onnxruntime/docs/genai/tutorials/phi3-python.md

---
title: Phi-3 tutorial
description: Small but mighty. Run Phi-3 with ONNX Runtime in 3 easy steps.
has_children: false
parent: Tutorials
grand_parent: Generate API (Preview)
nav_order: 2
---

# Run Phi-3 language models with the ONNX Runtime generate() API
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## Introduction
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Phi-3 ONNX models are hosted on HuggingFace and you can run them with the ONNX Runtime generate() API.

The mini (3.3B) and medium (14B) versions available now, with support. Both mini and medium have a short (4k) context version and a long (128k) context version. The long context version can accept much longer prompts and produce longer output text, but it does consume more memory.

Available models are:


* [https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-onnx](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-onnx)
* [https://huggingface.co/microsoft/Phi-3-mini-128k-instruct-onnx](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct-onnx)
* [https://huggingface.co/microsoft/Phi-3-medium-4k-instruct-onnx-cpu](https://huggingface.co/microsoft/Phi-3-medium-4k-instruct-onnx-cpu)
* [https://huggingface.co/microsoft/Phi-3-medium-4k-instruct-onnx-cuda](https://huggingface.co/microsoft/Phi-3-medium-4k-instruct-onnx-cuda)
* [https://huggingface.co/microsoft/Phi-3-medium-4k-instruct-onnx-directml](https://huggingface.co/microsoft/Phi-3-medium-4k-instruct-onnx-directml)
* [https://huggingface.co/microsoft/Phi-3-medium-128k-instruct-onnx-cpu](https://huggingface.co/microsoft/Phi-3-medium-128k-instruct-onnx-cpu)
* [https://huggingface.co/microsoft/Phi-3-medium-128k-instruct-onnx-cuda](https://huggingface.co/microsoft/Phi-3-medium-128k-instruct-onnx-cuda)
* [https://huggingface.co/microsoft/Phi-3-medium-128k-instruct-onnx-directml](https://huggingface.co/microsoft/Phi-3-medium-128k-instruct-onnx-directml/)


This tutorial downloads and runs the short context (4k) mini (3B) model variant. See the [model reference](#phi-3-onnx-model-reference) for download commands for the other variants.

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## Setup

1. Install the git large file system extension

   HuggingFace uses `git` for version control. To download the ONNX models you need `git lfs` to be installed, if you do not already have it.

   * Windows: `winget install -e --id GitHub.GitLFS` (If you don't have winget, download and run the `exe` from the [official source](https://docs.github.com/en/repositories/working-with-files/managing-large-files/installing-git-large-file-storage?platform=windows))
   * Linux: `apt-get install git-lfs`
   * MacOS: `brew install git-lfs`

   Then run `git lfs install`

2. Install the HuggingFace CLI

   ```bash
   pip install huggingface-hub[cli]
   ```

## Choose your platform

Are you on a Windows machine with GPU?
* I don't know → Review [this guide](https://www.microsoft.com/en-us/windows/learning-center/how-to-check-gpu) to see whether you have a GPU in your Windows machine and confirm that your GPU is [DirectML-supported](https://github.com/microsoft/DirectML?tab=readme-ov-file#hardware-requirements)
* Yes → Follow the instructions for [DirectML](#run-with-directml).
* No → Do you have an NVIDIA GPU?
  * I don't know → Review [this guide](https://docs.nvidia.com/cuda/cuda-installation-guide-microsoft-windows/index.html#verify-you-have-a-cuda-capable-gpu) to see whether you have a CUDA-capable GPU.
  * Yes → Follow the instructions for [NVIDIA CUDA GPU](#run-with-nvidia-cuda).
  * No → Follow the instructions for [CPU](#run-on-cpu).
 
**Note: Only one package and model is required based on your hardware. That is, only execute the steps for one of the following sections**

## Run with DirectML


1. Download the model

   ```bash
   huggingface-cli download microsoft/Phi-3-mini-4k-instruct-onnx --include directml/* --local-dir .
   ```

   This command downloads the model into a folder called `directml`.


2. Install the generate() API

   ```
   pip install numpy
   pip install --pre onnxruntime-genai-directml
   ```

   You should now see `onnxruntime-genai-directml` in your `pip list`.

3. Run the model

   Run the model with [phi3-qa.py](https://github.com/microsoft/onnxruntime-genai/blob/main/examples/python/phi3-qa.py).

   ```cmd
   curl https://raw.githubusercontent.com/microsoft/onnxruntime-genai/main/examples/python/phi3-qa.py -o phi3-qa.py
   python phi3-qa.py -m directml\directml-int4-awq-block-128
   ```

   Once the script has loaded the model, it will ask you for input in a loop, streaming the output as it is produced the model. For example:

   ```bash
   Input: Tell me a joke about GPUs

   Certainly! Here\'s a light-hearted joke about GPUs:


   Why did the GPU go to school? Because it wanted to improve its "processing power"!


   This joke plays on the double meaning of "processing power," referring both to the computational abilities of a GPU and the idea of a student wanting to improve their academic skills.
   ```

## Run with NVIDIA CUDA

1. Download the model

   ```bash
   huggingface-cli download microsoft/Phi-3-mini-4k-instruct-onnx --include cuda/cuda-int4-rtn-block-32/* --local-dir .
   ```

   This command downloads the model into a folder called `cuda`.

