Sentence Transformers is a Python library for using and training dense embedding, reranker (cross encoder), and sparse embedding models for a wide range of applications, such as retrieval augmented generation, semantic search, semantic textual similarity, paraphrase mining, and more. In this blogpost, I’ll show you how to use it to finetune a sparse encoder/embedding model and explain why you might want to do so. This results in sparse-encoder/example-inference-free-splade-distilbert-base-uncased-nq, a cheap model that works especially well in hybrid search or retrieve […]

We built one of the most powerful computer-use AIs in the world. And made it directly accessible from your browser. On March 31st, we released Holo3, our most advanced computer-use model to date. Building something powerful is one thing; making it accessible and easy to use is another. We’re doing both. HoloTab is a Chrome extension that navigates the web just like a person would. It automates tasks across any website with zero setup or technical skills required. You describe […]

VAKRA Dataset | LeaderBoard | Release Blog | GitHub | Submit to Leaderboard We recently introduced VAKRA, a tool-grounded, executable benchmark for evaluating how well AI agents reason and act in enterprise-like environments. Unlike traditional benchmarks that test isolated skills, VAKRA measures compositional reasoning across APIs and documents, using full execution traces to assess whether agents can reliably complete multi-step workflows. VAKRA provides an executable environment where agents interact with over 8,000+ locally hosted APIs backed by real databases spanning […]

Sentence Transformers is a Python library for using and training embedding and reranker models for applications like retrieval augmented generation, semantic search, and more. In my previous blogpost, I introduced the new multimodal capabilities, showing how to use embedding and reranker models that handle text, images, audio, and video. In this blogpost, I’ll show you how to train or finetune these multimodal models on your own data. As a practical example, I’ll walk through finetuning Qwen/Qwen3-VL-Embedding-2B for Visual Document Retrieval […]

Making transformers models available in mlx-lm using a Skill and test harness TL;DR We provide a Skill and a test harness to help port language models from transformers to mlx-lm, so they become (almost) instantly available the moment they are added to transformers. The Skill is designed to support contributors and reviewers as an aide, not an automation. We explain why we did it, how, and comment about how to meaningfully contribute to open source in the age of agents. […]

TL;DR — We extend the RLVE framework from single-turn reasoning puzzles to multi-turn, tool-augmented e-commerce conversations. EcomRLVE-GYM provides 8 verifiable environments — product discovery, substitution, cart building, returns, order tracking, policy QA, bundle planning, and multi-intent journeys — each with procedural problem generation, a 12-axis difficulty curriculum, and algorithmically verifiable rewards. We train a Qwen 3 8B model with DAPO over 300 steps and present early results demonstrating that environment scaling and adaptive difficulty transfer to agentic, real-world task completion. […]

Training a high-quality OCR model requires a large quantity of annotated image-text pairs: images with precise bounding boxes, transcriptions, and ideally reading order information at the word, line, and paragraph level. Every approach to curating this data comes with tradeoffs. Existing benchmark datasets like ICDAR and Total-Text have clean labels but limited scale, typically tens of thousands of images skewed toward English and Chinese. Manual annotation produces the highest quality labels but is expensive and slow, making it impractical at […]