EC Speculative Decoding Training Platform
Advanced platform for training and evaluating speculative decoding algorithms with focus on EC (Efficient Compression) models. Optimize inference speed while maintaining accuracy.
Performance Metrics
Speedup Ratio
2.4x
+12% improvement
Accuracy
94.7%
Maintained
Tokens/sec
1,240
+35% increase
Memory Usage
4.2GB
+8% increase
Training Configuration
Model Parameters
Training Progress
Loss
0.124
Accuracy
94.7%
Training Visualization
Loss Curve
Accuracy Progress
Model Evaluation
| Algorithm | Speedup | Accuracy | Latency (ms) | Memory (MB) | Status |
|---|---|---|---|---|---|
| EC-SpecDeco v2.1 | 2.4x | 94.7% | 42.3 | 4,200 | Optimal |
| EC-SpecDeco v1.8 | 1.9x | 93.2% | 56.7 | 3,800 | Good |
| Standard Decoding | 1.0x | 95.1% | 102.4 | 3,500 | Baseline |
| Custom EC Model | 2.7x | 94.3% | 38.9 | 4,500 | Optimal |
Speculative Decoding Algorithm
Our EC (Efficient Compression) speculative decoding algorithm improves inference speed by predicting multiple tokens ahead before verification. The approach uses a smaller, faster draft model to propose token sequences which are then verified by the larger target model.
Draft Model
Smaller, faster model that proposes candidate tokens for verification
Verification
Target model verifies proposed tokens in parallel batches
Acceptance
Validated tokens are accepted, rejected tokens trigger re-generation
This approach achieves significant speedups by reducing the number of expensive operations on the large model while maintaining high accuracy through verification steps.