Customer SupportLLM - BaselineSFT with LoRA

Customer support automation requires a model that can respond consistently, follow support-style instructions, and remain efficient enough to fine-tune and evaluate within realistic compute limits. The challenge was to build a fine-tuning workflow for causal LLMs that could transform support data into reliable training examples, adapt models with PEFT/LoRA, and support repeatable experiments without requiring full-model training. The system also needed to handle memory constraints, batch inference, and demo-ready validation for stakeholder review.

Optimized and fine-tuned causal language models for enterprise-grade English customer support using PEFT/LoRA and SFTTrainer. Engineered robust data preprocessing pipelines and memory-efficient training workflows leveraging BitsAndBytes (4-bit/8-bit quantization), while developing high-performance batch inference utilities and integrating modelsinto validated stakeholder demos.

The project delivered a practical supervised fine-tuning pipeline for customer support LLM adaptation using PEFT/LoRA, SFTTrainer, and memory-efficient quantization workflows. It established reusable data preparation and batch inference utilities, enabling faster iteration across baseline models and fine-tuned variants. By combining LoRA-based adaptation with 4-bit/8-bit BitsAndBytes workflows, the project reduced training overhead while keeping the model evaluation and demo process accessible, repeatable, and suitable for enterprise support use cases.