gemma

Google's Embedding Gemma 2 embedding model can now run 100% free and locally on Mac, enabling private and cost-free text embedding workflows.

A Reddit discussion moving away from benchmarks and coding to compare open-weight local models (Gemma 31B/4 finetunes, Qwen 3.8B 27B, Muse Glimmer 30B) for long-form creative writing, including user experiences with custom finetunes and a self-correcting writing harness.

Emma Scharfmann highlights how EmbeddingGemma has enabled numerous AI for Science projects (medical, geo, oncology, PubMed embeddings) and notes that EmbeddingGemma 2 was released on Hugging Face, opening new possibilities for scientific research.

Google DeepMind launches EmbeddingGemma 2, a 740M-parameter open (Apache 2.0) multimodal embedding model that unifies text, code, images, video, and audio into a shared embedding space optimized for on-device inference, built on the Gemma 4 architecture with 8K context and Matryoshka truncation for storage efficiency.

Google DeepMind unveiled a 740M-parameter multimodal model that is competitive across benchmarks, outperforming some specialist models more than twice its size, enabling developers to add multimodal search features like finding video moments via voice memo and pair it with Gemma 4 for private on-device use.

Google DeepMind 发布 EmbeddingGemma 2，这是其首个原生多模态开源嵌入模型，支持在统一向量空间中处理代码、图像、音频和视频，面向端侧设备部署。

autotrust/GEV-26B-Decide-NVFP4 is an NVFP4 4-bit quantized release of GEV-26B-Decide that reduces weights from 49.5 GB to 18 GB (−66% GPU memory) by quantizing only the 3,840 routed expert MLPs, with benchmark results within run-to-run noise of the bf16 model and support for native W4A4 inference on Blackwell GPUs via vLLM.

作者自建了魔兽世界私服和浏览器客户端（jankcraft.xyz），并开发了自定义 MCP 与 agent harness，让 LLM 通过 websocket 控制浏览器客户端在游戏内行动，同时推荐了 Qwen 和 Gemma 等本地模型配置以运行该 agent。

Satlyt, founded by a former Google and SpaceX product manager, raised an $8 million seed round to build software that lets AI models run on satellites, positioning itself as a hardware-agnostic 'cloud layer' for orbital computing similar to VMware or Snowflake.

Jev-Omni is the first open-weight model to extend typed decisions to multimodal, supporting text, images, audio, and video, and directly returning the probability distribution of options without generating explanations.

Jev-Omni is a multimodal decision classifier built on Gemma 4 12B IT, fine-tuned to handle text, images, audio, and video, providing probability scores for options based on questions.

A factory worker built an autonomous AI entity using Gemma 4 31B on Ollama, with self-identity, memory, and a blog, demonstrating personalized AI interaction through an open-source framework.

The author expresses hope that the upcoming gemma 5 model family will maintain a chat-focused philosophy and avoid becoming overly code-oriented like Qwen models, valuing creativity and less robotic behavior as seen in gemma 4.

A developer created a minimal 700-line C implementation for running the Gemma 4 E2B LLM on CPUs, outperforming llama.cpp in speed.

Darkbloom, a network of Mac machines for serving AI tokens, has moved to a paid tier on OpenRouter, reaching 4.5B tokens served and $102K ARR, with users earning $120-200 per month per machine.

Speculation surrounds Google's upcoming Gemma SF Celebration event, where the open-source model family has reached 1 billion downloads, leading to questions about a potential new model announcement.

Announcing the launch of Awesome Gemma, a GitHub repository that curates resources, tools, and projects for Google DeepMind's Gemma models, including model cards, setup guides, and fine-tuning recipes.

A demonstration of the Gemma 4 E4B AI model running locally on an iPad using Apple MLX, showcased as an engaging application for children.

Google is suggested to release a 120B dense multimodal Gemma model to compete with OpenAI and Anthropic, targeting Western enterprises wary of Chinese models.

This paper proposes HybridRAG-BN, a retrieval-augmented framework for Bangla knowledge-base question answering that combines hybrid retrieval, Gemma-based generation, and LoRA fine-tuned verification, achieving first place with F1 scores of 0.71654 and 0.72912.