Running Multiple Gemma 4 LoRAs in Production — A Practical Guide to Merging and Dynamic Adapter Switching
You've trained several LoRAs on Gemma 4 — summarization, translation, code review. How do you serve them without tripling your GPU bill? A working notebook on pre-merge rank checks, Weighted and TIES merges, dynamic switching, and measuring switch churn, written with Antigravity alongside.
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 with Apple Silicon and MLX
An M3 Max fine-tunes Gemma 4 27B with LoRA in about 40 minutes. A Colab-free MLX setup: memory sizing, rank vs. scale, overnight runs, base-vs-tuned evaluation, and Ollama.
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.
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.