2. Install the generate() API

   ```
   pip install numpy
   pip install --pre onnxruntime-genai-cuda --index-url=https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/onnxruntime-genai/pypi/simple/
   ```

3. Run the model

   Run the model with [phi3-qa.py](https://github.com/microsoft/onnxruntime-genai/blob/main/examples/python/phi3-qa.py).

   ```bash
   curl https://raw.githubusercontent.com/microsoft/onnxruntime-genai/main/examples/python/phi3-qa.py -o phi3-qa.py
   python phi3-qa.py -m cuda/cuda-int4-rtn-block-32 
   ```

      Once the script has loaded the model, it will ask you for input in a loop, streaming the output as it is produced the model. For example:

   ```bash
   Input: Tell me a joke about creative writing
 
   Output:  Why don't writers ever get lost? Because they always follow the plot! 
   ```

## Run on CPU

1. Download the model

   ```bash
   huggingface-cli download microsoft/Phi-3-mini-4k-instruct-onnx --include cpu_and_mobile/cpu-int4-rtn-block-32-acc-level-4/* --local-dir .
   ```

   This command downloads the model into a folder called `cpu_and_mobile`

2. Install the generate() API for CPU
   
   ```
   pip install numpy
   pip install --pre onnxruntime-genai
   ```

3. Run the model

   Run the model with [phi3-qa.py](https://github.com/microsoft/onnxruntime-genai/blob/main/examples/python/phi3-qa.py).

   ```bash
   curl https://raw.githubusercontent.com/microsoft/onnxruntime-genai/main/examples/python/phi3-qa.py -o phi3-qa.py
   python phi3-qa.py -m cpu_and_mobile/cpu-int4-rtn-block-32-acc-level-4
   ```

   Once the script has loaded the model, it will ask you for input in a loop, streaming the output as it is produced the model. For example:

   ```bash
   Input: Tell me a joke about generative AI

   Output:  Why did the generative AI go to school?

   To improve its "creativity" algorithm!
   ```

## Phi-3 ONNX model reference

### Phi-3 mini 4k context CPU

```bash
huggingface-cli download microsoft/Phi-3-mini-4k-instruct-onnx --include cpu_and_mobile/cpu-int4-rtn-block-32-acc-level-4/* --local-dir .
python phi3-qa.py -m cpu_and_mobile/cpu-int4-rtn-block-32-acc-level-4
```

### Phi-3 mini 4k context CUDA

```bash
huggingface-cli download microsoft/Phi-3-mini-4k-instruct-onnx --include cuda/cuda-int4-rtn-block-32/* --local-dir .
python phi3-qa.py -m cuda/cuda-int4-rtn-block-32
```

### Phi-3 mini 4k context DirectML

```bash
huggingface-cli download microsoft/Phi-3-mini-4k-instruct-onnx --include directml/* --local-dir .
python phi3-qa.py -m directml\directml-int4-awq-block-128
```

### Phi-3 mini 128k context CPU

```bash
huggingface-cli download microsoft/Phi-3-mini-128k-instruct-onnx --include cpu_and_mobile/cpu-int4-rtn-block-32-acc-level-4/* --local-dir .
python phi3-qa.py -m cpu_and_mobile/cpu-int4-rtn-block-32-acc-level-4
```

### Phi-3 mini 128k context CUDA

```bash
huggingface-cli download microsoft/Phi-3-mini-128k-instruct-onnx --include cuda/cuda-int4-rtn-block-32/* --local-dir .
python phi3-qa.py -m cuda/cuda-int4-rtn-block-32
```

### Phi-3 mini 128k context DirectML

```bash
huggingface-cli download microsoft/Phi-3-mini-128k-instruct-onnx --include directml/* --local-dir .
python phi3-qa.py -m directml\directml-int4-awq-block-128
```

### Phi-3 medium 4k context CPU

```bash
git clone https://huggingface.co/microsoft/Phi-3-medium-4k-instruct-onnx-cpu
python phi3-qa.py -m Phi-3-medium-4k-instruct-onnx-cpu/cpu-int4-rtn-block-32-acc-level-4
```

### Phi-3 medium 4k context CUDA

```bash
git clone https://huggingface.co/microsoft/Phi-3-medium-4k-instruct-onnx-cuda
python phi3-qa.py -m Phi-3-medium-4k-instruct-onnx-cuda/cuda-int4-rtn-block-32
```

### Phi-3 medium 4k context DirectML

```bash
git clone https://huggingface.co/microsoft/Phi-3-medium-4k-instruct-onnx-directml
python phi3-qa.py -m Phi-3-medium-4k-instruct-onnx-directml/directml-int4-awq-block-128
```

### Phi-3 medium 128k context CPU

```bash
git clone https://huggingface.co/microsoft/Phi-3-medium-128k-instruct-onnx-cpu
python phi3-qa.py -m Phi-3-medium-128k-instruct-onnx-cpu/cpu-int4-rtn-block-32-acc-level-4
```

### Phi-3 medium 128k context CUDA

```bash
git clone https://huggingface.co/microsoft/Phi-3-medium-128k-instruct-onnx-cuda
python phi3-qa.py -m Phi-3-medium-128k-instruct-onnx-cuda/cuda-int4-rtn-block-32
```

### Phi-3 medium 128k context DirectML

```bash
git clone https://huggingface.co/microsoft/Phi-3-medium-128k-instruct-onnx-directml
python phi3-qa.py -m Phi-3-medium-128k-instruct-onnx-directml/directml-int4-awq-block-128
```