Create README.md

README.md

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+---
+license: apache-2.0
+base_model:
+- microsoft/Florence-2-large
+tags:
+- robotics
+- vla
+pipeline_tag: robotics
+datasets:
+- Facebear/XVLA-Soft-Fold
+---
+
+#  X-VLA 0.9B (Soft Fold Edition)
+
+
+**Repository:** [2toINF/X-VLA](https://github.com/2toinf/X-VLA)
+
+**Authors:** [2toINF](https://github.com/2toINF) | **License:** Apache 2.0
+
+**Paper:** *Zheng et al., 2025, “X-VLA: Soft-Prompted Transformer as Scalable Cross-Embodiment Vision-Language-Action Model”* ([arXiv:2510.10274](https://arxiv.org/pdf/2510.10274))
+
+
+## 🚀 Overview
+
+Successful generalist **Vision-Language-Action (VLA)** models rely on effective training across diverse robotic platforms with large-scale, cross-embodiment, heterogeneous datasets.
+To facilitate and leverage the heterogeneity in rich robotic data sources, **X-VLA** introduces a **Soft Prompt approach** with minimally added parameters: we infuse prompt-learning concepts into cross-embodiment robot learning, introducing **separate sets of learnable embeddings** for each distinct embodiment.
+
+These embodiment-specific prompts empower VLA models to exploit cross-embodiment features effectively.
+Our architecture—**a clean, flow-matching-based VLA design relying exclusively on soft-prompted standard Transformers**—achieves superior scalability and simplicity.
+
+Trained on **Bridge Data** and evaluated across **six simulations** and **three real-world robots**, the 0.9B-parameter X-VLA simultaneously achieves **state-of-the-art performance** across diverse benchmarks, demonstrating flexible dexterity and fast adaptation across embodiments, environments, and tasks.
+
+🌐 **Project Website:** [https://thu-air-dream.github.io/X-VLA/](https://thu-air-dream.github.io/X-VLA/)
+
+
+<video controls autoplay loop muted playsinline width="720">
+  <source src="https://huggingface.co/2toINF/X-VLA-0.9B-WidowX/resolve/main/demo.mp4" type="video/mp4">
+</video>
+
+## ⚙️ Usage
+### 🔹 Load the model
+
+```python
+from transformers import AutoModel
+
+model = AutoModel.from_pretrained(
+    "2toINF/X-VLA-WidowX",
+    trust_remote_code=True
+)
+```
+### 🔹 Start FastAPI server
+
+```python
+from transformers import AutoProcessor
+processor = AutoProcessor.from_pretrained("2toINF/X-VLA-WidowX", trust_remote_code=True)
+model.run(processor, host="0.0.0.0", port=8000)
+```
+### 🔹 Client-server evaluation
+
+You can run the provided evaluation client from our GitHub:
+👉 [2toINF/X-VLA – Client &amp; Server Code](https://github.com/2toINF/X-VLA)
+
+
+## 🧩 Architecture
+
+| Component                         | Role                                                                       |
+| :-------------------------------- | :------------------------------------------------------------------------- |
+| **Florence 2 Encoder**      | Vision-Language representation backbone (encoder-only).                    |
+| **SoftPromptedTransformer** | Flow-matching action denoiser using learnable soft prompts per embodiment. |
+| **Action Hub**              | Defines action spaces, masking rules, pre/post-processing, and losses.     |
+
+## 🧠 Training Summary
+
+| Setting           | Value                                           |
+| :---------------- | :---------------------------------------------- |
+| Training Data     | Bridge Data V2    |
+| Parameters        | ≈ 0.9 B                                        |
+| Action Mode       | `ee6d`                                         |
+| Precision         | BP16                                            |
+| Framework         | PyTorch + Transformers                          |
+
+---
+## 🪪 License
+```
+Copyright 2025 2toINF (https://github.com/2toINF)
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+http://www.apache.org/licenses/LICENSE-2.0
+```
+---
+## 📚 Citation
+```bibtex
+@article{zheng2025x,
+  title   = {X-VLA: Soft-Prompted Transformer as Scalable Cross-Embodiment Vision-Language-Action Model},
+  author  = {Zheng, Jinliang and Li, Jianxiong and Wang, Zhihao and Liu, Dongxiu and Kang, Xirui
+             and Feng, Yuchun and Zheng, Yinan and Zou, Jiayin and Chen, Yilun and Zeng, Jia and others},
+  journal = {arXiv preprint arXiv:2510.10274},
+  year    = {2025}
+}
+```
+---
+## 🌐 Links
+
+- 📄 **Paper:** [arXiv 2510.10274](https://arxiv.org/abs/2510.10274)
+- 💻 **Code & Client/Server:** [GitHub – 2toINF/X-VLA](https://github.com/2toINF/X-VLA)
+- 🤖 **Model Hub:** [Hugging Face – 2toINF/X-VLA-0.9B-WidowX](https://huggingface.co/2toINF/X-VLA-0.9B-WidowX)