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fine-tuning-Phi2-with-webglm-qa-with-lora

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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
    results: []

fine-tuning-Phi2-with-webglm-qa-with-lora

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: 0.1016

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: 50
  • training_steps: 500
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss
No log 0.2 10 8.0315
No log 0.4 20 6.2055
No log 0.6 30 2.7735
No log 0.8 40 0.6055
4.4745 1.0 50 0.5323
4.4745 1.2 60 0.4631
4.4745 1.39 70 0.4075
4.4745 1.59 80 0.3566
4.4745 1.79 90 0.3155
0.3331 1.99 100 0.2869
0.3331 2.19 110 0.2624
0.3331 2.39 120 0.2453
0.3331 2.59 130 0.2288
0.3331 2.79 140 0.2095
0.1947 2.99 150 0.1978
0.1947 3.19 160 0.1886
0.1947 3.39 170 0.1766
0.1947 3.59 180 0.1691
0.1947 3.78 190 0.1626
0.1486 3.98 200 0.1562
0.1486 4.18 210 0.1510
0.1486 4.38 220 0.1489
0.1486 4.58 230 0.1439
0.1486 4.78 240 0.1364
0.1232 4.98 250 0.1314
0.1232 5.18 260 0.1306
0.1232 5.38 270 0.1295
0.1232 5.58 280 0.1256
0.1232 5.78 290 0.1228
0.1084 5.98 300 0.1195
0.1084 6.18 310 0.1165
0.1084 6.37 320 0.1156
0.1084 6.57 330 0.1147
0.1084 6.77 340 0.1120
0.0964 6.97 350 0.1100
0.0964 7.17 360 0.1100
0.0964 7.37 370 0.1087
0.0964 7.57 380 0.1080
0.0964 7.77 390 0.1071
0.0905 7.97 400 0.1065
0.0905 8.17 410 0.1061
0.0905 8.37 420 0.1053
0.0905 8.57 430 0.1044
0.0905 8.76 440 0.1036
0.0843 8.96 450 0.1028
0.0843 9.16 460 0.1021
0.0843 9.36 470 0.1019
0.0843 9.56 480 0.1018
0.0843 9.76 490 0.1016
0.0819 9.96 500 0.1016

Framework versions

  • PEFT 0.7.1
  • Transformers 4.36.2
  • Pytorch 2.0.0
  • Datasets 2.15.0
  • Tokenizers 0.15.0