---
license: mit
base_model:
- google/paligemma-3b-pt-224
tags:
- openpi0
- jax
datasets:
- IPEC-COMMUNITY/bridge_orig_lerobot
---



download the model

```bash
huggingface-cli download --resume-download --local-dir-use-symlinks False ${model} --local-dir $(basename ${model})
```

launch the openpi0 server, please create the [openpi](https://github.com/Physical-Intelligence/openpi/) environment first

```bash
export OPENPI_DATA_HOME=/PATH/TO/OPENPI_DATA_HOME
export LEROBOT_HOME=/PATH/TO/LEROBOT_HOME

uv run scripts/serve_policy.py policy:checkpoint \
    --policy.config=pi0_fast_bridge_fft_pt_tokenizer \
    --policy.dir=$THE_MODEL_PATH
```

### DataConfig
```python
@dataclasses.dataclass(frozen=True)
class LeRobotBridgeDataConfig(DataConfigFactory):
    use_quantile_norm: bool = True

    # Action keys that will be used to read the action sequence from the dataset.
    action_sequence_keys: Sequence[str] = ("action",)

    prompt_from_task: bool = True

    @override
    def create(self, assets_dirs: pathlib.Path, model_config: _model.BaseModelConfig) -> DataConfig:
        # Make inputs look like they come from the Libero environment
        repack_transform = _transforms.Group(
            inputs=[
                _transforms.RepackTransform(
                    {
                        "observation/primary_image": "observation.images.image_0",
                        # "observation/left_yellow_image": "observation.images.image_1",
                        # "observation/right_blue_image": "observation.images.image_2",
                        # "observation/wirst_image": "observation.images.image_3",
                        "observation/state": "observation.state",
                        "actions": "action",
                        "prompt": "prompt",
                    }
                )
            ]
        )

        # Prepare data for policy training
        # Convert images to uint8 numpy arrays, add masks
        data_transforms = _transforms.Group(
            inputs=[
                bridge_policy.BridgeInputs(
                    action_dim=model_config.action_dim,
                    model_type=model_config.model_type,
                )
            ],
            outputs=[bridge_policy.BridgeOutputs()],
        )

        # Model transforms include things like tokenizing the prompt and action targets
        model_transforms = ModelTransformFactory()(model_config)

        return dataclasses.replace(
            self.create_base_config(assets_dirs),
            repack_transforms=repack_transform,
            data_transforms=data_transforms,
            model_transforms=model_transforms,
            use_quantile_norm=self.use_quantile_norm,
            action_sequence_keys=self.action_sequence_keys,
            prompt_from_task=self.prompt_from_task,
        )
```