LLM Context Extender is a tool designed to extend the context window of large language models (LLMs). It achieves this by adjusting the RoPE's base frequency and scaling attention logits, enabling LLMs to effectively adapt to larger context windows. The tool has validated the superiority of its method in terms of fine-tuning performance and robustness, demonstrating remarkable efficiency in expanding the context window of LLaMA-2-7B-Chat to 16,384 with only 100 samples and 6 training steps. It further explores the impact of data composition and training curriculum on context window expansion for specific downstream tasks, suggesting fine-tuning LLMs with long dialogues as a good starting point.