In this paper review, we discussed the recent release of Phi-2, a small language model (SLM) developed by Hugging Face and AI21 Labs. We covered its architecture, training data, benchmarks, and deployment options. The key takeaways from this research are: 1. SLMs have fewer parameters than large language models (LLMs), making them more efficient in terms of memory usage and computational resources. 2. Phi-2 is trained on a diverse range of text data, including synthetic math and coding problems generated using GPT-3.5. 3. The model demonstrates competitive performance on various benchmarks, such as MMLU, HellaSwag, and TriviaQA, while being smaller in size compared to other open-source models like LLaMA. 4. Deployment options for Phi-2 include using tools like Ollama and LLM studio, which allow users to run the model locally on their hardware or even host it as a server. 5. There is ongoing research into extending the context length of SLMs through techniques like self-context extension, which could lead to more advanced applications in the future.