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--- |
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license: mit |
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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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- ONNXRuntime |
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- phi3 |
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- nlp |
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- conversational |
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- custom_code |
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inference: false |
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language: |
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- en |
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--- |
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# EmbeddedLLM/Phi-3-mini-4k-instruct-062024 ONNX |
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## Model Summary |
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This model is an ONNX-optimized version of [microsoft/Phi-3-mini-4k-instruct (June 2024)](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct), designed to provide accelerated inference on a variety of hardware using ONNX Runtime(CPU and DirectML). |
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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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## ONNX Models |
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Here are some of the optimized configurations we have added: |
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- **ONNX model for int4 DirectML:** ONNX model for AMD, Intel, and NVIDIA GPUs on Windows, quantized to int4 using AWQ. |
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## Usage |
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### Installation and Setup |
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To use the EmbeddedLLM/Phi-3-mini-4k-instruct-062024 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/Phi-3-mini-4k-instruct-062024-onnx --include="onnx/directml/Phi-3-mini-4k-instruct-062024-int4/*" --local-dir .\Phi-3-mini-4k-instruct-062024-int4 |
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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==1.26.4 |
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pip install onnxruntime-directml |
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pip install --pre onnxruntime-genai-directml==0.3.0 |
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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 .\Phi-3-mini-4k-instruct-062024-int4 |
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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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## Model Description |
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- **Developed by:** Microsoft |
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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 Phi-3-mini-4k-instruct-062024 for ONNX Runtime inference, optimized for DirectML. |
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## Performance Metrics |
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<!-- These are the evaluation metrics being used, ideally with a description of why. --> |
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### DirectML |
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We measured the performance of DirectML on AMD Ryzen 9 7940HS /w Radeon 78 |
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| Prompt Length | Generation Length | Average Throughput (tps) | |
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|---------------------------|-------------------|-----------------------------| |
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| 128 | 128 | 53.46686 | |
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| 128 | 256 | 53.11233 | |
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| 128 | 512 | 57.45816 | |
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| 128 | 1024 | 33.44713 | |
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| 256 | 128 | 76.50182 | |
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| 256 | 256 | 66.68873 | |
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| 256 | 512 | 70.83862 | |
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| 256 | 1024 | 34.64715 | |
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| 512 | 128 | 85.10079 | |
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| 512 | 256 | 68.64049 | |
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| 512 | 512 | - | |
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| 512 | 1024 | - | |
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| 1024 | 128 | - | |
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| 1024 | 256 | - | |
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| 1024 | 512 | - | |
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| 1024 | 1024 | - | |
