--- base_model: Daewon0808/prm800k_llama_fulltune library_name: peft license: llama3.1 tags: - generated_from_trainer model-index: - name: mmlu_math_noaugse1_llama_lora results: [] --- # mmlu_math_noaugse1_llama_lora This model is a fine-tuned version of [Daewon0808/prm800k_llama_fulltune](https://huggingface.co/Daewon0808/prm800k_llama_fulltune) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2988 - Prm accuracy: 0.8571 - Prm precision: 0.8739 - Prm recall: 0.9720 - Prm specificty: 0.2105 - Prm npv: 0.5714 - Prm f1: 0.9204 - Prm f1 neg: 0.3077 - Prm f1 auc: 0.5912 - Prm f1 auc (fixed): 0.8807 ## 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: 0.0001 - train_batch_size: 2 - eval_batch_size: 4 - seed: 908932403 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - total_eval_batch_size: 32 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Prm accuracy | Prm precision | Prm recall | Prm specificty | Prm npv | Prm f1 | Prm f1 neg | Prm f1 auc | Prm f1 auc (fixed) | |:-------------:|:------:|:----:|:---------------:|:------------:|:-------------:|:----------:|:--------------:|:-------:|:------:|:----------:|:----------:|:------------------:| | No log | 0 | 0 | 0.3535 | 0.8333 | 0.8772 | 0.9346 | 0.2632 | 0.4167 | 0.9050 | 0.3226 | 0.5989 | 0.8195 | | 0.2794 | 0.0246 | 5 | 0.3514 | 0.8333 | 0.8707 | 0.9439 | 0.2105 | 0.4 | 0.9058 | 0.2759 | 0.5772 | 0.8155 | | 0.2101 | 0.0493 | 10 | 0.4065 | 0.8571 | 0.856 | 1.0 | 0.0526 | 1.0 | 0.9224 | 0.1 | 0.5263 | 0.8377 | | 0.2629 | 0.0739 | 15 | 0.4323 | 0.8571 | 0.856 | 1.0 | 0.0526 | 1.0 | 0.9224 | 0.1 | 0.5263 | 0.8598 | | 0.2389 | 0.0985 | 20 | 0.3076 | 0.8492 | 0.8729 | 0.9626 | 0.2105 | 0.5 | 0.9156 | 0.2963 | 0.5866 | 0.8613 | | 0.1863 | 0.1232 | 25 | 0.3250 | 0.8651 | 0.875 | 0.9813 | 0.2105 | 0.6667 | 0.9251 | 0.32 | 0.5959 | 0.8610 | | 0.2801 | 0.1478 | 30 | 0.3046 | 0.8492 | 0.8729 | 0.9626 | 0.2105 | 0.5 | 0.9156 | 0.2963 | 0.5866 | 0.8694 | | 0.2207 | 0.1724 | 35 | 0.2880 | 0.8571 | 0.8803 | 0.9626 | 0.2632 | 0.5556 | 0.9196 | 0.3571 | 0.6129 | 0.8731 | | 0.25 | 0.1970 | 40 | 0.2856 | 0.8651 | 0.8879 | 0.9626 | 0.3158 | 0.6 | 0.9238 | 0.4138 | 0.6392 | 0.8709 | | 0.2113 | 0.2217 | 45 | 0.2917 | 0.8571 | 0.8803 | 0.9626 | 0.2632 | 0.5556 | 0.9196 | 0.3571 | 0.6129 | 0.8719 | | 0.1327 | 0.2463 | 50 | 0.2954 | 0.8651 | 0.8879 | 0.9626 | 0.3158 | 0.6 | 0.9238 | 0.4138 | 0.6392 | 0.8662 | | 0.1599 | 0.2709 | 55 | 0.3042 | 0.8730 | 0.8889 | 0.9720 | 0.3158 | 0.6667 | 0.9286 | 0.4286 | 0.6439 | 0.8637 | | 0.1855 | 0.2956 | 60 | 0.3409 | 0.8413 | 0.8655 | 0.9626 | 0.1579 | 0.4286 | 0.9115 | 0.2308 | 0.5603 | 0.8711 | | 0.2119 | 0.3202 | 65 | 0.3051 | 0.8651 | 0.8879 | 0.9626 | 0.3158 | 0.6 | 0.9238 | 0.4138 | 0.6392 | 0.8660 | | 0.156 | 0.3448 | 70 | 0.3285 | 0.8413 | 0.8655 | 0.9626 | 0.1579 | 0.4286 | 0.9115 | 0.2308 | 0.5603 | 0.8721 | | 0.3401 | 0.3695 | 75 | 0.3260 | 0.8571 | 0.8678 | 0.9813 | 0.1579 | 0.6 | 0.9211 | 0.25 | 0.5696 | 0.8674 | | 0.1796 | 