In this tutorial, we'll explore how to run Local Language Models (LLMs) using OpenClaw and Ollama. This guide is designed for developers and enthusiasts looking to harness the power of LLMs on their own hardware, providing a hands-on approach to implementing an efficient local LLM setup. ## Prerequisites Before diving into the steps, ensure you have the following: 1. **Basic knowledge of Python**: Familiarity with running Python scripts and understanding basic programming concepts. 2. **Docker installed**: OpenClaw and Ollama utilize Docker for containerization. You can [install Docker](https://docs.docker.com/get-docker/) based on your operating system. 3. **OpenClaw account**: If you haven't already, create an account on OpenClaw Hub (stormap.ai). 4. **Ollama CLI**: Make sure you have the Ollama CLI installed. You can follow their [installation guide](https://ollama.com/docs/install/) for your respective OS. ## Step-by-Step Instructions ### Step 1: Setting Up Your Environment First, let's ensure your environment is ready for running Local LLMs. 1. **Install Docker**: If Docker is not installed, follow the installation guide for your operating system. ```bash # For Ubuntu sudo apt update sudo apt install docker.io ``` 2. **Install Ollama**: Use the command line to install Ollama. ```bash curl -sSfL https://ollama.com/download | sh ``` 3. **Verify Installations**: Check if Docker and Ollama are installed correctly. ```bash # Check Docker docker --version # Check Ollama ollama --version ``` ### Step 2: Pulling the Required Docker Images Next, you need to pull the necessary Docker images. OpenClaw integrates with various models, and Ollama serves as the interface to run these models. 1. **Open your terminal** and run the following commands: ```bash # Pull the OpenClaw image docker pull openclaw/openclaw:latest # Pull the Ollama image docker pull ollama/ollama:latest ``` ### Step 3: Running OpenClaw with Ollama Now that you have the images, it's time to start the containers and set up OpenClaw to work with Ollama. 1. **Start OpenClaw Container**: ```bash docker run -d -p 8080:8080 openclaw/openclaw:latest ``` This command starts the OpenClaw server and maps port 8080 of your host to port 8080 of the container. 2. **Start Ollama Container**: ```bash docker run -d -p 8081:8080 ollama/ollama:latest ``` This command starts the Ollama server on a different port (8081) of your host. ### Step 4: Configuring the LLM With both containers running, you can now configure the Local LLMs. OpenClaw provides a simple API for this purpose. 1. **Create a Configuration File**: Create a new configuration file named `config.json` in your project directory. ```json { "model": "gpt-3", "endpoint": "http://localhost:8080", "parameters": { "max_tokens": 150, "temperature": 0.7 } } ``` 2. **Load the Configuration**: In your Python script, load this configuration file and set up a connection to the OpenClaw server. ```python import requests import json with open('config.json') as config_file: config = json.load(config_file) def query_openclaw(prompt): response = requests.post( f"{config['endpoint']}/generate", json={ "model": config['model'], "prompt": prompt, "parameters": config['parameters'] } ) return response.json() ``` ### Step 5: Interacting with the LLM Now that you have your LLM configured, you can interact with it using a simple prompt. 1. **Write a function to test the LLM**: ```python def main(): prompt = "Explain the significance of the Turing Test." response = query_openclaw(prompt) print("Response from LLM:", response['text']) if __name__ == "__main__": main() ``` 2. **Run your script**: Execute your script to see the interaction with the Local LLM. ```bash python your_script.py ``` ### Troubleshooting Tips 1. **Container Not Starting**: If either Docker container fails to start, check the logs using: ```bash docker logs ``` Replace `` with the actual ID of your container. 2. **Port Conflicts**: Ensure that the ports you are using (8080 and 8081) are not occupied by other services on your machine. 3. **Network Issues**: If you encounter connectivity issues, ensure Docker is configured to allow network traffic and that your firewall settings are not blocking connections. 4. **Model Not Found**: If you receive an error regarding the model, verify that the model name in your `config.json` matches the models available in OpenClaw. ## Next Steps Congratulations! You’ve successfully set up a local LLM using OpenClaw and Ollama. Here are some related topics you may want to explore next: - **Customizing LLM Parameters**: Learn how to fine-tune the parameters for better output quality. - **Integrating with Web Applications**: Discover how to integrate your LLM with Flask or Django for web-based applications. - **Exploring Other Models**: Investigate other available models within OpenClaw to expand your capabilities. By leveraging these tools, you're well on your way to creating powerful applications with local LLMs. Happy coding!