How good are general purpose AI agents? We built an open evaluation framework to find out. Most evaluations in AI report a simple result: what score each model got on which benchmarking task. When you deploy an agent, you’re not just choosing a model. You’re choosing a full system: what tools the agent can use, how it plans its steps, what it remembers between actions, how it recovers when something goes wrong. Change any of those and the same model […]

PaddleOCR 3.5 brings OCR and document parsing tasks closer to the Hugging Face ecosystem. With this release, supported PaddleOCR models can run with Hugging Face Transformers as an inference backend by setting: engine=”transformers” PaddleOCR continues to provide OCR model series such as PP-OCRv5 and document parsing model series such as PaddleOCR-VL 1.5, while Transformers becomes one of the supported backends for running them. Try the live demo on Hugging Face Spaces: https://huggingface.co/spaces/PaddlePaddle/paddleocr-3.5-transformers-demo What changed? PaddleOCR 3.5 introduces a more flexible […]

Today I’m releasing six new Sentence Transformers CrossEncoder rerankers, state-of-the-art at their respective sizes, built on top of the Ettin ModernBERT encoders, together with the data and full training recipe that produced them: The models were trained with a distillation recipe: pointwise MSE on mixedbread-ai/mxbai-rerank-large-v2 scores over cross-encoder/ettin-reranker-v1-data, which is a subset of lightonai/embeddings-pre-training mixed with a reranked subset of lightonai/embeddings-fine-tuning. Our six rerankers paired with google/embeddinggemma-300m on MTEB(eng, v2) Retrieval. See Results for five more embedder pairings. If you’re […]

🧠 Models: https://huggingface.co/collections/allenai/olmoearth | 📄 Tech Report: https://allenai.org/papers/olmoearth_v1_1 | 💻 Code: https://github.com/allenai/olmoearth_pretrain We released OlmoEarth (v1) in November 2025. Since then, partners have applied it across a wide range of tasks, from tracking mangrove change to classifying drivers of forest loss to producing country-scale crop-type maps in days, scaling deployments to national, continental, and global areas. Every release moves us closer to our mission: bringing state-of-the-art AI to organizations and communities working to protect people and our planet. When OlmoEarth […]

In April, we released DharmaOCR — a pair of specialized small language models for structured OCR, alongside a benchmark and the accompanying paper. The models and the benchmark are available on Hugging Face. Together they form part of a broader effort at Dharma to study how    

Large language models (LLMs) have become the default interface for code generation, math problem solving, summarization, document understanding, and many other developer workflows. Under the hood, though, many LLMs still generate text the same way: one token at a time, and each token depends on the tokens that appeared before it. As such, these models are called autoregressive, since they consume their own outputs. That autoregressive (AR) approach has been remarkably successful. It is stable to train, simple to serve, […]