davanstrien (Daniel van Strien)

reacted to sergiopaniego's post with 🚀 about 20 hours ago

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Meet OpenEnv 👋, an open ecosystem of environments for intelligent agents. Build, share, and test agents safely and consistently.

Ideal for training with TRL (we include examples🤓), deployment, and community collaboration via the HF Hub

Blog: https://huggingface.co/blog/openenv
Hub for Environments:

openenv
OpenEnv repo: https://github.com/meta-pytorch/OpenEnv
Try it out using TRL: https://huggingface.co/docs/trl/main/en/openenv

reacted to stefan-it's post with 😎🔥 about 22 hours ago

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Wohoo 🥳 I have finished my 2025 GPU workstation build and I am very excited to train new awesome open source models on it.

I built my last GPU workstation 5 years ago featuring an AMD Ryzen 5900X, 64GB of G.SKILL Trident Z RGB on an ASRock X570 Taichi cooled by an Alphacool Eisbär 420. GPU was a Zotac RTX 3090 AMP Extreme. Unfortunately, I was never satisfied with the case - some Fractal Define 7, as it is definitely too small, airflow is not optimal as I had to open the front door all the time and it also arrived with a partly damaged side panel.

For my new build, I've used the following components: an outstanding new AMD Ryzen 9950X3D with 64GB of Corsair Dominator Titanium (what a name). As a huge Noctua fan - warm greetings to my Austrian neighbors - I am using the brand new Noctua NH-D15 G2 on an ASRock X870E Taichi in an amazing Lian Li LANCOOL III chassis. One joke that only NVIDIA Blackwell users will understand: you definitely need a tempered glass panel to check if your GPU cables/connectors start melting 😂 And the best is yet to come: I returned my previously bought Zotac RTX 5090 Solid to the eBay seller (because of... missing ROPs, only NVIDIA Blackwell users will again understand) and bought a Zotac 5090 AMP Extreme INFINITY (yes, the long name indicates that this is the flagship model from Zotac) from a more trustworthy source (NBB in Germany).

I am so happy to start training and fine-tuning new open source models - stay tuned!!!

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posted an update about 2 months ago

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I fine-tuned a smol VLM to generate specialized art history metadata!

https://huggingface.co/davanstrien/iconclass-vlm: Qwen2.5-VL-3B trained using SFT to generate ICONCLASS codes (think Dewey Decimal for art!)

Trained with TRL + HF Jobs - single UV script, no GPU needed!

Space to explore predictions on a test set: davanstrien/iconclass-predictions

Blog soon!

replied to clem's post 3 months ago

The model could be super depressed and stressed out!

replied to clem's post 3 months ago

Hope so!

replied to clem's post 3 months ago

Yeah, quite bold that they put health + legal use cases so prominently

reacted to clem's post with 🔥 3 months ago

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Thread to gossip during the

openai GPT-5 livestream: https://www.youtube.com/watch?v=0Uu_VJeVVfo. Feel free to post your impressions below!

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replied to clem's post 3 months ago

Very off topic, but on the theme of music to welcome aliens, this short film is lovely: https://www.youtube.com/watch?v=Jr83bJsT6OA!

reacted to dmoxy's post with 👍 3 months ago

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🥐 Got a Croissant URL?

Here is the fastest way to ingest it into a high-performance multimodal database.

Workflow #1: Croissant Ingestion is live on ApertureDB Cloud — the first release in our Summer Workflows series.

Plug in any MLCommons Croissant-formatted dataset and this ready-to-run workflow will:

✅Parse Croissant metadata
📥Download all linked assets (images, text, video, etc.)
📦Ingest them into ApertureDB, preserving structure and relationships
All with just a few lines of Python.

Whether you are working with public datasets from Hugging Face or prepping production-ready data pipelines — this is the ingestion flow you’ve been waiting for.

🎥 Watch a quick demo→ https://www.youtube.com/watch?v=6cWcZ2G53gE
🔗 Try it → https://cloud.aperturedata.io/signup?campaign=WF1Croissant
📚 Docs → https://docs.aperturedata.io/workflows/ingest_from_croissant

💬 Tell us what you think about this workflow and what you'd like to see us build next. Hit the comments with your ideas— We're listening!

We are launching one new workflow every Wednesday — 12 in total.
Follow along all summer☀️.

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posted an update 5 months ago

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Inspired by Hugging Face's official MCP server, I've developed a complementary tool that exposes my semantic search API to enhance discovery across the HF platform.

Key capabilities:

- AI-powered semantic search for models and datasets
- Parameter count analysis via safetensors metadata
- Trending content discovery
- Find similar models/datasets functionality
- 11 tools total for enhanced ecosystem navigation

The semantic search goes beyond simple keyword matching, understanding context and relationships between different models and datasets.

