SARLO-80: Worldwide Slant SAR Language Optic Dataset at 80 cm Resolution
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VibecoderMcSwagginsÂ
updated
a
dataset
5 days ago
reach-vbÂ
authored
a
paper
11 days ago
ilijalichkovskiÂ
authored
a
paper
about 1 month ago
Post
2556
AI for Scientific Discovery Won't Work Without Fixing How We Collaborate.
My co-author @cgeorgiaw and I just published a paper challenging a core assumption: that the main barriers to AI in science are technical. They're not. They're social.
Key findings:
đ¨ The "AI Scientist" myth delays progress: Waiting for AGI devalues human expertise and obscures science's real purpose: cultivating understanding, not just outputs.
đ Wrong incentives: Datasets have 100x longer impact than models, yet data curation is undervalued.
â ď¸ Broken collaboration: Domain scientists want understanding. ML researchers optimize performance. Without shared language, projects fail.
đ Fragmentation costs years: Harmonizing just 9 cancer files took 329 hours.
Why this matters: Upstream bottlenecks like efficient PDE solvers could accelerate discovery across multiple sciences. CASP mobilized a community around protein structure, enabling AlphaFold. We need this for dozens of challenges.
Thus, we're launching Hugging Science! A global community addressing these barriers through collaborative challenges, open toolkits, education, and community-owned infrastructure. Please find all the links below!
Paper: AI for Scientific Discovery is a Social Problem (2509.06580)
Join:
hugging-science
Discord: https://discord.com/invite/VYkdEVjJ5J
My co-author @cgeorgiaw and I just published a paper challenging a core assumption: that the main barriers to AI in science are technical. They're not. They're social.
Key findings:
đ¨ The "AI Scientist" myth delays progress: Waiting for AGI devalues human expertise and obscures science's real purpose: cultivating understanding, not just outputs.
đ Wrong incentives: Datasets have 100x longer impact than models, yet data curation is undervalued.
â ď¸ Broken collaboration: Domain scientists want understanding. ML researchers optimize performance. Without shared language, projects fail.
đ Fragmentation costs years: Harmonizing just 9 cancer files took 329 hours.
Why this matters: Upstream bottlenecks like efficient PDE solvers could accelerate discovery across multiple sciences. CASP mobilized a community around protein structure, enabling AlphaFold. We need this for dozens of challenges.
Thus, we're launching Hugging Science! A global community addressing these barriers through collaborative challenges, open toolkits, education, and community-owned infrastructure. Please find all the links below!
Paper: AI for Scientific Discovery is a Social Problem (2509.06580)
Join:
hugging-science
Discord: https://discord.com/invite/VYkdEVjJ5J
davanstrienÂ
posted
an
update
3 months ago
Post
1356
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!
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!
jrosserukÂ
authored
a
paper
4 months ago
Post
400
New blog post alert! "What is the Hugging Face Community Building?", with
@yjernite
and
@irenesolaiman
What 1.8 Million Models Reveal About Open Source Innovation: Our latest deep dive into the Hugging Face Hub reveals patterns that challenge conventional AI narratives:
đ Models become platforms for innovation Qwen, Llama, and Gemma models have spawned entire ecosystems of specialized variants. Looking at derivative works shows community adoption better than any single metric.
đ Datasets reveal the foundation layer â Most downloaded datasets are evaluation benchmarks (MMLU, Squad, GLUE) â Universities and research institutions dominate foundational data â Domain-specific datasets thrive across finance, healthcare, robotics, and science â Open actors provide the datasets that power most AI development
đď¸ Research institutions lead the charge: AI2 (Allen Institute) emerges as one of the most active contributors, alongside significant activity from IBM, NVIDIA, and international organizations. The open source ecosystem spans far beyond Big Tech.
đ Interactive exploration tools: We've built several tools to help you discover patterns!
ModelVerse Explorer - organizational contributions
DataVerse Explorer - dataset patterns
Organization HeatMap - activity over time
Base Model Explorer - model family trees
Semantic Search - find models by capability
đ Academic research is thriving: Researchers are already producing valuable insights, including recent work at FAccT 2025: "The Brief and Wondrous Life of Open Models." We've also made hub datasets, weekly snapshots, and other data available for your own analysis.
The bottom line: AI development is far more distributed, diverse, and collaborative than popular narratives suggest. Real innovation happens through community collaboration across specialized domains.
Read: https://huggingface.co/blog/evijit/hf-hub-ecosystem-overview
What 1.8 Million Models Reveal About Open Source Innovation: Our latest deep dive into the Hugging Face Hub reveals patterns that challenge conventional AI narratives:
đ Models become platforms for innovation Qwen, Llama, and Gemma models have spawned entire ecosystems of specialized variants. Looking at derivative works shows community adoption better than any single metric.
đ Datasets reveal the foundation layer â Most downloaded datasets are evaluation benchmarks (MMLU, Squad, GLUE) â Universities and research institutions dominate foundational data â Domain-specific datasets thrive across finance, healthcare, robotics, and science â Open actors provide the datasets that power most AI development
đď¸ Research institutions lead the charge: AI2 (Allen Institute) emerges as one of the most active contributors, alongside significant activity from IBM, NVIDIA, and international organizations. The open source ecosystem spans far beyond Big Tech.
đ Interactive exploration tools: We've built several tools to help you discover patterns!
ModelVerse Explorer - organizational contributions
DataVerse Explorer - dataset patterns
Organization HeatMap - activity over time
Base Model Explorer - model family trees
Semantic Search - find models by capability
đ Academic research is thriving: Researchers are already producing valuable insights, including recent work at FAccT 2025: "The Brief and Wondrous Life of Open Models." We've also made hub datasets, weekly snapshots, and other data available for your own analysis.
The bottom line: AI development is far more distributed, diverse, and collaborative than popular narratives suggest. Real innovation happens through community collaboration across specialized domains.
Read: https://huggingface.co/blog/evijit/hf-hub-ecosystem-overview
Post
5973
Excited to onboard FeatherlessAI on Hugging Face as an Inference Provider - they bring a fleet of 6,700+ LLMs on-demand on the Hugging Face Hub đ¤Ż
Starting today, you'd be able to access all those LLMs (OpenAI compatible) on HF model pages and via OpenAI client libraries too! đĽ
Go, play with it today: https://huggingface.co/blog/inference-providers-featherless
P.S. They're also bringing on more GPUs to support all your concurrent requests!
Starting today, you'd be able to access all those LLMs (OpenAI compatible) on HF model pages and via OpenAI client libraries too! đĽ
Go, play with it today: https://huggingface.co/blog/inference-providers-featherless
P.S. They're also bringing on more GPUs to support all your concurrent requests!
- 1 reply
davanstrienÂ
posted
an
update
6 months ago
Post
3652
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
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
- 1 reply
Post
1738
The HF Policy Team submitted our response to the 2025 National Artificial Intelligence (AI) Research and Development (R&D) Strategic Plan.
Blog (with link to full pdf response):
https://huggingface.co/blog/evijit/us-ai-research-strategy-rfi
Blog (with link to full pdf response):
https://huggingface.co/blog/evijit/us-ai-research-strategy-rfi
Post
4642
hey hey
@mradermacher
- VB from Hugging Face here, we'd love to onboard you over to our optimised xet backend! đĽ
as you know we're in the process of upgrading our storage backend to xet (which helps us scale and offer blazingly fast upload/ download speeds too): https://huggingface.co/blog/xet-on-the-hub and now that we are certain that the backend can scale with even big models like Llama 4/ Qwen 3 - we;re moving to the next phase of inviting impactful orgs and users on the hub over as you are a big part of the open source ML community - we would love to onboard you next and create some excitement about it in the community too!
in terms of actual steps - it should be as simple as one of the org admins to join hf.co/join/xet - we'll take care of the rest.
