README.md

---
license: llama3
base_model: meta-llama/Meta-Llama-3-8B
language:
- en
tags:
- openchat
- llama3
- C-RLFT
- ONNX
- DML
- DirectML
- ONNXRuntime
- conversational
- custom_code
pipeline_tag: text-generation
---
# openchat-3.6-8b-20240522 ONNX

## Model Summary

This repository contains the ONNX-optimized version of [openchat/openchat-3.6-8b-20240522](https://huggingface.co/openchat/openchat-3.6-8b-20240522), designed to accelerate inference using ONNX Runtime. These optimizations are specifically tailored for CPU and DirectML. DirectML is a high-performance, hardware-accelerated DirectX 12 library for machine learning, offering GPU acceleration across a wide range of supported hardware and drivers, including those from AMD, Intel, NVIDIA, and Qualcomm.

## Optimized Configurations

The following optimized configurations are available:

- **ONNX model for int4 DirectML:** Optimized 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 EmbeddedLLM/openchat-3.6-8b-20240522-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/openchat-3.6-8b-20240522-onnx --include="onnx/directml/*" --local-dir .\openchat-3.6-8b-20240522-onnx
```

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 .\openchat-3.6-8b-20240522-onnx
```

### 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

## Citation
```
@article{wang2023openchat,
  title={OpenChat: Advancing Open-source Language Models with Mixed-Quality Data},
  author={Wang, Guan and Cheng, Sijie and Zhan, Xianyuan and Li, Xiangang and Song, Sen and Liu, Yang},
  journal={arXiv preprint arXiv:2309.11235},
  year={2023}
}
```