MLX Bridge: Prototype Fine-Tuning on Mac, Deploy on GPU
MLX Bridge enables developers to prototype and fine-tune machine learning models on Mac devices using Apple Silicon, then seamlessly deploy the optimized
Someone built a workaround for prototyping LLM fine-tuning on Mac before moving to cloud GPUs.
The setup:
# On Mac - uses MLX from unsloth_mlx import FastLanguageModel
# On cloud GPU - uses original Unsloth from unsloth import FastLanguageModelRest of the code stays identical. Just swap one import line.
Turns out cloud GPU costs rack up quick during experimentation, but newer Macs have 64-512GB unified memory just sitting there. This lets someone test locally first, then push the exact same training script to CUDA without rewriting anything.
Not affiliated with official Unsloth - just a personal project that solved a “context switch” problem for Mac users who fine-tune models.
Github: https://github.com/ARahim3/unsloth-mlx
Pretty handy for the prototype-on-laptop, scale-on-cloud workflow.
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