\n ## Bar Chart: Speedup Comparison of Language Models ### Overview This bar chart compares the speedup achieved by three different training configurations – Vanilla, Base, and Instruct – for two language models: Yi-34B and DeepSeek-Coder-33B. The speedup is measured on the y-axis, while the x-axis represents the language model being evaluated. ### Components/Axes * **X-axis:** Language Model (Yi-34B, DeepSeek-Coder-33B) * **Y-axis:** Speedup (Scale ranges from 1.0 to 1.6, with increments of 0.1) * **Legend:** * Vanilla (Color: Orange) * Base (Color: Blue) * Instruct (Color: Teal) ### Detailed Analysis The chart consists of six bars, two for each language model, representing the speedup for each training configuration. **Yi-34B:** * **Vanilla:** The bar is positioned at approximately 1.00 on the y-axis. * **Base:** The bar reaches approximately 1.31 on the y-axis. The line slopes upward from the Vanilla bar. * **Instruct:** The bar reaches approximately 1.26 on the y-axis. The line slopes downward from the Base bar. **DeepSeek-Coder-33B:** * **Vanilla:** The bar is positioned at approximately 1.00 on the y-axis. * **Base:** The bar reaches approximately 1.54 on the y-axis. The line slopes upward from the Vanilla bar. * **Instruct:** The bar reaches approximately 1.39 on the y-axis. The line slopes downward from the Base bar. ### Key Observations * The "Base" configuration consistently provides the highest speedup for both language models. * The "Instruct" configuration provides a speedup that is lower than the "Base" configuration but higher than the "Vanilla" configuration. * DeepSeek-Coder-33B shows a greater speedup overall compared to Yi-34B, particularly in the "Base" configuration. * The "Vanilla" configuration has a speedup of 1.00 for both models, indicating no speedup relative to a baseline. ### Interpretation The data suggests that the "Base" training configuration is the most effective for accelerating both Yi-34B and DeepSeek-Coder-33B. The "Instruct" configuration offers a moderate speedup, while the "Vanilla" configuration provides no speedup. The larger speedup observed with DeepSeek-Coder-33B suggests that this model benefits more from the "Base" training approach than Yi-34B. This could be due to differences in model architecture, training data, or other factors. The consistent pattern of "Base" > "Instruct" > "Vanilla" indicates a clear hierarchy in the effectiveness of these training configurations. The speedup values provide quantitative evidence of the performance gains achieved by each configuration.