LLaMA Model Comparison: LLaMA-3.1 vs LLaMA-3.2 Base Models
The LLaMA 3.1 and 3.2 base models in this analysis include versions with various parameter sizes—1B, 3B, 8B, and 70B—designed to provide robust performance across a range of language understanding tasks. These models showcase consistent conditioning, with alpha values clustering in the HTSR safe range (2-6), though some outliers exhibit higher alpha values indicative of underfit layers.
Key Insights:
- Alpha Distribution and Stability: Both LLaMA-3.1 and LLaMA-3.2 models display a well-distributed alpha clustering within the HTSR safe range, indicating strong layer conditioning across versions. The average alpha across these models is around 4.0, reinforcing their stability for generalization.
- Underfit Layers: There are some layers, especially in the larger parameter models, that show higher alpha values, suggesting underfitting. However, the majority of layers stay within the safe range, which maintains the model's robustness.
- Dks and Scale Consistency: The Dks and scale values across LLaMA-3.1 and LLaMA-3.2 models are generally stable, with the scale values reflecting good conditioning for instructional fine-tuning, even though a few deviations exist in Dks metrics.
The LLaMA-3.2 models show some improvements in stability over the 3.1 models, particularly in the 1B and 3B versions, suggesting better conditioning in the newer versions. These models, despite minor underfitting in select layers, are well-suited for further instruction fine-tuning applications.
For additional insights into applying WeightWatcher for instruction fine-tuning analysis, refer to this blog post.