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fine-tuning-dolphin-mistral-with-webglm-qa-with-lora_1

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metadata
license: apache-2.0
library_name: peft
tags:
  - generated_from_trainer
base_model: cognitivecomputations/dolphin-2.8-mistral-7b-v02
model-index:
  - name: fine-tuning-dolphin-mistral-with-webglm-qa-with-lora_1
    results: []

fine-tuning-dolphin-mistral-with-webglm-qa-with-lora_1

This model is a fine-tuned version of cognitivecomputations/dolphin-2.8-mistral-7b-v02 on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2999

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: 60
  • training_steps: 700
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss
1.7558 0.16 10 1.4842
1.4966 0.32 20 1.3367
1.2328 0.48 30 1.1282
0.9873 0.64 40 1.0817
0.9661 0.8 50 0.9967
0.8808 0.96 60 0.8844
0.7455 1.13 70 0.7337
0.6018 1.29 80 0.6164
0.4899 1.45 90 0.5440
0.4402 1.61 100 0.4971
0.4154 1.77 110 0.4555
0.4025 1.93 120 0.4238
0.3992 2.09 130 0.4007
0.3585 2.25 140 0.3862
0.3369 2.41 150 0.3666
0.3328 2.57 160 0.3537
0.3216 2.73 170 0.3423
0.2859 2.89 180 0.3303
0.2967 3.05 190 0.3211
0.2933 3.22 200 0.3114
0.2716 3.38 210 0.3097
0.255 3.54 220 0.3053
0.2731 3.7 230 0.2990
0.2729 3.86 240 0.2972
0.2701 4.02 250 0.3030
0.2558 4.18 260 0.3042
0.2612 4.34 270 0.3301
0.3048 4.5 280 0.4564
0.5437 4.66 290 0.7938
1.5888 4.82 300 1.5418
0.6588 4.98 310 0.4630
0.5345 5.14 320 0.9088
1.1475 5.31 330 1.6381
1.6442 5.47 340 2.0495
2.2517 5.63 350 1.7558
0.9492 5.79 360 0.5187
0.3727 5.95 370 0.3763
0.3139 6.11 380 0.3376
0.2896 6.27 390 0.3195
0.283 6.43 400 0.3106
0.2646 6.59 410 0.3105
0.2674 6.75 420 0.3256
0.3482 6.91 430 0.4016
0.4193 7.07 440 0.6300
0.7397 7.23 450 1.0617
1.1954 7.4 460 1.6157
1.6177 7.56 470 1.8019
1.2996 7.72 480 0.9151
0.6605 7.88 490 0.5433
0.416 8.04 500 0.4012
0.3412 8.2 510 0.3685
0.3322 8.36 520 0.3928
0.3516 8.52 530 0.3641
0.3406 8.68 540 0.4061
0.3772 8.84 550 0.4145
0.3695 9.0 560 0.5453
0.5824 9.16 570 0.7332
0.5139 9.32 580 0.4839
0.3798 9.49 590 0.3758
0.319 9.65 600 0.3438
0.3082 9.81 610 0.3301
0.3017 9.97 620 0.3225
0.2862 10.13 630 0.3156
0.2586 10.29 640 0.3109
0.2878 10.45 650 0.3082
0.2766 10.61 660 0.3056
0.2834 10.77 670 0.3042
0.2513 10.93 680 0.3020
0.2762 11.09 690 0.3007
0.28 11.25 700 0.2999

Framework versions

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