Final Nuke of legacy files

alexnet_places365.pth_mlx.npz

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-oid sha256:587f2f379063fb722563b86d9e7fea2321119b571c6bff7e09e309abf6dbf0b4
-size 117002764

resnet50_places365.pth_mlx.npz

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-version https://git-lfs.github.com/spec/v1
-oid sha256:c7e4496e460a4cbec41e02f169c7be9c0e3cebe28036ac917105ba386471c47b
-size 48691562

resnet50_places365_t7_mlx.npz

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-version https://git-lfs.github.com/spec/v1
-oid sha256:cbfc6e4d63fb8824df3a8c60d82581106679b2061a654fd9d9ab62d798b94f99
-size 48536532

train_dream.py

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-# TODO: Implement Fine-Tuning Logic
-
-"""
-DeepDream Training / Fine-Tuning Script (Placeholder)
-
-Goal:
-Allow users to fine-tune these base models (VGG, GoogLeNet, etc.) on their own datasets
-to create custom Dream styles.
-
-Steps to Implement:
-1.  Load Dataset: Use `torchvision.datasets.ImageFolder` or custom loader for user images.
-2.  Load Model: Use our MLX models (need to add `train()` mode with dropout/grad support if missing,
-    or simpler: use PyTorch for training -> export to MLX).
-    *Easier path:* Train in PyTorch using standard scripts, then use `export_*.py` to bring it here.
-3.  Training Loop: Standard classification training or style transfer fine-tuning.
-4.  Export: Save the fine-tuned weights to `.pth`, then run export script.
-
-Usage:
-    python train_dream.py --data /path/to/images --epochs 10 --model vgg16
-"""
-
-import argparse
-
-def main():
-    print("--- DeepDream-MLX Training Stub ---")
-    print("Feature coming soon.")
-    print("Current Workflow: Train in PyTorch -> Use export_*.py -> Dream in MLX")
-
-if __name__ == "__main__":
-    main()