Add model card and pipeline tag

Hi! I'm Niels, part of the community science team at Hugging Face.

This pull request improves the model card by adding relevant metadata and links to the research paper, project page, and code repository. It also adds installation instructions and a sample usage section for inference, along with the `image-to-3d` pipeline tag to improve the model's discoverability on the Hub.

README.md

@@ -1,3 +1,54 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+pipeline_tag: image-to-3d
+---
+
+# Lite3R: A Model-Agnostic Framework for Efficient Feed-Forward 3D Reconstruction
+
+Official implementation of **Lite3R**, a model-agnostic framework for efficient feed-forward 3D reconstruction from multi-view images.
+
+Lite3R introduces a systematic approach to compress large-scale 3D reconstruction models while maintaining reconstruction quality. The framework combines Sparse Linear Attention (SLA), FP8-Aware Quantization-Aware Training (QAT), and Partial Attention Distillation.
+
+[**Paper**](https://huggingface.co/papers/2605.11354) | [**Website**](https://aigeeksgroup.github.io/Lite3R/) | [**Code**](https://github.com/AIGeeksGroup/Lite3R)
+
+## Installation
+
+```bash
+# Clone the repository
+git clone https://github.com/AIGeeksGroup/Lite3R.git
+cd Lite3R
+
+# Create conda environment
+conda create -n lite3r python=3.10
+conda activate lite3r
+
+# Install dependencies
+pip install -r requirements.txt
+```
+
+## Sample Usage
+
+### Inference
+
+After downloading the model checkpoints from this repository, you can run inference using the following command:
+
+```bash
+python inference.py \
+  --model vggt \
+  --checkpoint checkpoints/fp8_qat_1ep/vggt/vggt_fp8_qat_1ep.pt \
+  --input_dir examples/input \
+  --output_dir examples/output
+```
+
+## Citation
+
+If you find this work useful, please cite:
+
+```bibtex
+@article{zhang2026lite3r,
+  title={Lite3R: A Model-Agnostic Framework for Efficient Feed-Forward 3D Reconstruction},
+  author={Zhang, Haoyu and Zhang, Zeyu and Zhou, Zedong and Zhao, Yang and Tang, Hao},
+  journal={arXiv preprint arXiv:2605.11354},
+  year={2026}
+}
+```