Author(s): Tarun Singh

Originally published on Towards AI.

A clean, from-scratch walkthrough (with code) to tune a 270M-param LLM on chess — no cloud required.

Google dropped Gemma 3 270M, a compact instruction-tuned model you can actually run locally. With 4-bit loading, inference needs only a few hundred MB of memory — yes, sub-0.5 GB territory — so tinkering on a modest machine is finally comfortable. We’ll fine-tune it on a “fill the missing chess move” task, evaluate, and export for local use. Google AI for Developers

The article discusses the release of Google’s 270M instruction-tuned model, Gemma 3, which allows for local low-memory usage and fine-tuning for specific tasks like chess move predictions. It guides readers through various aspects of utilizing this model, including loading it, crafting a specialized dataset, applying LoRA tuning for efficient performance, and conducting supervised fine-tuning with the TRL framework. The article emphasizes the model’s practicality for developers interested in building and deploying machine learning applications without reliance on cloud resources.

Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI