StableCode Tutorial: Getting Started with Stability AI's Coding Assistant

What is StableCode from Stability AI?

StableCode, the latest offering from Stability AI, is an innovative generative AI product designed to enhance the coding experience for developers at all levels. This advanced tool serves as a powerful assistant for both experienced programmers seeking greater efficiency and newcomers looking to strengthen their coding skills.

Base Model

The foundation of StableCode is a comprehensive model that underwent initial training on a wide range of programming languages, sourced from the stack-dataset (v1.2) provided by BigCode. To refine its capabilities, the base model was further trained using popular languages such as Python, Go, Java, JavaScript, C, Markdown, and C++. This training involved a substantial dataset, comprising a staggering 560 billion tokens of code. This robust foundation equips StableCode with a deep understanding of various programming languages and structures.

Instruction Model

This model has been meticulously fine-tuned for specific use cases, focusing on solving intricate programming challenges. By exposing it to around 120,000 pairs of code instruction and corresponding responses in Alpaca format, the instruction model has been sharpened to provide intelligent solutions for complex coding tasks.

Long-Context Window Model

StableCode introduces an advanced long-context window model that excels at generating single and multi-line autocomplete suggestions. Compared to previous open models with limited context windows, this new model is designed to handle significantly more code at once—approximately 2 to 4 times more. As a result, developers can effortlessly review or edit the equivalent of multiple average-sized Python files concurrently. This extended context window is particularly beneficial for those eager to expand their coding expertise and take on larger coding challenges.

Using StableCode: A Step-by-Step Tutorial

In this tutorial, we will learn how to use StableCode to generate code completion. We will explore each model and see how it works. Additionally, we will learn how to use StableCode in Google Colab and the Hugging Face Inference API to run StableCode, even if you don't have a powerful GPU.

Implementation in Google Colab

1. Setting up the project: Start by creating a new Notebook in Google Colab. Go to Google Colab and create a new Notebook named "StableCode Tutorial".
2. Install required packages: Set the Runtime type to Python 3 and Hardware accelerator to GPU. Install or update Python packages related to natural language processing (NLP) and machine learning.
3. Working with StableCode - Base Model: Add a new code cell to run StableCode - Base Model. Define a function to run the model using a prompt.
4. Using StableCode - Instruction Model: Change BASE_MODEL to INSTRUCTION_MODEL in the from_pretrained() function and provide your desired prompt.
5. Implementing StableCode - Long Context Window Model: Switch to LONG_CONTEXT_WINDOW_MODEL in the from_pretrained() function and input your prompt.

Implementation with Hugging Face Inference API

1. Create an account: Go to Hugging Face, create a new account, or log in if you already have one.
2. Create a new token: Generate a token for using Hugging Face Inference API from your profile.
3. Run StableCode with Hugging Face Inference API: Visit StableCode model page, select Inference API, and copy the provided code snippet.

Conclusion

Thank you for following along with this tutorial. If you have any questions or need further assistance, feel free to reach out to me on LinkedIn or Twitter. I'd love to hear from you!

Explore more about AI tools Read our tutorial on Python Coding Skills Hugging Face Stability AI