gemma

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.

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.

This paper reports an activation study of Gemma 3 4B IT showing that the model's internal representations distinguish necessary falsehoods (impossibilities) from contingent falsehoods, with impossibility directions orthogonal to truth directions and overlapping with semantic anomaly directions.

A developer created a task-aware GGUF quantization pipeline that uses tensor-level bit allocation to improve Gemma 4 12B Q3 coding performance by 8.55% over a hand-tuned imatrix while increasing model size by only 0.119%.

Google Developers showcase a fully offline voice translator built with Gemma 4, Google Antigravity, and a Raspberry Pi 5. The open-source GitHub repo provides code for on-device inference, a custom web UI, and deploy scripts for a portable AI appliance.

The author shares experiments using a custom WebUI to let Gemma and Qwen models inspect their own logprobs to detect hallucinations. Initial observations suggest that first-recall token probabilities can indicate uncertainty, though both models struggle to read their own logprobs.

A local benchmark comparing Muse Glimmer 30B, Qwen 3.6 27B, and Gemma4 31B, noting request counts and final scores, with links to detailed results.

A user shares observations about Muse-Glimmer's reasoning traces, noting they appear disorganized and repetitive compared to Qwen and Gemma models, and asks the community about their experiences.

An annotated from-scratch reimplementation of Google's DiffusionGemma, a 26B open-weight state diffusion language model, explaining its architecture, sampling procedure, and design choices.

DiffusionGemma technical report released on arXiv, with ongoing work on llama.cpp pull requests to enable faster local inference on limited VRAM.

The Gemma team is hosting an in-person event on August 20 to celebrate the upcoming 1 billion downloads of Gemma models, featuring live demos and members of the open models community.

A user shares an observation that Qwen and Gemma tokenize code very differently, with Qwen using far fewer tokens for the same HTML/JS input, which may explain differences in coding and language performance. They also note a potential retraining project by LiquidAI using a more efficient tokenizer.