Predibase has introduced a series of updates aimed at enhancing the fine-tuning and deployment of small language models (SLMs) in production, emphasizing improvements in speed, reliability, and cost-effectiveness. Notable updates include the introduction of new models like Solar Pro Preview, which has demonstrated superior performance across numerous benchmarks, and the implementation of Turbo LoRA for faster and more accurate model inference. Additionally, Predibase now supports synthetic data generation from minimal seed data to train SLMs efficiently, deployment health analytics for real-time performance monitoring, and seamless continuation of model training from any checkpoint. The platform also offers support for large datasets over 1 GB, prompt prefix caching for faster inference, and the ability to update live deployments without downtime. Integration with Comet’s Opik further enables users to track and evaluate fine-tuning jobs with detailed analytics, facilitating the optimization of model performance in production environments.