Lightweight LLMs for 3GPP Specifications: Fine-Tuning, Retrieval-Augmented Generation and Quantization

Interpreting complex 3GPP telecommunications standards for question and answering (QA) poses a challenge for general-purpose LLMs due to their specialized terminology and high computational demands, limiting their use in resourceconstrained environments. This work explores an efficient, opensource approach using the TeleQnA dataset of 10,000 telecom questions and the TSpec-LLM repository of processed 3GPP documents. We enhance a lightweight Llama 3.2 (3B parameters) model, quantized from 16-bit precision to 4 bits, through finetuning and RAG to improve accuracy without heavy resource reliance. Unlike prior resource-intensive or proprietary solutions, our method reduces memory demands, enabling deployment on modest hardware like edge devices or softwarized networks. Shared via GitHub repositories [1], this approach advances costeffective, reproducible AI for telecommunications QA, supporting contexts where budgets, computation, or public internet access are limited.