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Mistral-7B-v0.1_mbe_positive

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Mistral-7B-v0.1_mbe_positive / README.md
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metadata
license: apache-2.0
library_name: peft
tags:
  - generated_from_trainer
base_model: mistralai/Mistral-7B-v0.1
datasets:
  - mbe
metrics:
  - accuracy
model-index:
  - name: Mistral-7B-v0.1_mbe_positive
    results: []

Mistral-7B-v0.1_mbe_positive

This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on the mbe dataset. It achieves the following results on the evaluation set:

  • Loss: 1.0233
  • Accuracy: 0.6809

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: 3e-05
  • train_batch_size: 4
  • eval_batch_size: 4
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 16
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: constant
  • lr_scheduler_warmup_ratio: 0.03
  • num_epochs: 5.0

Training results

Training Loss Epoch Step Validation Loss Accuracy
2.3217 0.07 10 0.7263 0.4901
0.56 0.13 20 0.6898 0.5526
0.5281 0.2 30 0.6465 0.5888
0.994 0.27 40 0.7351 0.5987
0.4785 0.33 50 0.6004 0.6118
0.4732 0.4 60 0.5783 0.6349
0.4466 0.47 70 0.5714 0.6414
0.8737 0.53 80 0.5673 0.6184
0.4471 0.6 90 0.5631 0.6283
0.46 0.67 100 0.5504 0.6349
0.3294 0.73 110 0.6010 0.625
0.6526 0.8 120 0.5731 0.6283
0.3712 0.87 130 0.5379 0.6447
0.3341 0.93 140 0.5409 0.6283
0.552 1.0 150 0.5311 0.6382
0.4681 1.07 160 0.5371 0.6414
0.3119 1.14 170 0.6172 0.6283
0.3082 1.2 180 0.5361 0.6513
0.5217 1.27 190 0.5468 0.625
0.3888 1.34 200 0.5891 0.6316
0.2841 1.4 210 0.5429 0.6283
0.2728 1.47 220 0.5247 0.6382
0.5563 1.54 230 0.5004 0.6513
0.2862 1.6 240 0.4741 0.6546
0.2289 1.67 250 0.5441 0.6513
0.2481 1.74 260 0.5171 0.6513
0.329 1.8 270 0.5371 0.6546
0.1741 1.87 280 0.5412 0.6678
0.2888 1.94 290 0.5131 0.6711
0.4157 2.0 300 0.4555 0.6447
0.1982 2.07 310 0.5670 0.6612
0.106 2.14 320 0.7943 0.6678
0.1718 2.2 330 0.7496 0.6645
0.214 2.27 340 0.6264 0.6842
0.1571 2.34 350 0.6139 0.6316
0.1432 2.4 360 0.6199 0.6842
0.1038 2.47 370 0.6368 0.6974
0.1728 2.54 380 0.7889 0.6678
0.14 2.6 390 0.7952 0.6546
0.1522 2.67 400 0.7745 0.6579
0.1345 2.74 410 0.7231 0.6513
0.1587 2.8 420 0.7154 0.6480
0.1391 2.87 430 0.6923 0.6513
0.129 2.94 440 0.6484 0.6711
0.2092 3.01 450 0.5822 0.6743
0.015 3.07 460 1.1217 0.6579
0.051 3.14 470 1.5790 0.6480
0.0999 3.21 480 1.5168 0.6678
0.1776 3.27 490 1.2342 0.6875
0.0612 3.34 500 1.0371 0.6974
0.0858 3.41 510 1.0277 0.6776
0.0316 3.47 520 1.0387 0.6809
0.1899 3.54 530 0.8185 0.6908
0.1517 3.61 540 0.7054 0.6842
0.0324 3.67 550 0.8505 0.6842
0.0646 3.74 560 1.0057 0.6612
0.1038 3.81 570 1.0027 0.6645
0.0844 3.87 580 0.9926 0.6513
0.0986 3.94 590 0.9246 0.6579
0.0627 4.01 600 0.8539 0.6546
0.0513 4.07 610 0.9247 0.6513
0.0484 4.14 620 1.1128 0.6546
0.0244 4.21 630 1.2702 0.6480
0.0672 4.27 640 1.7169 0.6414
0.0824 4.34 650 1.6627 0.6414
0.0068 4.41 660 1.3425 0.6349
0.044 4.47 670 1.2208 0.6612
0.0378 4.54 680 1.2891 0.6447
0.0411 4.61 690 1.3528 0.6612
0.0215 4.67 700 1.2606 0.6678
0.0438 4.74 710 1.2515 0.6546
0.0936 4.81 720 1.0858 0.6645
0.0305 4.87 730 0.9839 0.6579
0.0282 4.94 740 1.0233 0.6809

Framework versions

  • PEFT 0.7.1
  • Transformers 4.37.2
  • Pytorch 2.1.2+cu121
  • Datasets 2.17.1
  • Tokenizers 0.15.1