## Line Chart: Llama-3.1-8B-Instruct Performance vs. Compression Rate ### Overview The chart compares the average accuracy of different compression methods for the Llama-3.1-8B-Instruct model across compression rates (1x to 8x). Five data series are plotted, with the Baseline (dashed black line) serving as a reference point at 95% accuracy. ### Components/Axes - **X-axis (Compression Rate)**: Labeled "Compression Rate" with markers at 1x, 2x, 4x, and 8x. - **Y-axis (Average Accuracy)**: Labeled "Average Accuracy" with increments from 0 to 100. - **Legend**: Located in the bottom-right corner, with five entries: - **Baseline**: Dashed black line (95% accuracy). - **Minicache**: Teal squares (■). - **Single SVD**: Teal diamonds (◆). - **xKV-2 (Ours)**: Orange circles (●). - **xKV-4 (Ours)**: Red circles (●). ### Detailed Analysis 1. **Baseline**: A horizontal dashed black line at 95% accuracy, representing the uncompressed model's performance. 2. **Minicache**: Starts at ~85% at 1x, declines steeply to ~35% at 8x. Data points are teal squares. 3. **Single SVD**: Begins at ~90% at 1x, drops to ~70% at 8x. Data points are teal diamonds. 4. **xKV-2 (Ours)**: Starts at ~90% at 1x, declines to ~70% at 8x. Data points are orange circles. 5. **xKV-4 (Ours)**: Starts at ~90% at 1x, declines to ~85% at 8x. Data points are red circles. ### Key Observations - **Baseline Dominance**: The Baseline (95%) remains constant and significantly higher than all compressed methods. - **Minicache Degradation**: Minicache shows the steepest decline in accuracy with increasing compression. - **xKV-4 Superiority**: xKV-4 consistently outperforms xKV-2 across all compression rates, maintaining ~5% higher accuracy. - **Compression Trade-off**: All methods show reduced accuracy as compression increases, with Minicache being the most affected. ### Interpretation The chart demonstrates that compression reduces model accuracy, but the xKV-2 and xKV-4 methods (marked as "Ours") preserve performance better than Minicache and Single SVD. The Baseline represents the theoretical maximum accuracy without compression. Notably, xKV-4's smaller degradation suggests it is the most efficient compression method for this model, retaining ~85% of baseline accuracy even at 8x compression. This implies that xKV-4 offers a favorable balance between compression and performance for Llama-3.1-8B-Instruct.