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Hugging Face
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lysandre
updated
a
dataset
about 19 hours ago
coyotte508
updated
a
Space
about 21 hours ago
mishig
updated
a
Space
1 day ago
yonigozlan
updated
a
dataset
2 days ago
Molbap
updated
a
dataset
2 days ago
a-r-r-o-w
updated
a
dataset
2 days ago
stevhliu
updated
a
dataset
3 days ago
Post
1261
You can now bill your inference costs from all our inference partners (together, fireworks, fal, sambanova, cerebras, hyperbolic,...) to your Hugging Face organization.
Useful to drive more company-wide usage of AI without the billing headaches!
Useful to drive more company-wide usage of AI without the billing headaches!
- 1 reply
Post
1449
Reasoning models like o3 and o4-mini are advancing faster than ever, but imagine what will be possible when they can run locally in your browser! 🤯
Well, with 🤗 Transformers.js, you can do just that! Here's Zyphra's new ZR1 model running at over 100 tokens/second on WebGPU! ⚡️
Giving models access to browser APIs (like File System, Screen Capture, and more) could unlock an entirely new class of web experiences that are personalized, interactive, and run locally in a secure, sandboxed environment.
For now, try out the demo! 👇
webml-community/Zyphra-ZR1-WebGPU
Well, with 🤗 Transformers.js, you can do just that! Here's Zyphra's new ZR1 model running at over 100 tokens/second on WebGPU! ⚡️
Giving models access to browser APIs (like File System, Screen Capture, and more) could unlock an entirely new class of web experiences that are personalized, interactive, and run locally in a secure, sandboxed environment.
For now, try out the demo! 👇
webml-community/Zyphra-ZR1-WebGPU
- 1 reply
Post
1318
Just tested something this morning that feels kind of game-changing for how we publish, discover, and consume news with AI: connecting Claude directly to the New York Times through MCP.
Picture this: You ask Claude about a topic, and it instantly pulls verified and trusted NYT content — no more guessing if the info is accurate.
The cool part? Publishers stay in control of what they share via API, and users get fast, reliable access through the AI tools they already use. Instead of scraping random stuff off the web, we get a future where publishers actively shape how their journalism shows up in AI.
It’s still a bit technical to set up right now, but this could get super simple soon — like installing apps on your phone, but for your chatbot. And you keep the brand connection, too.
Not saying it solves everything, but it’s definitely a new way to distribute content — and maybe even find some fresh value in the middle of this whole news + AI shakeup. Early movers will have a head start.
Curious what folks think — could MCPs be a real opportunity for journalism?
Picture this: You ask Claude about a topic, and it instantly pulls verified and trusted NYT content — no more guessing if the info is accurate.
The cool part? Publishers stay in control of what they share via API, and users get fast, reliable access through the AI tools they already use. Instead of scraping random stuff off the web, we get a future where publishers actively shape how their journalism shows up in AI.
It’s still a bit technical to set up right now, but this could get super simple soon — like installing apps on your phone, but for your chatbot. And you keep the brand connection, too.
Not saying it solves everything, but it’s definitely a new way to distribute content — and maybe even find some fresh value in the middle of this whole news + AI shakeup. Early movers will have a head start.
Curious what folks think — could MCPs be a real opportunity for journalism?
- 1 reply
Post
2445
I just released Sentence Transformers v4.1; featuring ONNX and OpenVINO backends for rerankers offering 2-3x speedups and improved hard negatives mining which helps prepare stronger training datasets. Details:
🏎️ ONNX, OpenVINO, Optimization, Quantization
- I've added ONNX and OpenVINO support with just one extra argument: "backend" when loading the CrossEncoder reranker, e.g.:
- The
- I've uploaded ~340 ONNX & OpenVINO models for all existing models under the cross-encoder Hugging Face organization. You can use these without having to export when loading.
⛏ Improved Hard Negatives Mining
- Added 'absolute_margin' and 'relative_margin' arguments to
-
-
- Inspired by the excellent NV-Retriever paper from NVIDIA.
And several other small improvements. Check out the full release notes here: https://github.com/UKPLab/sentence-transformers/releases/tag/v4.1.0
With this release, I introduce near-feature parity between the SentenceTransformer embedding & CrossEncoder reranker models, which I've wanted to do for quite some time! With rerankers very strongly supported now, it's time to look forward to other useful architectures!
🏎️ ONNX, OpenVINO, Optimization, Quantization
- I've added ONNX and OpenVINO support with just one extra argument: "backend" when loading the CrossEncoder reranker, e.g.:
CrossEncoder("cross-encoder/ms-marco-MiniLM-L6-v2", backend="onnx")- The
export_optimized_onnx_model, export_dynamic_quantized_onnx_model, and export_static_quantized_openvino_model functions now work with CrossEncoder rerankers, allowing you to optimize (e.g. fusions, gelu approximations, etc.) or quantize (int8 weights) rerankers.- I've uploaded ~340 ONNX & OpenVINO models for all existing models under the cross-encoder Hugging Face organization. You can use these without having to export when loading.
