Hugging Face

Hugging Face Tasks is a documentation project for everyone to start building with machine learning! 🤗📖 Link in comments 💬 This month we have shipped a lot of newbies, updated many models and datasets, completely renewed feature extraction task page to have modern retrieval embeddings with RAG scenarios and added a new task page for vision language models 🤩

We just removed the requirement to have a payment method attached to your org when creating competitions on Hugging Face 🚀 Now, universities, organizations & private individuals can create free-tier competitions without the need to worry about CC. https://lnkd.in/dJp82BGv

🔥 🔥 Generate 3D characters with CharacterGen (SIGGRAPH'24) in high-quality shapes and textures! - Highley useful for downstream applications such as animation and game dev - Code, demo, and pretrained weights available now on 🤗 Hugging Face Official Demo on 🤗 Hugging Face Spaces: https://lnkd.in/gVaQ6B4w Model on 🤗 Hugging Face Hub: https://lnkd.in/gew2cyZq CharacterGen (SIGGRAPH'24) 👀 To get more details on the Project, visit their website: https://lnkd.in/gsXcyGmX 👩💻 GitHub Code: https://lnkd.in/gPpbJuXw 😀 To build a Gradio demo locally for CharacterGen: https://lnkd.in/gJgb-xyp

I had an incredible time at EuroPython over the past three days! 🎉 Presenting my poster, receiving my EuroPython cookie, and connecting with the vibrant Python community were absolute AMAZING 🐍 🌟 I also had the pleasure of delivering a ⚡️ lightning talk about AutoQuizzer, where we challenged Llama 3 to a quiz showdown. Shoutout to Stefano Fiorucci for creating such an impressive demo with #Haystack, Groq, and Gradio! 🙌 Catch the lightning talk recording here: https://lnkd.in/dR2ctzdB Explore the AutoQuizzer Demo: https://lnkd.in/dp-M72XR Already looking forward to EuroPython 2025. See you all there! 👋 #europython2024 #Python

Microsoft presents SpreadsheetLLM Encoding Spreadsheets for Large Language Models paper page: https://lnkd.in/eqvykDdT Spreadsheets, with their extensive two-dimensional grids, various layouts, and diverse formatting options, present notable challenges for large language models (LLMs). In response, we introduce SpreadsheetLLM, pioneering an efficient encoding method designed to unleash and optimize LLMs' powerful understanding and reasoning capability on spreadsheets. Initially, we propose a vanilla serialization approach that incorporates cell addresses, values, and formats. However, this approach was limited by LLMs' token constraints, making it impractical for most applications. To tackle this challenge, we develop SheetCompressor, an innovative encoding framework that compresses spreadsheets effectively for LLMs. It comprises three modules: structural-anchor-based compression, inverse index translation, and data-format-aware aggregation. It significantly improves performance in spreadsheet table detection task, outperforming the vanilla approach by 25.6% in GPT4's in-context learning setting. Moreover, fine-tuned LLM with SheetCompressor has an average compression ratio of 25 times, but achieves a state-of-the-art 78.9% F1 score, surpassing the best existing models by 12.3%. Finally, we propose Chain of Spreadsheet for downstream tasks of spreadsheet understanding and validate in a new and demanding spreadsheet QA task. We methodically leverage the inherent layout and structure of spreadsheets, demonstrating that SpreadsheetLLM is highly effective across a variety of spreadsheet tasks.

Open-source AI strikes back with AuraFlow v0.1🤩 - Largest open-sourced flow-based T2I model with Apache 2.0 license - 6.8B parameters - DiT Encoder blocks - Better instruction following - GenEval score 0.703 with prompt enhancement 🥳 Congratulations to Simo, Batuhan & the whole fal ai team on this epic release!👏 Explore AuraFlow with the community Gradio demo: https://lnkd.in/gY27BETy Find the open-source AuraFlow model on Hugging Face Hub: https://lnkd.in/g4ttnJTw Read the fal ai official release blog: https://lnkd.in/gNiK9uYN

Lovely time speaking at EuroPython'24. Some really, really amazing talks and libraries I am taking back home. EuroPython, thanks for the opportunity 🇨🇿 Slides: https://lnkd.in/g8ENyECd

Today, I am super excited to introduce Lynx, the leading hallucination detection model! 🚀 Hallucinations make it really hard to ship AI products. To address this problem, developers have begun to use general-purpose LLMs like GPT-4 to evaluate other LLMs (‘LLM-as-a-judge’). However, general-purpose LLMs weren’t designed to be great at evaluation. Prior research shows that current LLM-as-a-judge approaches are unreliable and inconsistent. Enter Lynx. Lynx beats GPT-4o and all state-of-the-art LLMs on RAG hallucination tasks. And we’ve open sourced it ✨ You can use quantized Lynx-8B locally, use Lynx-70B with GPUs, or just reach out to Patronus AI for easy API access 😃 We are thrilled to launch Lynx with our Day 1 Integration Partners: NVIDIA, MongoDB, and Nomic AI. ⚡ At Patronus AI, our mission is to make high quality LLM evaluation accessible to everyone. The best is yet to come. Download quantized Lynx-8B on Hugging Face: https://lnkd.in/eaTpM3u6 Download Lynx-70B on Hugging Face: https://lnkd.in/eauKirMc Read the arXiv paper: https://lnkd.in/e--_R8Cg  Read our blog: https://lnkd.in/eJ4AgX8T Use Lynx with NVIDIA's Nemo Guardrails: https://lnkd.in/ec2yJFrZ Read about our Nomic AI Atlas integration: https://lnkd.in/eKxeq65D

Real Time Object Tracking with RT-DETR🔥 Simply upload a video, set up a confidence threshold, or try the examples given in the demo 👇 Demo: https://lnkd.in/gEXMka-2 Do read Merve Noyan's amazing post on LinkedIn with more details on RT-DETR model over here: https://lnkd.in/gH6Mqc4j

🚀Introducing LLaVA-NeXT Interleave: Now AI can understand and reason with multiple images at once! ⬇️Learn everything about Llava-Next-Interleave that you would ever need - Opens up multi-image scenarios like multi-frame videos, multi-view 3D, and multiple inter-leaved images. - An all round LMM that can understand videos, images, and 3D - Interleave data format unifies different tasks. - New datasets on 🤗Hub: 1️⃣M4-Instruct, high-quality dataset, 1.1M samples from domains: multi-image, video, 3D & single-image 2️⃣LLaVA-Interleave Bench - Set of tasks to evaluate multi-image capabilities LLaVA-NeXT-Interleave💪 - Attached video show how Llava-Interleave can explain jokes and understand content spread in multiple images and videos🤯 - Model has SoTA Performance, both, in multiple and single images - Matches in performance with LLaVA-NeXT for single image - Improved performance in video tasks Gradio Multimodal Demo for LLaVA-NeXT-Interleave😍 : https://lnkd.in/gAaPrQtT Models and Datasets are on 🤗@huggingface Hub: https://lnkd.in/g2JEj2Uv