## Line Chart: Average Math-benchmark Accuracy vs Compression-Rate on Llama3.2-3B Model ### Overview The chart visualizes the relationship between **Latent Compression-Rate** (x-axis) and two metrics: **Averaged Math Accuracy** (y-axis, blue line) and **Token Counts** (y-axis, red line). The data suggests a non-linear trade-off between compression efficiency and model performance. ### Components/Axes - **X-axis (Latent Compression-Rate)**: Ranges from 5 to 30 in increments of 5. - **Y-axis (Averaged Math Accuracy)**: Ranges from 27.2 to 28.0 in increments of 0.2. - **Legend**: Located in the top-right corner, with: - **Blue line/circles**: Represents **Average Math Accuracy**. - **Red line/circles**: Represents **Token Counts**. ### Detailed Analysis #### Average Math Accuracy (Blue Line) - **Data Points**: - At **5**: 27.2 - At **15**: 28.1 (peak) - At **30**: 27.9 - **Trend**: Increases sharply from 5 to 15, then declines slightly from 15 to 30. #### Token Counts (Red Line) - **Data Points**: - At **5**: 580 - At **15**: 520 - At **30**: 480 - **Trend**: Consistently decreases as compression-rate increases. ### Key Observations 1. **Optimal Compression-Rate for Accuracy**: The highest math accuracy (28.1) occurs at a compression-rate of 15, suggesting a potential "sweet spot" before performance degradation. 2. **Token Efficiency**: Token counts drop linearly (580 → 480) as compression-rate increases, indicating reduced computational/resource demands. 3. **Divergence at Extremes**: At compression-rate 5, accuracy is lowest (27.2) but tokens are highest (580). At 30, accuracy stabilizes near 27.9 while tokens drop to 480. ### Interpretation The chart highlights a **non-linear trade-off**: - **Math Accuracy** improves with moderate compression (up to 15) but declines at higher rates (30), possibly due to over-compression degrading model fidelity. - **Token Counts** decrease monotonically, reflecting efficient resource utilization at higher compression rates. - The divergence between the two metrics implies a **compromise**: Higher compression reduces tokens but risks accuracy loss beyond a critical threshold. This analysis aligns with typical model compression behavior, where excessive compression can harm performance despite resource savings.