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ai2.bsky.social
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How can we better understand how models make predictions and which components of a training dataset are shaping their behaviors? In April we introduced OLMoTrace, a feature that lets you trace the outputs of language models back to their full training data in real time. 🧵

🚨 We're hiring a #ResearchScientist in #AI for Scientific Discovery at Ai2! Are you passionate about intelligent agents, data-driven discovery, and AI systems that accelerate science? Join us in shaping the future of research. 🧬🧠 Apply now: job-boards.greenhouse.io/thealleninst...

Today we’re releasing a prototype of Genesys, an autonomous multi-agent LLM discovery system that aims to discover new types of language model architectures. We found Genesys can discover novel architectures competitive with the industry-standard transformer. 🧵

Hear Ai2 Senior Researcher @natolambert speak at @VentureBeat Transform Wednesday, 6/25! Join us at 2:30 PM PT for his talk, “A Taxonomy for Next-Generation Reasoning Models.” See you there! #VBTransform

How well can today’s models generalize, compose, or even innovate on unseen problems? OMEGA Ω is a new math benchmark that pushes LLMs beyond pattern-matching to test true mathematical reasoning. ⚡ allenai.org/blog/omega

New updates for olmOCR, our fully open toolkit for transforming documents (PDFs & images) into clean markdown. We released: 1️⃣ New benchmark for fair comparison of OCR engines and APIs 2️⃣ Improved inference that is faster and cheaper to run 3️⃣ Docker image for easy deployment

Congrats to the team! 📜🏆... Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models.

We are #1 on the @huggingface heatmap - this is what true openness looks like!🥇🎉 750+ models 230+ datasets And counting... Come build with us huggingface.co/spaces/cfahl...

We’ve just made it even easier to build with our open models and datasets ✨ What’s in our docs platform? ⚙️ Setup instructions 🚀 Hosting + deployment ⭐ Integration examples ⚡ Optimization tips

Congrats to Ai2's @sewonm.bsky.social won__min on the recognition! Her work is pushing the boundaries of how we can integrate private data into language model training flexibly and securely.

As we’ve been working towards training a new version of OLMo, we wanted to improve our methods for measuring the Critical Batch Size (CBS) of a training run, to unlock greater efficiency. but we found gaps between the methods in the literature and our practical needs for training OLMo. 🧵