Instructions to use melihcatal/codedp-cpt-models-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use melihcatal/codedp-cpt-models-v2 with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
| { | |
| "audit/delta": 1e-05, | |
| "audit/embedding/auc": 0.52, | |
| "audit/embedding/empirical_epsilon/0.01": 3.023197554051876, | |
| "audit/embedding/empirical_epsilon/0.05": 3.4791953936219215, | |
| "audit/embedding/empirical_epsilon_details/0.01/correct_guesses": 100.0, | |
| "audit/embedding/empirical_epsilon_details/0.01/epsilon": 3.023197554051876, | |
| "audit/embedding/empirical_epsilon_details/0.01/num_guesses": 100.0, | |
| "audit/embedding/empirical_epsilon_details/0.05/correct_guesses": 100.0, | |
| "audit/embedding/empirical_epsilon_details/0.05/epsilon": 3.4791953936219215, | |
| "audit/embedding/empirical_epsilon_details/0.05/num_guesses": 100.0, | |
| "audit/loss/auc": 0.997368, | |
| "audit/loss/empirical_epsilon/0.01": 3.023197554051876, | |
| "audit/loss/empirical_epsilon/0.05": 3.4791953936219215, | |
| "audit/loss/empirical_epsilon_details/0.01/correct_guesses": 100.0, | |
| "audit/loss/empirical_epsilon_details/0.01/epsilon": 3.023197554051876, | |
| "audit/loss/empirical_epsilon_details/0.01/num_guesses": 100.0, | |
| "audit/loss/empirical_epsilon_details/0.05/correct_guesses": 100.0, | |
| "audit/loss/empirical_epsilon_details/0.05/epsilon": 3.4791953936219215, | |
| "audit/loss/empirical_epsilon_details/0.05/num_guesses": 100.0, | |
| "audit/num_canaries": 500.0, | |
| "audit/num_members": 250.0, | |
| "audit/paper_guess_fraction": 0.2, | |
| "audit/paper_guess_steps": 20.0, | |
| "energy/codecarbon/cpu_count": 8.0, | |
| "energy/codecarbon/cpu_energy": 0.07906645068697907, | |
| "energy/codecarbon/cpu_power": 80.40813034674923, | |
| "energy/codecarbon/cpu_utilization_percent": 8.855084745762712, | |
| "energy/codecarbon/duration": 3685.56630371185, | |
| "energy/codecarbon/emissions": 0.16952444726420043, | |
| "energy/codecarbon/emissions_rate": 4.599685185244585e-05, | |
| "energy/codecarbon/energy_consumed": 4.865380342226571, | |
| "energy/codecarbon/gpu_count": 8.0, | |
| "energy/codecarbon/gpu_energy": 4.748948588600271, | |
| "energy/codecarbon/gpu_power": 4660.729357242909, | |
| "energy/codecarbon/gpu_utilization_percent": 93.662247129579, | |
| "energy/codecarbon/latitude": 47.4843, | |
| "energy/codecarbon/longitude": 8.212, | |
| "energy/codecarbon/pue": 1.0, | |
| "energy/codecarbon/ram_energy": 0.03736530293931913, | |
| "energy/codecarbon/ram_power": 38.0, | |
| "energy/codecarbon/ram_total_size": 256.0, | |
| "energy/codecarbon/ram_used_gb": 515.4519262068776, | |
| "energy/codecarbon/ram_utilization_percent": 26.020803717878625, | |
| "energy/codecarbon/water_consumed": 0.0, | |
| "energy/codecarbon/wue": 0.0, | |
| "eval/duration_sec": 14.38155033509247, | |
| "eval/loss": 0.7171374095434493, | |
| "perf/audit_duration_sec": 7.9593279850669205, | |
| "perf/epoch_duration_sec": 1156.3163079482038, | |
| "perf/epoch_samples": 53331.0, | |
| "perf/epoch_samples_per_sec": 46.12146316143534, | |
| "perf/epoch_tokens": 43842337.0, | |
| "perf/epoch_tokens_per_sec": 37915.522507673464, | |
| "perf/gradient_accumulation_steps": 4.0, | |
| "perf/logical_batch_size": 32.0, | |
| "perf/logical_token_count": 25716.0, | |
| "perf/samples_per_sec": 7.637238639755865, | |
| "perf/step_duration_sec": 4.189996084896848, | |
| "perf/tokens_per_sec": 6137.475901873807, | |
| "system/cuda_epoch_peak_memory_gb": 81.2615852355957, | |
| "system/cuda_max_memory_allocated_gb": 81.2615852355957, | |
| "system/cuda_memory_allocated_gb": 17.816345691680908, | |
| "train/epoch_canary_loss": 1.9868655627132747, | |
| "train/epoch_loss": 0.7704904898906849, | |
| "train/epoch_real_loss": 0.762895334188057, | |
| "train/lr": 6.50087836514208e-06, | |
| "train/step_canary_loss": 0.08935546875, | |
| "train/step_loss": 0.691263422369957, | |
| "train/step_real_loss": 0.691263422369957 | |
| } |