You can deploy this model and start earning money today!

Tacoin/
openpi-pi0.5-libero-onnx

Name: openpi-pi0.5-libero-onnx
Availability: InStock

Views

No views yet

OpenPi VLA Models (ONNX Export)

This repository contains ONNX-exported versions of the OpenPi VLA (Vision-Language-Action) models, specifically tuned for NVIDIA Jetson Thor (Blackwell) and GeForce RTX Ada/Blackwell GPUs.

These models are derived from the Pi0.5 Libero baseline (OpenVLA architecture).

📊 Performance Benchmarks (Jetson Thor)

Variant	Latency (ms)	Throughput (QPS)	GPU Mem (Est)	Status	Notes
W8A16 (QDQ)	181.81	6.37	~6.3 GiB	✅ Recommended	Production Ready. Verified accuracy parity (MSE 0.0061).
FP16	184.54	6.26	~13.0 GiB	✅ Verified	Baseline. Native FP16 execution.
W8A8 (Sim)	128.37	8.38	~3.7 GiB	🚧 Benchmark	Fastest (1.4x speedup), requires calibration.
INT4 (Sim)	183.15	6.33	~6.3 GiB	🚧 Experimental	Parity speed with W8A16.

Memory Note: INT8 quantization reduces runtime VRAM usage by approximately 3.2x compared to FP32 (4GB vs 13GB measured on runtime).

📂 Repository Structure & "Why is this 231GB?"

We preserve all precision variants and intermediate export states for research reproducibility.

Folder	Precision	Description	Size	Contents
`final_w8a16/`	W8A16	[USE THIS] Clean, collapsed ONNX model.	~12GB	`model.w8a16.onnx`, `.data`
`final_fp16/`	FP16	Baseline high-precision export.	~24GB	`model.fp32.onnx`
`final_w4a4/`	W4A4	Experimental INT4/INT4 export.	~12GB	Granular (Many files)
`final_w8a16_new/`	W8A16	Uncollapsed dev export (Duplicate state).	~12GB	Granular (Many files)
`checkpoints/`	PyTorch	Raw Archive. Original JAX->PyTorch weights.	~100GB	`.pt` files, config, raw layers

Recommendation: Users should download ONLY final_w8a16/ for deployment.

⚠️ Environment & Compatibility Notes

1. NVIDIA Jetson Thor (SM 11.0)

Driver Requirement: CUDA 13.0+ / Driver 560+
Issue: Standard onnxruntime and PyTorch binaries may not fully support SM 11.0 (Blackwell) instructions yet.
Fix: Use the NVIDIA NGC containers or compile from source.

2. Quantization Support

W4A4: Requires a custom TensorRT backend to execute trt.DequantizeLinear ops efficiently. CPU execution is not supported.
W8A16: Uses standard ONNX QDQ nodes (QuantizeLinear/DequantizeLinear). Compatible with standard ONNX Runtime (GPU) and TensorRT.

🛠️ Deployment Code

Python (ONNX Runtime)

python

import onnxruntime as ort
import os
import tensorrt_libs

# 1. Register TensorRT Plugins (Critical for QDQ models)
os.environ["LD_LIBRARY_PATH"] = os.environ.get("LD_LIBRARY_PATH", "") + ":" + os.path.dirname(tensorrt_libs.__file__)

# 2. Load Model
model_path = "final_w8a16/model.w8a16.onnx"
providers = [
    ("TensorrtExecutionProvider", {
        "device_id": 0,
        "trt_fp16_enable": True,
        "trt_int8_enable": True,    # Enable INT8 Tensor Cores
        "trt_engine_cache_enable": True,
    }),
    "CUDAExecutionProvider"
]

session = ort.InferenceSession(model_path, providers=providers)
print("Model loaded successfully!")

TensorRT CLI (`trtexec`)

bash

# Compile W8A16 model to Engine
trtexec --onnx=final_w8a16/model.w8a16.onnx         --fp16 --int8         --saveEngine=model.engine         --verbose

Tacoin/openpi-pi0.5-libero-onnx