Robotics
Transformers
lap
vision-language-action
embodied-ai
manipulation
vla
robot-learning
multimodal
Instructions to use lihzha/LAP-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lihzha/LAP-3B with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("lihzha/LAP-3B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
update readme download instructions
Browse files
README.md
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📄 Paper: https://arxiv.org/abs/2602.10556
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💻 Code: https://github.com/lihzha/lap
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## Model Summary
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- container placement
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- towel manipulation
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## Limitations
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- bimanual robots
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- dexterous hands
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- mobile manipulation systems
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## Intended Use
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The model is **not intended for safety-critical deployments without additional validation**.
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## Citation
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📄 Paper: https://arxiv.org/abs/2602.10556
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💻 Code: https://github.com/lihzha/lap
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## Download
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You can download the LAP checkpoint directly from the Hugging Face Hub.
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### Using the Hugging Face CLI (recommended)
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Install the Hugging Face Hub CLI:
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```bash
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pip install -U huggingface_hub
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````
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Download the checkpoint to the expected directory:
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```bash
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hf download lihzha/LAP-3B --local-dir ./checkpoint/lap
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```
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After downloading, the checkpoint will be located at:
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```
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./checkpoint/lap
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```
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This matches the default path expected by the LAP codebase.
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### Alternative: Python API
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You can also download the checkpoint programmatically:
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```python
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from huggingface_hub import snapshot_download
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snapshot_download(
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repo_id="lihzha/LAP-3B",
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local_dir="./checkpoint/lap"
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)
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```
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## Model Summary
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- container placement
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- towel manipulation
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## Limitations
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- bimanual robots
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- dexterous hands
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- mobile manipulation systems
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## Intended Use
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The model is **not intended for safety-critical deployments without additional validation**.
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## Citation
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