Mistral-7B Base Model Comparison: Versions v0.1, v0.2, and v0.3
The Mistral-7B models, across versions v0.1, v0.2, and v0.3, demonstrate a unique pattern in their layer conditioning, characterized by a significant number of underfit layers (alpha > 6). Despite this, the models maintain a strong average alpha value around 4.0 (excluding the underfit layers), indicating good overall layer conditioning. This balance makes Mistral-7B models particularly useful for instruction fine-tuning, where stable generalization and consistent performance are essential.
Key Insights:
- Underfit Layers: Each Mistral-7B version has numerous layers falling outside the HTSR safe range, specifically in the underfit zone. However, this doesn’t prevent the models from performing well in instruction fine-tuning tasks.
- Average Alpha Value: The models maintain an average alpha close to 4.0 when excluding underfit layers, which aligns with well-conditioned behavior and strong generalization capacity.
- Instruction Fine-Tuning Suitability: Despite their underfit layers, Mistral-7B models are effective for instruction fine-tuning, leveraging their stable, conditioned layers to handle complex tasks effectively.
For more insights on the role of models like Mistral-7B in instruction fine-tuning, refer to this blog post.