| == training exited 20:22 |
| step 123/124 loss 0.0204 191.7s/step ~0.1 h left |
| step 124/124 loss 0.0152 141.9s/step ~0.0 h left |
| Final validation loss: 0.0173 |
| Adapter saved to /Users/umer/Documents/6 - mix/endpoint-audit/training/models/auditor-lora/final |
| == base model 20:22 |
| /Users/umer/Documents/6 - mix/endpoint-audit/training/.venv/lib/python3.11/site-packages/transformers/generation/configuration_utils.py:590: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.7` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`. |
| warnings.warn( |
| /Users/umer/Documents/6 - mix/endpoint-audit/training/.venv/lib/python3.11/site-packages/transformers/generation/configuration_utils.py:595: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.8` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`. |
| warnings.warn( |
| /Users/umer/Documents/6 - mix/endpoint-audit/training/.venv/lib/python3.11/site-packages/transformers/generation/configuration_utils.py:612: UserWarning: `do_sample` is set to `False`. However, `top_k` is set to `20` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_k`. |
| warnings.warn( |
| The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results. |
| [1/30] ai_classification 26s |
| [2/30] finding 80s |
| [3/30] finding 136s |
| [4/30] finding 194s |
| [5/30] finding 207s |
| [6/30] finding 219s |
| [7/30] summary 354s |
| [8/30] ai_classification 368s |
| [9/30] finding 395s |
| [10/30] finding 427s |
| [11/30] finding 446s |
| [12/30] finding 491s |
| [13/30] finding 544s |
| [14/30] summary 716s |
| [15/30] ai_classification 853s |
| [16/30] finding 879s |
| [17/30] finding 913s |
| [18/30] finding 943s |
| [19/30] finding 975s |
| [20/30] finding 989s |
| [21/30] summary 1163s |
| [22/30] ai_classification 1301s |
| [23/30] finding 1332s |
| [24/30] finding 1362s |
| [25/30] finding 1381s |
| [26/30] finding 1393s |
| [27/30] finding 1438s |
| [28/30] summary 1599s |
| [29/30] ai_classification 1666s |
| [30/30] finding 1695s |
| |
| Results for base model on 30 test examples: |
| task n valid JSON grounded fields match |
| ai_classification 5 0% 0% 0% |
| finding 21 0% 0% 0% |
| summary 4 0% 0% 0% |
| == fine-tuned v0.1 pilot 20:51 |
| /Users/umer/Documents/6 - mix/endpoint-audit/training/.venv/lib/python3.11/site-packages/transformers/generation/configuration_utils.py:590: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.7` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`. |
| warnings.warn( |
| /Users/umer/Documents/6 - mix/endpoint-audit/training/.venv/lib/python3.11/site-packages/transformers/generation/configuration_utils.py:595: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.8` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`. |
| warnings.warn( |
| /Users/umer/Documents/6 - mix/endpoint-audit/training/.venv/lib/python3.11/site-packages/transformers/generation/configuration_utils.py:612: UserWarning: `do_sample` is set to `False`. However, `top_k` is set to `20` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_k`. |
| warnings.warn( |
| The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results. |
| [1/30] ai_classification 41s |
| [2/30] finding 84s |
| [3/30] finding 133s |
| [4/30] finding 167s |
| [5/30] finding 188s |
| [6/30] finding 214s |
| [7/30] summary 325s |
| [8/30] ai_classification 386s |
| [9/30] finding 434s |
| [10/30] finding 462s |
| [11/30] finding 492s |
| [12/30] finding 517s |
| [13/30] finding 551s |
| [14/30] summary 659s |
| [15/30] ai_classification 762s |
| [16/30] finding 784s |
| [17/30] finding 814s |
| [18/30] finding 843s |
| [19/30] finding 862s |
| [20/30] finding 887s |
| [21/30] summary 1009s |
| [22/30] ai_classification 1104s |
| [23/30] finding 1123s |
| [24/30] finding 1148s |
| [25/30] finding 1172s |
| [26/30] finding 1189s |
| [27/30] finding 1212s |
| [28/30] summary 1338s |
| [29/30] ai_classification 1370s |
| [30/30] finding 1387s |
|
|
| Results for adapter training/models/auditor-lora/final on 30 test examples: |
| task n valid JSON grounded fields match |
| ai_classification 5 100% 100% 100% |
| finding 21 100% 100% 100% |
| summary 4 100% 100% 0% |
| == done 21:14 |
|
|
