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": 0.53716, | |
| "audit/embedding/empirical_epsilon/0.01": 0.0, | |
| "audit/embedding/empirical_epsilon/0.05": 0.0, | |
| "audit/embedding/empirical_epsilon_details/0.01/correct_guesses": 0.0, | |
| "audit/embedding/empirical_epsilon_details/0.01/epsilon": 0.0, | |
| "audit/embedding/empirical_epsilon_details/0.01/num_guesses": 0.0, | |
| "audit/embedding/empirical_epsilon_details/0.05/correct_guesses": 0.0, | |
| "audit/embedding/empirical_epsilon_details/0.05/epsilon": 0.0, | |
| "audit/embedding/empirical_epsilon_details/0.05/num_guesses": 0.0, | |
| "audit/loss/auc": 0.52628, | |
| "audit/loss/empirical_epsilon/0.01": 0.0, | |
| "audit/loss/empirical_epsilon/0.05": 0.0, | |
| "audit/loss/empirical_epsilon_details/0.01/correct_guesses": 0.0, | |
| "audit/loss/empirical_epsilon_details/0.01/epsilon": 0.0, | |
| "audit/loss/empirical_epsilon_details/0.01/num_guesses": 0.0, | |
| "audit/loss/empirical_epsilon_details/0.05/correct_guesses": 0.0, | |
| "audit/loss/empirical_epsilon_details/0.05/epsilon": 0.0, | |
| "audit/loss/empirical_epsilon_details/0.05/num_guesses": 0.0, | |
| "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": 16.0, | |
| "energy/codecarbon/cpu_energy": 0.10578202327142265, | |
| "energy/codecarbon/cpu_power": 81.12029064705573, | |
| "energy/codecarbon/cpu_utilization_percent": 10.068375181572941, | |
| "energy/codecarbon/duration": 4876.212828015909, | |
| "energy/codecarbon/emissions": 0.19531666760349806, | |
| "energy/codecarbon/emissions_rate": 4.005499236647774e-05, | |
| "energy/codecarbon/energy_consumed": 5.605621433386851, | |
| "energy/codecarbon/gpu_count": 8.0, | |
| "energy/codecarbon/gpu_energy": 5.4502873277263575, | |
| "energy/codecarbon/gpu_power": 4036.488899527526, | |
| "energy/codecarbon/gpu_utilization_percent": 93.63337829425193, | |
| "energy/codecarbon/latitude": 47.4843, | |
| "energy/codecarbon/longitude": 8.212, | |
| "energy/codecarbon/pue": 1.0, | |
| "energy/codecarbon/ram_energy": 0.049552082389069795, | |
| "energy/codecarbon/ram_power": 38.0, | |
| "energy/codecarbon/ram_total_size": 128.0, | |
| "energy/codecarbon/ram_used_gb": 529.1190595498197, | |
| "energy/codecarbon/ram_utilization_percent": 26.690475202324134, | |
| "energy/codecarbon/water_consumed": 0.0, | |
| "energy/codecarbon/wue": 0.0, | |
| "eval/duration_sec": 14.139388957060874, | |
| "eval/loss": 0.8476120976900514, | |
| "perf/audit_duration_sec": 5.879508854821324, | |
| "perf/epoch_duration_sec": 1581.0388780001085, | |
| "perf/epoch_samples": 58247.0, | |
| "perf/epoch_samples_per_sec": 36.840966285204786, | |
| "perf/epoch_tokens": 44136681.0, | |
| "perf/epoch_tokens_per_sec": 27916.25279691381, | |
| "perf/logical_batch_size": 34.0, | |
| "perf/logical_token_count": 24675.0, | |
| "perf/physical_batches": 9.0, | |
| "perf/samples_per_sec": 6.377925321669768, | |
| "perf/step_duration_sec": 5.330887127900496, | |
| "perf/tokens_per_sec": 4628.685509182398, | |
| "privacy/epsilon": 7.99463488083978, | |
| "system/cuda_epoch_peak_memory_gb": 46.08257722854614, | |
| "system/cuda_max_memory_allocated_gb": 46.08257722854614, | |
| "system/cuda_memory_allocated_gb": 16.34225034713745, | |
| "train/epoch_canary_loss": 7.866599485030913, | |
| "train/epoch_loss": 2.0618881598471557, | |
| "train/epoch_real_loss": 1.3318002155782387, | |
| "train/lr": 6.5139112762079875e-06, | |
| "train/step_canary_loss": 10.15625, | |
| "train/step_loss": 1.804337599698235, | |
| "train/step_real_loss": 1.2823430746793747 | |
| } |