Improve model card: Add pipeline tag, paper link, project page, and code link (#1)

- Improve model card: Add pipeline tag, paper link, project page, and code link (7a052d17f4ec15a0d1b4ad0ecb6390f0378bf21b)


Co-authored-by: Niels Rogge <[email protected]>

README.md

@@ -1,91 +1,76 @@
----
-license: apache-2.0
----
-
-# PPTmodel4UnitreeG1
-
-This is a post-pre-trained model specifically designed for Unitree G1 robot applications. The model has been fine-tuned using the [Agibot2UnitreeG1Retarget dataset](https://huggingface.co/datasets/l2aggle/Agibot2UnitreeG1Retarget) to enhance its performance on robotic whole-body manipulation.
-
-## Model Description
-
-This model underwent post-pre-training using specialized robotics data to improve its understanding and generation capabilities for Unitree G1 humanoid robot applications. The training process leveraged the Agibot2UnitreeG1Retarget dataset, which contains motion retargeting data specifically curated for Unitree G1.
-
-## Dataset
-
-The model was trained on the [Agibot2UnitreeG1Retarget dataset](https://huggingface.co/datasets/l2aggle/Agibot2UnitreeG1Retarget), which provides comprehensive motion retargeting data for converting motion patterns to UnitreeG1 robot format.
-
-## Model Files
-
-The model consists of the following files:
-- `config.json` - Model configuration
-- `model.safetensors.index.json` - SafeTensors index file
-- `model-00001-of-00002.safetensors` - Model weights (part 1)
-- `model-00002-of-00002.safetensors` - Model weights (part 2)
-- `trainer_state.json` - Training state information
-- `training_args.bin` - Training arguments
-- `experiment_cfg/` - Experimental configuration files
-
-## Download and Usage
-
-### Method 1: Using Hugging Face Hub
-
-```python
-from transformers import AutoModel, AutoTokenizer
-
-# Download and load the model
-model = AutoModel.from_pretrained("l2aggle/PPTmodel4UnitreeG1")
-tokenizer = AutoTokenizer.from_pretrained("l2aggle/PPTmodel4UnitreeG1")
-```
-
-### Method 2: Using Git LFS
-
-```bash
-# Clone the repository
-git clone https://huggingface.co/l2aggle/PPTmodel4UnitreeG1
-
-# Navigate to the model directory
-cd PPTmodel4UnitreeG1
-```
-
-### Method 3: Direct Download
-
-You can also download individual files directly from the [model repository](https://huggingface.co/l2aggle/PPTmodel4UnitreeG1/tree/main) on Hugging Face.
-
-## Requirements
-
-- Python 3.7+
-- PyTorch
-- Transformers library
-- SafeTensors
-
-## Installation
-
-```bash
-pip install torch transformers safetensors
-```
-
-## License
-
-This model is released under the Apache 2.0 license.
-
-<!-- ## Citation
-
-If you use this model in your research, please consider citing:
-
-```bibtex
-@misc{pptmodel4unitreeg1,
-  title={PPTmodel4UnitreeG1: Post-Pre-trained Model for UnitreeG1 Robot Applications},
-  author={l2aggle},
-  year={2024},
-  url={https://huggingface.co/l2aggle/PPTmodel4UnitreeG1}
-}
-```
-
-## Related Resources
-
-- Dataset: [Agibot2UnitreeG1Retarget](https://huggingface.co/datasets/l2aggle/Agibot2UnitreeG1Retarget)
-- Model Repository: [PPTmodel4UnitreeG1](https://huggingface.co/l2aggle/PPTmodel4UnitreeG1)
-
-## Contact
-
-For questions or issues regarding this model, please open an issue in the model repository or contact the author through Hugging Face. -->
+---
+license: apache-2.0
+pipeline_tag: robotics
+library_name: transformers
+---
+
+# PPTmodel4UnitreeG1
+
+This is the PPTmodel4UnitreeG1 model presented in [TrajBooster: Boosting Humanoid Whole-Body Manipulation via Trajectory-Centric Learning](https://huggingface.co/papers/2509.11839).
+
+Project page: https://jiachengliu3.github.io/TrajBooster/
+Code: https://github.com/jiachengliu3/OpenTrajBooster
+
+This model is a post-pre-trained model specifically designed for Unitree G1 robot applications. The model has been fine-tuned using the [Agibot2UnitreeG1Retarget dataset](https://huggingface.co/datasets/l2aggle/Agibot2UnitreeG1Retarget) to enhance its performance on robotic whole-body manipulation.
+
+## Model Description
+
+This model underwent post-pre-training using specialized robotics data to improve its understanding and generation capabilities for Unitree G1 humanoid robot applications. The training process leveraged the Agibot2UnitreeG1Retarget dataset, which contains motion retargeting data specifically curated for Unitree G1.
+
+## Dataset
+
+The model was trained on the [Agibot2UnitreeG1Retarget dataset](https://huggingface.co/datasets/l2aggle/Agibot2UnitreeG1Retarget), which provides comprehensive motion retargeting data for converting motion patterns to UnitreeG1 robot format.
+
+## Model Files
+
+The model consists of the following files:
+- `config.json` - Model configuration
+- `model.safetensors.index.json` - SafeTensors index file
+- `model-00001-of-00002.safetensors` - Model weights (part 1)
+- `model-00002-of-00002.safetensors` - Model weights (part 2)
+- `trainer_state.json` - Training state information
+- `training_args.bin` - Training arguments
+- `experiment_cfg/` - Experimental configuration files
+
+## Download and Usage
+
+### Method 1: Using Hugging Face Hub
+
+```python
+from transformers import AutoModel, AutoTokenizer
+
+# Download and load the model
+model = AutoModel.from_pretrained("l2aggle/PPTmodel4UnitreeG1")
+tokenizer = AutoTokenizer.from_pretrained("l2aggle/PPTmodel4UnitreeG1")
+```
+
+### Method 2: Using Git LFS
+
+```bash
+# Clone the repository
+git clone https://huggingface.co/l2aggle/PPTmodel4UnitreeG1
+
+# Navigate to the model directory
+cd PPTmodel4UnitreeG1
+```
+
+### Method 3: Direct Download
+
+You can also download individual files directly from the [model repository](https://huggingface.co/l2aggle/PPTmodel4UnitreeG1/tree/main) on Hugging Face.
+
+## Requirements
+
+- Python 3.7+
+- PyTorch
+- Transformers library
+- SafeTensors
+
+## Installation
+
+```bash
+pip install torch transformers safetensors
+```
+
+## License
+
+This model is released under the Apache 2.0 license.