Add model card for GTO (#1)

- Add model card for GTO (197173e787237f4c9f39679925804f748a2e80ed)


Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

README.md

@@ -0,0 +1,49 @@
+---
+license: apache-2.0
+pipeline_tag: text-generation
+tags:
+- speculative-decoding
+- gto
+---
+
+# Bridging Draft Policy Misalignment: Group Tree Optimization for Speculative Decoding
+
+Group Tree Optimization (GTO) is a framework designed to address draft policy misalignment in speculative decoding. While standard methods optimize for a single greedy path, GTO aligns training with the actual tree-based decoding policy used during inference. This is achieved through a Draft Tree Reward objective and a stable Group-based Draft Policy Training scheme.
+
+- **Paper:** [Bridging Draft Policy Misalignment: Group Tree Optimization for Speculative Decoding](https://huggingface.co/papers/2509.22134)
+- **Repository:** [https://github.com/hsj576/GTO](https://github.com/hsj576/GTO)
+
+## Performance
+GTO achieves state-of-the-art acceleration for LLM inference:
+- **5.6x faster** than vanilla autoregressive decoding.
+- **7% faster** than previous state-of-the-art methods like EAGLE-3.
+
+## Usage
+
+To use this model for accelerated inference, please follow the setup instructions in the [official GTO repository](https://github.com/hsj576/GTO).
+
+### Inference via Web UI
+The codebase provides a web interface for testing the acceleration. After setting up the environment and cloning the repo, you can run:
+
+```bash
+python -m application.webui --ea-model-path [path of GTO weight] \ 
+    --base-model-path [path of the original model] \
+    --model-type [vicuna\llama3\qwen] \
+    --total-token [int]
+```
+
+The `total-token` parameter represents the number of draft tokens. Adjusting this based on your specific device and model can achieve better results.
+
+## Citation
+If you find this work useful, please cite:
+```bibtex
+@article{hu2025bridging,
+  title={Bridging Draft Policy Misalignment: Group Tree Optimization for Speculative Decoding},
+  author={Hu, Shijing and Li, Jingyang and Lu, Zhihui and Zhou, Pan},
+  journal={arXiv preprint arXiv:2509.22134},
+  year={2025}
+}
+```
+
+## Acknowledgements
+The implementation is based on the open-source repository of [EAGLE](https://github.com/SafeAILab/EAGLE/tree/main). This project has been influenced by many projects in the LLM community, such as [HASS](https://github.com/HArmonizedSS/HASS) and [GRIFFIN](https://github.com/hsj576/GRIFFIN).