Fix OutOfMemoryError

✅ Solution

OutOfMemoryError in PyTorch usually stems from allocating more GPU memory than available. Fix this by reducing batch size, model size, or sequence length, and explicitly release unused tensors with `del` and `torch.cuda.empty_cache()` to free up memory. Consider using gradient accumulation or mixed-precision training (e.g., with `torch.cuda.amp`) to further lower memory footprint.