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Daniel van Strien PRO

davanstrien

https://danielvanstrien.xyz/

AI & ML interests

Machine Learning Librarian

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updated a dataset less than a minute ago

davanstrien/grpo-completions

published a dataset 6 minutes ago

davanstrien/grpo-completions

posted an update about 1 hour ago

📊 Introducing "Hugging Face Dataset Spotlight" 📊 I'm excited to share the first episode of our AI-generated podcast series focusing on nice datasets from the Hugging Face Hub! This first episode explores mathematical reasoning datasets: - https://huggingface.co./datasets/SynthLabsAI/Big-Math-RL-Verified: Over 250,000 rigorously verified problems spanning multiple difficulty levels and mathematical domains - https://huggingface.co./datasets/open-r1/OpenR1-Math-220k: 220,000 math problems with multiple reasoning traces, verified for accuracy using Math Verify and Llama-3.3-70B models. - https://huggingface.co./datasets/facebook/natural_reasoning: 1.1 million general reasoning questions carefully deduplicated and decontaminated from existing benchmarks, showing superior scaling effects when training models like Llama3.1-8B-Instruct. Plus a bonus segment on https://huggingface.co./datasets/bespokelabs/bespoke-manim! https://www.youtube.com/watch?v=-TgmRq45tW4

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davanstrien's activity

updated a dataset less than a minute ago

davanstrien/grpo-completions

Updated less than a minute ago

published a dataset 6 minutes ago

davanstrien/grpo-completions

Updated less than a minute ago

posted an update about 1 hour ago

Post

📊 Introducing "Hugging Face Dataset Spotlight" 📊

I'm excited to share the first episode of our AI-generated podcast series focusing on nice datasets from the Hugging Face Hub!

This first episode explores mathematical reasoning datasets:

- SynthLabsAI/Big-Math-RL-Verified: Over 250,000 rigorously verified problems spanning multiple difficulty levels and mathematical domains
- open-r1/OpenR1-Math-220k: 220,000 math problems with multiple reasoning traces, verified for accuracy using Math Verify and Llama-3.3-70B models.
- facebook/natural_reasoning: 1.1 million general reasoning questions carefully deduplicated and decontaminated from existing benchmarks, showing superior scaling effects when training models like Llama3.1-8B-Instruct.

Plus a bonus segment on bespokelabs/bespoke-manim!

https://www.youtube.com/watch?v=-TgmRq45tW4

reacted to stefan-it's post with 🔥 about 2 hours ago

Post

708

After running some 3DMark and FurMark benchmarks on Windows to make sure that my new 5090 is not causing melting cables [1] and some nice shots with a thermal camera (I don't think that's too much), running some fine-tuning experiments with my favorite Flair & Transformers libraries are very easy to perform.

Important steps:

Good idea is to start with a fresh Ubuntu 24.04 installation with latest CUDA 12.8 and the open NVIDIA driver - follow more advices from [2]:

sudo apt -y install cuda-toolkit-12-8 nvidia-open

I tried update from an existing Ubuntu installation with an older CUDA and driver version and it resulted in a non-startable system.

If you are using PyTorch 2.6 with built CUDA 12.6 it will result in:

NVIDIA Graphics Device with CUDA capability sm_120 is not compatible with the current PyTorch installation.
The current PyTorch install supports CUDA capabilities sm_50 sm_60 sm_70 sm_75 sm_80 sm_86 sm_90.

But no worries! For PyTorch you need just to use a nightly 2.7 version that was built with CUDA 12.8. This can easily done via:

pip install --pre torch --index-url https://download.pytorch.org/whl/nightly/cu128

After that the latest Flair version can be installed and fine-tuning will work!

References:

[1]: https://www.reddit.com/r/nvidia/comments/1inpox7/rtx_50_series_12vhpwr_megathread/
[2]: https://developer.nvidia.com/cuda-downloads?target_os=Linux&target_arch=x86_64&Distribution=Ubuntu&target_version=24.04&target_type=deb_network

liked a model about 2 hours ago

chandar-lab/NeoBERT

Updated about 8 hours ago • 17 • 9

updated 2 datasets about 6 hours ago

data-is-better-together/fineweb-c-progress

Viewer • Updated about 6 hours ago • 796 • 256 • 3

librarian-bots/model_cards_with_metadata

Viewer • Updated about 6 hours ago • 618k • 1.07k • 13

updated a dataset about 10 hours ago

librarian-bots/dataset_cards_with_metadata

Viewer • Updated about 10 hours ago • 219k • 1.05k • 12

updated a dataset about 11 hours ago

librarian-bots/dataset-columns

Viewer • Updated about 11 hours ago • 4.95M • 571

liked a dataset about 21 hours ago

eth-nlped/mathdial

Viewer • Updated 2 days ago • 2.86k • 149 • 4

posted an update about 21 hours ago

Post

1461

Quick POC: Turn a Hugging Face dataset card into a short podcast introducing the dataset using all open models.

I think I'm the only weirdo who would enjoy listening to something like this though 😅

Here is an example for eth-nlped/stepverify