0.3941 | 80 | 0.3070 | 0.8413 | 0.8655 | 0.9626 | 0.1579 | 0.4286 | 0.9115 | 0.2308 | 0.5603 | 0.8667 | | 0.235 | 0.4187 | 85 | 0.3169 | 0.8492 | 0.8607 | 0.9813 | 0.1053 | 0.5 | 0.9170 | 0.1739 | 0.5433 | 0.8728 | | 0.1436 | 0.4433 | 90 | 0.2999 | 0.8492 | 0.8729 | 0.9626 | 0.2105 | 0.5 | 0.9156 | 0.2963 | 0.5866 | 0.8746 | | 0.178 | 0.4680 | 95 | 0.2941 | 0.8492 | 0.8729 | 0.9626 | 0.2105 | 0.5 | 0.9156 | 0.2963 | 0.5866 | 0.8736 | | 0.1733 | 0.4926 | 100 | 0.3088 | 0.8571 | 0.8739 | 0.9720 | 0.2105 | 0.5714 | 0.9204 | 0.3077 | 0.5912 | 0.8775 | | 0.125 | 0.5172 | 105 | 0.3066 | 0.8571 | 0.8739 | 0.9720 | 0.2105 | 0.5714 | 0.9204 | 0.3077 | 0.5912 | 0.8802 | | 0.154 | 0.5419 | 110 | 0.2838 | 0.8730 | 0.8957 | 0.9626 | 0.3684 | 0.6364 | 0.9279 | 0.4667 | 0.6655 | 0.8795 | | 0.1651 | 0.5665 | 115 | 0.2887 | 0.8651 | 0.8879 | 0.9626 | 0.3158 | 0.6 | 0.9238 | 0.4138 | 0.6392 | 0.8832 | | 0.1942 | 0.5911 | 120 | 0.3150 | 0.8571 | 0.8739 | 0.9720 | 0.2105 | 0.5714 | 0.9204 | 0.3077 | 0.5912 | 0.8898 | | 0.2232 | 0.6158 | 125 | 0.3050 | 0.8571 | 0.8739 | 0.9720 | 0.2105 | 0.5714 | 0.9204 | 0.3077 | 0.5912 | 0.8829 | | 0.203 | 0.6404 | 130 | 0.2900 | 0.8730 | 0.8889 | 0.9720 | 0.3158 | 0.6667 | 0.9286 | 0.4286 | 0.6439 | 0.8773 | | 0.2011 | 0.6650 | 135 | 0.2883 | 0.8730 | 0.8889 | 0.9720 | 0.3158 | 0.6667 | 0.9286 | 0.4286 | 0.6439 | 0.8819 | | 0.1789 | 0.6897 | 140 | 0.2964 | 0.8571 | 0.8739 | 0.9720 | 0.2105 | 0.5714 | 0.9204 | 0.3077 | 0.5912 | 0.8810 | | 0.17 | 0.7143 | 145 | 0.3107 | 0.8730 | 0.8760 | 0.9907 | 0.2105 | 0.8 | 0.9298 | 0.3333 | 0.6006 | 0.8842 | | 0.1512 | 0.7389 | 150 | 0.3141 | 0.8730 | 0.8760 | 0.9907 | 0.2105 | 0.8 | 0.9298 | 0.3333 | 0.6006 | 0.8842 | | 0.1368 | 0.7635 | 155 | 0.3115 | 0.8651 | 0.875 | 0.9813 | 0.2105 | 0.6667 | 0.9251 | 0.32 | 0.5959 | 0.8854 | | 0.1492 | 0.7882 | 160 | 0.3066 | 0.8571 | 0.8739 | 0.9720 | 0.2105 | 0.5714 | 0.9204 | 0.3077 | 0.5912 | 0.8844 | | 0.1946 | 0.8128 | 165 | 0.2986 | 0.8571 | 0.8739 | 0.9720 | 0.2105 | 0.5714 | 0.9204 | 0.3077 | 0.5912 | 0.8819 | | 0.1832 | 0.8374 | 170 | 0.2962 | 0.8651 | 0.8814 | 0.9720 | 0.2632 | 0.625 | 0.9244 | 0.3704 | 0.6176 | 0.8815 | | 0.168 | 0.8621 | 175 | 0.2970 | 0.8571 | 0.8739 | 0.9720 | 0.2105 | 0.5714 | 0.9204 | 0.3077 | 0.5912 | 0.8827 | | 0.1285 | 0.8867 | 180 | 0.2977 | 0.8571 | 0.8739 | 0.9720 | 0.2105 | 0.5714 | 0.9204 | 0.3077 | 0.5912 | 0.8810 | | 0.1991 | 0.9113 | 185 | 0.2979 | 0.8571 | 0.8739 | 0.9720 | 0.2105 | 0.5714 | 0.9204 | 0.3077 | 0.5912 | 0.8810 | | 0.1585 | 0.9360 | 190 | 0.2975 | 0.8571 | 0.8739 | 0.9720 | 0.2105 | 0.5714 | 0.9204 | 0.3077 | 0.5912 | 0.8815 | | 0.2369 | 0.9606 | 195 | 0.2977 | 0.8571 | 0.8739 | 0.9720 | 0.2105 | 0.5714 | 0.9204 | 0.3077 | 0.5912 | 0.8815 | | 0.1665 | 0.9852 | 200 | 0.2988 | 0.8571 | 0.8739 | 0.9720 | 0.2105 | 0.5714 | 0.9204 | 0.3077 | 0.5912 | 0.8807 | ### Framework versions - PEFT 0.12.0 - Transformers 4.46.0 - Pytorch 2.4.0+cu118 - Datasets 3.0.0 - Tokenizers 0.20.1