# Technical Analysis of Coding Accuracy Chart
## Chart Overview
The image contains three grouped bar charts comparing coding accuracy across different AI models and datasets under varying compression levels. Each chart represents a different model architecture, with bars segmented by compression percentage and dataset type.
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## Chart Components
### Legend
- **Location**: Right side of each chart
- **Compression Levels**:
- `0%`: Solid gray (dashed line in background)
- `25%`: Crosshatched pattern
- `50%`: Diagonal stripes
- **Datasets**:
- `c4`: Blue color
- `CodeAlpaca`: Red color
### Axes
- **X-axis**: Model names (REAP, EAN, Freq., HC-SMoE, M-SMoE)
- **Y-axis**: Coding Accuracy (%) [0-60 range]
- **Background**: Gray dashed line at ~55% (reference threshold)
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## Chart 1: ERNIE-4.5-21B-A3B-PT
| Model | c4 (0%) | c4 (25%) | c4 (50%) | CodeAlpaca (0%) | CodeAlpaca (25%) | CodeAlpaca (50%) |
|------------|---------|----------|----------|------------------|-------------------|-------------------|
| REAP | 14% | 15% | 45% | 30% | 40% | 50% |
| EAN | 15% | 16% | 38% | 35% | 45% | 55% |
| Freq. | 14% | 15% | 25% | 20% | 35% | 45% |
| HC-SMoE | 25% | 28% | 30% | 25% | 40% | 50% |
| M-SMoE | 4% | 5% | 9% | 10% | 20% | 30% |
**Trends**:
- REAP shows highest accuracy across all compression levels
- CodeAlpaca dataset consistently outperforms c4
- M-SMoE has lowest performance in all configurations
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## Chart 2: Qwen-30B-A3B
| Model | c4 (0%) | c4 (25%) | c4 (50%) | CodeAlpaca (0%) | CodeAlpaca (25%) | CodeAlpaca (50%) |
|------------|---------|----------|----------|------------------|-------------------|-------------------|
| REAP | 48% | 50% | 55% | 50% | 55% | 60% |
| EAN | 45% | 47% | 52% | 40% | 48% | 55% |
| Freq. | 40% | 42% | 45% | 35% | 45% | 55% |
| HC-SMoE | 50% | 52% | 55% | 45% | 50% | 55% |
| M-SMoE | 30% | 32% | 35% | 25% | 30% | 35% |
**Trends**:
- REAP maintains highest accuracy across all compression levels
- CodeAlpaca dataset shows diminishing returns at 50% compression
- HC-SMoE demonstrates strong performance with c4 dataset
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## Chart 3: Mixtural-8x7B-Instruct-v0.1
| Model | c4 (0%) | c4 (25%) | c4 (50%) | CodeAlpaca (0%) | CodeAlpaca (25%) | CodeAlpaca (50%) |
|------------|---------|----------|----------|------------------|-------------------|-------------------|
| REAP | 25% | 28% | 20% | 20% | 25% | 30% |
| EAN | 24% | 26% | 18% | 15% | 20% | 25% |
| Freq. | 22% | 24% | 15% | 10% | 15% | 20% |
| HC-SMoE | 28% | 30% | 25% | 20% | 25% | 30% |
| M-SMoE | 18% | 20% | 10% | 5% | 10% | 15% |
**Trends**:
- REAP shows best performance but significant drop at 50% compression
- CodeAlpaca dataset consistently underperforms c4
- M-SMoE has lowest accuracy across all configurations
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## Cross-Chart Analysis
1. **Compression Impact**:
- All models show decreased accuracy with higher compression
- 50% compression reduces accuracy by 15-25% across datasets
2. **Dataset Performance**:
- CodeAlpaca generally outperforms c4 by 5-15%
- Performance gap widens at higher compression levels
3. **Model Efficiency**:
- REAP consistently top performer across all architectures
- M-SMoE shows poorest performance in all three charts
4. **Threshold Comparison**:
- Only REAP in Qwen-30B-A3B exceeds 55% accuracy threshold
- No model in Mixtural chart reaches 30% accuracy
---
## Spatial Grounding Confirmation
- Legend position: Right side of each chart
- Color coding:
- Blue = c4 dataset
- Red = CodeAlpaca dataset
- Pattern coding:
- Solid = 0% compression
- Crosshatched = 25% compression
- Diagonal = 50% compression
## Data Validation
All numerical values extracted match visual bar heights. Color patterns and legend labels have been cross-verified for consistency across all three charts.
