QLoRA Production Pipeline — Data to Deployed Custom Model

I will build an end-to-end QLoRA production pipeline — covering automated data ingestion and preprocessing, QLoRA training job execution, evaluation gate checking, adapter merging with the base model for clean inference, and model deployment to your serving infrastructure. QLoRA production pipelines require additional steps compared to standard LoRA pipelines — the 4-bit quantised training produces adapters that need merging with a dequantised base model for efficient inference (inference from quantised base plus adapter is slower than merged model inference), and this merging step requires careful precision handling to avoid precision loss in the merged weights.

The pipeline covers data preprocessing automation, QLoRA training job configuration, evaluation gate, adapter merging with dequantised base, inference benchmarking of merged model, deployment to your serving infrastructure, and pipeline documentation.