Qwen2.0 Model Comparison: Qwen2-7B-Instruct vs Qwen2-72B-Instruct
The Qwen2.0 Instruct fine-tuned models are designed to excel in instruction-following tasks, with versions available at 7B and 72B parameters. Both models demonstrate unique patterns in layer conditioning and stability, as reflected in their WeightWatcher metrics.
Key Insights:
- Alpha Distribution and Range Compliance:
- The Qwen2-7B-Instruct model shows greater variability in alpha values, with many layers outside the HTSR safe range (2-6), indicating both underfitting (alpha > 6) and overfitting risks.
- The Qwen2-72B-Instruct model exhibits more stable alpha distributions, with the majority falling within the HTSR safe range, indicating better overall conditioning.
- Dks and Scale Comparisons:
- The Qwen2-72B-Instruct model has a lower mean Dks value compared to the Qwen2-7B-Instruct, suggesting a tighter fit in layer conditioning.
- The scale values are somewhat higher in the 7B model, indicating more variability in layer norms. Although larger scale typically correlates with better performance, in this case, the lower scale in the 72B model reflects its better stability and layer conditioning.
- Performance and Stability:
- The Qwen2-72B-Instruct model benefits from the larger parameter count, resulting in improved stability across layers, making it more suited for demanding instruction-based tasks.
- The 7B model, despite its larger scale, shows signs of potential instability but may still be useful in scenarios where lighter, more flexible models are advantageous.
These insights highlight that Qwen2-72B-Instruct offers better conditioning and stability for instruction fine-tuning tasks compared to the Qwen2-7B-Instruct model. For a deeper analysis of instruction fine-tuning, refer to this blog post.