Add model card and metadata for Wall-OSS-0.5

README.md

@@ -1,47 +1,27 @@
-# WALL-OSS
+---
+license: apache-2.0
+library_name: transformers
+pipeline_tag: robotics
+---
 
-<div align="left">
+# Wall-OSS-0.5
 
-<p align="center">
-    <img src="assets/logo.png" width="600"/>
-<p>
+Wall-OSS-0.5 is an open-source 4B Vision-Language-Action (VLA) model built upon a 3B VLM backbone augmented with action-generation components. It is designed to yield executable robot behavior directly from pretraining, rather than just serving as a better initialization for downstream policy learning.
 
-<div align="center">
+- **Paper:** [Wall-OSS-0.5 Technical Report](https://huggingface.co/papers/2605.30877)
+- **Repository:** [GitHub](https://github.com/X-Square-Robot/wall-x)
+- **Project Page:** [X-Square Robot Research](https://x2robot.com/en/research/68bc2cde8497d7f238dde690)
 
-[![Paper](https://img.shields.io/badge/📄%20Paper-PDF-EA1B22?style=for-the-badge&logo=adobeacrobatreader&logoColor=fff)](https://x2robot.cn-wlcb.ufileos.com/wall_oss.pdf)
-&nbsp;&nbsp;
-[![Hugging Face](https://img.shields.io/badge/Hugging%20Face-x--square--robot-FFB000?style=for-the-badge&logo=huggingface&logoColor=000)](https://huggingface.co/x-square-robot)
-&nbsp;&nbsp;
-[![GitHub](https://img.shields.io/badge/GitHub-181717?style=for-the-badge&logo=github&logoColor=fff)](https://github.com/X-Square-Robot/wall-x)
-&nbsp;&nbsp;
-[![Project Page](https://img.shields.io/badge/Project-1E90FF?style=for-the-badge&logo=google-chrome&logoColor=fff)](https://x2robot.com/en/research/68bc2cde8497d7f238dde690)
+## Model Description
 
-</div>
+The model is pretrained across more than 20 embodiments, processing over one million robot trajectories per epoch alongside a grounded multimodal corpus. It utilizes a gradient-bridged co-training recipe involving three objectives: discrete action prediction, multimodal prediction, and continuous flow matching. Wall-OSS-0.5 achieves non-trivial zero-shot real-robot behavior and demonstrates strong vision-language competence while strengthening embodied grounding.
 
-</div>
-
-## <a href="https://x2robot.cn-wlcb.ufileos.com/wall_oss.pdf" target="_blank"><strong>WALL-OSS: Igniting VLMs toward the Embodied Space</strong></a>
-
-We introduce **WALL-OSS**, an end-to-end embodied foundation model that leverages large-scale multimodal pretraining to achieve (1) embodiment-aware vision--language understanding, (2) strong language--action association, and (3) robust manipulation capability.
-Our approach employs a tightly coupled architecture and multi-strategies training curriculum that enables Unified Cross-Level CoT—seamlessly unifying instruction reasoning, subgoal decomposition, and fine-grained action synthesis within a single differentiable framework.
-Our results show that WALL-OSS attains high success on complex long-horizon manipulations, demonstrates strong instruction-following capabilities, complex   understanding and reasoning, and outperforms strong baselines, thereby providing a reliable and scalable path from VLMs to embodied foundation models.
-
-## 🎬 Video Demos
-
-<div align="center">
-    <video width="80%" controls>
-        <source src="https://x2robot.com/api/videos/file/wall-oss_top_720p-1.mp4" type="video/mp4">
-        Your browser does not support the video tag.
-    </video>
-    <p><strong>WALL-OSS in Action: Demonstrating advanced manipulation capabilities and embodied AI performance</strong></p>
-</div>
-
-
-
-## 🚀 Quick Start
+## Quick Start
 
 ### Installation
 
+To use Wall-OSS, you need to set up the environment and install the `wall-x` library from source:
+
 ```bash
 # Create conda environment
 conda create --name wallx python=3.10
@@ -64,7 +44,7 @@ import torch
 from wall_x.model.qwen2_5_based.modeling_qwen2_5_vl_act import Qwen2_5_VLMoEForAction
 
 # Load the model
-model_path = "X-Square-Robot/wall-oss-fast"  # or your local path
+model_path = "X-Square-Robot/wall-oss-0.5" 
 model = Qwen2_5_VLMoEForAction.from_pretrained(model_path)
 model.eval()
 
@@ -75,107 +55,7 @@ model = model.to(device).bfloat16()
 # Your inference code here...
 ```
 
