Studio's frontend exposes a Resume action and submits requests with
resume_from_checkpoint set to a previous run's output_dir. The CUDA
training paths in worker.py read this field from config and pass it to
trainer.train() (see lines 2729-2787 and 3108-3229). The MLX path
_run_mlx_training did neither: it never read config['resume_from_checkpoint']
and called trainer.train() with no args. The MLX trainer also did not
accept the kwarg, so even threading it through would have been a no-op.
With this PR + the unsloth-zoo companion PR adding the trainer-side
support (saves optimizer_state + trainer_state, accepts and applies
resume_from_checkpoint in MLXTrainer.train()), MLX Resume now works
end-to-end. Verified on M2 16GB with Qwen3-0.6B + unsloth/LaTeX_OCR:
loss at every post-resume step matches a fresh run bit for bit
(2.168627977371216 == 2.168627977371216 at step 6, etc).
Two lines: read the field near the other config.get() extractions in
_run_mlx_training, pass it as a kwarg at the trainer.train() call site.
Companion PR: unslothai/unsloth-zoo#751
0ac051177a