Example query: "Find around 10 reasoning Hugging Face datasets published in 2025 focusing on topics other than maths and science. Show a link and a short summary for each dataset." (results in video!)

https://github.com/davanstrien/hub-semantic-search-mcp

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reacted to cbensimon's post with 🔥 5 months ago

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6048

🚀 ZeroGPU medium size is now available as a power-user feature

Nothing too fancy for now—ZeroGPU Spaces still default to large (70GB VRAM)—but this paves the way for:
- 💰 size-based quotas / pricing (medium will offer significantly more usage than large)
- 🦣 the upcoming xlarge size (141GB VRAM)

You can as of now control GPU size via a Space variable. Accepted values:
- auto (future default)
- medium
- large (current default)

The auto mode checks total CUDA tensor size during startup:
- More than 30GB → large
- Otherwise → medium

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reacted to their post with 🔥 6 months ago

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2351

Came across a very nice submission from @marcodsn for the reasoning datasets competition (https://huggingface.co/blog/bespokelabs/reasoning-datasets-competition).

The dataset distils reasoning chains from arXiv research papers in biology and economics. Some nice features of the dataset:

- Extracts both the logical structure AND researcher intuition from academic papers
- Adopts the persona of researchers "before experiments" to capture exploratory thinking
- Provides multi-short and single-long reasoning formats with token budgets - Shows 7.2% improvement on MMLU-Pro Economics when fine-tuning a 3B model

It's created using the Curator framework with plans to scale across more scientific domains and incorporate multi-modal reasoning with charts and mathematics.

I personally am very excited about datasets like this, which involve creativity in their creation and don't just rely on $$$ to produce a big dataset with little novelty.

Dataset can be found here: marcodsn/academic-chains (give it a like!)

posted an update 6 months ago

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2351

Came across a very nice submission from @marcodsn for the reasoning datasets competition (https://huggingface.co/blog/bespokelabs/reasoning-datasets-competition).

The dataset distils reasoning chains from arXiv research papers in biology and economics. Some nice features of the dataset:

- Extracts both the logical structure AND researcher intuition from academic papers
- Adopts the persona of researchers "before experiments" to capture exploratory thinking
- Provides multi-short and single-long reasoning formats with token budgets - Shows 7.2% improvement on MMLU-Pro Economics when fine-tuning a 3B model

It's created using the Curator framework with plans to scale across more scientific domains and incorporate multi-modal reasoning with charts and mathematics.

I personally am very excited about datasets like this, which involve creativity in their creation and don't just rely on $$$ to produce a big dataset with little novelty.

Dataset can be found here: marcodsn/academic-chains (give it a like!)

reacted to jasoncorkill's post with 🔥 7 months ago

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3092

🔥 Yesterday was a fire day!
We dropped two brand-new datasets capturing Human Preferences for text-to-video and text-to-image generations powered by our own crowdsourcing tool!

Whether you're working on model evaluation, alignment, or fine-tuning, this is for you.

1. Text-to-Video Dataset (Pika 2.2 model):
Rapidata/text-2-video-human-preferences-pika2.2

2. Text-to-Image Dataset (Reve-AI Halfmoon):
Rapidata/Reve-AI-Halfmoon_t2i_human_preference

Let’s train AI on AI-generated content with humans in the loop.
Let’s make generative models that actually get us.

reacted to ajibawa-2023's post with 🔥 7 months ago

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Hi All, I recently released two Audio datasets which are generated using my earlier released dataset: ajibawa-2023/Children-Stories-Collection

First Audio Dataset:https://huggingface.co/datasets/ajibawa-2023/Audio-Children-Stories-Collection-Large has 5600++ stories in .mp3 format.

Second Audio Dataset:https://huggingface.co/datasets/ajibawa-2023/Audio-Children-Stories-Collection has 600 stories in .mp3 format.

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reacted to jasoncorkill's post with 🚀🔥 7 months ago

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3312

🚀 We tried something new!

We just published a dataset using a new (for us) preference modality: direct ranking based on aesthetic preference. We ranked a couple of thousand images from most to least preferred, all sampled from the Open Image Preferences v1 dataset by the amazing @data-is-better-together team.

📊 Check it out here:
Rapidata/2k-ranked-images-open-image-preferences-v1

We're really curious to hear your thoughts!
Is this kind of ranking interesting or useful to you? Let us know! 💬

If it is, please consider leaving a ❤️ and if we hit 30 ❤️s, we’ll go ahead and rank the full 17k image dataset!

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replied to jasoncorkill's post 7 months ago

This is very cool! I was always curious about doing something like this! Could be quite cool to train a "aesthic preference model" on this kind of dataset. Could be quite cool to try and use as a reward model for image gen training...

cc @sayakpaul @multimodalart @linoyts @davidberenstein1957 who might also find this data interesting :)

reacted to jasoncorkill's post with ❤️ 7 months ago

Post

3312

🚀 We tried something new!

We just published a dataset using a new (for us) preference modality: direct ranking based on aesthetic preference. We ranked a couple of thousand images from most to least preferred, all sampled from the Open Image Preferences v1 dataset by the amazing @data-is-better-together team.

📊 Check it out here:
Rapidata/2k-ranked-images-open-image-preferences-v1

We're really curious to hear your thoughts!
Is this kind of ranking interesting or useful to you? Let us know! 💬

If it is, please consider leaving a ❤️ and if we hit 30 ❤️s, we’ll go ahead and rank the full 17k image dataset!

6 replies

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