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How to replace OpenAI with Llama V2 using Genoss & Hugging Face ?

Integrating cutting-edge models like Llama V2 with platforms like Genoss and Hugging Face no longer has to be a complex task. In this guide, we've explored the seamless method of running Llama V2 using Genoss through Hugging Face's inference endpoint.

In the contemporary world of machine learning, we have a host of technologies, models, and services available. When we have to integrate multiple tools, things can get complex. But what if there's a seamless way to run Llama V2 using Genoss with Hugging Face? In this article, we are going to explore exactly how to do that. Let's break it down step by step.

Introduction

Llama V2 is a state-of-the-art LLM (Language Model) designed to fulfill various natural language processing tasks. Genoss is an open-source platform that enables us to run models like this quickly, and Hugging Face provides an ecosystem to host and manage models.

The goal here is to explain how to use Genoss to run the Llama V2 LLM model via the inference endpoint of Hugging Face by hosting it on Hugging Face servers.

Step-by-Step Guide

1. Getting Started with Genoss GPT

Go to Genoss GPT and download it using SSH or HTTPS.

git clone https://github.com/OpenGenerativeAI/GenossGPT.git

Open the project with Visual Studio or your preferred code editor.

2. Setting Up the Environment

Follow the readme instructions within the Genoss repository.
Install poetry, which allows you to easily install everything you need to handles the backend of Genoss. Run poetry install.

3. Configuring the Environment File

Go inside the demo folder and update the .env file.
Inside the demo folder, there's an env.example file.
You must add your HuggingFace API token, which you can create at HuggingFace under settings/token.

Hugging face API Token Creation

You also need to put your OpenAI API key. Go to https://platform.openai.com/account/api-keys
Finally, specify the custom HuggingFace endpoint URL.

4. Deploying the Model

Find the Llama V2 model on HuggingFace.

Deploy it in the region and cloud provider of your choice.
Choose the GPU you want and protect it, then create the endpoint.

5. Running Genoss

Add the URL from your deployed model to the .env file.
Run the command to start the stream.

6. Accessing Genoss, HuggingFace, and Llama V2

Now you can access Genoss, HuggingFace, and Llama V2 through the inference endpoint.
Run the backend and the demo
Using the commands in the readme
Access

You can also host other models locally.

Conclusion

Using Genoss to run Llama V2 with Hugging Face makes a seemingly complex process very simple. Not only does it streamline the deployment and use of the model, but it also enables scalability and ease of integration with other tools like OpenAI SDK.

It's an exciting time to be involved in machine learning and artificial intelligence, and the integration of Genoss, Hugging Face, and Llama V2 is a powerful example of what is possible.

Feel free to explore the possibilities, and let us know if you have any questions. Happy modeling!

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And here is the Youtube Video for more details 😘