File size: 2,650 Bytes
84a2633 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 |
---
license: apache-2.0
pipeline_tag: text-generation
tags:
- ONNX
- DML
- DirectML
- ONNXRuntime
- mistral
- conversational
- custom_code
inference: false
---
# Mistral-7B-Instruct-v0.3 ONNX
## Model Summary
The [Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) is an optimized version of the Mistral-7B model, fine-tuned for instruction-based tasks. This model is available in ONNX format to accelerate inference using ONNX Runtime, specifically optimized for 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.
## 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.
## 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-int4-onnx-directml --include directml/* --local-dir .\mistral-7b-instruct-v0.3
```
5. **Install necessary Python packages:**
```sh
pip install numpy
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
## Optimized Configurations
The following optimized configurations are available:
1. **ONNX model for int4 DML:** Optimized for AMD, Intel, and NVIDIA GPUs on Windows, quantized to int4.
2. **ONNX model for int4 CPU:** Optimized for CPU, using int4 quantization. |