Anyone know how to reset Claude web's MCP config? I connected mine when the HF MCP first released with just the default example spaces added. I added lots of other MCP spaces but Claude.ai doesn't update the available tools... "Disconnecting" the HF integration does nothing, deleting it and adding it again does nothing.
Refreshing tools works fine in VS Code because I can manually restart it in mcp.json, but claude.ai has no such option. Anyone got any ideas?
Dataset Viewer for PDFs just landed on Hugging Face 📖🤗 you can now preview all the PDFs easier than before!
on top of this, there's PdfFolder format to load the PDF datasets quicker 💨 > to use it, your dataset should follow a directory format like folder/train/doc1.pdf, folder/train/doc1.pdf > if you want to include bounding boxes, labels etc. you can keep them in a metadata.csv file in the same folder 🤝
🖼️ VLMs/OCR > moonshotai/Kimi-VL-A3B-Thinking-2506 is a powerful reasoning vision LM, 3B active params, smarter with less tokens, supports long documents, videos 👏 (OS) > nanonets/Nanonets-OCR-s is 3.75B params OCR model based on Qwen2.5VL-3B-Instruct (OS)
🗣️ Audio > Google released google/magenta-realtime, real time music generation & audio synthesis (cc-by-4) > kyutai released new speech-to-text models that come in 1B & 2B (kyutai/stt-1b-en_fr, stt-2b-en_fr) with 0.5s and 2.5s delay
y'all have been asking my opinion on how OCR models compare to each other 👀 I will leave three apps to compare newest models by @prithivMLmods instead ⤵️ > compare Nanonets-OCR-s, Qwen2-VL-OCR-2B-Instruct, RolmOCR, Aya-Vision prithivMLmods/Multimodal-OCR > SmolDocling, Nanonets-OCR-s, MonkeyOCR, Typhoon-OCR-7B prithivMLmods/Multimodal-OCR2 > docscopeOCR, MonkeyOCR, coreOCR prithivMLmods/core-OCR
🌐 Clinical Trials Dataset now available on Hugging Face! 🧬
I’ve just released a comprehensive, ML-ready dataset featuring 500,000+ clinical trial records sourced directly from ClinicalTrials.gov for biomedical NLP, healthcare analytics, and clinical research applications 🤗
I wanted to produce the most complete and up-to-date dump with all raw data partially flattened to simplify extraction, self-querying and processing.
Do you have any ideas about what we can do with it? Using descriptions to enhance specialized embedding models?
so far I figured out > for fact-checks, you need a relatively bigger size (7B is ok!) > Gemma 3 gets downgrade without pan and scan (especially for 📑) > Qwen2.5VL-32B is very talkative, great for reasoning but not good for simple tasks 🗣️
Extending datasets just got a whole lot easier! 🚀 With Sheets, I was able to create a Spanish version of the popular fka/awesome-chatgpt-prompts dataset in just a few minutes ⏱️.
Want to try it out for yourself? Head over to the Sheets space and see how easy it is to extend and modify existing datasets 🤯. The possibilities are endless! 🌐
the method is simple: find which tokens have the highest attention score, merge rest of the tokens based on similarity, then merge both
their method is both training-free and for fine-tuning the authors report 5 point improvement on average of vision language tasks + 8x improvement in prefilling time for Llava-Next 7B and 13B 🤯
removing redundant tokens improve image token quality too 🥹
we have launched Kernel Hub: easy optimized kernels for all models on Hugging Face 🔥 use them right away! it's where the community populates optimized kernels 🤝
this release comes in three parts > Kernel Hub: contains (as of now) 14 kernels > kernels: Python library to load kernels from Kernel Hub > kernel-builder: Nix package to build kernels for PyTorch (made using PyTorch C++ frontend)
when building models, your regular workflow should be pulling kernels from Hub and building your model with them 🤗 here's a practical example with RMSNorm: 1. pull the kernel from Hub with get_kernel 2. decorate with use_kernel_forward_from_hub 3. inject it to your model we'd love to hear your feedback! 🙏🏻 we also welcome kernel contributions by community 🥹💗
Dolphin: new OCR model by ByteDance with MIT license 🐬
the model first detects element in the layout (table, formula etc) and then parses each element in parallel for generation Model: ByteDance/Dolphin Try the demo: ByteDance/Dolphin
