Step-by-Step Guide: Running DeepCoder-14B-Preview Locally with Ollama

About 2 min

Running DeepCoder-14B-Preview Locally: A Step-by-Step Guide

Are you eager to dive into the world of AI-assisted coding with the latest open-source model, DeepCoder-14B-Preview? This impressive model, developed by Agentica and Together AI, offers a powerful tool for code generation and reasoning tasks. In this guide, we'll explore how to run DeepCoder-14B-Preview locally, leveraging the lightweight framework of Ollama.

Why Choose DeepCoder Locally?

Privacy: Keep sensitive code off cloud servers by running models on your local machine.
Cost: Avoid subscription fees by hosting your AI coding assistant locally.
Customization: Tailor the model to fit your specific development needs without relying on cloud services.

Prerequisites for Running DeepCoder Locally

Before you begin, ensure your system meets the following requirements:

Hardware:

RAM: 32GB minimum for smaller models; 64GB recommended for the 14B model.
GPU: An NVIDIA RTX 3090 or better with 24GB+ VRAM for optimal performance with the 14B model. Alternatively, a 12GB RTX 3060 can be used with quantized models.
CPU: For smaller models (1.5B), a CPU with sufficient cores like an Intel i9 or AMD Ryzen 9 works well.

Software:

Operating System: Linux (Ubuntu 20.04+), macOS, or Windows (via WSL2).
Git: Necessary for cloning repositories.
Docker: Optional for containerized deployment.
Python: Version 3.9+ required for scripting and API interactions.

Dependencies:

Ollama: This lightweight framework is essential for running DeepCoder locally.
DeepCoder Model Files: Downloadable from the Ollama library.

Step-by-Step Installation

1. Install Ollama

Ollama is the backbone for running DeepCoder locally. To install it:

# For Linux
curl -fsSL https://ollama.com/install.sh | sh

# For macOS using Homebrew
brew install ollama

Verify Installation: Run ollama --version to check the installed version.

Start the Ollama Service: Launch Ollama in the background with ollama serve &. This will start the server on localhost:11434, exposing an API for model interactions.

2. Download the DeepCoder Model

Use the following command to download the model:

# For the default version
ollama pull deepcoder

# For a specific version
ollama pull deepcoder:14b-preview

Monitor the download progress. Ensure you have a stable internet connection as the 14B model is a multi-gigabyte download.

Verify Model Installation: Run ollama list to confirm DeepCoder is available among your installed models.

Integration and Usage

With DeepCoder and Ollama in place, you can integrate it with tools like VS Code for a seamless coding experience. For instance, you can use the VS Code Copilot extension to access the model's capabilities directly within your IDE.

Install the VS Code Copilot extension.
Set up the Ollama API provider in your VS Code client settings.
Select DeepCoder as your model from the API provider's options.

Now, you're all set to explore the capabilities of DeepCoder!

Future Enhancements and Community Engagement

The open-source nature of DeepCoder invites community involvement. Developers can fine-tune the model further or extend its capabilities. The use of GRPO+ for training suggests potential areas of improvement in RL algorithms for more stable and effective model development.

Conclusion

Hosting DeepCoder-14B-Preview locally offers a powerful, customizable AI coding assistant. Combining this model with Ollama provides a cost-effective solution for developers. For hosting or expanding your computational capabilities, consider exploring server options like those offered by LightNode: visit LightNode. Whether you're diving into coding challenges or exploring AI's potential, running DeepCoder locally is a promising start towards achieving your development goals.