# Technical Document Extraction: INT3/g128 Performance Analysis ## Chart Overview The image presents a comparative bar chart analyzing the performance of three quantization methods (RTN, GPTQ, AWQ) across two Vicuna model sizes (7B and 13B). The chart uses color-coded bars to represent three performance categories: Quantized Win (blue), Tie (yellow), and Quantized Lost (red). --- ### Legend & Axis Labels - **Legend**: Located on the right side of the chart - Blue: Quantized Win - Yellow: Tie - Red: Quantized Lost - **X-axis**: Numerical scale from 0 to 80 (performance metric) - **Y-axis**: Model configurations - Sub-chart (a): Vicuna-7B - Sub-chart (b): Vicuna-13B --- ### Data Extraction #### Sub-chart (a): Vicuna-7B | Model | Quantized Win | Tie | Quantized Lost | |-------|---------------|-----|----------------| | RTN | 6 | 3 | 71 | | GPTQ | 4 | 1 | 75 | | AWQ | 23 | 5 | 52 | #### Sub-chart (b): Vicuna-13B | Model | Quantized Win | Tie | Quantized Lost | |-------|---------------|-----|----------------| | RTN | 14 | 9 | 57 | | GPTQ | 17 | 6 | 57 | | AWQ | 22 | 11 | 47 | --- ### Spatial Grounding & Color Verification 1. **Legend Position**: Right-aligned, adjacent to both sub-charts 2. **Color Consistency**: - All blue bars correspond to "Quantized Win" values - Yellow bars match "Tie" metrics - Red bars represent "Quantized Lost" outcomes 3. **Axis Alignment**: - X-axis values increase left-to-right (0-80) - Y-axis models are vertically stacked per sub-chart --- ### Trend Verification 1. **Vicuna-7B (Sub-chart a)**: - RTN shows the highest Quantized Lost (71) - AWQ demonstrates the strongest Quantized Win performance (23) - GPTQ has the lowest Quantized Win (4) and highest Tie (1) 2. **Vicuna-13B (Sub-chart b)**: - AWQ maintains lead in Quantized Wins (22) - RTN and GPTQ show identical Quantized Lost counts (57) - Tie values increase across all models from 7B to 13B versions --- ### Component Isolation 1. **Header**: "INT3/g128" title at top-center 2. **Main Chart**: - Two vertically stacked sub-charts (a/b) - Each sub-chart contains three grouped bars per model 3. **Footer**: X-axis labels and numerical scale --- ### Data Table Reconstruction | Model | Vicuna-7B: Quantized Win | Vicuna-7B: Tie | Vicuna-7B: Quantized Lost | Vicuna-13B: Quantized Win | Vicuna-13B: Tie | Vicuna-13B: Quantized Lost | |-------|--------------------------|----------------|---------------------------|---------------------------|-----------------|----------------------------| | RTN | 6 | 3 | 71 | 14 | 9 | 57 | | GPTQ | 4 | 1 | 75 | 17 | 6 | 57 | | AWQ | 23 | 5 | 52 | 22 | 11 | 47 | --- ### Critical Observations 1. **Performance Scaling**: - Quantized Win values increase by 2-3x when moving from 7B to 13B models - Tie values show proportional growth (e.g., RTN: 3→9, GPTQ: 1→6) 2. **Quantized Lost Trends**: - RTN maintains highest loss rates in both configurations - AWQ demonstrates most significant improvement in 13B version (52→47) 3. **Color-Coded Validation**: - All red bars (Quantized Lost) exceed 47 in both sub-charts - Yellow bars (Tie) never exceed 11 in either configuration