Update README.md

README.md

@@ -1,57 +1,61 @@
-# Gemma3 Embedding Model: ONNX Conversion Demonstration
-
-This repository demonstrates the conversion and comparison of the Gemma3 embedding model from Hugging Face to ONNX format using optimum-onnx. It includes scripts for both ONNX and PyTorch inference pipelines, as well as a comparison of their outputs.
-
-## Files
-
-- `onnx_gemma3_pipeline.py`: Runs the Gemma3 embedding model using ONNXRuntime, including post-processing steps (Pooling, Dense, Normalize) with ONNX exported layers.
-- `pytorch_gemma3_pipeline.py`: Runs the original Gemma3 embedding model using PyTorch and SentenceTransformer for reference.
-- `compare_gemma3_onnx_vs_pytorch.py`: Compares the output embeddings and cosine similarities between the ONNX and PyTorch pipelines.
-- `download_missing_hf_files.py`: Downloads required files from Hugging Face and exports Dense layers to ONNX.
-- `gemma3_mean_pooling_basic.py`: The most basic implementation, running Gemma3 ONNX inference with only mean pooling (no Dense or Normalize stages).
-
-## Pipeline Differences
-
-Both pipelines use ONNXRuntime for transformer inference via `ORTModelForFeatureExtraction`. The key difference is in post-processing:
-
-- **ONNX pipeline** (`onnx_gemma3_pipeline.py`): Uses ONNXRuntime for both the transformer and Dense layers (exported to ONNX), making most of the pipeline ONNX-based except for normalization.
-- **PyTorch pipeline** (`pytorch_gemma3_pipeline.py`): Uses ONNXRuntime for the transformer, but all post-processing (Pooling, Dense, Normalize) is performed with PyTorch modules from SentenceTransformer.
-
-This demonstrates how ONNX conversion can offload more computation for faster, hardware-agnostic inference, while the PyTorch pipeline serves as the reference implementation.
-
-## Setup
-
-1. Install dependencies:
-	 ```sh
-	 pip install git+https://github.com/simondanielsson/optimum-onnx.git@feature/add-gemma3-export
-	 pip install git+https://github.com/huggingface/[email protected]
-	 pip install sentence-transformers onnxruntime safetensors huggingface_hub
-	 ```
-2. Export the ONNX model:
-	 ```sh
-	 optimum-cli export onnx --model google/embeddinggemma-300m-qat-q4_0-unquantized embeddinggemma-300m-onnx
-	 python download_missing_hf_files.py
-	 ```
-
-## Usage
-
-- Run the ONNX pipeline:
-	```sh
-	python onnx_gemma3_pipeline.py
-	```
-- Run the PyTorch pipeline:
-	```sh
-	python pytorch_gemma3_pipeline.py
-	```
-- Compare outputs:
-	```sh
-	python compare_gemma3_onnx_vs_pytorch.py
-	```
-
-## Results
-
-The comparison script prints cosine similarities between sample word embeddings (e.g., "apple", "banana", "car") for both ONNX and PyTorch pipelines, demonstrating the fidelity of the ONNX conversion.
-
-## References
-- [Optimum-ONNX Gemma3 PR](https://github.com/huggingface/optimum-onnx/pull/50)
-- [Gemma3 Model](https://huggingface.co/google/embeddinggemma-300m-qat-q4_0-unquantized)
+---
+base_model:
+- google/embeddinggemma-300m-qat-q4_0-unquantized
+---
+# Gemma3 Embedding Model: ONNX Conversion Demonstration
+
+This repository demonstrates the conversion and comparison of the Gemma3 embedding model from Hugging Face to ONNX format using optimum-onnx. It includes scripts for both ONNX and PyTorch inference pipelines, as well as a comparison of their outputs.
+
+## Files
+
+- `onnx_gemma3_pipeline.py`: Runs the Gemma3 embedding model using ONNXRuntime, including post-processing steps (Pooling, Dense, Normalize) with ONNX exported layers.
+- `pytorch_gemma3_pipeline.py`: Runs the original Gemma3 embedding model using PyTorch and SentenceTransformer for reference.
+- `compare_gemma3_onnx_vs_pytorch.py`: Compares the output embeddings and cosine similarities between the ONNX and PyTorch pipelines.
+- `download_missing_hf_files.py`: Downloads required files from Hugging Face and exports Dense layers to ONNX.
+- `gemma3_mean_pooling_basic.py`: The most basic implementation, running Gemma3 ONNX inference with only mean pooling (no Dense or Normalize stages).
+
+## Pipeline Differences
+
+Both pipelines use ONNXRuntime for transformer inference via `ORTModelForFeatureExtraction`. The key difference is in post-processing:
+
+- **ONNX pipeline** (`onnx_gemma3_pipeline.py`): Uses ONNXRuntime for both the transformer and Dense layers (exported to ONNX), making most of the pipeline ONNX-based except for normalization.
+- **PyTorch pipeline** (`pytorch_gemma3_pipeline.py`): Uses ONNXRuntime for the transformer, but all post-processing (Pooling, Dense, Normalize) is performed with PyTorch modules from SentenceTransformer.
+
+This demonstrates how ONNX conversion can offload more computation for faster, hardware-agnostic inference, while the PyTorch pipeline serves as the reference implementation.
+
+## Setup
+
+1. Install dependencies:
+	 ```sh
+	 pip install git+https://github.com/simondanielsson/optimum-onnx.git@feature/add-gemma3-export
+	 pip install git+https://github.com/huggingface/[email protected]
+	 pip install sentence-transformers onnxruntime safetensors huggingface_hub
+	 ```
+2. Export the ONNX model:
+	 ```sh
+	 optimum-cli export onnx --model google/embeddinggemma-300m-qat-q4_0-unquantized embeddinggemma-300m-onnx
+	 python download_missing_hf_files.py
+	 ```
+
+## Usage
+
+- Run the ONNX pipeline:
+	```sh
+	python onnx_gemma3_pipeline.py
+	```
+- Run the PyTorch pipeline:
+	```sh
+	python pytorch_gemma3_pipeline.py
+	```
+- Compare outputs:
+	```sh
+	python compare_gemma3_onnx_vs_pytorch.py
+	```
+
+## Results
+
+The comparison script prints cosine similarities between sample word embeddings (e.g., "apple", "banana", "car") for both ONNX and PyTorch pipelines, demonstrating the fidelity of the ONNX conversion.
+
+## References
+- [Optimum-ONNX Gemma3 PR](https://github.com/huggingface/optimum-onnx/pull/50)
+- [Gemma3 Model](https://huggingface.co/google/embeddinggemma-300m-qat-q4_0-unquantized)