AI & ML••KR

Fine-tuning Gemma 4 MoE — Customizing Arena #6 with 3.8B Active Parameters

Apply QLoRA to Gemma 4 26B MoE. Expert layer LoRA strategies, Dense vs MoE comparison, MoE-specific training tips, and Ollama deployment. LoRA Series Part 4.

Fine-tuning Gemma 4 MoE — Customizing Arena #6 with Just 3.8B Active Parameters

Series: Part 1: LoRA Theory | Part 2: QLoRA + Custom Data | Part 3: Eval + Deploy | Part 4 (this post)

Parts 1-3 covered LoRA fundamentals through deployment using Qwen 2.5 7B. Part 4 levels up — we apply LoRA to a Gemma 4 MoE model.

Why Gemma 4? Three reasons:

  1. MoE architecture: 26B total params, only 3.8B active. Inference cost is 4B-class, but performance is Arena #6
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