A Deep Dive into Sub GB Llama Scout Quants: PPL, KLD, and Top P Metrics

In the world of model optimization and quantization, understanding how different quantization strategies affect model performance is key to building more efficient systems. The latest analysis comparing several sub-50GB Llama 4 Scout quant models sheds light on critical metrics—PPL (Perplexity), KLD (Kullback-Leibler Divergence), and Top P (Top Probability). These metrics provide insight into how close these quant models are to the full BF16 model and whether they maintain good performance while reducing model size.

This analysis isn’t just about the numbers—it’s a search for balance. The goal is to see whether the quant models that were altered (through changes like PR modifications) deliver tangible benefits over the original setup. While some models have slightly larger sizes, the focus here is to figure out if these adjustments improve or hinder the final output. Through this comparison, the performance trade-offs of several sub-50GB Llama 4 Scout quant models are evaluated to inform future choices.

Analyzing Sub-50GB Llama 4 Scout Quants: PPL, KLD, and Top P Metrics

The analysis focuses on a variety of sub-50GB Llama 4 Scout quant models, including versions published by the author and Unsloth, using a similar setup with and without PR changes. A special mention goes to Artus at BeaverAI Club for assisting with the extensive KLD calculations, which would have been too time-consuming otherwise. The overall aim of this experiment is to assess the effectiveness of PR changes on quant model performance.

The table below presents data on several models, showing their size, mean PPL, KLD, and other critical values like RMS (Root Mean Square) Delta P, and the percentage of the same top. It’s clear that the differences between models are subtle but significant, with each quant performing better in some areas than others.

|-|–|–|-|-|–|–|-|-|–|-|-|

| Size (GB) | 26.32 | 24.57 | 30.17 | 28.56 | 34.34 | 35.4 | 44 | 40.57 | 42.6 | 44.96 | 41.66 |
| Mean PPL | 11.81 | 13.79 | 10.55 | 11.66 | 9.85 | 10.30 | 9.02 | 9.88 | 9.31 | 9.27 | 9.76 |
| KLD Mean | 0.691 | 0.933 | 0.464 | 0.664 | 0.361 | 0.376 | 0.217 | 0.332 | 0.185 | 0.164 | 0.244 |

The table highlights the quantization’s size and performance in terms of mean PPL, KLD, and other important stats, showing that smaller models tend to have higher PPL, indicating a trade-off between model size and prediction accuracy. The KLD values also point to some interesting differences, with smaller models generally having higher KLD, which reflects a greater divergence from the original BF16 weights.

Interestingly, performance per GB (calculated by inversing the PPL, KLD, and RMS values) is a crucial metric when considering which quant models are most efficient. The data suggests that larger models like IQ3_XXS may have slightly worse KLD values but still perform better in terms of PPL per megabyte, showing that some larger models strike a better balance in preserving model quality despite their increased size.

What Undercode Says:

The analysis presented offers valuable insights into the ongoing debate about how to optimize large language models like Llama 4 for more efficient deployment. The metrics KLD, PPL, and Top P each reveal different facets of performance, but none can be looked at in isolation. For example, PPL is useful but can be misleading, as even large models like the full BF16 still achieve high perplexity. KLD, on the other hand, provides a clearer picture of how a quant model’s weights diverge from the original model, making it particularly useful for understanding the effectiveness of different quantization strategies.

The comparison between various models, especially IQ3_XXS and UD-Q2_K_XL, is particularly telling. While IQ3_XXS might be marginally larger, it shows strong performance in terms of mean PPL and RMS. However, its max KLD is higher, which is worth considering when thinking about whether that trade-off is acceptable. On the flip side, models like UD-Q2_K_XL have a slightly better performance in terms of KLD but come at the cost of a larger model size.

Overall, this comparison does not lead to a simple conclusion of one model being universally better than another. Instead, it highlights that there are different strengths and weaknesses to consider. The real takeaway is that achieving optimal performance isn’t just about shrinking the model as much as possible—it’s about finding the right balance between model size and the specific needs of the task at hand.

For instance, if you need the absolute smallest model and are okay with slightly higher PPL, models like IQ2_S and UD-IQ1_M could be good choices. However, if you are aiming for a more balanced performance across different metrics, then models like IQ3_XXS and UD-Q2_K_XL are more likely to offer better all-around results.

It’s clear that the goal of quantization is not just size reduction, but rather the maintenance of high-quality performance across various dimensions. By examining metrics like KLD, PPL, and Top P, the researcher is working to understand how these changes affect the model’s predictive capabilities. This kind of analysis is critical in determining whether the changes made in quantization result in meaningful improvements or merely shifts in model behavior that aren’t necessarily beneficial in all scenarios.

Fact Checker Results:

The data provided is consistent with expected outcomes for quantized models in terms of KLD and PPL, showing a reasonable trade-off between model size and performance.
The PPL metric, while informative, is not the sole indicator of model quality and should be interpreted alongside other metrics like KLD and RMS.
The findings align with current best practices in model optimization, confirming that larger models generally perform better in terms of preservation of original weights, despite their size.