Hugging Face

Are the latest VLM-Based OCR models better than "traditional" OCR systems? With new vision-language models for OCR dropping almost weekly, I wanted to create an easier way for GLAM professionals to evaluate/vibe check how existing OCR compares to newer VLM-based OCR. I previously shared a space which allowed you to upload your own images for testing, but I think it could be more useful to compare results across a larger number of images. To help with this, I've built OCR Time Capsule - a simple comparison tool using 11,000+Scottish school exam papers (1888-1963) from the National Library of Scotland as a test case. 📚 Dataset: https://lnkd.in/eWQBK8FZ 🔗 Browse Results: https://lnkd.in/eyX4zJhK 🔧 Process Your Own: https://lnkd.in/eq2U2F_q Key Features: - Visual page browser to quickly scan through documents - Side-by-side comparison of XML OCR vs VLM output - Quality metrics showing character-level improvements - Export functionality for further analysis 🚀 Next Steps: I'm planning to add more example datasets & OCR models using HF Jobs. Feel free to suggest collections to test with - I need image + existing OCR! Even better: if your institution has digitised collections, consider uploading them to Hugging Face! Would love to see more GLAM datasets on the Hub 🤗 Drop a comment with dataset suggestions or links! #DigitalLibraries #OCR #GLAM #DigitalHumanities #AI

FLUX.1-Krea-dev, the new best open image generation model? The answer is........ Run your own experiments! I've just done a quick experiment, and I am impressed by its ability to follow prompts and the realism of the images, but it also fails sometimes (see the baseball's pizza 😂 ) You can run similar experiments, for free, thanks to fal and Hugging Face Inference Providers. Now available on AISheets: https://lnkd.in/dVu2f2SB

Fast LoRA inference for Flux with Diffusers and PEFT 🚨 There are great materials that demonstrate how to optimize inference for popular image generation models, such as Flux. However, very few cover how to serve LoRAs fast, despite LoRAs being an inseparable part of their adoption. In our latest post, Benjamin Bossan and I show different techniques to optimize LoRA inference for the Flux family of models for image generation. Our recipe includes the use of: 1. `torch.compile` 2. Flash Attention 3 (when compatible) 3. Dynamic FP8 weight quantization (when compatible) 4. Hotswapping for avoiding recompilation during swapping new LoRAs 🤯 We have tested our recipe with Flux.1-Dev on both H100 and RTX 4090. We achieve at least a *2x speedup* in either of the GPUs. We believe our recipe is grounded in the reality of how LoRA-based use cases are generally served. So, we hope this will be beneficial to the community 🤗 Even though our recipe was tested primarily with NVIDIA GPUs, it should also work with AMD GPUs. Learn the details and the full code here: https://lnkd.in/gYh8MSF7

"Browse the merchant websites for blue t-shirts, and show me what I would look like in three of them, use my photo" Virtual try-on agent, powered by Claude and MCP servers🔥👇 Great work, as always, by Freddy Boulton! Here's a quick 2-minute guide on how to build your own MCP-powered agents: https://lnkd.in/gWU4AduY

hf jobs: synthetic data pipelines as a service, at lightning speed ⚡ Here's a real example: > Using Kimi-K2-Instruct to generate a dataset of diverse and accurate questions and answers, thanks to Inference Providers and Groq! (You can generate a 10,000-row dataset in less than an hour). < Script, dataset, prompts config, and docs in the first comment

𝐀𝐈 𝐒𝐡𝐨𝐩𝐩𝐢𝐧𝐠 𝐀𝐬𝐬𝐢𝐬𝐭𝐚𝐧𝐭 𝐓𝐫𝐢𝐞𝐬 𝐨𝐧 𝐂𝐥𝐨𝐭𝐡𝐞𝐬 𝐅𝐨𝐫 𝐌𝐞 LLMs excel at text-based tasks. But so many use-cases go beyond text. I personally hate shopping and trying on clothes. Can AI solve this for me? In my latest blog, I show how you can use Gradio to connect an LLM (Anthropic Claude) with one of the thousands of AI models on Hugging Face (IDM-VTON virtual-try on). The result is an intelligent assistant that can browse the web and show me what I would look like in different outfits! Python developers, check out Gradio and Hugging Face. It's the fastest ways to turbocharge an LLM! Read the full details here ➡️: https://lnkd.in/eFWaUgWZ

I just ran batch inference on a 30B parameter LLM across 4 GPUs with a single Python command! The secret? Modern AI infrastructure where everyone handles their specialty: 📦 UV (by Astral) handles dependencies via uv scripts 🖥️ Hugging Face Jobs handles GPU orchestration   🧠 Qwen AI team handles the model (Qwen3-30B-A3B-Instruct-2507) ⚡ vLLM handles efficient batched inference I'm very excited about using uv scripts as a nice way of packaging fairly simple but useful ML tasks in a somewhat reproducible way. This combined with Jobs opens up some nice oppertunities for making pipelines that require different types of compute. Technical deep dive and code examples: https://lnkd.in/e5BEBU95

🚀 Introducing JSVibes - vibe testing open models for simple, yet useful web development tasks - A list of 50 web micro-apps you can play with - Implementations by kimi k2 and qwen3-code, side-by-side - Judged by kimi k2 The Qwen3-code win rate is 88%. Personally, I don't always agree; you'd better play with the apps yourself! Built with Hugging Face AISheets

Cerebras Systems is very proud to be offering Qwen3-235B 2507 with our strategic partner , Hugging Face ! https://lnkd.in/dDBcqHpc Thank you Clem Delangue 🤗 Vaibhav Srivastav and Julien Chaumond for the partnership!

Don't use any ML tracking solution for your training runs before reading this! 🔥 Technical Report: https://lnkd.in/gqhguKbP