TurboVLA-Libero-0.22B

License & Attribution

This repository contains weights or code derived from the TurboVLA foundational architecture developed by Hugging Face and the TurboVLA Authors.

• License: Distributed under the https://apache.org.
• Original Model Base: https://huggingface.co/H-EmbodVis/TurboVLA
• Copyright Notice: Copyright 2025-2026 Hugging Face & the TurboVLA Authors.
• Original Authors: Hengyi Xie, Chenfei Yao, Xianjin Wu, Yingying Zhu, Dingkang Liang, Xiang Bai, Han Ding
• Research Paper: https://arxiv.org/abs/2607.27205

Script to load this model:

import sys
import torch
import yaml
from pathlib import Path
from huggingface_hub import snapshot_download

# 1. Pull down your fully packaged repository tree to an isolated workspace cache
repo_id = "Man1103/TurboVLA-Libero-0.22B"
cache_dir = Path("./turbovla_cached_checkpoint")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

print(f"Streaming ready assets from {repo_id}...")
snapshot_download(repo_id=repo_id, local_dir=cache_dir)

# 2. Dynamically stitch the repository's native execution files into your Python context
# This allows you to load TurboVLA natively without manual git clones
sys.path.append(str(cache_dir))

# 3. Import the architecture templates included inside your repository package
from turbovla.models.turbovla import TurboVLAPolicy 

# 4. Parse your embedded structural configurations
with open(cache_dir / "config.yaml", "r") as f:
    config = yaml.safe_load(f)

# 5. Build the structural skeleton and map your ready weights directly onto your GPU
print("Assembling TurboVLA direct V+L -> A mapping blueprint...")
model = TurboVLAPolicy(config["model_config"]).to(device)

# Automatically match any .pth or weight binaries stored inside your repo
weight_file = list(cache_dir.glob("**/*.pth"))[0] 
checkpoint = torch.load(weight_file, map_location=device)

# Load the ready state dictionary safely into position
model.load_state_dict(checkpoint["model_state_dict"] if "model_state_dict" in checkpoint else checkpoint)
model.eval()

print(f"\n--- SUCCESS ---")
print(f"Your fully ready model is loaded onto {device} and primed for 32Hz LIBERO rollouts!")