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