Add Fara-7b recipes

microsoft-Fara-7B/builtin/.gitignore

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+# Generated model artifacts
+models/
+
+# Python bytecode
+__pycache__/
+*.pyc
+
+# Olive cache
+.olive-cache/
+
+# Temp and log files
+*.temp
+*.bak
+*.log
+*_log.txt
+optimize_log.txt

microsoft-Fara-7B/builtin/README.md

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+# Fara-7B ONNX Runtime GenAI Example
+
+This example demonstrates how to convert [Fara-7B](https://huggingface.co/microsoft/Fara-7B) vision-language model to ONNX format using Olive and run inference with ONNX Runtime GenAI.
+
+Fara-7B is Microsoft's agentic small language model designed for computer use, based on Qwen 2.5 VL (7B). The pipeline exports three sub-models (vision encoder, text embedding, text decoder), applies graph optimizations, and quantizes all three sub-models.
+
+## Prerequisites
+
+```bash
+pip install -r requirements.txt
+```
+
+Install ONNX Runtime GenAI based on your target device:
+
+| Device | Install Command |
+|--------|-----------------|
+| GPU (CUDA) | `pip install onnxruntime-genai-cuda --index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/ORT-Nightly/pypi/simple` |
+| CPU | `pip install onnxruntime-genai --index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/ORT-Nightly/pypi/simple` |
+
+## Steps
+
+### 1. Export & Optimize Models
+
+All graph transformations and quantization are declared in the JSON config files inside `cpu_and_mobile/` and `cuda/`. The top-level `optimize.py` script orchestrates the three Olive runs and generates the GenAI runtime configs.
+
+| Command | Description |
+|---------|-------------|
+| `python optimize.py --config-dir cpu_and_mobile --device cpu` | Full pipeline: export, optimize, INT4 quantize (CPU) |
+| `python optimize.py --config-dir cuda --device gpu` | Full pipeline with FP16 + INT4 (CUDA) |
+| `python optimize.py --config-dir cpu_and_mobile --skip-export` | Regenerate configs only (models already exported) |
+
+> **Note:** The text model is always exported as INT4 via ModelBuilder. The vision encoder is graph-optimized and quantized by Olive passes. The embedding model's Gather-based embedding table is quantized using GatherBlockQuantized.
+>
+> The vision encoder is exported for a single image using the Dynamo exporter. At runtime, ONNX Runtime GenAI handles multiple images by calling the vision encoder once per image and concatenating the results — so there is no upper bound on the number of images passed to the model.
+
+### 2. Run Inference
+
+From the top-level model directory:
+
+```bash
+# Text-only (CPU models, default)
+python inference.py --prompt "What is the capital of France?"
+
+# With a single image
+python inference.py --prompt "Describe this image" --image screenshot.png
+
+# CUDA models
+python inference.py --model_path cuda/models --prompt "Describe this image" --image screenshot.png
+
+# Interactive mode
+python inference.py --interactive
+```
+
+## Directory Structure
+
+```
+microsoft-Fara-7B/
+└── builtin/
+    ├── optimize.py                # End-to-end Olive pipeline + GenAI config generation
+    ├── user_script.py             # Olive callbacks: model loading, dummy inputs, IO configs
+    ├── inference.py               # ONNX Runtime GenAI inference
+    ├── codes/                     # Custom Qwen2.5-VL PyTorch model adapted for ONNX export
+    ├── cpu_and_mobile/
+    │   ├── embedding.json         # Olive config: export → optimize → INT4
+    │   ├── vision.json            # Olive config: Dynamo export → graph surgeries → INT4
+    │   ├── text.json              # Olive config: ModelBuilder INT4
+    │   └── models/                # Exported ONNX models (generated)
+    └── cuda/
+        ├── embedding.json         # Olive config with FP16 + CUDA EP
+        ├── vision.json            # Olive config with FP16 + CUDA EP
+        ├── text.json              # ModelBuilder INT4 with CUDA EP
+        └── models/                # Exported CUDA ONNX models (generated)
+```