Reasoning on AMC 2023 (accuracy)
90.63Accuracy (AMC 2023)DS-Qwen 14B
Evaluation Results
| Method | Links | |
|---|---|---|
| DS-Qwen 14BModel Size=14B, 0-shot=true2025.05 | 90.63 | |
| CATSModel Size=14B, 0-shot=true2025.05 | 88.75 | |
| Layer-wise CapreseModel Size=14B, Sparse Algorithm=CATS, 0-shot=true2025.05 | 88.75 | |
| E2E CapreseModel Size=14B, Sparse Algorithm=CATS, 0-shot=true2025.05 | 87.5 | |
| Layer-wise CapreseModel Size=14B, Sparse Algorithm=GRIFFIN, 0-shot=true2025.05 | 86.88 | |
| E2E CapreseModel Size=14B, Sparse Algorithm=GRIFFIN, 0-shot=true2025.05 | 84.38 | |
| GRIFFINModel Size=14B, 0-shot=true2025.05 | 80.63 | |
| E2E CapreseModel Size=7B, Sparse Algorithm=CATS, 0-shot=true2025.05 | 78.13 | |
| DS-Qwen 7BModel Size=7B, 0-shot=true2025.05 | 75 | |
| E2E CapreseModel Size=7B, Sparse Algorithm=GRIFFIN, 0-shot=true2025.05 | 71.88 | |
| Layer-wise CapreseModel Size=7B, Sparse Algorithm=CATS, 0-shot=true2025.05 | 70 | |
| Layer-wise CapreseModel Size=7B, Sparse Algorithm=GRIFFIN, 0-shot=true2025.05 | 64.38 | |
| GRIFFINModel Size=7B, 0-shot=true2025.05 | 62.5 | |
| CATSModel Size=7B, 0-shot=true2025.05 | 62.5 | |
| DS-Qwen 1.5BModel Size=1.5B, 0-shot=true2025.05 | 60.83 | |
| Layer-wise CapreseModel Size=1.5B, Sparse Algorithm=CATS, 0-shot=true2025.05 | 48.13 | |
| E2E CapreseModel Size=1.5B, Sparse Algorithm=CATS, 0-shot=true2025.05 | 47.5 | |
| CATSModel Size=1.5B, 0-shot=true2025.05 | 37.5 | |
| E2E CapreseModel Size=1.5B, Sparse Algorithm=GRIFFIN, 0-shot=true2025.05 | 34.38 | |
| Layer-wise CapreseModel Size=1.5B, Sparse Algorithm=GRIFFIN, 0-shot=true2025.05 | 28.13 | |
| GRIFFINModel Size=1.5B, 0-shot=true2025.05 | 16.88 |