Upload README.md

README.md

@@ -1,62 +1,62 @@
----
-license: mit
-pipeline_tag: text-generation
-tags:
- - ONNX
- - ONNXRuntime
- - ONNXRuntimeWeb
- - phi3
- - transformers.js
- - transformers
- - nlp
- - conversational
- - custom_code
-inference: false
----
-
-# Phi-3 Mini-4K-Instruct ONNX model for in-browser inference
-
-<!-- Provide a quick summary of what the model is/does. -->
-Running Phi3-mini-4K entirely in the browser! Check out this [demo](https://guschmue.github.io/ort-webgpu/chat/index.html).
-
-This repository hosts the optimized Web version of ONNX Phi-3-mini-4k-instruct model to accelerate inference in the browser with ONNX Runtime Web.
-
-[The Phi-3-Mini-4K-Instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) is a 3.8B parameters, lightweight, state-of-the-art open model trained with the Phi-3 datasets that includes both synthetic data and the filtered publicly available websites data with a focus on high-quality and reasoning dense properties. When assessed against benchmarks testing common sense, language understanding, math, code, long context and logical reasoning, Phi-3 Mini-4K-Instruct showcased a robust and state-of-the-art performance among models with less than 13 billion parameters. 
-
-## How to run
-
-[ONNX Runtime Web](https://onnxruntime.ai/docs/tutorials/web/build-web-app.html) is a JavaScript library to enable web developers to deploy machine learning models directly in web browsers, offering multiple backends leveraging hardware acceleration. WebGPU backend is recommended to run Phi-3-mini efficiently. 
-
-
-Here is an [E2E example](https://github.com/microsoft/onnxruntime-inference-examples/tree/main/js/chat) for running this optimized  Phi3-mini-4K for the web, with ONNX Runtime harnessing WebGPU.
-
-
-**Supported devices and browser with WebGPU**: Chrome 113+ and Edge 113+ for Mac, Windows, ChromeOS, and Chrome 121+ for Android. Pls visit [here](https://github.com/gpuweb/gpuweb/wiki/Implementation-Status#safari-in-progress) for tracking WebGPU support in browsers
-
-## Performance Metrics
-Performance vary between GPUs. The more powerful the GPU, the faster the speed. On a NVIDIA GeForce RTX 4090: ~42 tokens/second 
-
-
-## Additional Details
-
-To obtain other optimized Phi3-mini-4k ONNX models for server platforms, Windows, Linux, Mac desktops, and mobile, please visit [Phi-3-mini-4k-instruct onnx model](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-onnx). The model differences in the web version compared to other versions:
-
-1. the model is fp16 with int4 block quantization for weights
-2. the 'logits' output is fp32 
-3. the model uses MHA instead of GQA
-4. onnx and external data file need to stay below 2GB to be cacheable in chromium
-
-To optimize a fine-tuned Phi3-mini-4k model to run with ONNX Runtime Web, please follow [this Olive example](https://github.com/microsoft/Olive/tree/main/examples/phi3). [Olive](https://github.com/microsoft/OLive) is an easy-to-use model optimization tool for generating an optimized ONNX model to efficiently run with ONNX Runtime across platforms.
-
-
-## Model Description
-
-- **Developed by:**  Microsoft
-- **Model type:** ONNX
-- **Inference Language(s) (NLP):** JavaScript
-- **License:** MIT
-- **Model Description:** This is the web version of the Phi-3 Mini-4K-Instruct model for ONNX Runtime inference.
-
-
-## Model Card Contact
-guschmue, qining
+---
+license: mit
+pipeline_tag: text-generation
+tags:
+ - ONNX
+ - ONNXRuntime
+ - ONNXRuntimeWeb
+ - phi3
+ - transformers.js
+ - transformers
+ - nlp
+ - conversational
+ - custom_code
+inference: false
+---
+
+# Phi-3 Mini-4K-Instruct ONNX model for in-browser inference
+
+<!-- Provide a quick summary of what the model is/does. -->
+Running Phi3-mini-4K entirely in the browser! Check out this [demo](https://guschmue.github.io/ort-webgpu/chat/index.html).
+
+This repository hosts the optimized Web version of ONNX Phi-3-mini-4k-instruct model to accelerate inference in the browser with ONNX Runtime Web.
+
+[The Phi-3-Mini-4K-Instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) is a 3.8B parameters, lightweight, state-of-the-art open model trained with the Phi-3 datasets that includes both synthetic data and the filtered publicly available websites data with a focus on high-quality and reasoning dense properties. When assessed against benchmarks testing common sense, language understanding, math, code, long context and logical reasoning, Phi-3 Mini-4K-Instruct showcased a robust and state-of-the-art performance among models with less than 13 billion parameters. 
+
+## How to run
+
+[ONNX Runtime Web](https://onnxruntime.ai/docs/tutorials/web/build-web-app.html) is a JavaScript library to enable web developers to deploy machine learning models directly in web browsers, offering multiple backends leveraging hardware acceleration. WebGPU backend is recommended to run Phi-3-mini efficiently. 
+
+
+Here is an [E2E example](https://github.com/microsoft/onnxruntime-inference-examples/tree/main/js/chat) for running this optimized  Phi3-mini-4K for the web, with ONNX Runtime harnessing WebGPU.
+
+
+**Supported devices and browser with WebGPU**: Chrome 113+ and Edge 113+ for Mac, Windows, ChromeOS, and Chrome 121+ for Android. Pls visit [here](https://github.com/gpuweb/gpuweb/wiki/Implementation-Status#safari-in-progress) for tracking WebGPU support in browsers
+
+## Performance Metrics
+Performance vary between GPUs. The more powerful the GPU, the faster the speed. On a NVIDIA GeForce RTX 4090: ~42 tokens/second 
+
+
+## Additional Details
+
+To obtain other optimized Phi3-mini-4k ONNX models for server platforms, Windows, Linux, Mac desktops, and mobile, please visit [Phi-3-mini-4k-instruct onnx model](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-onnx). The model differences in the web version compared to other versions:
+
+1. the model is fp16 with int4 block quantization for weights
+2. the 'logits' output is fp32 
+3. the model uses MHA instead of GQA
+4. onnx and external data file need to stay below 2GB to be cacheable in chromium
+
+To optimize a fine-tuned Phi3-mini-4k model to run with ONNX Runtime Web, please follow [this Olive example](https://github.com/microsoft/Olive/tree/main/examples/phi3). [Olive](https://github.com/microsoft/OLive) is an easy-to-use model optimization tool for generating an optimized ONNX model to efficiently run with ONNX Runtime across platforms.
+
+
+## Model Description
+
+- **Developed by:**  Microsoft
+- **Model type:** ONNX
+- **Inference Language(s) (NLP):** JavaScript
+- **License:** MIT
+- **Model Description:** This is the web version of the Phi-3 Mini-4K-Instruct model for ONNX Runtime inference.
+
+
+## Model Card Contact
+guschmue, qining