license: apache-2.0
library_name: lerobot
pipeline_tag: robotics
WALL-OSS: Wall-OSS-0.5
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 provide executable robot behavior directly from pretraining. The model was introduced in the Wall-OSS-0.5 Technical Report.
Before task-specific fine-tuning, Wall-OSS-0.5 achieves non-trivial zero-shot real-robot behavior. After fine-tuning, it serves as a strong adaptation prior, significantly outperforming previous baselines in manipulation tasks.
🎬 Video Demos
WALL-OSS in Action: Demonstrating advanced manipulation capabilities and embodied AI performance
🚀 Quick Start
Installation
# Create conda environment
conda create --name wallx python=3.10
conda activate wallx
# Install base requirements
pip install torch torchvision transformers
pip install huggingface_hub
# Install Wall-X from GitHub
git clone https://github.com/X-Square-Robot/wall-x.git
cd wall-x
pip install -e .
Basic Usage
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-flow" # or your local path
model = Qwen2_5_VLMoEForAction.from_pretrained(model_path)
model.eval()
# Configuration
device = "cuda" if torch.cuda.is_available() else "cpu"
model = model.to(device).bfloat16()
# Basic inference example
batch_size = 1
seq_length = 50
# Prepare inputs (example with synthetic data)
input_ids = torch.randint(0, len(model.processor.tokenizer), (batch_size, seq_length), dtype=torch.long).to(device)
attention_mask = torch.ones((batch_size, seq_length), dtype=torch.long).to(device)
proprioception = torch.randn((batch_size, 1, 20), dtype=torch.float32).to(device).bfloat16()
with torch.no_grad():
outputs = model(input_ids=input_ids, attention_mask=attention_mask, proprioception=proprioception, mode="validate")
print(f"Output logits shape: {outputs.logits.shape}")
🎯 Supervised Fine-Tuning (SFT)
For training Wall-X on your robotics datasets using the LeRobot format, please refer to the Training Documentation.
📄 Citation
If you find WALL-OSS models useful, please cite:
@misc{walloss_paper_2025,
title = {Wall-OSS-0.5 Technical Report},
author = {X Square Robot},
year = {2026},
howpublished = {\url{https://huggingface.co/papers/2605.30877}},
note = {Technical Report}
}