How Google Uses Fine-tuning

DiffusionGemma is an experimental text-generation model built on the Gemma 4 architecture that uses diffusion-based parallel generation instead of token-by-token autoregression, enabling much faster inference, bidirectional context awareness, and real-time self-correction while remaining deployable on consumer GPUs. Its architecture generates and refines 256-token blocks in parallel through iterative denoising, allowing it to handle complex constraint-based tasks such as Sudoku more effectively than traditional language models and demonstrating strong gains from fine-tuning. The model integrates with vLLM and other popular inference frameworks, giving developers access to a new non-autoregressive approach that combines high performance, efficient long-context scaling, and straightforward customization and deployment.

Google has announced the Google Colab Command-Line Interface (CLI), a new tool that allows developers and AI agents to connect local terminals to remote Colab runtimes for frictionless execution. The lightweight CLI enables users to easily request high-powered GPUs, run local Python scripts remotely, and seamlessly retrieve artifact logs or models like fine-tuned Gemma 3 adapters. By integrating directly into standard terminal environments, the tool is highly programmable and ready to be used by AI agents such as Antigravity or Claude Code to manage complex machine learning pipelines.

The Google Tunix Hackathon on Kaggle challenged developers to transform small, non-reasoning base models into general reasoning engines using Kaggle TPUs and a limited compute budget. The winning teams achieved this by implementing multi-stage post-training pipelines that combined Supervised Fine-Tuning (SFT) with advanced alignment techniques like GRPO and SimPO. Ultimately, the competition democratized AI development by proving that highly capable, structured reasoning models can be successfully trained by the community using accessible, open-source resources.

This article presents a blueprint for building trustable AI systems, demonstrated through a real-world field test at Thunderhill Raceway where Google Developer Experts built a real-time AI racing c...

This article demonstrates how to fine-tune FunctionGemma, a specialized 270M parameter Gemma 3 model designed for function calling in agentic AI systems.

The article discusses how to fine-tune the Gemma 3 270M model for on-device applications, enabling developers to create custom AI models without the need for expensive hardware.

The article provides an in-depth exploration of the EmbeddingGemma architecture, detailing its origins, embedding generation process, and the comprehensive training methodology.

The article discusses the contributions of the community to the Unlock Global Communication with Gemma competition on Kaggle, focusing on adapting large language models (LLMs) for diverse linguisti...

TxGemma is a collection of open models designed to enhance the efficiency of therapeutic development by utilizing large language models.

The article discusses the PaliGemma architecture, a lightweight open vision-language model (VLM) inspired by PaLI-3.

The article discusses the release of Gemma 2, a new suite of open models that sets a new standard for performance and accessibility in conversational AI.

The article presents three engaging project ideas utilizing Gemma, a family of open models for AI tasks including text generation and code completion.

The article discusses the release of the Gemma 2 model with 27 billion parameters, highlighting its capabilities in Keras and integration with JAX for efficient model training.

The article discusses how to tune Gemini models using Google AI Studio or the Gemini API, emphasizing the benefits of Parameter Efficient Tuning (PET) over traditional fine-tuning methods.

The article introduces Gemma models in Keras, a family of lightweight, state-of-the-art open models that leverage the same technology as the Gemini models.

The article discusses the tuning capabilities of MakerSuite, a tool designed to help developers customize large language models (LLMs) quickly and efficiently.