Are you ready to dive into the fascinating world of Language Learning Models (LLM) with LangServe and Retrieval-Augmented Generation (RAG)? This guide will walk you through the steps needed for a basic setup, while putting an emphasis on application and ease of use.

What You’ll Need

• Python installed on your system.
• Access to HuggingFace Model Hub.
• Terminal or command line for executing commands.
• Ngrok for creating a public URL.
• A basic understanding of how to navigate directories and execute command-line commands.

Installation Steps

First, we need to install the necessary packages and retrieve the model files. Here’s how you can do that:

Step 1: Installing HuggingFace Hub

Open your terminal and run the following command:

bash
pip install huggingface-hub

Step 2: Downloading the EEVE-Korean-Instruct Model

Use the command below to download the model from HuggingFace:

bash
huggingface-cli download heegyu/EEVE-Korean-Instruct-10.8B-v1.0-GGUF ggml-model-Q5_K_M.gguf --local-dir ___ --local-dir-use-symlinks False

Step 3: Setting Up the Model File

Once downloaded, you can configure your model file (e.g., ‘EEVE-Korean-Instruct-10.8B-v1.0’) using the template structure provided below:

TEMPLATE
- if .System s 
.System s- end s
Human: .Prompt ss
Assistant:SYSTEM
PARAMETER stop s

This configuration helps define how your assistant lights up on user queries.

Step 4: Using Ollama

Next, let’s create the model using Ollama with the below command:

bash
ollama create EEVE-Korean-10.8B -f EEVE-Korean-Instruct-10.8B-v1.0-GGUFModelfile

Running the Server

To run your LangServe application, execute:

bash
python server.py

This will start your local instance of the application.

Tunneling with Ngrok

To make your application accessible online, you can use Ngrok. Start it with the following command:

bash
ngrok http localhost:8000

This command will generate a public URL for your local server, making it easy for others to access it.

Troubleshooting

If you run into any issues along the way, consider the following troubleshooting steps:

• Error messages during installation: Ensure Python and pip are correctly installed and your environment paths are set up.
• Model not downloading: Check your internet connection and ensure you have access to the HuggingFace links.
• Server not starting: Confirm that the server.py file is in the correct directory and that you’re in its working folder when executing the command.
• Ngrok connection issues: Make sure Ngrok is correctly set up and running; refer to the Ngrok dashboard for additional details.

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Conclusion

Integrating LLM with RAG creates a powerful tool that can significantly enhance application capabilities. Keep experimenting with your setup, explore various features, and continuously adapt to new advancements.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.