|
--- |
|
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. |
