Instructions to use WWZzz/pi0_lora_robotwin_beat_block_hammer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use WWZzz/pi0_lora_robotwin_beat_block_hammer with PEFT:
Task type is invalid.
- Transformers
How to use WWZzz/pi0_lora_robotwin_beat_block_hammer with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("WWZzz/pi0_lora_robotwin_beat_block_hammer", dtype="auto") - Notebooks
- Google Colab
- Kaggle
| === Training Example Data Info === | |
| This example is saved from the raw training dataset (before data_processor). | |
| Data is saved in unnormalized form to match testing phase format. | |
| Sample keys: | |
| image: shape=torch.Size([3, 3, 480, 640]), dtype=torch.uint8 | |
| state: shape=torch.Size([14]), dtype=torch.float32 | |
| action: shape=torch.Size([16, 14]), dtype=torch.float32 | |
| is_pad: shape=torch.Size([16]), dtype=torch.bool | |
| raw_lang: str | |
| reasoning: str | |
| timestamp: int64 | |
| episode_id: int64 | |
| __index__: int | |
| dataset_id: str | |
| Files saved: | |
| - camera_{i}.png: raw images from dataset (unnormalized, original resolution) | |
| - state_raw.csv: raw state values (unnormalized) | |
| - action_raw.csv: raw action values (unnormalized) | |
| - raw_lang.txt: language instruction (if available) | |
| - reasoning.json: reasoning data (if available) | |
| - info.txt: this file | |
| Note: These raw values can be directly compared with testing phase examples. | |