Stable AI has recently released a new state-of-the-art model, Stable-Code-3B, designed for code completion in various programming languages with multiple additional capabilities. The model is a follow-up on the Stable Code Alpha 3B. It is trained on 1.3 trillion tokens including both natural language data and code data in 18 programming languages and codes. Compared to existing models CodeLLaMA 7b, the stable-code-3b is 60% smaller, maintaining the high-level performance of the model.
In conclusion, the stable-code-3b model represents a powerful tool for developers seeking a foundational base in natural language processing applications. However, it’s crucial to note that the model comes with limitations and potential biases. As a base model, it requires careful evaluation and fine-tuning for safe and reliable performance in specific downstream applications. Developers should be aware of possible undesirable behaviors, and it’s recommended to thoroughly assess and correct these aspects before deployment to ensure the model aligns with ethical and safety standards.
Pragati Jhunjhunwala is a consulting intern at MarktechPost. She is currently pursuing her B.Tech from the Indian Institute of Technology(IIT), Kharagpur. She is a tech enthusiast and has a keen interest in the scope of software and data science applications. She is always reading about the developments in different field of AI and ML.