Methods to Improve AI Code Generation (Paperback)

Mohd Rashid

ISBN 10: 516106367X / ISBN 13: 9785161063675

Published by Rashid Publications, 2026

About this Item

Paperback. In recent years, transformer-based AI language models have gained prominence due to their powerful capabilities in a variety of tasks including generation of images, video, text and code. Large Language Models (LLMs) exist with parameters counts of a trillion parameters and greater. Such models are proprietary and unavailable for organizations to deploy privately. Even if such deployments were possible, the tremendous resource requirements of LLMs preclude their deployment on infrastructure smaller than enterprise and hyper-scale data centers. Small Language Models (SLMs), with far lower parameter counts of billions or fewer are a viable alternative for use on small servers and edge devices including PCs. While SLMs possess similar generative capabilities as LLMs, the reduction in model size is correlated with a decrease in accuracy when evaluated across a broad range of generative applications, including code generation in multiple languages.To mitigate this shortcoming, an SLM may be fine-tuned with a curated code dataset consisting of code examples in a target programming language. This praxis presents results illustrating how two fine-tuned SLMs variants have been created that improve average accuracy in C++ code generation by more than 9%, and Rust code generation by more than 14%. This praxis defines the problem space of code generation with SLMs and provides an in-depth review of the methods used to create the ensemble model. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Seller Inventory # 9785161063675

Bibliographic Details

• Title: Methods to Improve AI Code Generation (Paperback)
• Publisher: Rashid Publications
• Publication Date: 2026
• Language: English
• ISBN 10: 516106367X
• ISBN 13: 9785161063675
• Binding: Paperback
• Condition: new

About this title

Synopsis

In recent years, transformer-based AI language models have gained prominence due to their powerful capabilities in a variety of tasks including generation of images, video, text and code. Large Language Models (LLMs) exist with parameters counts of a trillion parameters and greater. Such models are proprietary and unavailable for organizations to deploy privately. Even if such deployments were possible, the tremendous resource requirements of LLMs preclude their deployment on infrastructure smaller than enterprise and hyper-scale data centers. Small Language Models (SLMs), with far lower parameter counts of billions or fewer are a viable alternative for use on small servers and edge devices including PCs. While SLMs possess similar generative capabilities as LLMs, the reduction in model size is correlated with a decrease in accuracy when evaluated across a broad range of generative applications, including code generation in multiple languages.

To mitigate this shortcoming, an SLM may be fine-tuned with a curated code dataset consisting of code examples in a target programming language. This praxis presents results illustrating how two fine-tuned SLMs variants have been created that improve average accuracy in C++ code generation by more than 9%, and Rust code generation by more than 14%.