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fine-tuning-Phi2-with-webglm-qa-with-lora_2

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metadata
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 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