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sft-zephyr-7b-sft-qlora-ultrafeedback

PEFT
Safetensors
trl
sft
Generated from Trainer
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metadata
base_model: mistralai/Mistral-7B-v0.1
library_name: peft
license: apache-2.0
tags:
  - trl
  - sft
  - generated_from_trainer
model-index:
  - name: sft-zephyr-7b-sft-qlora-ultrafeedback-binarized-20241011-162008
    results: []

sft-zephyr-7b-sft-qlora-ultrafeedback-binarized-20241011-162008

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

  • Loss: 0.9805

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: 0.0002
  • train_batch_size: 32
  • eval_batch_size: 64
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 1.0

Training results

Training Loss Epoch Step Validation Loss
1.0557 0.0105 20 1.2313
1.0725 0.0209 40 1.1635
1.0261 0.0314 60 1.1410
1.1577 0.0419 80 1.1209
1.1619 0.0523 100 1.1067
1.1234 0.0628 120 1.0981
1.0256 0.0733 140 1.0901
1.1511 0.0837 160 1.0850
1.2364 0.0942 180 1.0802
1.1676 0.1047 200 1.0761
1.2327 0.1151 220 1.0731
1.0082 0.1256 240 1.0695
0.9324 0.1361 260 1.0666
1.0435 0.1465 280 1.0630
0.8484 0.1570 300 1.0588
0.962 0.1675 320 1.0588
0.9531 0.1779 340 1.0549
0.8902 0.1884 360 1.0518
1.1103 0.1988 380 1.0485
1.0641 0.2093 400 1.0455
0.9541 0.2198 420 1.0431
1.0081 0.2302 440 1.0427
0.9761 0.2407 460 1.0407
1.0654 0.2512 480 1.0391
1.1185 0.2616 500 1.0367
1.0337 0.2721 520 1.0357
0.9059 0.2826 540 1.0335
1.1223 0.2930 560 1.0318
1.1514 0.3035 580 1.0300
1.0715 0.3140 600 1.0294
1.1336 0.3244 620 1.0263
1.0148 0.3349 640 1.0246
1.0242 0.3454 660 1.0238
1.1316 0.3558 680 1.0220
1.0114 0.3663 700 1.0216
1.1682 0.3768 720 1.0207
1.1026 0.3872 740 1.0180
1.0854 0.3977 760 1.0182
0.8933 0.4082 780 1.0164
1.0233 0.4186 800 1.0153
1.1105 0.4291 820 1.0140
0.8441 0.4396 840 1.0124
0.963 0.4500 860 1.0113
1.0488 0.4605 880 1.0093
0.8147 0.4710 900 1.0084
1.0005 0.4814 920 1.0081
0.959 0.4919 940 1.0071
0.8878 0.5024 960 1.0062
1.238 0.5128 980 1.0048
0.9114 0.5233 1000 1.0032
1.0474 0.5338 1020 1.0017
0.9858 0.5442 1040 1.0009
0.9642 0.5547 1060 1.0007
0.9116 0.5651 1080 0.9992
0.9444 0.5756 1100 0.9978
1.0698 0.5861 1120 0.9970
0.9379 0.5965 1140 0.9959
0.8902 0.6070 1160 0.9950
1.0654 0.6175 1180 0.9941
1.1352 0.6279 1200 0.9935
1.0493 0.6384 1220 0.9922
0.9792 0.6489 1240 0.9913
0.8634 0.6593 1260 0.9903
0.8152 0.6698 1280 0.9898
1.0059 0.6803 1300 0.9890
0.9244 0.6907 1320 0.9884
0.9918 0.7012 1340 0.9876
1.0536 0.7117 1360 0.9872
0.9883 0.7221 1380 0.9866
0.9426 0.7326 1400 0.9863
0.8653 0.7431 1420 0.9855
0.863 0.7535 1440 0.9849
0.9217 0.7640 1460 0.9847
1.0365 0.7745 1480 0.9844
0.8865 0.7849 1500 0.9841
1.1006 0.7954 1520 0.9836
0.9393 0.8059 1540 0.9832
0.8455 0.8163 1560 0.9826
1.1479 0.8268 1580 0.9823
1.0578 0.8373 1600 0.9820
0.7279 0.8477 1620 0.9818
0.973 0.8582 1640 0.9815
1.1227 0.8687 1660 0.9812
0.9897 0.8791 1680 0.9811
0.8196 0.8896 1700 0.9810
0.9309 0.9001 1720 0.9808
0.8774 0.9105 1740 0.9808
0.9671 0.9210 1760 0.9807
1.0849 0.9314 1780 0.9807
1.0233 0.9419 1800 0.9806
0.9742 0.9524 1820 0.9806
1.029 0.9628 1840 0.9806
1.0048 0.9733 1860 0.9806
0.9348 0.9838 1880 0.9805
0.8959 0.9942 1900 0.9805

Framework versions

  • PEFT 0.12.0
  • Transformers 4.45.2
  • Pytorch 2.4.0+cu121
  • Datasets 2.20.0
  • Tokenizers 0.20.0