or latest Unsloth per their guidance

🦥 Finetune Gemma 3 with Unsloth (simple 4-bit LoRA)

Minimal example to finetune Google's Gemma 3 Instruct models with Unsloth using 4-bit loading + LoRA. Small, readable, and runnable on a CUDA GPU.

• Models: 270M, 1B, 4B, 12B, 27B
• Dataset: FineTome-100k (ShareGPT-style multi-turn chats)
• Method: Parameter-efficient LoRA (not full FT)

Reference: Unsloth’s Gemma 3 notes: unsloth.ai/blog/gemma3

Install

pip install -r requirements.txt
# or latest Unsloth per their guidance
pip install --upgrade --force-reinstall --no-cache-dir unsloth unsloth_zoo

Run

python finetune_gemma3.py

Outputs are saved to finetuned_model/.

What the script does

1. Loads Gemma 3 with 4-bit quantization via Unsloth’s FastModel.
2. Attaches LoRA adapters to attention/MLP projections.
3. Prepares FineTome-100k by applying the Gemma 3 chat template.
4. Trains with TRL’s SFTTrainer for a few demo steps.
5. Saves the finetuned weights.

Change model or settings

Edit the top of finetune_gemma3.py:

• MODEL_NAME (e.g., unsloth/gemma-3-270m-it, unsloth/gemma-3-1b-it)
• MAX_SEQ_LEN, LOAD_IN_4BIT, FULL_FINETUNING

Note: 4-bit/8-bit loading requires a CUDA GPU. On Mac (M1/M2), run on CPU/MPS without quantization or use a GPU machine.