fix(pre-commit): 🎨 auto format pre-commit hooks

Author: pre-commit-ci[bot]

CHANGELOG.md

@@ -9,6 +9,7 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
 ### Added
 
 - Added live free/total GPU memory (`free_mem`, `torch.cuda.mem_get_info()` in MB) next to `max_mem` in the training progress bar. Unlike `max_mem`, `free_mem` is not process-local and not a peak — it reflects the whole device, including other workloads sharing the GPU, at the instant it is read. It typically does not rise when this process frees a tensor while the caching allocator retains that block; explicit cache release or allocator reclamation can return it to the driver. It is closer to "room left for a new allocation beyond what every process already claimed" than to the full headroom this run has for a bigger `batch_size`. Same `trainer.fit()`-only scope as `max_mem`. ([#1314](https://github.com/roboflow/rf-detr/issues/1314))
+
 - Restored peak GPU memory (`max_mem` in MB) in the training progress bar, dropped during the PyTorch Lightning migration (PR #794) along with `rfdetr.engine`. Only covers `trainer.fit()` (training and its periodic in-training validation) — PTL's own progress-bar classes never call `get_metrics()` outside `trainer.state.fn == "fit"`, so a standalone `RFDETR.evaluate()` progress bar shows no metrics at all, not just `max_mem`, same as before this change. ([#974](https://github.com/roboflow/rf-detr/issues/974))
 
 - `WeightedMultiSourceBatchSampler` (`rfdetr.datasets.multi_source`) fixes the per-source composition of every training batch when training on a `ConcatDataset` of several datasets, so a small hand-labelled set is not drowned out by a large public one. Sources are recycled with reshuffling when they run out mid-epoch, epoch length can be driven by the largest or smallest source, and batches are sharded across DDP ranks. Opt-in: no existing training path changes. ([#1287](https://github.com/roboflow/rf-detr/pull/1287))