Hi! We open-source PrunaSuperPoint in collaboration with Eclipse Aidge for the DeepGreen project
It's an optimized version of SuperPoint built for faster keypoint detection on edge devices. - Up to 2.1× faster on Jetson Orin Nano, with optimizations applicable to other runtimes. - The distilled model retains strong keypoint coverage across indoor and outdoor data, with low descriptor differences from the original model. - We structurally prune the most expensive convolutional layers, recover performance through distillation, and accelerate keypoint selection with hierarchical top-k, all while preserving the original architecture’s core behavior. Check it here: PrunaAI/PrunaSuperPoint
Today, we open-source Pruna-Qwen-Image-2.1, a set of a few-step LoRA adapters that make Qwen-Image-2. up to 6.3× faster for image generation and editing.
Pruna OSS is turning 1! To mark this milestone, we're launching the First Prune initiative.
What's First Prune: If you contribute to open issues at our GitHub repo, you earn Pruna Inference API credits.
How you can participate: • Pick an open issue labelled "first-prune" and assign it to you • Submit your PR and mark it ready for review by April 30 • Find out more in the PR template when you open a PR
More OSS than ever with the latest pruna 0.3.2 release. It extends existing algorithm families, such as compilers, kernels, and pruners, and adds new ones, including decoders, distillers, enhancers, and recoverers. But it's not only a collection of algorithms; instead, you can easily combine them to get the biggest efficiency win.
This first unit of the course sets you up with all the fundamentals to become a pro in agents.
- What's an AI Agent? - What are LLMs? - Messages and Special Tokens - Understanding AI Agents through the Thought-Action-Observation Cycle - Thought, Internal Reasoning and the Re-Act Approach - Actions, Enabling the Agent to Engage with Its Environment - Observe, Integrating Feedback to Reflect and Adapt
Introducing the Synthetic Data Generator, a user-friendly application that takes a no-code approach to creating custom datasets with Large Language Models (LLMs). The best part: A simple step-by-step process, making dataset creation a non-technical breeze, allowing anyone to create datasets and models in minutes and without any code.
🚀 We will be generating a preference dataset for DPO/ORPO and cleaning it with AI feedback during our upcoming meetup!
In this session, we'll walk you through the essentials of building a distilabel pipeline by exploring two key use cases: cleaning an existing dataset and generating a preference dataset for DPO/ORPO. You’ll also learn how to make the most of AI feedback, integrating Argilla to gather human feedback and improve the overall data quality.
This session is perfect for you - if you’re getting started with distilabel or synthetic data - if you want to learn how to use LLM inference endpoints for **free** - if you want to discover new functionalities - if you want to provide us with new feedback
distilabel 1.3.0 is out! This release contains many core improvements and new tasks that help us building argilla/magpie-ultra-v0.1!
Distributed pipeline execution with Ray, new Magpie tasks, reward models, components for dataset diversity based on sentence embeddings, Argilla 2.0 compatibility and many more features!
🔌 You can now integrate with the Hugging Face Hub and get started in under five minutes. 🪂 A single Dataset class is now designed to handle multiple tasks. 🔧 It’s 100 times simpler to configure your dataset now with the new SDK! 📖 The documentation has been revamped to be cleaner and more user-friendly. 🍌 A new feature automates splitting annotation tasks among a team. ✍️ The layout has been made more flexible to accommodate many use cases.
Data is essential for training good AI systems. We believe that the amazing community built around open machine learning can also work on developing amazing datasets together.
To explore how this can be done, Argilla and Hugging Face are thrilled to announce a collaborative project where we’re asking Hugging Face community members to build a dataset consisting of LLM prompts collectively.
What are we doing? Using an instance of Argilla — a powerful open-source data collaboration tool — hosted on the Hugging Face Hub, we are collecting ratings of prompts based on their quality.
How Can You Contribute? It’s super simple to start contributing:
1. Sign up if you don’t have a Hugging Face account
