Small Language Models (SLMs) have shown remarkable performance in general domain language understanding, reasoning and coding tasks, but their capabilities in the medical domain, particularly concerning radiology text, is less explored. In this study, we investigate the application of SLMs for general radiology knowledge specifically question answering related to understanding of symptoms, radiological appearances of findings, differential diagnosis, assessing prognosis, and suggesting treatments w.r.t diseases pertaining to different organ systems. Additionally, we explore the utility of SLMs in handling text-related tasks with respect to radiology reports within AI-driven radiology workflows.We fine-tune Phi2, a SLM with 2.7 billion parameters using high-quality educational content from Radiopaedia, a collaborative online radiology resource. The resulting language model, RadPhi2-Base, exhibits the ability to address general radiology queries across various systems (e.g., chest, cardiac). Furthermore, we investigate Phi2 for instruction tuning, enabling it to perform specific tasks. By fine-tuning Phi2 on general domain tasks followed by radiology-specific tasks related to chest X-ray reports, we create RadPhi2. Our empirical results reveal that Rad-Phi2-Base and Rad-Phi2 perform comparably or even outperform larger models such as Mistral-7B-Instruct-v0.2 and GPT-4 providing concise and precise answers.