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README.md

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----
-license: other
-license_name: nvidia-open-model-license
-license_link: >-
-  https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf
----
+---
+license: other
+license_name: nvidia-open-model-license
+license_link: >-
+  https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf
+base_model:
+- microsoft/Phi-3.5-mini-instruct
+---
+
+
+# Phi-3.5-mini-Instruct ONNX INT4
+
+### Model Developer: Microsoft 
+
+### Model Description
+
+The NVIDIA Phi-3.5-mini-Instruct ONNX INT4 model is the quantized version of the Microsoft Phi-3.5-mini-Instruct model which has 3.8B parameters and is a dense decoder-only Transformer model using the same tokenizer as Phi-3 Mini. It supports 128K context length, therefore the model is capable of several long context tasks including long document/meeting summarization, long document QA, long document information retrieval. For more information, please check [here](https://huggingface.co/microsoft/Phi-3.5-mini-instruct). The NVIDIA Phi-3.5-mini-Instruct ONNX INT4 model is quantized with [TensorRT Model Optimizer](https://github.com/NVIDIA/TensorRT-Model-Optimizer). 
+
+This model is ready for commercial and research use case. 
+
+Steps followed to generate this quantized model: 
+
+*  1. Download Microsoft Phi-3.5-mini-Instruct model in Pytorch bfloat16 format from HuggingFace.  
+
+*  2. Convert PyTorch model to ONNX FP16 using onnxruntime-genai model builder.  
+
+*  3. Quantize Phi-3.5-mini-Instruct ONNX FP16 model to Phi-3.5-mini-Instruct ONNX INT4 AWQ model using TensorRT Model Optimizer – Windows.   
+
+### Third-Party Community Consideration 
+This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party’s requirements for this application and use case; see link to the Non-NVIDIA [Phi3.5-Mini-Instruct Model Card](https://huggingface.co/microsoft/Phi-3.5-mini-instruct).
+ 
+
+### License/Terms of Use: 
+GOVERNING TERMS: Use of this model is governed by the NVIDIA Open Model License Agreement (found at https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf). ADDITIONAL INFORMATION: Gemma Terms of Use (found at https://ai.google.dev/gemma/terms). 
+
+### Reference: 
+
+[MIT License](https://huggingface.co/microsoft/Phi-3.5-mini-instruct/blob/main/LICENSE ) 
+
+[Phi3.5-mini-Instruct Model Card](https://huggingface.co/microsoft/Phi-3.5-mini-instruct ) 
+
+[Readme](https://huggingface.co/microsoft/Phi-3.5-mini-instruct/blob/main/README.md)   
+
+### Model Architecture: 
+
+Phi-3.5-mini has 3.8B parameters and is a dense decoder-only Transformer model using the same tokenizer as Phi-3 Mini.
+
+**Architecture Type:** Transformer <br> 
+
+**Network Architecture:** Phi3 <br> 
+
+**Input** 
+
+* Input Type: Text. It is best suited for prompts using chat format. 
+
+* Input Format: String 
+
+* Input Parameters: Sequence (1D)
+
+* Other Properties Related to Input: Supports Arabic, Chinese, Czech, Danish, Dutch, English, Finnish, French, German, Hebrew, Hungarian, Italian, Japanese, Korean, Norwegian, Polish, Portuguese, Russian, Spanish, Swedish, Thai, Turkish, Ukrainian  
+
+**Output**  
+
+* Output Type: Text 
+
+* Output Format: String  
+
+* Output Parameters: Sequence (1D)
+
+## Software Integration: 
+ 
+* **Supported Hardware Microarchitecture Compatibility :** Nvidia Ampere and newer GPUs. 6GB or higher VRAM GPUs are recommended. Higher VRAM may be required for larger context length use cases.  
+
+* **Supported Operating System(s):**  Windows  
+
+## Model Version(s):  1.0  
+
+## Training, Testing, and Evaluation Datasets:   
+Refer to [Phi3.5-mini-Instruct Model Card](https://huggingface.co/microsoft/Phi-3.5-mini-instruct) for the details. 
+
+### Calibration Dataset: cnn_daily mail used for calibration.  
+
+Link: https://huggingface.co/datasets/abisee/cnn_dailymail
+
+* Data Collection Method by dataset: Automated
+
+* Labeling Method by dataset: [Unknown] 
+
+### Evaluation Dataset: 
+
+Link: https://people.eecs.berkeley.edu/~hendrycks/data.tar
+
+* Data Collection Method by dataset  - Unknown
+
+* Labeling Method by dataset  - Not Applicable 
+
+## Evaluation Results: 
+
+**MMLU (5# shots):**  
+
+With GenAI ORT->DML backend, we got below mentioned accuracy numbers on a desktop RTX 4090 GPU system.  
+
+"overall_accuracy": 65.51 
+
+**Test configuration:** 
+
+* **GPU:** RTX 4090, RTX 3090.   
+
+* **Windows 11:** 23H2 
+
+* **NVIDIA Graphics driver:** R565 or higher 
+
+## Inference: 
+Inference Backend: [Onnxruntime-GenAI-DirectML](https://onnxruntime.ai/docs/genai/howto/install.html#directml) 
+
+We used GenAI ORT->DML backend for inference. The instructions to use this backend are given in readme.txt file available under Files section.  
+
+
+## Ethical Considerations:
+
+NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications.  When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.  
+
+Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).