Running OpenHands-LM-32B-V0.1 Locally: Unlocking Autonomous Software Development

Imagine having an autonomous software development assistant by your side, creating code snippets, solving GitHub issues, and organizing projects efficiently. OpenHands-LM-32B-V0.1 is a game-changing model designed to empower software development with its open-source capabilities. In this article, we'll explore how to run this model locally, leveraging its potential to transform your coding workflow.

Introduction to OpenHands-LM

OpenHands-LM is built upon the Qwen Coder 2.5 Instruct foundation and fine-tuned using a reinforcement learning framework developed by SWE-Gym. This 32B parameter model achieves impressive performance on software engineering tasks, particularly addressing GitHub issues with a verified resolve rate of 37.2% on the SWE-Bench Verified benchmark. Being relatively compact, it can run locally on hardware like a single NVIDIA GeForce 3090 GPU, making it accessible for developers to manage and optimize their projects without relying on cloud services.

Why Run OpenHands-LM Locally?

Local deployment offers several advantages:

• Security and Privacy: Running models locally ensures that sensitive project data remains secure within your environment, reducing the risk of exposure via external APIs.
• Customization: You can fine-tune the model with your specific development workflow, enhancing its performance on tasks unique to your projects.
• Cost-Effectiveness: By minimizing dependency on external API calls, you save on service costs while maintaining control over data access.

Setting Up OpenHands-LM Locally

Prerequisites

1. Hardware Requirements: Ensure you have a suitable GPU (e.g., NVIDIA GeForce 3090) and at least 16 GB of RAM for smooth operation.

2. Software Setup: Install Docker and Docker Desktop on your system (Windows, macOS, or Linux).

   For macOS and Windows:

   • Ensure Docker Desktop is installed and configured to use the default Docker socket.
   • Verify that your system is running the latest version of Docker.

   For Linux:

   • Install Docker and have at least Ubuntu 22.04 or a similar Linux distribution.

Steps to Run OpenHands-LM

1. Download OpenHands LM Model:

   • Visit Hugging Face to directly download OpenHands-LM-32B-V0.1. It's ~20 GB.

2. Create an OpenAI-Compatible Endpoint:

   • Use a model serving framework like SGLang or vLLM to create a local OpenAI-compatible endpoint.

3. Configure OpenHands Agent:

   • Point your OpenHands agent to the newly set-up model following instructions provided by OpenHands documentation.

Example Setup with Docker

Here's a simplified setup guide using Docker to run OpenHands:

1. Install Docker:

   # For Ubuntu
   sudo apt-get update
   sudo apt-get install docker.io -y
   
   # For Windows (with WSL)
   wsl --install -d Ubuntu

2. Pull and Run OpenHands Docker Image:

   Since there isn't a specific Docker image for OpenHands-LM, you typically run OpenHands with its main container and connect it to your local model interface.

   docker pull docker.all-hands.dev/all-hands-ai/openhands:0.30

   Then, start OpenHands following its official installation guide:

   docker run -it --rm --pull=always \
   -e SANDBOX_RUNTIME_CONTAINER_IMAGE=docker.all-hands.dev/all-hands-ai/runtime:0.30 \
   -e LOG_ALL_EVENTS=true \
   -v /var/run/docker.sock:/var/run/docker.sock \
   -v ~/.openhands-state:/.openhands-state \
   -p 3000:3000 \
   --add-host host.docker.internal:host-gateway \
   --name openhands-app \
   docker.all-hands.dev/all-hands-ai/openhands:0.30

3. OpenHands UI:

   • Access your OpenHands setup at http://localhost:3000 in your web browser.

4. Link OpenHands with the Local Model:

   • Ensure OpenHands is connected to your locally hosted OpenHands-LM-32B-V0.1 model via the model's endpoint.

Challenges and Solutions

While running OpenHands-LM locally, you might face issues related to hardware performance, environment setup, or model sensitivity to quantization levels. Here are some tips:

1. Hardware Upgrades:

   • If you encounter performance bottlenecks, consider updating your GPU for better processing power.

2. Environment Adjustments:

   • Ensure Docker and the model server framework (like SGLang) are properly installed and updated.

3. Quantization Optimization:

   • Be cautious with quantization levels; running at lower levels may affect model performance.

Integration with LightNode VPS

For those needing scalability or looking to host their development projects remotely while maintaining control over OpenHands-LM, using a LightNode VPS (Virtual Private Server) is an excellent option. LightNode offers flexible server configurations suitable for high-performance tasks like running local AI models.

Why Choose LightNode for Hosting OpenHands-LM?:

1. Customizable Resources:

   • Allocate resources according to your needs, ensuring efficient model execution.

2. Security Features:

   • Leverage robust security measures to protect your projects and data.

3. Scalability:

   • Easily scale your infrastructure based on project demands.

Consider migrating your setup to LightNode today to enhance your development workflow:
Visit LightNode to explore tailored VPS solutions.

Conclusion

Running OpenHands-LM-32B-V0.1 locally opens up new avenues for software development by offering autonomy, customization, and enhanced project security. By integrating this powerful model into your workflow, you can automate code writing, issue resolution, and project management tasks with unprecedented efficiency. As the future of software development becomes increasingly reliant on AI-assisted tools, harnessing the full potential of models like OpenHands-LM will be crucial for staying ahead in the industry.