Efficient Telecom Specific LLM: TSLAM-Mini with QLoRA and Digital Twin Data

While general-purpose Large Language Models (LLMs) have demonstrated remarkable proficiency across diverse natural language tasks, their inherent lack of domain-specific knowledge often renders them inadequate for specialized telecom applications, such as intricate network optimization, real-time fault diagnosis, and automated configuration management. To bridge this capability gap, we introduce TSLAM-Mini, a meticulously fine-tuned iteration of the Phi-4 Mini Instruct 4B model. TSLAM-Mini is specifically tailored for telecommunications tasks, leveraging a comprehensive dataset of 100,000 samples that span 20 consolidated and critical telecommunications categories. These categories, delineated in Section 3, encompass a wide spectrum from foundational networking principles (e.g., Network Fundamentals, IP Routing, MPLS) to advanced and emerging areas (e.g., Network Security, Automation, OSS/BSS, RAN, Mobile Core, Satellite Communications, and Ethical AI). The foundational dataset was synthesized utilizing Ne-toAI's DigiTwin platform, which facilitates the creation of high-fidelity digital replicas of network devices and environments. This approach allows for the generation of realistic network operation data, further enriched by insights from seasoned Subject Matter Experts (SMEs) and normative information extracted from pertinent Request for Comments (RFCs), ensuring profound domain relevance. The fine-tuning process employs Quantized Low-Rank Adaptation (QLoRA), a Parameter-Efficient Fine-Tuning (PEFT) technique, to optimize training efficiency and computational footprint, thereby enabling deployment on resource-constrained edge devices or embedded systems. This research endeavors to significantly enhance TSLAM-Mini's capacity to deliver precise, context-aware, and actionable responses to complex telecom challenges, thereby contributing to the paradigm of intelligent, resilient, and autonomous network management and advancing the frontier of applied LLMs in the telecommunications sector.