0ac051177aStudio'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 |
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| .. | ||
| assets | ||
| auth | ||
| core | ||
| hub | ||
| loggers | ||
| models | ||
| plugins | ||
| requirements | ||
| routes | ||
| state | ||
| storage | ||
| tests | ||
| utils | ||
| __init__.py | ||
| _platform_compat.py | ||
| cloudflare_tunnel.py | ||
| colab.py | ||
| main.py | ||
| run.py | ||
| startup_banner.py | ||