⛏ Improved Hard Negatives Mining
- Added 'absolute_margin' and 'relative_margin' arguments to
mine_hard_negatives. -
absolute_margin ensures that sim(query, negative) < sim(query, positive) - absolute_margin, i.e. an absolute margin between the negative & positive similarities.-
relative_margin ensures that sim(query, negative) < sim(query, positive) * (1 - relative_margin), i.e. a relative margin between the negative & positive similarities.- Inspired by the excellent NV-Retriever paper from NVIDIA.
And several other small improvements. Check out the full release notes here: https://github.com/UKPLab/sentence-transformers/releases/tag/v4.1.0
With this release, I introduce near-feature parity between the SentenceTransformer embedding & CrossEncoder reranker models, which I've wanted to do for quite some time! With rerankers very strongly supported now, it's time to look forward to other useful architectures!
Post
4169
If you've followed the progress of robotics in the past 18 months, you've likely noticed how robotics is increasingly becoming the next frontier that AI will unlock.
At Hugging Face—in robotics and across all AI fields—we believe in a future where AI and robots are open-source, transparent, and affordable; community-built and safe; hackable and fun. We've had so much mutual understanding and passion working with the Pollen Robotics team over the past year that we decided to join forces!
You can already find our open-source humanoid robot platform Reachy 2 on the Pollen website and the Pollen community and people here on the hub at
pollen-robotics
We're so excited to build and share more open-source robots with the world in the coming months!
At Hugging Face—in robotics and across all AI fields—we believe in a future where AI and robots are open-source, transparent, and affordable; community-built and safe; hackable and fun. We've had so much mutual understanding and passion working with the Pollen Robotics team over the past year that we decided to join forces!
You can already find our open-source humanoid robot platform Reachy 2 on the Pollen website and the Pollen community and people here on the hub at
pollen-robotics
We're so excited to build and share more open-source robots with the world in the coming months!
- 1 reply
Post
3924
sooo many open AI releases past week, let's summarize! 🤗
merve/april-11-releases-67fcd78be33d241c0977b9d2
multimodal
> Moonshot AI released Kimi VL Thinking, first working open-source multimodal reasoning model and Kimi VL Instruct, both 16B MoEs with 3B active params (OS)
> InternVL3 released based on Qwen2.5VL, 7 ckpts with various sizes (1B to 78B)
LLMs
> NVIDIA released Llama-3_1-Nemotron-Ultra-253B-v1 an LLM built on Llama 405B for reasoning, chat and tool use
> Agentica released DeepCoder-14B-Preview, fine-tuned version of DeepSeek-R1-Distilled-Qwen-14B on problem-test pairs, along with the compiled dataset
> Zyphra/ZR1-1.5B is a new small reasoning LLM built on R1-Distill-1.5B (OS)
> Skywork-OR1-32B-Preview is a new reasoning model by Skywork
Image Generation
> HiDream releases three new models, HiDream I1 Dev, I1 Full, and I1 fast for image generation (OS)
*OS ones have Apache 2.0 or MIT licenses
merve/april-11-releases-67fcd78be33d241c0977b9d2
multimodal
> Moonshot AI released Kimi VL Thinking, first working open-source multimodal reasoning model and Kimi VL Instruct, both 16B MoEs with 3B active params (OS)
> InternVL3 released based on Qwen2.5VL, 7 ckpts with various sizes (1B to 78B)
LLMs
> NVIDIA released Llama-3_1-Nemotron-Ultra-253B-v1 an LLM built on Llama 405B for reasoning, chat and tool use
> Agentica released DeepCoder-14B-Preview, fine-tuned version of DeepSeek-R1-Distilled-Qwen-14B on problem-test pairs, along with the compiled dataset
> Zyphra/ZR1-1.5B is a new small reasoning LLM built on R1-Distill-1.5B (OS)
> Skywork-OR1-32B-Preview is a new reasoning model by Skywork
Image Generation
> HiDream releases three new models, HiDream I1 Dev, I1 Full, and I1 fast for image generation (OS)
*OS ones have Apache 2.0 or MIT licenses
- 4 replies
Post
2061
Want AI that truly understands your country's culture? Public institutions are sitting on the next AI revolution - and here's the practical guide to unlock it.
I've had fascinating conversations recently about sovereign AI, with people trying to solve this recurring question: "How do we build AI that truly understands our culture?"
This guide by @evijit and @yjernite brings lots of insights about this question. It's not just about throwing data at models. It's about partnering cultural expertise with tech infrastructure in ways we're just starting to figure out.
