Finetuning model for AI saftey
Fine-tuned DeBERTa and Qwen 4B models for AI security guardrails, including prompt safety and policy enforcement. Optimized inference using vLLM and quantization for scalable, cost-efficient deployment.
Business Challenge
Enterprises needed scalable AI guardrails to detect unsafe prompts, sensitive data, and policy violations with low latency. High false-positive rates required frequent model fine-tuning, while manual data preparation, labeling, and retraining made continuous improvement time-consuming and operationally expensive.
Solution Delivered
Built an automated synthetic data generation pipeline that creates targeted training examples based on false positives identified in production. Integrated an AutoResearch-driven training pipeline to automate experimentation, fine-tuning, and model evaluation, enabling continuous improvement of DeBERTa and Qwen 4B guardrail models with reduced manual effort.
Outcomes Achieved
Enabled faster experimentation and iteration on new AI models through automated data generation and fine-tuning. Established a continuous improvement process to reduce false positives, enhance model accuracy, and accelerate deployment of updated AI guardrails with minimal manual effort.
Measurable Business Outcome
Reduced model development and iteration time from 3–4 days to just 1 day through automated data generation and fine-tuning. Improved guardrail model accuracy from 70% to 85% within 4–5 iterations, enabling faster experimentation, reduced manual effort, and continuous model improvement.