Gemma 4 Fine-Tuning in Practice: Preventing Data Starvation, Overfitting, and Quality Problems
A practitioner's guide to Gemma 4 fine-tuning—covering data quality validation, LoRA vs QLoRA selection, overfitting prevention with early stopping, checkpoint selection, and pre-deployment quality evaluation with complete code examples.
Tuning Gemma 4 for Yourself — A Realistic LoRA / QLoRA Workflow on a Solo Developer's Budget
Full fine-tuning of Gemma 4 is out of reach for most individuals, but LoRA / QLoRA makes personalization realistic on a solo budget. This guide walks through data prep, training settings, evaluation, and wiring the result into an Antigravity workflow — from hard-earned practical experience.
Fine-Tuning Gemma 4 in Practice: From Google Colab to Vertex AI
A practical guide to fine-tuning Gemma 4 with QLoRA on Google Colab (free GPU) and Vertex AI. Covers dataset preparation, 4-bit quantization, LoRA adapter configuration, training, and inference — with working code throughout.
Gemma 4 Production Fine-tuning Complete Guide — Dataset Design, QLoRA Optimization, Evaluation Pipeline & Cloudflare Workers Deployment
A complete guide to fine-tuning Gemma 4 with QLoRA for production use. Covers dataset design, evaluation pipelines, and deploying to Cloudflare Workers AI — all within Antigravity, with fully working code examples.
Fine-Tuning Gemma 4 with Antigravity: A Practical Guide to Building Custom AI Models
Learn how to fine-tune Gemma 4 using LoRA/QLoRA and integrate your custom model into Antigravity. From dataset preparation to local deployment, this step-by-step guide covers everything with code examples.