An example? The National Library of Norway already has 150+ AI models on Hugging Face. They're not just digitizing books - they're building AI that thinks in Norwegian, understands Norwegian values, and serves Norwegian citizens.
This is sovereign AI in practice: technology that understands your culture, values, and languages.
Especially loved the practical examples on how to do this:
- Real examples from museums, libraries, and government agencies
- How to convert complex documents (PDFs, PowerPoints) into ML-ready formats
- Code templates for processing public data
- Technical recipes for sharing datasets on open platforms
The stakes? Citizens' ability to leverage their collective digital intelligence.
The technology is ready. The infrastructure exists. The guide shows exactly how to use it. What's needed is your cultural expertise to shape these tools.
Check it out: https://huggingface.co/blog/evijit/public-org-data-ai
P.s.: Building cool projects in a public institution? Share them in the comments for others to learn from!
I've had fascinating conversations recently about sovereign AI, with people trying to solve this recurring question: "How do we build AI that truly understands our culture?"
This guide by @evijit and @yjernite brings lots of insights about this question. It's not just about throwing data at models. It's about partnering cultural expertise with tech infrastructure in ways we're just starting to figure out.
An example? The National Library of Norway already has 150+ AI models on Hugging Face. They're not just digitizing books - they're building AI that thinks in Norwegian, understands Norwegian values, and serves Norwegian citizens.
This is sovereign AI in practice: technology that understands your culture, values, and languages.
Especially loved the practical examples on how to do this:
- Real examples from museums, libraries, and government agencies
- How to convert complex documents (PDFs, PowerPoints) into ML-ready formats
- Code templates for processing public data
- Technical recipes for sharing datasets on open platforms
The stakes? Citizens' ability to leverage their collective digital intelligence.
The technology is ready. The infrastructure exists. The guide shows exactly how to use it. What's needed is your cultural expertise to shape these tools.
Check it out: https://huggingface.co/blog/evijit/public-org-data-ai
P.s.: Building cool projects in a public institution? Share them in the comments for others to learn from!
Post
2768
Do chatbots lie about Céline Dion? We now have answers, not speculation.
Ai2 just released OLMoTrace and it's a game-changer for transparency. You can literally see where an AI's responses come from in its training data - in real time.
The demo shows results about Céline. So I tried it out myself! Watch what happens in the video.
For journalists, researchers studying hallucinations and anyone who needs to trust their AI, this is like getting X-ray vision into AI systems. When the model made claims, I could instantly verify them against original sources. When it hallucinated, I could see why.
You can finally 1) understand how LLMs actually work and 2) verify if what they're saying is true. No more blind trust.
This pushes the open data movement to the next level.
👉 Blog post: https://allenai.org/blog/olmotrace
👉 Paper: https://www.datocms-assets.com/64837/1743890415-olmotrace.pdf
P.S.: A word of caution: never use a chatbot as a knowledge base. It's not Google. Better use it with a connection to the internet.
Ai2 just released OLMoTrace and it's a game-changer for transparency. You can literally see where an AI's responses come from in its training data - in real time.
The demo shows results about Céline. So I tried it out myself! Watch what happens in the video.
For journalists, researchers studying hallucinations and anyone who needs to trust their AI, this is like getting X-ray vision into AI systems. When the model made claims, I could instantly verify them against original sources. When it hallucinated, I could see why.
You can finally 1) understand how LLMs actually work and 2) verify if what they're saying is true. No more blind trust.
This pushes the open data movement to the next level.
👉 Blog post: https://allenai.org/blog/olmotrace
👉 Paper: https://www.datocms-assets.com/64837/1743890415-olmotrace.pdf
P.S.: A word of caution: never use a chatbot as a knowledge base. It's not Google. Better use it with a connection to the internet.
- 1 reply
Post
4044
🎨 Designers, meet OmniSVG! This new model helps you create professional vector graphics from text/images, generate editable SVGs from icons to detailed characters, convert rasters to vectors, maintain style consistency with references, and integrate into your workflow.
@OmniSVG
@OmniSVG
-
- 2 replies
davanstrien
posted
an
update
10 days ago
Post
1592
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.
Post
789
As
xet-team
infrastructure begins backing hundreds of repositories on the Hugging Face Hub, we’re getting to put on our researcher hats and peer into the bytes. 👀 🤓
IMO, one of the most interesting ideas Xet storage introduces is a globally shared store of data.
When you upload a file through Xet, the contents are split into ~64KB chunks and deduplicated, but what if those same chunks already exist in another repo on the Hub?
