gemma-tuner-multimodal

Gemma Tuner lets you fine-tune Google's Gemma 4 and 3n models on your Mac, no cloud GPU required. Text, images, and audio, all via Apple Silicon's MPS backend. You bring a CSV of training data, point the wizard at a HuggingFace checkpoint, and watch the training run on your local GPU with a real-time visualizer showing loss curves, attention heatmaps, and memory pressure.

The 2B and 4B parameter models are the sweet spot for consumer hardware. 16GB RAM minimum, 32GB recommended. It streams training data from Google Cloud Storage or BigQuery for datasets larger than your SSD. Exports land in HuggingFace SafeTensors format with guides for Core ML and GGUF conversion if you want to deploy on-device.

Solo ML practitioners get local fine-tuning without paying $2-5/hr for cloud GPUs. Small teams prototyping custom Gemma models can iterate locally before scaling to cloud training. The wizard CLI makes the setup approachable even if you're not a PyTorch expert.

The catch: Gemma only. No Llama, no Mistral, no other model families. Larger Gemma weights (26B+) are not supported. Audio fine-tuning on non-Mac platforms requires CUDA. And you still need a HuggingFace account with Gemma's license accepted before you can download the weights.