LoRA

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.