Qwen2 is a family of large language models developed by Alibaba's DAMO Academy, designed for high-performance language understanding and generation tasks, particularly in Chinese. The Qwen2 models come in various sizes, with the largest model being Qwen-2-72B, which has 72 billion parameters. These models are built to handle complex and demanding tasks, providing deep insights and highly accurate language processing.

The Qwen2 lineup includes models like Qwen-2-0.5B, Qwen-2-1.5B, and scales up to Qwen-2-72B, offering different computational options based on the task requirements. As with other large language models, performance typically improves with larger model sizes, making Qwen-2-72B the most powerful in the series. These models are optimized not just for Chinese but also perform well in multilingual contexts, making them versatile for various applications.

Below we compare the smallest Qwen2 base model with it's Instruct Fine-Tuned component

The Qwen2-0.5B-Instruct model's alpha values consistently fall within the 2-6 range, as predicted by HTSR theory, indicating well-regularized layers. In contrast, the Qwen2-0.5B base model has many layers with alpha values below 2, suggesting underfitting and weaker generalization. Notice, however, that despite this, the Instruct model looks great as it follows the theoretical predictions nearly perfectly