Create README.md

README.md

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+---
+license: apache-2.0
+datasets:
+- berkeley-nest/Nectar
+language:
+- en
+tags:
+ - reward model
+ - RLHF
+ - RLAIF
+ - ONNX
+ - DML
+ - DirectML
+ - ONNXRuntime
+ - mistral
+ - conversational
+ - custom_code
+---
+# 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
+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}
+}
+```