If we can detect and reuse them, we skip them as well saving time and bandwidth for AI builders. More on how that works here:
🔗 https://huggingface.co/blog/from-chunks-to-blocks#scaling-deduplication-with-aggregation
Because of this, different repositories can share bytes we store. That opens up something cool - we can draw a graph of which repos actually share data at the chunk level, where:
- Nodes = repositories
- Edges = shared chunks
- Edge thickness = how much they overlap
xet-team/repo-graph
Come find the many BERT islands. Or see how datasets relate in practice, not just in theory. See how libraries or tasks can tie repositories together. You can play around with node size using storage/likes/downloads too.
The result is a super fun visualization from @saba9 and @znation that I’ve already lost way too much time to. I'm excited to see how the networks grow as we add more repositories!
xet-team
infrastructure begins backing hundreds of repositories on the Hugging Face Hub, we’re getting to put on our researcher hats and peer into the bytes. 👀 🤓IMO, one of the most interesting ideas Xet storage introduces is a globally shared store of data.
When you upload a file through Xet, the contents are split into ~64KB chunks and deduplicated, but what if those same chunks already exist in another repo on the Hub?
If we can detect and reuse them, we skip them as well saving time and bandwidth for AI builders. More on how that works here:
🔗 https://huggingface.co/blog/from-chunks-to-blocks#scaling-deduplication-with-aggregation
Because of this, different repositories can share bytes we store. That opens up something cool - we can draw a graph of which repos actually share data at the chunk level, where:
- Nodes = repositories
- Edges = shared chunks
- Edge thickness = how much they overlap
xet-team/repo-graph
Come find the many BERT islands. Or see how datasets relate in practice, not just in theory. See how libraries or tasks can tie repositories together. You can play around with node size using storage/likes/downloads too.
The result is a super fun visualization from @saba9 and @znation that I’ve already lost way too much time to. I'm excited to see how the networks grow as we add more repositories!
Post
2908
What does it mean when models share the same bytes?
We've investigated some quants and have seen that a considerable portion of quantizations of the same model share the same bytes and can be deduplicated to save considerable upload time for quantizers on the Hub.
This space where we crack open a repo from @bartowski shows we can get significant dedupe xet-team/quantization-dedup
You can get a sense of why by reading this write-up: https://github.com/bartowski1182/llm-knowledge/blob/main/quantization/quantization.md
But what about finetuned models?
Since going into production the
xet-team
has migrated hundreds of repositories on the Hub to our storage layer, including classic "pre-Hub" open-source models like
FacebookAI/xlm-roberta-large (XLM-R) from
FacebookAI
XLM-R, introduced in 2019, set new benchmarks for multilingual NLP by learning shared representations across 100 languages. It was then fine-tuned on English, Spanish, Dutch, and German, generating language-specific derivations for each - check out the paper here Unsupervised Cross-lingual Representation Learning at Scale (1911.02116)
These finetunes share much of the same architecture and layout as XLM-R with similar training methods and goals. It makes sense that they would share bytes, but it's still fascinating to see.
We put together a similar space to explore these models to see where they overlap - check it out for yourself xet-team/finetune-dedupe
The darker each block in the heatmap, the more the bytes are shared. Clicking on a repos blocks shows all other repos that share blocks.
We've investigated some quants and have seen that a considerable portion of quantizations of the same model share the same bytes and can be deduplicated to save considerable upload time for quantizers on the Hub.
This space where we crack open a repo from @bartowski shows we can get significant dedupe xet-team/quantization-dedup
You can get a sense of why by reading this write-up: https://github.com/bartowski1182/llm-knowledge/blob/main/quantization/quantization.md
But what about finetuned models?
Since going into production the
xet-team
has migrated hundreds of repositories on the Hub to our storage layer, including classic "pre-Hub" open-source models like
FacebookAI/xlm-roberta-large (XLM-R) from
FacebookAI
XLM-R, introduced in 2019, set new benchmarks for multilingual NLP by learning shared representations across 100 languages. It was then fine-tuned on English, Spanish, Dutch, and German, generating language-specific derivations for each - check out the paper here Unsupervised Cross-lingual Representation Learning at Scale (1911.02116)
These finetunes share much of the same architecture and layout as XLM-R with similar training methods and goals. It makes sense that they would share bytes, but it's still fascinating to see.
We put together a similar space to explore these models to see where they overlap - check it out for yourself xet-team/finetune-dedupe
The darker each block in the heatmap, the more the bytes are shared. Clicking on a repos blocks shows all other repos that share blocks.
- 1 reply
Post
1890
Need to convert CSV to Parquet?
Use https://www.chatdb.ai/tools/csv-to-parquet-converter. It does the job instantly.
@cfahlgren1 provides many other tools on his website. Approved and bookmarked!
Use https://www.chatdb.ai/tools/csv-to-parquet-converter. It does the job instantly.
@cfahlgren1 provides many other tools on his website. Approved and bookmarked!
- 2 replies