p.s. you'd need to have a the latest hf_xet version of huggingface_hub lib but everything else should be the same: https://huggingface.co/docs/hub/storage-backends#using-xet-storage
p.p.s. this is fully backwards compatible so everything will work as it should! đ¤
as you know we're in the process of upgrading our storage backend to xet (which helps us scale and offer blazingly fast upload/ download speeds too): https://huggingface.co/blog/xet-on-the-hub and now that we are certain that the backend can scale with even big models like Llama 4/ Qwen 3 - we;re moving to the next phase of inviting impactful orgs and users on the hub over as you are a big part of the open source ML community - we would love to onboard you next and create some excitement about it in the community too!
in terms of actual steps - it should be as simple as one of the org admins to join hf.co/join/xet - we'll take care of the rest.
p.s. you'd need to have a the latest hf_xet version of huggingface_hub lib but everything else should be the same: https://huggingface.co/docs/hub/storage-backends#using-xet-storage
p.p.s. this is fully backwards compatible so everything will work as it should! đ¤
-
- 16 replies
evijitÂ
authored
a
paper
7 months ago
davanstrienÂ
posted
an
update
8 months ago
Post
2370
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 $$$ to produce a big dataset with little novelty.
Dataset can be found here: marcodsn/academic-chains (give it a like!)
The dataset distils reasoning chains from arXiv research papers in biology and economics. Some nice features of the dataset:
- Extracts both the logical structure AND researcher intuition from academic papers
- Adopts the persona of researchers "before experiments" to capture exploratory thinking
- Provides multi-short and single-long reasoning formats with token budgets - Shows 7.2% improvement on MMLU-Pro Economics when fine-tuning a 3B model
It's created using the Curator framework with plans to scale across more scientific domains and incorporate multi-modal reasoning with charts and mathematics.
I personally am very excited about datasets like this, which involve creativity in their creation and don't just rely on $$$ to produce a big dataset with little novelty.
Dataset can be found here: marcodsn/academic-chains (give it a like!)
evijitÂ
authored
2
papers
8 months ago
davanstrienÂ
posted
an
update
8 months ago
Post
1767
I've created a v1 dataset (
davanstrien/reasoning-required) and model (
davanstrien/ModernBERT-based-Reasoning-Required) to help curate "wild text" data for generating reasoning examples beyond the usual code/math/science domains.
- I developed a "Reasoning Required" dataset with a 0-4 scoring system for reasoning complexity
- I used educational content from HuggingFaceFW/fineweb-edu, adding annotations for domains, reasoning types, and example questions
My approach enables a more efficient workflow: filter text with small models first, then use LLMs only on high-value content.
This significantly reduces computation costs while expanding reasoning dataset domain coverage.
- I developed a "Reasoning Required" dataset with a 0-4 scoring system for reasoning complexity
- I used educational content from HuggingFaceFW/fineweb-edu, adding annotations for domains, reasoning types, and example questions
My approach enables a more efficient workflow: filter text with small models first, then use LLMs only on high-value content.
This significantly reduces computation costs while expanding reasoning dataset domain coverage.
reach-vbÂ
authored
a
paper
8 months ago
evijitÂ
authored
a
paper
9 months ago
Post
6171
đAraClip is now fully integrated with Hugging Face đ¤
AraClip is a specialized CLIP model that was created by @pain and optimized for Arabic text-image retrieval tasksđĽ
đ Try it out đ
đ¤ model: Arabic-Clip/araclip
𧊠Gradio demo: Arabic-Clip/Araclip-Simplified
đ website: https://arabic-clip.github.io/Arabic-CLIP/
AraClip is a specialized CLIP model that was created by @pain and optimized for Arabic text-image retrieval tasksđĽ
đ Try it out đ
đ¤ model: Arabic-Clip/araclip
𧊠Gradio demo: Arabic-Clip/Araclip-Simplified
đ website: https://arabic-clip.github.io/Arabic-CLIP/
- 2 replies
loganboltonÂ
authored
a
paper
9 months ago