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--- |
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license: apache-2.0 |
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pipeline_tag: text-generation |
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tags: |
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- ONNX |
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- DML |
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- DirectML |
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- ONNXRuntime |
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- mistral |
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- conversational |
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- custom_code |
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inference: false |
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--- |
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# Mistral-7B-Instruct-v0.3 ONNX |
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## Model Summary |
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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. |
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## Model Description |
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- **Developed by:** Mistral AI |
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- **Model type:** ONNX |
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- **Language(s) (NLP):** Python, C, C++ |
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- **License:** Apache License Version 2.0 |
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- **Model Description:** This model is a conversion of the Mistral-7B-Instruct-v0.3 for ONNX Runtime inference, optimized for CPU and DirectML. |
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## Usage |
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### Installation and Setup |
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To use the Mistral-7B-Instruct-v0.3 ONNX model on Windows with DirectML, follow these steps: |
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1. **Create and activate a Conda environment:** |
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```sh |
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conda create -n onnx python=3.10 |
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conda activate onnx |
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``` |
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2. **Install Git LFS:** |
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```sh |
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winget install -e --id GitHub.GitLFS |
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``` |
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3. **Install Hugging Face CLI:** |
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```sh |
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pip install huggingface-hub[cli] |
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``` |
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4. **Download the model:** |
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```sh |
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huggingface-cli download EmbeddedLLM/mistral-7b-instruct-v0.3-int4-onnx-directml --include directml/* --local-dir .\mistral-7b-instruct-v0.3 |
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``` |
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5. **Install necessary Python packages:** |
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```sh |
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pip install numpy |
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pip install onnxruntime-directml |
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pip install --pre onnxruntime-genai-directml |
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``` |
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6. **Install Visual Studio 2015 runtime:** |
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```sh |
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conda install conda-forge::vs2015_runtime |
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``` |
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7. **Download the example script:** |
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```sh |
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Invoke-WebRequest -Uri "https://raw.githubusercontent.com/microsoft/onnxruntime-genai/main/examples/python/phi3-qa.py" -OutFile "phi3-qa.py" |
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``` |
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8. **Run the example script:** |
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```sh |
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python phi3-qa.py -m .\mistral-7b-instruct-v0.3 |
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``` |
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### Hardware Requirements |
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- **Minimum Configuration:** |
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- **Windows:** DirectX 12-capable GPU (AMD/Nvidia) |
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- **CPU:** x86_64 / ARM64 |
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- **Tested Configurations:** |
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- **GPU:** AMD Ryzen 8000 Series iGPU (DirectML) |
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- **CPU:** AMD Ryzen CPU |
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## Optimized Configurations |
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The following optimized configurations are available: |
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1. **ONNX model for int4 DML:** Optimized for AMD, Intel, and NVIDIA GPUs on Windows, quantized to int4. |
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2. **ONNX model for int4 CPU:** Optimized for CPU, using int4 quantization. |
