Llamas, model merging, massive ASR for data collection, 3D ML, on-device ML, quantization, model judging, ML in browser, healthcare applications, education, intersection of art and ML.🦙
We’re launching a FREE and CERTIFIED course on Agents!
We're thrilled to announce the launch of the Hugging Face Agents course on Learn! This interactive, certified course will guide you through building and deploying your own AI agents.
Here's what you'll learn:
- Understanding Agents: We'll break down the fundamentals of AI agents, showing you how they use LLMs to perceive their environment (observations), reason about it (thoughts), and take actions. Think of a smart assistant that can book appointments, answer emails, or even write code based on your instructions. - Building with Frameworks: You'll dive into popular agent frameworks like LangChain, LlamaIndex and smolagents. These tools provide the building blocks for creating complex agent behaviors. - Real-World Applications: See how agents are used in practice, from automating SQL queries to generating code and summarizing complex documents. - Certification: Earn a certification by completing the course modules, implementing a use case, and passing a benchmark assessment. This proves your skills in building and deploying AI agents. Audience
This course is designed for anyone interested in the future of AI. Whether you're a developer, data scientist, or simply curious about AI, this course will equip you with the knowledge and skills to build your own intelligent agents.
Enroll today and start building the next generation of AI agent applications!
Multimodal 🖼️ > Google shipped a PaliGemma 2, new iteration of PaliGemma with more sizes: 3B, 10B and 28B, with pre-trained and captioning variants 👏 > OpenGVLab released InternVL2, seven new vision LMs in different sizes, with sota checkpoint with MIT license ✨ > Qwen team at Alibaba released the base models of Qwen2VL models with 2B, 7B and 72B ckpts
LLMs 💬 > Meta released a new iteration of Llama 70B, Llama3.2-70B trained further > EuroLLM-9B-Instruct is a new multilingual LLM for European languages with Apache 2.0 license 🔥 > Dataset: CohereForAI released GlobalMMLU, multilingual version of MMLU with 42 languages with Apache 2.0 license > Dataset: QwQ-LongCoT-130K is a new dataset to train reasoning models > Dataset: FineWeb2 just landed with multilinguality update! 🔥 nearly 8TB pretraining data in many languages!
Image/Video Generation 🖼️ > Tencent released HunyuanVideo, a new photorealistic video generation model > OminiControl is a new editing/control framework for image generation models like Flux
Audio 🔊 > Indic-Parler-TTS is a new text2speech model made by community
Have you tried out 🤗 Transformers.js v3? Here are the new features: ⚡ WebGPU support (up to 100x faster than WASM) 🔢 New quantization formats (dtypes) 🏛 120 supported architectures in total 📂 25 new example projects and templates 🤖 Over 1200 pre-converted models 🌐 Node.js (ESM + CJS), Deno, and Bun compatibility 🏡 A new home on GitHub and NPM
📣 Sentence Transformers v3.2.0 is out, marking the biggest release for inference in 2 years! 2 new backends for embedding models: ONNX (+ optimization & quantization) and OpenVINO, allowing for speedups up to 2x-3x AND Static Embeddings for 500x speedups at 10-20% accuracy cost.
1️⃣ ONNX Backend: This backend uses the ONNX Runtime to accelerate model inference on both CPU and GPU, reaching up to 1.4x-3x speedup depending on the precision. We also introduce 2 helper methods for optimizing and quantizing models for (much) faster inference. 2️⃣ OpenVINO Backend: This backend uses Intel their OpenVINO instead, outperforming ONNX in some situations on CPU.
Usage is as simple as SentenceTransformer("all-MiniLM-L6-v2", backend="onnx"). Does your model not have an ONNX or OpenVINO file yet? No worries - it'll be autoexported for you. Thank me later 😉
🔒 Another major new feature is Static Embeddings: think word embeddings like GLoVe and word2vec, but modernized. Static Embeddings are bags of token embeddings that are summed together to create text embeddings, allowing for lightning-fast embeddings that don't require any neural networks. They're initialized in one of 2 ways:
1️⃣ via Model2Vec, a new technique for distilling any Sentence Transformer models into static embeddings. Either via a pre-distilled model with from_model2vec or with from_distillation where you do the distillation yourself. It'll only take 5 seconds on GPU & 2 minutes on CPU, no dataset needed. 2️⃣ Random initialization. This requires finetuning, but finetuning is extremely quick (e.g. I trained with 3 million pairs in 7 minutes). My final model was 6.6% worse than bge-base-en-v1.5, but 500x faster on CPU.
Dataset highlights: - 223,739 documents from doc4web.ru, a document hosting platform for students and teachers - Primarily in Russian, with some English and potentially other languages - Each entry includes: URL, title, download link, file path, and content (where available) - Contains original document files in addition to metadata - Data reflects a wide range of educational topics and materials - Licensed under Creative Commons Zero (CC0) for unrestricted use
The dataset can be used for analyzing educational content in Russian, text classification tasks, and information retrieval systems. It's also valuable for examining trends in educational materials and document sharing practices in the Russian-speaking academic community. The inclusion of original files allows for in-depth analysis of various document formats and structures.
Meta AI vision has been cooking @facebook They shipped multiple models and demos for their papers at @ECCV🤗
Here's a compilation of my top picks: - Sapiens is family of foundation models for human-centric depth estimation, segmentation and more, all models have open weights and demos 👏
All models have their demos and even torchscript checkpoints! A collection of models and demos: facebook/sapiens-66d22047daa6402d565cb2fc - VFusion3D is state-of-the-art consistent 3D generation model from images
The Nobel Prize background for Hopfield and Hinton's work on neural networks is pure gold. It's a masterclass in explaining AI basics.
Key takeaways from the conclusion: - ML applications are expanding rapidly. We're still figuring out which will stick. - Ethical discussions are crucial as the tech develops. - Physics 🤝 AI: A two-way street of innovation.
Some mind-blowing AI applications in physics: - Discovering the Higgs particle - Cleaning up gravitational wave data - Hunting exoplanets - Predicting molecular structures - Designing better solar cells
We're just scratching the surface. The interplay between AI and physics is reshaping both fields.
Bonus: The illustrations accompanying the background document are really neat. (Credit: Johan Jarnestad/The Royal Swedish Academy of Sciences)
