Introducing EmbeddingGemma 2, an open multimodal embedding model optimized for privacy-first use cases
Google for Developers Blog - News about Web, Mobile, AI and Cloud
EmbeddingGemma 2 is a compact, open-source multimodal embedding model that maps text, code, images, video, and audio into a unified 768-dimensional space. Developers can use the sentence-transformers library to selectively load modular modality encoders—ranging from 270M to 740M parameters—to optimize memory usage. Additionally, Matryoshka Representation Learning enables dynamic dimension truncation down to 128d, significantly reducing vector database storage requirements while maintaining high retrieval performance.
EmbeddingGemma: Powerful and Lightweight Text Representations
We introduce EmbeddingGemma, a new lightweight, open text embedding model based on the Gemma 3 language model family. Our innovative training recipe strategically captures knowledge from larger models via encoder-decoder initialization and geometric embedding distillation. We improve model robustness and expressiveness with a spread-out regularizer, and ensure generalizability by merging checkpoints from varied, optimized mixtures. Evaluated on the Massive Text Embedding Benchmark (MTEB) across multilingual, English, and code domains, EmbeddingGemma (300M) achieves state-of-the-art results. Notably, it outperforms prior top models, both proprietary and open, with fewer than 500M parameters, and provides performance comparable to models double its size, offering an exceptional performance-to-cost ratio. Remarkably, this lead persists when quantizing model weights or truncating embedding outputs. This makes EmbeddingGemma particularly well-suited for low-latency and high-throughput use cases such as on-device applications. We provide ablation studies exploring our key design choices. We release EmbeddingGemma to the community to promote further research.
unsloth/embeddinggemma-2-GGUF · Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
The Beginner’s Guide to Text Embeddings & Techniques | deepset Blog
Text embeddings represent human language to computers, enabling tasks like semantic search. Here, we introduce sparse and dense vectors in a non-technical way.
Google for Developers Blog - News about Web, Mobile, AI and Cloud
Discover EmbeddingGemma, Google's new on-device embedding model designed for efficient on-device AI, enabling features like RAG and semantic search.
Ollama's new engine for multimodal models· Ollama Blog
Ollama now supports new multimodal models with its new engine.
Fast Embeddings on GPUs
Fast and accurate search is vital to all of Perplexity, from Search and Computer to our API Platform. Behind the scenes, the heavy lifting is done by embedding
OpenMedia
OpenMedia works to keep the Internet open, affordable, and surveillance-free. We create community-driven campaigns to engage, educate, and empower people to safeguard the Internet.
qwen3.5:27b
Qwen 3.5 is a family of open-source multimodal models that delivers exceptional utility and performance.
Linkup - SPARSEUP: Linkup's Open-Source Sparse Embedding Model
Linkup Research releases SPARSEUP, an open-source sparse embedding model under 150M parameters. SOTA on BEIR-13, sub-millisecond retrieval, Apache 2.0 license.
MCIF: Multimodal Crosslingual Instruction-Following Benchmark from Scientific Talks
Recent advances in large language models have laid the foundation for multimodal LLMs (MLLMs), which unify text, speech, and vision within a single framework. As these models are rapidly evolving toward general-purpose instruction following across diverse and complex tasks, a key frontier is evaluating their crosslingual and multimodal capabilities over both short- and long-form inputs. However, existing benchmarks fall short in evaluating these dimensions jointly: they are often limited to English, mostly focus on a single modality at a time, rely on short-form inputs, or lack human annotations--hindering comprehensive assessment of model performance across languages, modalities, and task complexity. To address these gaps, we introduce MCIF (Multimodal Crosslingual Instruction Following), the first crosslingual human-annotated benchmark based on scientific talks on NLP and beyond. MCIF evaluates instruction following in crosslingual, multimodal settings over different input lengths and spans four macro-tasks: recognition, translation, question answering, and summarization. It covers three core modalities (speech, vision, and text) and four diverse languages (English, German, Italian, and Chinese), fully aligned across all dimensions. This parallel design enables a systematic evaluation of MLLMs' abilities to interpret instructions across languages and effectively integrate multimodal contextual information. Our benchmarking and analysis of 23 models highlight universal challenges across modalities and tasks, indicating substantial room for improvement in future MLLMs development. MCIF is released under CC-BY 4.0 license to promote open research.
Introducing FlexOlmo: a new paradigm for language model training and data collaboration | Ai2
Explore how FlexOlmo enables collaborative language model training without sacrificing data privacy or control, introducing a new, flexible approach to building shared AI models.
Introducing Gemma 4 12B: a unified, encoder-free multimodal model
An overview of Gemma 4 12B, a model designed to bring high-performance multimodal intelligence directly to your laptop.
