Improve model card: add metadata, paper link, and sample usage (#1)

- Improve model card: add metadata, paper link, and sample usage (4390121a0ce82ae032e2e68eeaaed9451a64ae7d)
- Resolve merge conflict with main branch (7dd7a90a0457b412f96738ba5845ad9731b4e640)


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

README.md

@@ -1,7 +1,106 @@
 ---
 license: apache-2.0
+library_name: transformers
 pipeline_tag: robotics
 tags:
-- vision-language-action
 - vla
----
+- vision-language-action
+- real-time
+---
+
+# Xiaomi-Robotics-0
+
+**Xiaomi-Robotics-0** is an advanced Vision-Language-Action (VLA) model with 4.7B parameters, specifically engineered for high-performance robotic reasoning and seamless real-time execution.
+
+- **Paper:** [Xiaomi-Robotics-0: An Open-Sourced Vision-Language-Action Model with Real-Time Execution](https://huggingface.co/papers/2602.12684)
+- **Project Page:** [xiaomi-robotics-0.github.io](https://xiaomi-robotics-0.github.io/)
+- **Repository:** [GitHub - Xiaomi-Robotics-0](https://github.com/XiaomiRobotics/Xiaomi-Robotics-0)
+
+## Model Description
+Xiaomi-Robotics-0 is first pre-trained on large-scale cross-embodiment robot trajectories and vision-language data, providing it with broad and generalizable action-generation capabilities. It utilizes a carefully designed training recipe and deployment strategy to address inference latency, enabling fast and smooth real-time rollouts on consumer-grade GPUs.
+
+## Quick Start: Deployment
+
+The model is compatible with the Hugging Face `transformers` ecosystem. By leveraging Flash Attention 2 and bfloat16 precision, it can be run efficiently for robotic manipulation tasks.
+
+```python
+import torch
+from transformers import AutoModel, AutoProcessor
+
+# 1. Load model and processor 
+model_path = "XiaomiRobotics/Xiaomi-Robotics-0"
+model = AutoModel.from_pretrained(
+    model_path, 
+    trust_remote_code=True, 
+    attn_implementation="flash_attention_2", 
+    dtype=torch.bfloat16
+).cuda().eval()
+processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True, use_fast=False)
+
+
+# 2. Construct the prompt with multi-view inputs
+language_instruction = "Pick up the red block."
+instruction = (
+    f"<|im_start|>user
+The following observations are captured from multiple views.
+"
+    f"# Base View
+<|vision_start|><|image_pad|><|vision_end|>
+"
+    f"# Left-Wrist View
+<|vision_start|><|image_pad|><|vision_end|>
+"
+    f"Generate robot actions for the task:
+{language_instruction} /no_cot<|im_end|>
+"
+    f"<|im_start|>assistant
+<cot></cot><|im_end|>
+"
+)
+
+# 3. Prepare inputs
+# Assuming `image_base`, `image_wrist`, and `proprio_state` are already loaded
+inputs = processor(
+    text=[instruction],
+    images=[image_base, image_wrist], # [PIL.Image, PIL.Image]
+    videos=None,
+    padding=True,
+    return_tensors="pt",
+).to(model.device)
+
+# Add proprioceptive state and action mask
+robot_type = "libero"
+inputs["state"] = torch.from_numpy(proprio_state).to(model.device, model.dtype).view(1, 1, -1)
+inputs["action_mask"] = processor.get_action_mask(robot_type).to(model.device, model.dtype)
+
+# 4. Generate action 
+with torch.no_grad():
+    outputs = model(**inputs)
+    
+# Decode raw outputs into actionable control commands
+action_chunk = processor.decode_action(outputs.actions, robot_type=robot_type)
+print(f"Generated Action Chunk Shape: {action_chunk.shape}")
+```
+
+## Benchmark Results
+
+The model has been evaluated extensively on standard simulation benchmarks:
+
+| Benchmark | Description | Performance |
+| :--- | :--- | :--- |
+| **LIBERO** | Fine-tuned on four LIBERO suites. | **98.7%** (Avg Success) |
+| **CALVIN** | Fine-tuned on ABCD→D Split. | **4.80** (Avg Length) |
+| **SimplerEnv** | Fine-tuned on Fractal dataset (Google Robot). | **85.5%** (VM) / **74.7%** (VA) |
+| **SimplerEnv** | Fine-tuned on Bridge dataset (WidowX). | **79.2%** |
+
+## Citation
+
+```bibtex
+@misc{robotics2026xiaomi,
+  title        = {Xiaomi-Robotics-0: An Open-Sourced Vision-Language-Action Model with Real-Time Execution},
+  author       = {Xiaomi Robotics},
+  howpublished={\url{https://xiaomi-robotics-0.github.io}},
+  year         = {2026},
+  note={Project Website}
+}
+```