Engineering a Sales-Domain Expert Foundation Model via Quantized Cross-Architecture Merging: Part II—Advanced Training Methodologies and Domain-Specific Fine-Tuning

This paper continues the exposition of the twelve-phase engineering pipeline for constructing a sales-domain super-expert foundation model, extending the work presented in Part I. It focuses on the advanced training methodologies that bridge the unified foundation model established in the earlier phases with the domain-specific behavioural specialisation required for complex enterprise sales operations. Specifically, this instalment examines Phases 4, 5, and 7 of the pipeline: the design of a memory-efficient training infrastructure, the formulation of a curriculum learning and safety-aware training strategy, and the supervised fine-tuning process that adapts the merged foundation model to sales-oriented data distributions. The study investigates how parameter-efficient fine-tuning methods, curriculum sequencing of domain data, and optimised memory management facilitate effective domain adaptation under constrained computational conditions while preserving reasoning fidelity and instruction-following competence from the base model. To this end, the paper formally articulates the integration of LoRA and QLoRA techniques, defines the training objectives and a score-based curriculum procedure, and presents preliminary empirical validation through baseline comparisons and ablation studies. Furthermore, we discuss reproducibility considerations, including experiment configuration, artefact documentation, and data formatting standards, alongside the broader implications for accessibility, scalability, and alignment with enterprise-grade AI deployment practices. By situating these phases within contemporary research on low-rank adaptation, instruction tuning, and safety-aligned training, the paper establishes the technical groundwork necessary for the retrieval-augmented and agentic orchestration stages to be elaborated in subsequent parts of this research series.