The Qwen2.5 models, developed by Alibaba’s DAMO Academy, are a family of high-performance Large Language Models (LLMs) designed for sophisticated language understanding and generation tasks, particularly in Chinese. The Qwen2.5 lineup includes models of various sizes—0.5B, 1B, 3B, 7B, 14B, 32B, and the largest, 72B—offering options optimized for tasks of different complexity levels. For more information, visit the Qwen2.5 GitHub repository.
Analysis of Alpha Values
The alpha histogram for Qwen2.5 models (14B, 32B, and 72B) reveals the following insights:
1. HTSR Range Compliance: The majority of alpha values for these larger Qwen2.5 models (7B and above) fall within the HTSR safe range (2-6), indicating strong layer conditioning and minimal overfitting. This adherence to the HTSR range reflects effective regularization, with few layers exceeding the ideal alpha range, suggesting high stability.
2. Model Size and Alpha Distribution: As model size increases from 14B to 72B, the alpha values continue to cluster within the HTSR range, showing consistent stability across layers. The larger models maintain conditioning as they scale, showcasing the robustness of Alibaba’s training approach.
3. Comparison to Smaller Qwen2.5 Models: It’s worth noting that the smaller Qwen2.5 models 0.5B, 1B, 3B fall outside the HTSR safe range. These smaller models show a wider dispersion of alpha values, suggesting that they may be more prone to overfitting and instability compared to the larger Qwen2.5 models.
In summary, the larger Qwen2.5 models (7B and above) exhibit well-conditioned alpha distributions that align with HTSR stability guidelines, while the smaller versions show greater variability in stability. This trend underscores the scalability and robustness of the larger Qwen2.5 models for high-performance language tasks. For more insights on applying WeightWatcher to Instruct Fine-Tune models, refer to this blog.