| --- |
| 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.* |
