LoRA

Taiwan retail platform uniopen adapted Amazon Nova 2 Lite to its two-axis moderation policy using LoRA supervised fine-tuning in Amazon SageMaker AI, plus a prompt-format change. According to AWS, Per Behavior Macro F1 rose from 0.5852 to 0.8550 and Subject Type Macro F1 from 0.4162 to 0.8491, both above production targets.

Mistral AI reports that fine-tuning its Pixtral-12B vision model on satellite imagery increased classification accuracy from 56% to 91% on the Aerial Image Dataset. The company used LoRA (Low-Rank Adaptation) to train on 8,000 samples for under $10, reducing hallucinations from 5% to 0.1%.

Amazon Web Services published a practical guide for fine-tuning Amazon Nova models using the Nova Forge SDK's data mixing capabilities. According to AWS, blending customer data with Amazon-curated datasets preserved near-baseline MMLU scores while delivering a 12-point F1 improvement on a Voice of Customer classification task spanning 1,420 leaf categories.

AWS has enabled fine-tuned Amazon Nova Micro models to run on Bedrock's on-demand inference for text-to-SQL generation. According to AWS testing, a sample workload of 22,000 queries per month costs $0.80 monthly using the serverless approach, compared to higher costs with persistent model hosting. The solution uses LoRA fine-tuning on the sql-create-context dataset containing over 78,000 SQL examples.