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Samarth0710  authored a paper 20 days ago
Manimator: Transforming Research Papers into Visual Explanations
MaziyarPanahi  authored a paper about 1 month ago
INTELLECT-1 Technical Report
MaziyarPanahi  authored a paper about 1 month ago
OpenMed NER: Open-Source, Domain-Adapted State-of-the-Art Transformers for Biomedical NER Across 12 Public Datasets
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burtenshaw 
posted an update 2 days ago
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The open source AI community is just made of people who are passionate and care about their work. So we thought it would be cool to share our favourite icons of the community with a fun award.

Winners get free Hugging Face Pro Subscriptions, Merchandise, or compute credits for the hub.

🔗 Follow and nominate here: community-spotlight

This is a new initiative to recognise and celebrate the incredible work being done by community members. It's all about inspiring more collaboration and innovation in the world of machine learning and AI.

They're highlighting contributors in four key areas:
- model creators: building and sharing innovative and state-of-the-art models.
- educators: sharing knowledge through posts, articles, demos, and events.
- tool builders: creating the libraries, frameworks, and applications that we all use.
- community champions: supporting and mentoring others in forums.

Know someone who deserves recognition? Nominate them by opening a post in the Hugging Face community forum.
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eugenesiow 
authored a paper 6 days ago
takarajordan 
posted an update 10 days ago
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I'm currently looking into what makes a scientific paper more popular than others on a platform like Hugging Face. I conducted a huge array of tests, content length, time based information even semantic feature extraction to get to some sort of answer around...

What actually drives popularity of these papers, why do some papers get zero upvotes and why do some get thousands?

The answer is absolutely nothing. Yes that's right. Nothing about the actual paper itself drives popularity, the paper's popularity is driven by external factors like it's authors, external marketing and others.

So next time you see a research paper with a lot of upvotes, just remember it's not because of the efforts of the authors. Remain objective.
takarajordan 
posted an update 11 days ago
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cron + LLM api is cracked
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BrigitteTousi 
posted an update 27 days ago
clem 
posted an update about 1 month ago
BrigitteTousi 
posted an update about 1 month ago
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New interactive viz from AI World showing OpenAI's new open model gpt-oss-120b breaking into the top 50 most liked models of all time on the Hub in under a day! ☄️☄️☄️
MaziyarPanahi 
authored 2 papers about 1 month ago
merterbak 
posted an update about 1 month ago
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OpenAI is now open again! Check out OpenAI’s brand new gpt‑oss‑20b model hosted on ZeroGPU 🤗

merterbak/gpt-oss-20b-demo
takarajordan 
posted an update about 1 month ago
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What do you all actually think about the open source OpenAI models? Are they legitimately any good or are they hype?
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BrigitteTousi 
posted an update about 1 month ago
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This is what Hugging Face is all about. We want everyone, hobbyists, researchers and industry alike, to be able to contribute to AI because everyone is affected by it. Kudos to HF's @irenesolaiman for spreading the word!🔥🤗
AtAndDev 
posted an update about 2 months ago
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Qwen 3 Coder is a personal attack to k2, and I love it.
It achieves near SOTA on LCB while not having reasoning.
Finally people are understanding that reasoning isnt necessary for high benches...

Qwen ftw!

DECENTRALIZE DECENTRALIZE DECENTRALIZE
MaziyarPanahi 
posted an update about 2 months ago
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🧬 Breaking news in Clinical AI: Introducing the OpenMed NER Model Discovery App on Hugging Face 🔬

OpenMed is back! 🔥 Finding the right biomedical NER model just became as precise as a PCR assay!

I'm thrilled to unveil my comprehensive OpenMed Named Entity Recognition Model Discovery App that puts 384 specialized biomedical AI models at your fingertips.

🎯 Why This Matters in Healthcare AI:
Traditional clinical text mining required hours of manual model evaluation. My Discovery App instantly connects researchers, clinicians, and data scientists with the exact NER models they need for their biomedical entity extraction tasks.

🔬 What You Can Discover:
✅ Pharmacological Models - Extract "chemical compounds", "drug interactions", and "pharmaceutical" entities from clinical notes
✅ Genomics & Proteomics - Identify "DNA sequences", "RNA transcripts", "gene variants", "protein complexes", and "cell lines"
✅ Pathology & Disease Detection - Recognize "pathological formations", "cancer types", and "disease entities" in medical literature
✅ Anatomical Recognition - Map "anatomical systems", "tissue types", "organ structures", and "cellular components"
✅ Clinical Entity Extraction - Detect "organism species", "amino acids", 'protein families", and "multi-tissue structures"

💡 Advanced Features:
🔍 Intelligent Entity Search - Find models by specific biomedical entities (e.g., "Show me models detecting CHEM + DNA + Protein")
🏥 Domain-Specific Filtering - Browse by Oncology, Pharmacology, Genomics, Pathology, Hematology, and more
📊 Model Architecture Insights - Compare BERT, RoBERTa, and DeBERTa implementations
⚡ Real-Time Search - Auto-filtering as you type, no search buttons needed
🎨 Clinical-Grade UI - Beautiful, intuitive interface designed for medical professionals

Ready to revolutionize your biomedical NLP pipeline?

🔗 Try it now: OpenMed/openmed-ner-models
🧬 Built with: Gradio, Transformers, Advanced Entity Mapping
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ariG23498 
posted an update about 2 months ago
erikkaum 
posted an update about 2 months ago
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ZML just released a technical preview of their new Inference Engine: LLMD.

- Just 2.4GB container, which means fast startup times and efficient autoscaling
- Cross-Platform GPU Support: works on both NVIDIA and AMD GPUs.
- written in Zig

I just tried it out and deployed it on Hugging Face Inference Endpoints and wrote a quick guide 👇 You can try it in like 5 minutes!

https://huggingface.co/blog/erikkaum/test-driving-llmd-inference-engine
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erikkaum 
posted an update about 2 months ago
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We just released native support for @SGLang and @vllm-project in Inference Endpoints 🔥

Inference Endpoints is becoming the central place where you deploy high performance Inference Engines.

And that provides the managed infra for it. Instead of spending weeks configuring infrastructure, managing servers, and debugging deployment issues, you can focus on what matters most: your AI model and your users 🙌
burtenshaw 
posted an update about 2 months ago
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Kimi-K2 is ready for general use! In these notebooks I walk you through use cases like function calling and structured outputs.

🔗 burtenshaw/Kimi-K2-notebooks

You can swap it into any OpenAI compatible application via Inference Providers and get to work with an open source model.
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burtenshaw 
posted an update 2 months ago
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Inference for generative ai models looks like a mine field, but there’s a simple protocol for picking the best inference:

🌍 95% of users >> If you’re using open (large) models and need fast online inference, then use Inference providers on auto mode, and let it choose the best provider for the model. https://huggingface.co/docs/inference-providers/index

👷 fine-tuners/ bespoke >> If you’ve got custom setups, use Inference Endpoints to define a configuration from AWS, Azure, GCP. https://endpoints.huggingface.co/

🦫 Locals >> If you’re trying to stretch everything you can out of a server or local machine, use Llama.cpp, Jan, LMStudio or vLLM. https://huggingface.co/settings/local-apps#local-apps

🪟 Browsers >> If you need open models running right here in the browser, use transformers.js. https://github.com/huggingface/transformers.js

Let me know what you’re using, and if you think it’s more complex than this.
txya900619 
authored a paper 2 months ago