---
license: apache-2.0
pipeline_tag: text-generation
tags:
- ONNX
- DML
- DirectML
- ONNXRuntime
- mistral
- conversational
- custom_code
inference: false
language:
- en
---

# Mistral-7B-Instruct-v0.3 ONNX

## Model Summary

This model is an ONNX-optimized version of [Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3), designed to provide accelerated inference on a variety of hardware using ONNX Runtime(CPU and DirectML).
DirectML is a high-performance, hardware-accelerated DirectX 12 library for machine learning, providing GPU acceleration for a wide range of supported hardware and drivers, including AMD, Intel, NVIDIA, and Qualcomm GPUs.

## ONNX Models

Here are some of the optimized configurations we have added:
- **ONNX model for int4 DirectML:** ONNX model for AMD, Intel, and NVIDIA GPUs on Windows, quantized to int4 using AWQ.
- **ONNX model for int4 CPU and Mobile:** ONNX model for CPU and mobile using int4 quantization via RTN. There are two versions uploaded to balance latency vs. accuracy. Acc=1 is targeted at improved accuracy, while Acc=4 is for improved performance. For mobile devices, we recommend using the model with acc-level-4.

## Usage

### Installation and Setup

To use the Mistral-7B-Instruct-v0.3 ONNX model on Windows with DirectML, follow these steps:

1. **Create and activate a Conda environment:**
```sh
conda create -n onnx python=3.10
conda activate onnx
```

2. **Install Git LFS:**
```sh
winget install -e --id GitHub.GitLFS
```

3. **Install Hugging Face CLI:**
```sh
pip install huggingface-hub[cli]
```

4. **Download the model:**
```sh
huggingface-cli download EmbeddedLLM/mistral-7b-instruct-v0.3-onnx --include="onnx/directml/*" --local-dir .\mistral-7b-instruct-v0.3
```

5. **Install necessary Python packages:**
```sh
pip install numpy==1.26.4
pip install onnxruntime-directml
pip install --pre onnxruntime-genai-directml
```

6. **Install Visual Studio 2015 runtime:**
```sh
conda install conda-forge::vs2015_runtime
```

7. **Download the example script:**
```sh
Invoke-WebRequest -Uri "https://raw.githubusercontent.com/microsoft/onnxruntime-genai/main/examples/python/phi3-qa.py" -OutFile "phi3-qa.py"
```

8. **Run the example script:**
```sh
python phi3-qa.py -m .\mistral-7b-instruct-v0.3
```

### Hardware Requirements

**Minimum Configuration:**
- **Windows:** DirectX 12-capable GPU (AMD/Nvidia) 
- **CPU:** x86_64 / ARM64

**Tested Configurations:**
- **GPU:** AMD Ryzen 8000 Series iGPU (DirectML)
- **CPU:** AMD Ryzen CPU
  
## Model Description

- **Developed by:** Mistral AI
- **Model type:** ONNX
- **Language(s) (NLP):** Python, C, C++
- **License:** Apache License Version 2.0
- **Model Description:** This model is a conversion of the Mistral-7B-Instruct-v0.3 for ONNX Runtime inference, optimized for CPU and DirectML.