Domain-Specific Large Model Construction Guide

This article introduces the challenges of using general large language models (LLMs) for enterprise-specific tasks and presents two main solutions: Retrieval-Augmented Generation (RAG) and building domain-specific large models through fine‑tuning on domain data.

The guide is organized into six parts:

Differences between domain models and general models, highlighting dataset breadth versus depth, flexibility versus accuracy, and model complexity.

Methods for constructing high‑quality domain datasets from enterprise data, including challenges such as limited high‑quality data, costly preprocessing, and balancing data diversity.

Training methodology selection, covering full‑parameter fine‑tuning, parameter‑efficient methods (LoRA, Q‑LoRA), and the trade‑offs among hardware requirements, accuracy, and flexibility.

Construction of validation sets and multi‑dimensional evaluation (tokenization, syntactic analysis, semantic disambiguation, understanding, etc.).

Evaluation of domestic hardware (NVIDIA A800, MooreThread S3000/S4000, Huawei Ascend 910A) for inference speed and compatibility.

A Q&A session addressing automation of validation, performance with limited fine‑tuning data, handling of ambiguous queries, and iterative improvement of the pipeline.

Key recommendations include using SFT (Supervised Fine‑Tuning) to reduce the need for large high‑quality datasets, leveraging LLMs themselves for data extraction when preprocessing is constrained, balancing domain data proportion (≈30%) to trade off flexibility and accuracy, and adopting a step‑wise approach: start with Q‑LoRA, fallback to LoRA, and finally use full‑parameter fine‑tuning if needed.

The article also emphasizes the importance of building a baseline open‑source model for comparative radar‑chart evaluation and suggests that most issues can be mitigated by adjusting data quality or quantity rather than re‑training from scratch.