--- license: mit library_name: peft tags: - generated_from_trainer base_model: microsoft/phi-2 model-index: - name: fine-tuning-Phi2-with-webglm-qa-with-lora_2 results: [] --- # fine-tuning-Phi2-with-webglm-qa-with-lora_2 This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0749 ## 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-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 5 - total_train_batch_size: 10 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - training_steps: 1000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 0.2 | 10 | 8.2244 | | No log | 0.4 | 20 | 7.4785 | | No log | 0.6 | 30 | 5.5477 | | No log | 0.8 | 40 | 2.9270 | | 5.8579 | 1.0 | 50 | 0.6602 | | 5.8579 | 1.2 | 60 | 0.5707 | | 5.8579 | 1.39 | 70 | 0.5056 | | 5.8579 | 1.59 | 80 | 0.4537 | | 5.8579 | 1.79 | 90 | 0.4048 | | 0.4258 | 1.99 | 100 | 0.3539 | | 0.4258 | 2.19 | 110 | 0.3117 | | 0.4258 | 2.39 | 120 | 0.2848 | | 0.4258 | 2.59 | 130 | 0.2589 | | 0.4258 | 2.79 | 140 | 0.2344 | | 0.2253 | 2.99 | 150 | 0.2160 | | 0.2253 | 3.19 | 160 | 0.2037 | | 0.2253 | 3.39 | 170 | 0.1890 | | 0.2253 | 3.59 | 180 | 0.1794 | | 0.2253 | 3.78 | 190 | 0.1695 | | 0.1573 | 3.98 | 200 | 0.1607 | | 0.1573 | 4.18 | 210 | 0.1544 | | 0.1573 | 4.38 | 220 | 0.1518 | | 0.1573 | 4.58 | 230 | 0.1441 | | 0.1573 | 4.78 | 240 | 0.1365 | | 0.1251 | 4.98 | 250 | 0.1302 | | 0.1251 | 5.18 | 260 | 0.1286 | | 0.1251 | 5.38 | 270 | 0.1258 | | 0.1251 | 5.58 | 280 | 0.1228 | | 0.1251 | 5.78 | 290 | 0.1203 | | 0.1059 | 5.98 | 300 | 0.1159 | | 0.1059 | 6.18 | 310 | 0.1116 | | 0.1059 | 6.37 | 320 | 0.1112 | | 0.1059 | 6.57 | 330 | 0.1092 | | 0.1059 | 6.77 | 340 | 0.1046 | | 0.0905 | 6.97 | 350 | 0.1032 | | 0.0905 | 7.17 | 360 | 0.1028 | | 0.0905 | 7.37 | 370 | 0.1005 | | 0.0905 | 7.57 | 380 | 0.1011 | | 0.0905 | 7.77 | 390 | 0.0991 | | 0.0816 | 7.97 | 400 | 0.0973 | | 0.0816 | 8.17 | 410 | 0.0965 | | 0.0816 | 8.37 | 420 | 0.0954 | | 0.0816 | 8.57 | 430 | 0.0949 | | 0.0816 | 8.76 | 440 | 0.0938 | | 0.0722 | 8.96 | 450 | 0.0915 | | 0.0722 | 9.16 | 460 | 0.0907 | | 0.0722 | 9.36 | 470 | 0.0900 | | 0.0722 | 9.56 | 480 | 0.0893 | | 0.0722 | 9.76 | 490 | 0.0877 | | 0.0656 | 9.96 | 500 | 0.0872 | | 0.0656 | 10.16 | 510 | 0.0868 | | 0.0656 | 10.36 | 520 | 0.0867 | | 0.0656 | 10.56 | 530 | 0.0874 | | 0.0656 | 10.76 | 540 | 0.0863 | | 0.0614 | 10.96 | 550 | 0.0849 | | 0.0614 | 11.16 | 560 | 0.0834 | | 0.0614 | 11.35 | 570 | 0.0829 | | 0.0614 | 11.55 | 580 | 0.0827 | | 0.0614 | 11.75 | 590 | 0.0817 | | 0.0553 | 11.95 | 600 | 0.0817 | | 0.0553 | 12.15 | 610 | 0.0824 | | 0.0553 | 12.35 | 620 | 0.0826 | | 0.0553 | 12.55 | 630 | 0.0810 | | 0.0553 | 12.75 | 640 | 0.0814 | | 0.053 | 12.95 | 650 | 0.0804 | | 0.053 | 13.15 | 660 | 0.0807 | | 0.053 | 13.35 | 670 | 0.0792 | | 0.053 | 13.55 | 680 | 0.0792 | | 0.053 | 13.75 | 690 | 0.0792 | | 0.0495 | 13.94 | 700 | 0.0788 | | 0.0495 | 14.14 | 710 | 0.0783 | | 0.0495 | 14.34 | 720 | 0.0784 | | 0.0495 | 14.54 | 730 | 0.0779 | | 0.0495 | 14.74 | 740 | 0.0776 | | 0.0477 | 14.94 | 750 | 0.0773 | | 0.0477 | 15.14 | 760 | 0.0787 | | 0.0477 | 15.34 | 770 | 0.0772 | | 0.0477 | 15.54 | 780 | 0.0763 | | 0.0477 | 15.74 | 790 | 0.0759 | | 0.0456 | 15.94 | 800 | 0.0766 | | 0.0456 | 16.14 | 810 | 0.0770 | | 0.0456 | 16.33 | 820 | 0.0774 | | 0.0456 | 16.53 | 830 | 0.0770 | | 0.0456 | 16.73 | 840 | 0.0764 | | 0.0438 | 16.93 | 850 | 0.0754 | | 0.0438 | 17.13 | 860 | 0.0759 | | 0.0438 | 17.33 | 870 | 0.0762 | | 0.0438 | 17.53 | 880 | 0.0758 | | 0.0438 | 17.73 | 890 | 0.0761 | | 0.0415 | 17.93 | 900 | 0.0758 | | 0.0415 | 18.13 | 910 | 0.0754 | | 0.0415 | 18.33 | 920 | 0.0754 | | 0.0415 | 18.53 | 930 | 0.0753 | | 0.0415 | 18.73 | 940 | 0.0751 | | 0.0408 | 18.92 | 950 | 0.0752 | | 0.0408 | 19.12 | 960 | 0.0750 | | 0.0408 | 19.32 | 970 | 0.0749 | | 0.0408 | 19.52 | 980 | 0.0749 | | 0.0408 | 19.72 | 990 | 0.0749 | | 0.0403 | 19.92 | 1000 | 0.0749 | ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.0.0 - Datasets 2.15.0 - Tokenizers 0.15.0