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+# Model Export
+
+Export PyTorch models to multiple formats for inference and finetuning.
+
+### Export Formats
+
+Each exported model directory contains:
+
+| File | Format | Description |
+|---|---|---|
+| `model.pt` | PyTorch pickle | Full model object (`torch.save`) |
+| `model.pt2` | torch.export | `ExportedProgram` — supports `unflatten` for finetuning |
+| `model.ts` | TorchScript | Traced model (`torch.jit.trace`) |
+| `model.onnx` | ONNX | For cross-framework inference |
+| `metadata.json` | JSON | Model metadata (shapes, voxel sizes, channels, etc.) |
+| `README.md` | Markdown | Auto-generated model card |
+
+### Export a DaCapo model
+
+```python
+import cellmap_models.model_export.config as c
+c.EXPORT_FOLDER = "/path/to/export/folder"
+
+from cellmap_models.model_export.dacapo_model import export_dacapo_model
+
+export_dacapo_model("my_run_name", iteration=100000)
+```
+
+### Export any PyTorch model
+
+```python
+import torch
+import cellmap_models.model_export.config as c
+from cellmap_models.model_export import ModelMetadata, get_export_folder, export_torch_model
+import os
+
+c.EXPORT_FOLDER = "/path/to/export/folder"
+
+model = ...  # any torch.nn.Module
+model.eval()
+
+metadata = ModelMetadata(
+    model_name="my_model",
+    model_type="UNet",
+    framework="torch",
+    spatial_dims=3,
+    in_channels=1,
+    out_channels=2,
+    channels_names=["mito", "er"],
+    input_shape=[1, 1, 96, 96, 96],
+    output_shape=[1, 2, 96, 96, 96],
+    inference_input_shape=[96, 96, 96],
+    inference_output_shape=[96, 96, 96],
+    input_voxel_size=[8, 8, 8],
+    output_voxel_size=[8, 8, 8],
+    author="Your Name",
+    description="My segmentation model",
+)
+
+input_shape = (1, 1, 96, 96, 96)
+export_torch_model(model, input_shape, os.path.join(get_export_folder(), "my_model"), metadata=metadata)
+```
+
+### Load an exported model for inference
+
+```python
+from cellmap_models.model_export.cellmap_model import CellmapModel
+
+model = CellmapModel("/path/to/export/folder/my_model")
+
+print(model.metadata.channels_names)
+
+onnx_session = model.onnx_model        # ONNX Runtime session
+ts_model = model.ts_model              # TorchScript model
+pt_model = model.pytorch_model         # PyTorch pickle model
+exported = model.exported_model         # torch.export ExportedProgram
+```
+
+### Load an exported model for finetuning
+
+```python
+from cellmap_models.model_export.cellmap_model import CellmapModel
+
+cellmap_model = CellmapModel("/path/to/export/folder/my_model")
+model = cellmap_model.train()
+# Returns an nn.Module in train mode
+# Tries torch.export (model.pt2) + unflatten first, falls back to TorchScript
+```
+
+### Push an exported model to Hugging Face Hub
+
+```python
+from cellmap_models.model_export import push_to_huggingface
+
+push_to_huggingface(
+    folder_path="/path/to/export/folder/my_model",
+    repo_id="janelia-cellmap/my-model",
+)
+```
+
+Requires `pip install cellmap-models[huggingface]` or `pip install huggingface-hub`.
+You must be logged in via `huggingface-cli login` first.