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Gemma
4 posts about Gemma. Every summary links to the original.
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EmbeddingGemma 2: The Developer Guide
Modern search and RAG applications need to work across diverse content types, but finding models with strong retrieval accuracy and low latency without massive compute is challenging. EmbeddingGemma 2 is a compact open model under Apache 2.0, based on Gemma 4, that maps text, code, images, video, and audio into a unified 768-dimensional space with a modular architecture scaling from 270M to 740M parameters. Key capabilities include native multimodal retrieval, superior code understanding, modular memory footprint, and flexible vector storage via Matryoshka Representation Learning enabling dynamic truncation from 768 to 128 dimensions. Under the hood, it uses modular encoders—a text/code decoder, vision encoder, and audio encoder—that project into a shared space and can be loaded selectively to minimize memory. The developer guide demonstrates how to use sentence-transformers to load the model, embed with task prompts, handle media inputs, truncate dimensions, and choose configurations based on storage and recall needs.
Introducing Support for Local AI Models in the Antigravity SDK
The Antigravity SDK now supports local AI models, starting with Gemma 4 26B A4B via Google AI Edge’s LiteRT, enabling offline agentic assistance on local GPU and RAM with a recommended machine of 24GB VRAM or unified memory. Local execution eliminates API costs and rate limits while keeping code and requests on-device, offering privacy and offline resiliency. The SDK supports hybrid workflows where a cloud model like Gemini 3.8 Flash plans while local Gemma 4 instances execute, as in a code auditing task where 97.2% of tokens (3,322 of 3,417) ran locally. A built-in example generates a live-updating CLI resource monitor using psutil and rich from a single prompt, with the agent autonomously writing the script, creating requirements.txt, and testing the code entirely offline. The SDK also supports any OpenAI-compatible server like Ollama, LM Studio, or vLLM via LocalOpenAIAgentConfig without changing agent orchestration.
Autonomous LLM post-training with Tunix on TPUs
autofinetune applies autonomous research loops to LLM post-training, covering supervised fine-tuning (SFT) and reinforcement learning with GRPO through Tunix, Gemma, Cloud TPUs, Antigravity CLI, and Gemini Flash 3.7. A human defines the arena in program.md and supplies a self-contained run.py, while an agent edits hyperparameters, launches jobs, evaluates a target metric, keeps winning Git commits, reverts regressions, and logs results in results.tsv. On FunctionGemma 270M with google/mobile-actions, 20 Cloud TPU v5e-1 experiments tested LoRA settings, optimization choices, schedules, batch size, and seeds without changing the dataset, epochs, or architecture. A second study used Gemma 3 1B, GSM8K, and Cloud TPU v6e-1 for 40 GRPO experiments over two to three days, improving the combined numerical-and-format reward by about 10%.
Mastering Edge AI on Raspberry Pi with LiteRT and Gemma
Running edge AI on a Raspberry Pi 5 is presented as a way to build autonomous, low-latency systems that operate offline, with Reachy Mini serving as the demonstration. The setup combines LiteRT, LiteRT-LM, Gemma models, and LiteRT’s WebGPU (Vulkan) backend via ML Drift, splitting workloads between the CPU and VideoCore VII GPU. On CPU, Gemma 4 E2B reportedly reaches 99 tokens/sec prefill, 9 tokens/sec decode, and 1,432 MB peak memory; its tokenizer supports about 27.3 characters/sec in the voice demo. The pipeline runs YOLO object detection on the GPU, Moonshine speech recognition, Gemma reasoning, and text-to-speech on the CPU, while LiteRT CLI covers conversion, quantization, benchmarking, inference, and model execution from the LiteRT Hugging Face Community.
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