular NuminaMath-CoT dataset.
🐳 800k R1 reasoning traces: We generate two answers for 400k problems using DeepSeek R1. The filtered dataset contains 220k problems with correct reasoning traces.
📀 512 H100s running locally: Instead of relying on an API, we leverage vLLM and SGLang to run generations locally on our science cluster, generating 180k reasoning traces per day.
⏳ Automated filtering: We apply Math Verify to only retain problems with at least one correct answer. We also leverage Llama3.3-70B-Instruct as a judge to retrieve more correct examples (e.g for cases with malformed answers that can’t be verified with a rules-based parser)
📊 We match the performance of DeepSeek-Distill-Qwen-7B by finetuning Qwen-7B-Math-Instruct on our dataset.
🔎 Read our blog post for all the nitty gritty details: https://huggingface.co/blog/open-r1/update-2
hlarcher
authored
a
paper
about 2 months ago
lewtun
authored
a
paper
about 2 months ago
loubnabnl
authored
a
paper
about 2 months ago
clefourrier
authored
a
paper
about 2 months ago
guipenedo
authored
a
paper
about 2 months ago
thomwolf
authored
a
paper
about 2 months ago