How Google Uses Fine-tuning
13 engineering articles about Fine-tuning from Google's engineering team
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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...
Matt Thompson, Ajeet Mirwani
5 min read
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This article demonstrates how to fine-tune FunctionGemma, a specialized 270M parameter Gemma 3 model designed for function calling in agentic AI systems.
Juyeong Ji
5 min read
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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.
Ian Ballantyne, Jason Mayes
5 min read
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The article provides an in-depth exploration of the EmbeddingGemma architecture, detailing its origins, embedding generation process, and the comprehensive training methodology.
Henrique Schechter Vera, Juyeong Ji, Sahil Dua
7 min read
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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...
Glenn Cameron
6 min read
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TxGemma is a collection of open models designed to enhance the efficiency of therapeutic development by utilizing large language models.
Shekoofeh Azizi
4 min read
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The article discusses the PaliGemma architecture, a lightweight open vision-language model (VLM) inspired by PaLI-3.
Ju-yeong Ji, Ravin Kumar
6 min read
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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.
Ju-yeong Ji, Ravin Kumar
5 min read
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The article presents three engaging project ideas utilizing Gemma, a family of open models for AI tasks including text generation and code completion.
Ju-yeong Ji
7 min read
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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.
Martin Görner
5 min read
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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.
Cher Hu, Saravanan Ganesh
4 min read
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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.
Martin Görner
5 min read
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The article discusses the tuning capabilities of MakerSuite, a tool designed to help developers customize large language models (LLMs) quickly and efficiently.
Pranay Bhatia
3 min read
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