Data intelligence too cheap to meter: Refuel-LLM2-mini

RefuelLLM-2-mini, a new 1.5 billion parameter model, joins the Refuel-LLM family, demonstrating superior performance in data labeling tasks when compared to other models like Phi-3.5-mini and Qwen2.5-3B. Designed for data labeling, enrichment, and cleaning, RefuelLLM-2-mini is built on the Qwen2-1.5B base model and trained on a diverse corpus of over 2,750 datasets, including both human-annotated and synthetic data. It achieves high output quality and well-calibrated confidence scores, with low latency performance. The model is accessible through Refuel Cloud and is open-sourced on Hugging Face under a CC BY-NC 4.0 license, thanks to contributions from various open-source initiatives and infrastructure support from organizations like Mosaic and GCP.