---
library_name: transformers
pipeline_tag: robotics
base_model:
- openbmb/MiniCPM-RobotManip
---

This is a randomly initialized tiny model based on [openbmb/MiniCPM-RobotManip](https://huggingface.co/openbmb/MiniCPM-RobotManip) for testing and debugging.

| Feature | Source | This repo |
|---|---|---|
| Checkpoint tensor elements | 1.30B BF16, 235.81M F32 | 8.29M BF16, 459.10K F32 |
| Safetensors weight files | 3.54 GB (model.safetensors) | 18.45 MB (model.safetensors) |
| VLM text layers / width / head dimension | 24 / 1024 / 256 | 8 / 32 / 32 |
| VLM attention cadence | 3 linear + 1 full, repeated | Two 3-linear + 1-full cycles |
| Vision layers / width | 27 / 1152 | 2 / 32 |
| Action diffusion blocks / width | 16 / 768 | 2 / 32 |
| Action horizon / action-state dimensions | 30 / 80-80 | Same |
| Embodiment categories | 32 | Same |
| Text vocabulary rows | 248094 | Same |
| Stored VLM / action-head precision | BF16 / FP32 | Same |
| Input / output | Image, instruction, state → action chunk | Same |

| File path | Size |
|------|------|
| model.safetensors | 18.4MB |

### Example usage

#### Transformers (✅ verified)

```python
def run_transformers(model_id: str):
    import torch
    from PIL import Image
    from transformers import AutoModel, AutoProcessor

    model = AutoModel.from_pretrained(model_id, trust_remote_code=True, dtype=torch.bfloat16).eval().cuda()
    processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
    image = Image.new("RGB", (112, 112), "blue")
    inputs = processor.apply_chat_template(
        [
            {
                "role": "user",
                "content": [{"type": "image", "image": image}, {"type": "text", "text": "Pick up the blue object."}],
            }
        ],
        tokenize=True,
        add_generation_prompt=True,
        return_dict=True,
        return_tensors="pt",
        processor_kwargs={"padding": False},
    ).to(model.device)
    action = model.predict_action(
        state=torch.zeros(1, 1, 80, device=model.device),
        embodiment_id=torch.zeros(1, dtype=torch.long, device=model.device),
        **inputs,
    )
    assert action.shape == (1, 30, 80)
    assert torch.isfinite(action).all()
    print(action.shape)


if __name__ == "__main__":
    run_transformers(model_id="tiny-random/minicpm-robot-manip")
```

### Creating this repo

- Code: [.meta/create_model.py](.meta/create_model.py)
- Model structure: [.meta/model.log](.meta/model.log)
- Environment: [.meta/env.json](.meta/env.json)

### Change log

- 2026-10-27: Created a tiny MiniCPM RobotManip vision-language-action policy.

*This repo is generated by GPT-6.1-Sol.*