Opt Babylm1 No Non Preemption Laugh 1x Seed 211 5e 6

--- library_name: transformers tags: - generated_from_trainer model-index: - name: opt-babylm1_no_non_preemption_laugh-1x_seed-211_5e-6 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # opt-babylm1_no_non_preemption_laugh-1x_seed-211_5e-6 This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.9367 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 32 - eval_batch_size: 64 - seed: 211 - gradient_accumulation_steps: 8 - total_train_batch_size: 256 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 20.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-------:|:-----:|:---------------:| | 5.3161 | 0.4208 | 1000 | 5.2976 | | 4.6694 | 0.8415 | 2000 | 4.6908 | | 4.2021 | 1.2621 | 3000 | 4.1980 | | 3.8438 | 1.6829 | 4000 | 3.8286 | | 3.5462 | 2.1035 | 5000 | 3.5530 | | 3.3942 | 2.5243 | 6000 | 3.3906 | | 3.2987 | 2.9450 | 7000 | 3.3023 | | 3.2193 | 3.3656 | 8000 | 3.2339 | | 3.1623 | 3.7864 | 9000 | 3.1827 | | 3.0946 | 4.2070 | 10000 | 3.1522 | | 3.0869 | 4.6278 | 11000 | 3.1202 | | 3.0072 | 5.0484 | 12000 | 3.0940 | | 3.0147 | 5.4692 | 13000 | 3.0766 | | 3.0049 | 5.8899 | 14000 | 3.0568 | | 2.9662 | 6.3105 | 15000 | 3.0463 | | 2.9574 | 6.7313 | 16000 | 3.0327 | | 2.9126 | 7.1519 | 17000 | 3.0284 | | 2.9196 | 7.5727 | 18000 | 3.0135 | | 2.9209 | 7.9934 | 19000 | 3.0032 | | 2.8812 | 8.4140 | 20000 | 2.9985 | | 2.8838 | 8.8348 | 21000 | 2.9912 | | 2.8444 | 9.2554 | 22000 | 2.9896 | | 2.8562 | 9.6762 | 23000 | 2.9810 | | 2.8037 | 10.0968 | 24000 | 2.9771 | | 2.8218 | 10.5175 | 25000 | 2.9718 | | 2.8237 | 10.9383 | 26000 | 2.9612 | | 2.7891 | 11.3589 | 27000 | 2.9674 | | 2.8049 | 11.7797 | 28000 | 2.9583 | | 2.7692 | 12.2003 | 29000 | 2.9594 | | 2.7793 | 12.6210 | 30000 |...