Enhanced Quantization Performance Analysis

Comparative analysis of Bartoski, Unsloth, and main quantization methods

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Overview of Quantization Methods

This analysis compares three quantization approaches: Bartoski, Unsloth, and main across multiple performance dimensions.

Bartoski
Unsloth
Main

Key Metrics

  • Perplexity (PPL): Lower is better, measures model prediction quality
  • KLD: Measures distribution divergence, lower is better
  • Model Size: Storage requirements in MB/GB
  • Inference Speed: Token generation time in ms
  • 1/PPL/MB: Efficiency metric, higher is better
  • Delta Probabilities: Difference in token prediction probabilities

Quantization Benefits

  • Reduced model size
  • Faster inference and lower latency
  • Lower memory requirements during runtime
  • Deployment on resource-constrained devices
  • Energy efficiency in production environments

Analysis Goals

This dashboard compares quantization methods across 8 dimensions to identify optimal approaches for different use cases, highlighting each method's strengths and weaknesses in terms of performance, efficiency, and quality tradeoffs.

Graph 1: Perplexity vs. Bits per Weight

Shows how model perplexity (PPL) changes with varying bit-depth across different quantization methods.

Key Insights:

  • Perplexity generally improves (decreases) with higher bit rates across all methods
  • Bartoski shows more stable performance across different bit rates
  • Main method shows significant performance degradation below 3 bits
  • Unsloth maintains acceptable perplexity even at lower bit rates
  • Critical transition point occurs around 4 bits per weight

Graph 2: Accuracy Degradation Curve

Shows the relative performance degradation compared to the FP16 baseline model.

Key Insights:

  • Bartoski maintains the highest relative accuracy at lower bit rates
  • All methods show minimal degradation at 8-bit quantization
  • Main method shows steepest degradation curve below 4 bits
  • Unsloth demonstrates a more gradual degradation pattern
  • 4-bit quantization represents a good balance point for all methods

Graph 3: Layerwise Impact Heatmap

Visualizes which model layers are most affected by each quantization method.

Key Insights:

  • Attention layers show higher sensitivity to quantization across all methods
  • Main method shows more concentrated impact on specific layers
  • Bartoski demonstrates more uniform impact distribution across layers
  • Early layers (closer to input) show higher quantization impact for Unsloth
  • Final output layers are less affected in all methods, preserving prediction quality

Graph 4: Speed vs. Accuracy Tradeoff

Shows the relationship between inference speed and model accuracy across different configurations.

Key Insights:

  • Bartoski achieves better accuracy at comparable speeds
  • Unsloth offers the fastest inference but with some accuracy tradeoff
  • Main method shows more balanced performance but doesn't excel in either dimension
  • 4-bit configurations (highlighted) offer optimal balance for most methods
  • Lower bit rates provide diminishing returns in speed benefits below certain thresholds

Graph 5: Model Size vs. 1/PPL

Illustrates efficiency (1/PPL) per unit of model size, showing which method delivers the best performance-to-size ratio.

Key Insights:

  • Bartoski shows the highest efficiency (1/PPL) per unit of model size
  • Smaller bit-depth configurations show diminishing returns in efficiency
  • Unsloth provides good efficiency with minimal size requirements
  • Main method requires larger size to achieve comparable efficiency
  • 4-bit quantization represents the optimal efficiency point for all methods

Graph 6: Token Prediction Confidence Distribution

Shows the distribution of prediction confidence across different confidence ranges for each quantization method.

Key Insights:

  • Bartoski maintains higher percentage of high-confidence predictions across bit rates
  • Lower bit quantization shifts distribution toward medium and low confidence ranges
  • Main method shows more uniform distribution across confidence ranges
  • Unsloth maintains relatively consistent distribution pattern even at lower bit rates
  • All methods show significant confidence degradation at 2-bit quantization

Graph 7: Delta Log Probabilities

Shows how log probability distributions diverge between quantized models and the original FP16 model.

Key Insights:

  • Bartoski shows the narrowest delta distribution, indicating more accurate quantization
  • Main method has wider distribution tails, suggesting more outlier predictions
  • Unsloth demonstrates a more concentrated distribution around zero
  • Larger deltas indicate more significant divergence from the original model behavior
  • Narrower distributions correlate with better preservation of model behavior post-quantization

Graph 8: Multi-Metric Comparison

Provides a comprehensive comparison across multiple metrics using normalized scores (100% = best performance).

Key Insights:

  • Bartoski excels in perplexity and KLD metrics, showing superior quality preservation
  • Unsloth leads in inference speed and memory footprint, making it ideal for resource-constrained environments
  • Main method shows balanced performance but doesn't lead in any specific metric
  • No single method dominates across all metrics, highlighting the importance of selecting the right method for specific requirements
  • Efficiency (size-to-performance ratio) varies significantly across methods

Summary & Conclusions

Key findings and recommendations based on the comparative analysis.

Method Best For Limitations Optimal Bit Rate PPL Efficiency (1/PPL/MB)
Bartoski Quality-sensitive applications, NLP tasks requiring high accuracy Slightly larger model size compared to alternatives 4-bit 11.81 3.217
Unsloth Mobile devices, edge computing, latency-sensitive applications Some quality degradation at lower bit rates 4-bit 13.79 2.952
Main Balanced applications, general-purpose deployment No standout strengths in any specific dimension 5-bit 12.55 3.140

Key Takeaways

  • No One-Size-Fits-All: Different quantization methods excel in different dimensions. Choose based on your specific requirements.
  • Sweet Spot: 4-bit quantization offers the best balance between model size, accuracy, and inference speed across all methods.
  • Quality vs. Speed: Bartoski preserves model quality better, while Unsloth offers superior speed and resource efficiency.
  • Layer Sensitivity: Attention to layer-specific impact can guide optimized mixed-precision approaches for better results.
  • Confidence Distribution: Consider prediction confidence patterns when selecting a method for critical decision-making applications.

Recommendations

Based on this analysis, we recommend:

  • Use Bartoski for applications where prediction quality and accuracy are paramount
  • Use Unsloth for deployment on resource-constrained devices or latency-sensitive applications
  • Consider 4-bit quantization as the default starting point for most applications
  • Implement mixed precision approaches guided by the layerwise impact analysis for optimal results
  • Balance compression ratio with acceptable performance degradation based on application requirements