CelesteImperia/Qwen2-VL-2B-Instruct-OpenVINO-INT4

Celeste Imperia | Qwen2-VL-2B (OpenVINO INT4 Gold)

The specialized vision-reasoning engine for edge hardware. This is a hardware-optimized port of Alibaba's Qwen2-VL-2B-Instruct, forged using Intel's NNCF (Neural Network Compression Framework) for ultra-low latency execution on CPUs, GPUs, and NPUs.

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🐍 For Python Users (Optimum Intel)

To achieve the highest throughput on Intel or ARM64 hardware, use the optimum-intel library with the OpenVINO backend.

from optimum.intel import OVModelForVisualCausalLM
from transformers import AutoProcessor
from PIL import Image
import requests

model_id = "CelesteImperia/Qwen2-VL-2B-Instruct-OpenVINO-INT4"

# Load optimized model on CPU/GPU/NPU
model = OVModelForVisualCausalLM.from_pretrained(model_id, device="CPU")
processor = AutoProcessor.from_pretrained(model_id)

# Example: Simple Image Description
url = "[https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.png](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.png)"
image = Image.open(requests.get(url, stream=True).raw)
inputs = processor(text=["Describe this image."], images=[image], return_tensors="pt")

output = model.generate(**inputs, max_new_tokens=128)
print(processor.decode(output[0], skip_special_tokens=True))

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💻 For C# / .NET Users (OpenVINO .NET SDK)

As a Senior .NET developer, I have validated this vision-language model for native C# integration. This is ideal for building high-speed OCR or automated surveillance tools directly in the Windows/Intel ecosystem.

using OpenVinoSharp;
using System;

// 1. Initialize OpenVINO Core for Vision
Core core = new Core();

// 2. Load the Optimized Qwen2-VL INT4 Model (.xml / .bin)
Model model = core.read_model("qwen2_vl_int4.xml");
CompiledModel compiledModel = core.compile_model(model, "CPU");

Console.WriteLine("👁️ Qwen2-VL Vision Engine Initialized on .NET");
// [Inference logic for visual tokens goes here]

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📜 Gold Release: Change Log

This release optimizes the Qwen2-VL architecture for constrained edge environments, specifically targeting the Snapdragon X Elite (ARM64) and Intel Core Ultra ecosystems.

🛠️ Key Technical Upgrades:

• Weight Compression: Reduced memory footprint to ~1.8GB (Asymmetric INT4/INT8), making it viable for 8GB-16GB RAM laptops and edge gateways.
• Architecture: Native support for high-resolution image reasoning remains intact despite heavy quantization.
• NPU Ready: Validated for Intel Meteor Lake/Lunar Lake NPUs and Qualcomm NPUs (via OpenVINO ARM64), allowing for continuous scene description with minimal power draw.
• Multimodal Speed: Significant reduction in Time-to-First-Token (TTFT) compared to the original FP16 weights.

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🎯 Sample Vision Benchmarks

Test Case 1: High-Resolution OCR

Result: Highly accurate extraction of text from technical diagrams and complex documents. Latency: Optimized for real-time interaction on the i5-11400 / 64GB RAM validation rig.

Test Case 2: Zero-Latency Scene Analysis

Prompt: "Identify all safety hazards in this factory floor image." Accuracy: Successfully maintains spatial awareness and object-token alignment despite INT4 compression.

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🏗️ Technical Forge & Infrastructure

• Model Type: OpenVINO IR (INT4 Quantized)
• Optimization: Intel NNCF (Mixed INT4/INT8 Asymmetric)
• Validation Rig: Intel i5-11400 | 64GB DDR4 RAM | NVIDIA RTX 3090 + RTX A4000
• Primary Use Case: Image-text-to-text (Multimodal Reasoning)

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📜 License & Disclaimer

License: This port is distributed under the Apache License 2.0, aligning with the original Qwen2-VL release by Alibaba Cloud.

Disclaimer: This model is for research and development purposes. Vision AI can occasionally misinterpret visual data; verify critical OCR or safety-related outputs manually.

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☕ Support the Forge

Maintaining a dual-GPU AI workstation and hosting high-bandwidth models requires significant compute resources. If our open-source tools power your projects, consider supporting our development:

Platform	Support Link
Global & India	Support via Razorpay

Scan to support via UPI (India Only):

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Connect with the architect: Abhishek Jaiswal on LinkedIn