---
license: apache-2.0
base_model: openchat/openchat-3.5-0106
datasets:
- berkeley-nest/Nectar
language:
- en
tags:
 - reward model
 - RLHF
 - RLAIF
 - ONNX
 - DML
 - DirectML
 - ONNXRuntime
 - mistral
 - conversational
 - custom_code
pipeline_tag: text-generation
---
# Starling-LM-7B-beta ONNX

## Model Summary

This repository contains the ONNX-optimized version of [Starling-LM-7B-beta](https://huggingface.co/Nexusflow/Starling-LM-7B-beta), 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 Starling-LM-7B-beta 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/Starling-LM-7b-beta-onnx --include="onnx/directml/*" --local-dir .\Starling-LM-7B-beta-onnx
```

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 .\Starling-LM-7B-beta-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

## Model Description

- **Developed by:** The Nexusflow Team (Banghua Zhu, Evan Frick, Tianhao Wu, Hanlin Zhu, Karthik Ganesan, Wei-Lin Chiang, Jian Zhang, and Jiantao Jiao)
- **Model type:** Language Model fine-tuned with RLHF / RLAIF
- **License:** Apache-2.0 license under the condition that the model is not used to compete with OpenAI
- **Finetuned from model:** [Openchat-3.5-0106](https://huggingface.co/openchat/openchat-3.5-0106) (based on [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1))

We introduce Starling-LM-7B-beta, an open large language model (LLM) trained by Reinforcement Learning from AI Feedback (RLAIF). Starling-LM-7B-beta is trained from [Openchat-3.5-0106](https://huggingface.co/openchat/openchat-3.5-0106) with our new reward model [Nexusflow/Starling-RM-34B](https://huggingface.co/Nexusflow/Starling-RM-34B) and policy optimization method [Fine-Tuning Language Models from Human Preferences (PPO)](https://arxiv.org/abs/1909.08593). Harnessing the power of the ranking dataset, [berkeley-nest/Nectar](https://huggingface.co/datasets/berkeley-nest/Nectar), the upgraded reward model, [Starling-RM-34B](https://huggingface.co/Nexusflow/Starling-RM-34B), and the new reward training and policy tuning pipeline, Starling-LM-7B-beta scores an improved 8.12 in MT Bench with GPT-4 as a judge.

## License
The dataset, model and online demo is subject to the [Terms of Use](https://openai.com/policies/terms-of-use) of the data generated by OpenAI, and [Privacy Practices](https://chrome.google.com/webstore/detail/sharegpt-share-your-chatg/daiacboceoaocpibfodeljbdfacokfjb) of ShareGPT. Please contact us if you find any potential violation.

## Citation
```
@misc{starling2023,
title = {Starling-7B: Improving LLM Helpfulness & Harmlessness with RLAIF},
url = {},
author = {Zhu, Banghua and Frick, Evan and Wu, Tianhao and Zhu, Hanlin and Ganesan, Karthik and Chiang, Wei-Lin and Zhang, Jian and Jiao, Jiantao},
month = {November},
year = {2023}
}
```