-## 🎯 Supervised Fine-Tuning (SFT)
-
-For training Wall-X on your robotics datasets, please refer to our comprehensive training guide:
-
-**📖 [Training Documentation](https://github.com/X-Square-Robot/wall-x/blob/main/workspace/README.md)**
-
-The training process includes:
-- **Dataset Preparation**: How to prepare your robotics datasets in LeRobot format
-- **Configuration Setup**: Detailed configuration for GPU setup, model paths, and robot DOF settings
-- **Training Scripts**: Ready-to-use training scripts with proper hyperparameters
-
-### Quick Training Start
-
-```bash
-# Run training (see workspace/README.md for detailed configuration)
-bash ./workspace/lerobot_example/run.sh
-```
-
-## 🔮 Inference
-
-For detailed inference examples and model evaluation:
-
-**📖 [Inference Documentation](https://github.com/X-Square-Robot/wall-x/blob/main/scripts/)**
-
-### Basic Inference Example
-
-```python
-import torch
-from wall_x.model.qwen2_5_based.modeling_qwen2_5_vl_act import Qwen2_5_VLMoEForAction
-
-# Load model
-model_path = "X-Square-Robot/wall-x"
-model = Qwen2_5_VLMoEForAction.from_pretrained(model_path)
-model.eval()
-
-# Setup
-batch_size = 1
-seq_length = 50
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model = model.to(device).bfloat16()
-
-# Prepare inputs (example with synthetic data)
-torch.manual_seed(0)
-input_ids = torch.randint(0, len(model.processor.tokenizer), (batch_size, seq_length), dtype=torch.long)
-attention_mask = torch.ones((batch_size, seq_length), dtype=torch.long)
-moe_token_types = torch.zeros((batch_size, seq_length), dtype=torch.long)
-position_ids = torch.arange(seq_length, dtype=torch.long).unsqueeze(0).expand(batch_size, -1)
-
-# Robotics-specific inputs
-proprioception = torch.randn((batch_size, 1, 20), dtype=torch.float32)  # Joint states
-agent_pos_mask = torch.ones((batch_size, 1, 20), dtype=torch.float32)
-dof_mask = torch.ones((batch_size, 32, 20), dtype=torch.float32)  # DOF mask
-dataset_names = ["x2_normal"]
-
-# Move to device
-inputs = {
-    "input_ids": input_ids.to(device),
-    "attention_mask": attention_mask.to(device),
-    "moe_token_types": moe_token_types.to(device),
-    "position_ids": position_ids.to(device),
-    "proprioception": proprioception.to(device).bfloat16(),
-    "agent_pos_mask": agent_pos_mask.to(device).bfloat16(),
-    "dof_mask": dof_mask.to(device).bfloat16(),
-    "dataset_names": dataset_names,
-    "mode": "validate"
-}
-
-# Run inference
-with torch.no_grad():
-    outputs = model(**inputs)
-    print(f"Output logits shape: {outputs.logits.shape}")
-```
-
-### Advanced Inference Scripts
-
-For production-ready inference and evaluation scripts:
-
-```bash
-# Basic inference test
-python ./scripts/fake_inference.py
-
-# Generate open-loop comparison plots
-python ./scripts/draw_openloop_plot.py
-```
-
-**📁 [View all inference scripts](https://github.com/X-Square-Robot/wall-x/tree/main/scripts)**
-
-## 📚 Complete Documentation
-
-For comprehensive setup, training, and inference instructions:
-
-### 🚀 **[Visit our GitHub Repository](https://github.com/X-Square-Robot/wall-x)**
-
-The repository contains:
-- **Detailed Installation Guide**: Complete environment setup with all dependencies
-- **Training Tutorials**: Step-by-step SFT process with LeRobot datasets
-- **Inference Examples**: Multiple inference scripts and evaluation tools
-- **Configuration Templates**: Ready-to-use configs for different robot setups
-- **Troubleshooting Guide**: Common issues and solutions
-
-## 📄 Cite Us
+## Citation
 
 If you find WALL-OSS models useful, please cite:
 
@@ -187,4 +67,4 @@ If you find WALL-OSS models useful, please cite:
   howpublished = {\url{https://x2robot.cn-wlcb.ufileos.com/wall_oss.pdf}},
   note         = {White paper}
 }
-```
+```