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This guide shows how to use Apple Silicon and the Gemma 3 270M model to fine-tune a small language model offline in under 10 minutes. It walks through installing the uv/MLX toolchain, preparing a simple code-review dataset, and running the training on any M-chip Mac without a cloud GPU.
- Gemma 3 270M runs on ~830MB RAM and hits 150+ tokens/sec on an M3, fine-tuning in under 10 minutes via MLX's uv toolchain (as fast as 3 minutes on an M1 Air)
- A dataset of only ~60 Python code snippets paired with expert review notes was enough to teach the model to flag security issues (SQL injection, unclosed files, insecure shell commands) and style problems
- The entire workflow runs offline on Apple Silicon with no cloud GPU or rental fees required