# Gemma Trainer

Trigger this skill when the user wants to train, fine-tune, or adapt Gemma models (e.g.

- Repository: https://github.com/google-gemma/gemma-skills (1038 stars)
- Category: Data and ML
- License: Apache-2.0
- Last updated: 2026-10-28
- Runs scripts: yes, review before use

## Install

Claude Code: `npx skills add google-gemma/gemma-skills --skill gemma-trainer -g -a claude-code -y`
Codex: `npx skills add google-gemma/gemma-skills --skill gemma-trainer -g -a codex -y`
Cursor: `npx skills add google-gemma/gemma-skills --skill gemma-trainer -g -a cursor -y`
Gemini CLI: `npx skills add google-gemma/gemma-skills --skill gemma-trainer -g -a gemini-cli -y`

## Description

Trigger this skill when the user wants to train, fine-tune, or adapt Gemma models (e.g. SFT, DPO, RLHF, Reward Modeling) on local hardware. Covers TRL, Unsloth, dataset preparation, validation, and GGUF/LiteRT conversion.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Gemma Training and Fine-Tuning Skill

## 1. Core Principles: Local Fine-Tuning Setup

When training locally, memory efficiency and execution speed are huge. Always
guide the user to follow these best practices:

*   **Prioritize Unsloth**: For local single-GPU training, always recommend
    **Unsloth**. It supports Gemma 4 natively, uses up to 70% less memory, and
    is up to 2x faster than standard Hugging Face PEFT training.
*   **Fall Back to TRL**: For multi-GPU environments (using DDP/FSDP) or when
    Unsloth is unavailable, use Hugging Face **TRL** (`SFTTrainer`,
    `DPOTrainer`) coupled with **PEFT** and **bitsandbytes** (for QLoRA).
*   **Always use QLoRA (4-bit Quantization)**: Crucial for fitting Gemma models
    (like Gemma 4 12B/31B) into consumer VRAM.
*   **Manage Context Window & Max Length**: Although Gemma 4 supports up to a
    256K context window, recommend training with a context window of 2048 to
    8192 tokens locally to prevent Out-Of-Memory (OOM) errors.

## 2. Choosing the Right Training Method

Help the user choose the correct workflow based on their goal:

-   **Supervised Fine-Tuning (SFT)**: Teaching new domains, specialized task
    instructions, or custom output structures.
    -   Prerequisites: Raw text, instruction pairs, or chat logs.
    -   Output: Adaptor trained on prompt/completion pairs.
-   **Direct Preference Optimization (DPO)**: Aligning model style, behavior,
    tone, or safety with human preferences.
    -   Prerequisites: A previously SFT-trained Gemma model and preferred
        pairwise datasets.
    -   Output: Aligning model weights directly without a separate reward head.
-   **Reward Modeling (RM)**: Training a scoring system to evaluate response
    quality.
    -   Prerequisites: Binary preference pairwise datasets.
    -   Output: A classification-style reward head on top of Gemma.

## 3. Dataset Preparation & Validation

The rest: https://github.com/google-gemma/gemma-skills/blob/HEAD/skills/gemma-trainer/SKILL.md