LoRA
2 use cases using this technology
Marsh McLennan saves over 1 million hours with agentic AI assistant LenAI
Marsh McLennan
Professional services firm Marsh McLennan built LenAI, an AI assistant now used by its roughly 90,000 employees for around 20 million requests annually. To fix intent-recognition problems in the original GPT-3.5-based assistant, Marsh McLennan worked with Predibase to fine-tune a Llama-3.1-8B-Instruct model using LoRA and Turbo LoRA, improving accuracy by 10-12% over GPT-3.5 and 7% over GPT-4o-mini, and cutting response time below 4 seconds. Since its December 2023 launch, LenAI has handled over 25 million cumulative queries and saved more than 1 million hours of team time in its first year.
Checkr: Streamlining Background Check Classification with Fine-tuned Small Language Models
Checkr
Checkr automated adjudication of background checks using a tuned logistic regression model for 98% of cases, but the remaining 2% required classifying noisy records into 230 categories. After testing GPT-4 (80-82% accuracy on hard cases, ~15s latency, ~$12k cost) and GPT-4 with RAG, Checkr fine-tuned Llama-2-7b (85% accuracy) and then, in production on Predibase, Llama-3-8b-instruct, achieving 90% accuracy on the hardest 2% of cases with 0.15-second response times (30x faster than GPT-4) and a 5x cost reduction, using LoRA/LoRAX for parameter-efficient fine-tuning and multi-adapter serving.