IBM has made great strides in the field of software development by releasing a set of open source Granite code templates designed to make coding easier for users around the world. This action stems from the realization that although software plays an essential role in contemporary society, the coding process remains difficult and time-consuming. Even seasoned engineers often struggle to keep learning new things, adapting to new languages, and solving complex problems.
Large language models (LLMs) have gained importance in development environments, helping to increase efficiency and independence when handling complex programming tasks. The WatsonX Code Assistant (WCA) series, IBM’s newest innovation, uses the astonishing 20 billion parameter capabilities of the Granite big language code model. The utility of this technology in enterprise environments has been demonstrated by its role in converting COBOL applications into contemporary services optimized for IBM Z.
IBM has created four versions of the Granite code model, with parameter counts ranging from 3 to 34 billion, publicly available. These models are designed specifically for a variety of coding tasks, such as memory-constrained applications and application modernization. They have undergone an extensive evaluation process to ensure that they meet the highest requirements for performance and adaptability in a variety of coding tasks, including generation, debugging and explanation.
IBM’s commitment to democratizing access to cutting-edge technologies has been demonstrated by its choice to make these models available under an open source license. He has publicly published these models on sites such as Hugging Face, GitHub and RHEL AI. Additionally, these solutions are reliable and trustworthy enough to be adopted by businesses, provided that strict ethical standards are followed throughout data collection and model training.
Through its open source project, IBM hopes to remove barriers related to high prices of proprietary models and unclear licensing rules and accelerate the adoption of generative AI models in the business sector. With Granite code templates’ adaptability and enterprise workflow optimization, developers gain access to a powerful toolkit capable of automating repetitive coding activities, improving code quality and enable seamless integration between existing and contemporary applications.
Deliberately releasing models of different sizes allows developers to select, based on their particular requirements, the ideal compromise between computing efficiency and performance. There are variations with 34 billion, 20 billion, 8 billion and 3 billion parameters. These models, which are licensed under Apache 2.0, were trained using deep scaling on a massive dataset of 4.5 trillion tokens. They include 116 programming languages, which is a wide range.
The Stack, a comprehensive resource that performs both exact and fuzzy deduplication and filters low-quality code, was used in the training data for these models. To further improve the models’ capabilities, natural language data was merged with the code. This methodology ensures that models are properly prepared to handle a range of coding tasks effectively and efficiently.
In conclusion, IBM predicts that in the future, coding will be as natural as speaking with an AI assistant, allowing engineers to focus more on creative work and less on repetitive tasks. Granite code models are just the start of a larger plan by IBM to enable developers to use AI technologies to reshape computing in the future.
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Tanya Malhotra is a final year undergraduate from University of Petroleum and Energy Studies, Dehradun, pursuing BTech in Computer Engineering with specialization in Artificial Intelligence and Machine Learning.
She is passionate about data science, with good analytical and critical thinking, as well as a keen interest in learning new skills, leading groups and managing work in an organized manner.
