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Add Qwen3.5 VL model recipes (0.8B, 2B, 4B, 9B) (#268)
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# Generated model artifacts
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models/
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# Python bytecode
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__pycache__/
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*.pyc
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# Olive cache
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.olive-cache/
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# Qwen3.5-0.8B ONNX Runtime GenAI Example
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This example demonstrates how to convert [Qwen3.5-0.8B](https://huggingface.co/Qwen/Qwen3.5-0.8B) vision-language model to ONNX format using Olive and run inference with ONNX Runtime GenAI.
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Qwen3.5 is a hybrid architecture combining GatedDeltaNet linear attention layers with standard full attention layers. The pipeline exports three sub-models (vision encoder, text embedding, text decoder) and applies graph optimizations. For CPU, all three sub-models are quantized to INT4. For CUDA, the vision encoder and embedding use FP16 while the text decoder uses INT4.
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## Prerequisites
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```bash
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pip install -r requirements.txt
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```
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Install ONNX Runtime GenAI:
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| Device | Install Command |
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|--------|-----------------|
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| CPU | `pip install onnxruntime-genai --index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/ORT-Nightly/pypi/simple` |
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| GPU (CUDA) | `pip install onnxruntime-genai-cuda --index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/ORT-Nightly/pypi/simple` |
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## Steps
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### 1. Export & Optimize Models
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**CPU (INT4 quantized):**
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```bash
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python optimize.py --config-dir cpu_and_mobile --device cpu
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```
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**CUDA (FP16 vision/embedding + INT4 text decoder):**
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```bash
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python optimize.py --config-dir cuda --device gpu
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```
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This runs three Olive pipelines:
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- **embedding.json** — Exports the embedding fusion model (token embedding + image feature scatter)
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- **vision.json** — Exports the vision encoder (packed patches → image features)
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- **text.json** — Exports the text decoder via ModelBuilder (hybrid GatedDeltaNet + full attention, INT4)
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Then generates `genai_config.json` and `processor_config.json` for the ORT GenAI runtime.
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### 2. Output Structure
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```
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cpu_and_mobile/models/ # or cuda/models/
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├── embedding.onnx # Embedding fusion model
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├── embedding.onnx.data
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├── vision.onnx # Vision encoder
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├── vision.onnx.data
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├── text.onnx # Text decoder (hybrid)
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├── text.onnx.data
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├── genai_config.json # Runtime configuration
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├── processor_config.json # Image preprocessing
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├── tokenizer.json
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└── tokenizer_config.json
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```
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### 3. Run Inference
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```bash
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# Text-only
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python inference.py --prompt "What is the capital of France?"
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# Image + text
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python inference.py --image photo.jpg --prompt "Describe this image"
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# Interactive mode
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python inference.py --interactive
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# CUDA model
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python inference.py --model_path cuda/models --prompt "Hello"
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```
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Alternatively, use the built-in GenAI multimodal demo:
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```bash
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python -m onnxruntime_genai.models.model_mm -m cpu_and_mobile/models --max_length 4096
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```
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### 4. Evaluate on AI2D
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Run the AI2D (diagram understanding) benchmark to measure accuracy:
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```bash
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# ONNX only (100 samples, default)
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python eval.py
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# Compare ONNX vs PyTorch side-by-side
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python eval.py --pytorch_model Qwen/Qwen3.5-0.8B
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# Larger evaluation
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python eval.py --num_samples 500 --pytorch_model Qwen/Qwen3.5-0.8B
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# CUDA model
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python eval.py --model_path cuda/models
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```
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The eval script reports per-sample accuracy, average latency, and an ONNX-vs-PyTorch comparison summary.

Qwen-Qwen3.5-0.8B/builtin/codes/__init__.py

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