Google Gemma Reviews and Details

Social recommendations and mentions

• Your AI, Your Device, Your Data - Introducing Aide

  Google AI, for running this challenge and for the work on Gemma, especially for prioritizing multimodal and translation capabilities in the Gemma family of models, which is exactly what made an offline assistant like this realistic. - Source: dev.to / 2 months ago

• Gemma 4 is the small-model tier agent stacks were waiting for

  Google's positioning of Gemma 4 makes this explicit. The 26B and 31B variants are pitched for "advanced reasoning"; the E2B and E4B variants are pitched for "maximum compute and memory efficiency" and "mobile and IoT devices" (source). That second tier is the one most agent-system writeups skip past on the way to discussing the flagship. It is also the one that changes the architecture. - Source: dev.to / 2 months ago

• The Master Algorithm

  The goalpost-movers like to pick at each of those and say "yes, but, it doesn't do this thing very well and that thing doesn't really count because of the following hand-wavy reasons." But if you read the book, it's obvious that everything we all take for granted nowadays was far-off science fiction in 2015. AI researchers literally dreamed of machines that could do half of what ChatGPT or Claude does before... - Source: dev.to / 4 months ago

• Small models, big ideas: what Google Gemma and MoE mean for developers

  We at zyte-devrel try to stay plugged into what is happening in the AI and developer tooling space, not just because it is interesting, but because a lot of it starts having real implications for how we build and think about web data pipelines. Lately, one development that has had us genuinely curious is Google's new Gemma 4 model family, and specifically the direction it points toward with Mixture of Experts... - Source: dev.to / 4 months ago

• Unusual Capabilities of Nano Banana (Examples)

  Oh very nice I wasn't aware of that [1] [2]. Adding the links as well. [1] https://deepmind.google/models/gemma [2] https://huggingface.co/google/gemma-7b [2]. - Source: Hacker News / 11 months ago

• Using Ollama and Tailscale to power an Android app with Gemini 3 27B

  Running Gemma 3 27B with Ollama on a powerful laptop or a gaming PC gives great performances. It’s very versatile, has good “world knowledge” and is good at text transformation (text summarization, rewrite, etc…). And Ollama also supports system prompts, so you can use it to give the model a persona and turn into a fun chatbot. - Source: dev.to / 11 months ago

• Google DeepMind Unveils QuestBench to Enhance LLM Evaluation

  The evaluations included several state-of-the-art LLMs such as GPT-4o, Claude 3.5 Sonnet, and open-sourced Gemma models. The study found that LLMs performed well on GSM-Q and GSME-Q domains with over 80% accuracy, while struggling with Logic-Q and Planning-Q, where they barely exceeded 50% accuracy. - Source: dev.to / over 1 year ago

• Gemma 3 QAT Models: Bringing AI to Consumer GPUs

  It's an older image that they just reused for the blog post. It's on https://ai.google.dev/gemma for example. - Source: Hacker News / over 1 year ago

• Building with Gemma 3: A Developer's Guide to Google's AI Innovation

  Think of Gemma 3 as Google’s answer to GPT-style models, but it’s open and optimized to run anywhere — your laptop, a single server, or even a high-end phone! It’s the latest in Google’s Gemma family of AI models. Google took the same core tech that powers their gigantic Gemini models and distilled it into Gemma 3. The result: a set of models you can actually download and run yourself. Gemma 3 is all about... - Source: dev.to / over 1 year ago

• Google DeepMind Unveils TxGemma: New Open AI for Drug Discovery

  Google DeepMind has launched TxGemma, an open collection of AI models aimed at improving drug discovery and clinical trial predictions. Built on the Gemma model family, TxGemma is designed to streamline the drug development process and accelerate the identification of new treatments. - Source: dev.to / over 1 year ago

• Leverage open models like Gemma 2 on GKE with LangChain

  In my previous posts, we explored how LangChain simplifies AI application development and how to deploy Gemini-powered LangChain applications on GKE. Now, let's take a look at a slightly different approach: running your own instance of Gemma, Google's open large language model, directly within your GKE cluster and integrating it with LangChain. - Source: dev.to / over 1 year ago

• The 6 Best LLM Tools To Run Models Locally

  LM Studio can run any model file with the format gguf. It supports gguf files from model providers such as Llama 3.1, Phi 3, Mistral, and Gemma. To use LM Studio, visit the link above and download the app for your machine. Once you launch LM Studio, the homepage presents top LLMs to download and test. There is also a search bar to filter and download specific models from different AI providers. - Source: dev.to / almost 2 years ago

• Model Providers: Open Source vs. Closed-Source

  Google's latest open-source initiative, Gemma, includes models like Gemma 2B and Gemma 7B. These models are built on the same technology as the Gemini series and are designed to be versatile and accessible, supporting various frameworks and hardware platforms​. - Source: dev.to / about 2 years ago