Add comprehensive model card for Mixture of Horizons

This PR adds a comprehensive model card for the Mixture of Horizons in Action Chunking model.

It includes:
- The paper link: https://huggingface.co/papers/2511.19433
- The project page: https://timsty1.github.io/moh/
- The code repository: https://github.com/Timsty1/MixtureOfHorizons/tree/main
- Relevant metadata (`license: apache-2.0`, `pipeline_tag: robotics`, `library_name: transformers`).
- A summary of the model and its method.
- Detailed installation instructions, including the necessary modification to the `transformers` library.
- A sample Python code snippet for inference, directly from the GitHub README.
- The BibTeX citation.

This ensures better discoverability and usability of the model on the Hugging Face Hub.

README.md

@@ -0,0 +1,118 @@
+---
+license: apache-2.0
+pipeline_tag: robotics
+library_name: transformers
+---
+
+# Mixture of Horizons in Action Chunking
+
+This repository hosts the official models and code for the paper:
+[**Mixture of Horizons in Action Chunking**](https://huggingface.co/papers/2511.19433)
+
+Project Page: https://timsty1.github.io/moh/
+Code Repository: https://github.com/Timsty1/MixtureOfHorizons/tree/main
+
+## Introduction
+Vision-language-action (VLA) models have shown remarkable capabilities in robotic manipulation, but their performance is sensitive to the **action chunk length** used during training, termed **horizon**. This paper proposes a **mixture of horizons (MoH)** strategy to mitigate the inherent trade-off between long-term foresight and short-term precision observed with fixed horizons. MoH rearranges action chunks into segments with different horizons, processes them in parallel with a shared action transformer, and fuses outputs. This approach allows MoH to exploit both long-term foresight and short-term precision jointly within a single model, improving performance and generalizability with minimal overhead. MoH also enables dynamic inference with adaptive horizons, achieving higher throughput while preserving superior performance.
+
+<div align="center">
+  <table border="0" cellspacing="0" cellpadding="0">
+    <tr>
+      <td align="center" width="50%">
+        <img src="https://huggingface.co/Timsty/mixture_of_horizons/resolve/main/figure/study_of_horizons_pi0.png" alt="Trade-off Effect" width="100%">
+      </td>
+      <td align="center" width="50%">
+        <img src="https://huggingface.co/Timsty/mixture_of_horizons/resolve/main/figure/intro_motivation_v2.png" alt="Mixture of Horizons" width="100%">
+      </td>
+    </tr>
+    <tr>
+      <td align="center" valign="top">
+        Figure 1: Trade-off between long-term foresight and short-term precision induced by single horizon
+      </td>
+      <td align="center" valign="top">
+        Figure 2: Overview of the proposed mixture-of-horizons strategy
+      </td>
+    </tr>
+  </table>
+</div>
+
+## Quick Start
+
+### 1. Environment Setup
+
+Clone the repository and set up the conda environment:
+
+```bash
+git clone git@github.com:Timsty1/MixtureOfHorizons.git
+conda create -n moh -y python=3.10
+conda activate moh
+pip install uv
+cd MixtureOfHorizons
+uv pip install -r requirements.txt
+pip install packages/libero
+pip install packages/openpi-client
+```
+
+### 2. Modify Transformers Library
+
+This implementation requires modifying the `transformers` library to support PyTorch-type $\pi$ series models, which rely on *gemma*, *paligemma*, and *siglip*.
+
+First, locate your conda environment path:
+```bash
+conda info --base
+```
+Then, copy the provided files to the transformers library directory (replace `YOUR_CONDA_DIR` with the path found above):
+```bash
+cp -r ./src/openpi/models_pytorch/transformers_replace/* YOUR_CONDA_DIR/envs/moh/lib/python3.10/site-packages/transformers/
+```
+
+### 3. Inference with Code
+You can use our provided "eagenerate" for speedup generation just like using 'generate' from Hugging Face. Here is an example.
+
+```python
+import torch
+from eagle.model.ea_model import EaModel
+from fastchat.model import get_conversation_template
+
+# Replace with paths to your base model and EAGLE model checkpoints
+# Example: base_model_path = "lmsys/vicuna-13b-v1.3", EAGLE_model_path = "Timsty/mixture_of_horizons"
+base_model_path = "path/to/your/base_model"
+EAGLE_model_path = "path/to/your/eagle_model"
+
+model = EaModel.from_pretrained(
+    base_model_path=base_model_path,
+    ea_model_path=EAGLE_model_path,
+    torch_dtype=torch.float16,
+    low_cpu_mem_usage=True,
+    device_map="auto",
+    total_token=-1
+)
+model.eval()
+your_message="Hello"
+conv = get_conversation_template("vicuna") # Use the correct template for your base model
+conv.append_message(conv.roles[0], your_message)
+conv.append_message(conv.roles[1], None)
+prompt = conv.get_prompt()
+input_ids=model.tokenizer([prompt]).input_ids
+input_ids = torch.as_tensor(input_ids).cuda()
+output_ids=model.eagenerate(input_ids,temperature=0.5,max_new_tokens=512)
+output=model.tokenizer.decode(output_ids[0])
+print(output)
+```
+**Note:** Vicuna, LLaMA2-Chat, and LLaMA3-Instruct are both chat models. You need to use the correct chat template, otherwise it will cause abnormal output from the model and affect the performance of EAGLE.
+
+## ❤️ Acknowledgment
+
+We express our gratitude to [OpenPi](https://github.com/Physical-Intelligence/openpi/tree/main), [LIBERO](https://github.com/Lifelong-Robot-Learning/LIBERO), and [RoboTwin](https://robotwin-platform.github.io/) for their open-source contributions.
+
+## 📝 Citation
+If you feel that this paper, models, or codes are helpful, please cite our paper, thanks for your support!
+
+```bibtex
+@article{jing2025mixture_of_horizons,
+  title={Mixture of Horizons in Action Chunking},
+  author={Jing, Dong and Wang, Gang and Liu, Jiaqi and Tang, Weiliang and Sun, Zelong and Yao, Yunchao and Wei, Zhenyu and Liu, Yunhui and Lu, Zhiwu and Ding, Mingyu},
+  journal={arXiv preprint arXiv:2511.19433},
+  year={2025}
+}
+```