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BlindSolitaire
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metadata
license: other
base_model: Gryphe/MythoMax-L2-13b
tags:
  - generated_from_trainer
model-index:
  - name: lora-out-10
    results: []

Built with Axolotl

lora-out-10

This model is a fine-tuned version of Gryphe/MythoMax-L2-13b on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3963

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: 2
  • eval_batch_size: 2
  • seed: 42
  • gradient_accumulation_steps: 3
  • total_train_batch_size: 6
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_steps: 20
  • num_epochs: 1

Training results

Training Loss Epoch Step Validation Loss
1.9065 0.01 200 1.9889
1.764 0.01 400 1.8015
1.6177 0.02 600 1.6777
1.5756 0.03 800 1.5862
1.4847 0.03 1000 1.5134
1.5603 0.04 1200 1.4480
1.347 0.05 1400 1.3939
1.2312 0.06 1600 1.3450
1.3749 0.06 1800 1.3037
1.2194 0.07 2000 1.2657
1.2434 0.08 2200 1.2297
1.2037 0.08 2400 1.1983
1.2709 0.09 2600 1.1679
1.1537 0.1 2800 1.1405
1.1408 0.1 3000 1.1154
1.0502 0.11 3200 1.0921
1.0453 0.12 3400 1.0717
0.9332 0.13 3600 1.0462
1.1569 0.13 3800 1.0284
1.146 0.14 4000 1.0092
1.0325 0.15 4200 0.9916
0.9936 0.15 4400 0.9748
0.9131 0.16 4600 0.9592
0.9426 0.17 4800 0.9416
0.8971 0.17 5000 0.9278
0.8258 0.18 5200 0.9124
0.8038 0.19 5400 0.8991
0.9663 0.2 5600 0.8841
0.7214 0.2 5800 0.8726
0.8789 0.21 6000 0.8621
0.8095 0.22 6200 0.8492
0.8446 0.22 6400 0.8379
0.8704 0.23 6600 0.8261
0.8175 0.24 6800 0.8165
0.7169 0.24 7000 0.8066
0.6899 0.25 7200 0.7952
0.8256 0.26 7400 0.7860
0.7186 0.27 7600 0.7759
0.8891 0.27 7800 0.7664
0.7919 0.28 8000 0.7580
0.8378 0.29 8200 0.7487
0.6804 0.29 8400 0.7390
0.6921 0.3 8600 0.7308
0.7263 0.31 8800 0.7221
0.6268 0.31 9000 0.7145
0.6954 0.32 9200 0.7067
0.8179 0.33 9400 0.6992
0.5709 0.34 9600 0.6929
0.7092 0.34 9800 0.6858
0.7396 0.35 10000 0.6774
0.5967 0.36 10200 0.6702
0.6984 0.36 10400 0.6636
0.5621 0.37 10600 0.6578
0.5342 0.38 10800 0.6512
0.6695 0.38 11000 0.6442
0.7242 0.39 11200 0.6374
0.6046 0.4 11400 0.6317
0.5994 0.41 11600 0.6260
0.6133 0.41 11800 0.6201
0.6707 0.42 12000 0.6138
0.6454 0.43 12200 0.6078
0.6534 0.43 12400 0.6013
0.6382 0.44 12600 0.5963
0.6659 0.45 12800 0.5908
0.6608 0.45 13000 0.5851
0.6864 0.46 13200 0.5793
0.6046 0.47 13400 0.5748
0.5331 0.48 13600 0.5692
0.5314 0.48 13800 0.5640
0.5528 0.49 14000 0.5594
0.5617 0.5 14200 0.5545
0.6572 0.5 14400 0.5488
0.6108 0.51 14600 0.5441
0.4776 0.52 14800 0.5387
0.493 0.52 15000 0.5357
0.5464 0.53 15200 0.5305
0.5519 0.54 15400 0.5260
0.4209 0.55 15600 0.5225
0.4759 0.55 15800 0.5173
0.5357 0.56 16000 0.5129
0.6064 0.57 16200 0.5091
0.4835 0.57 16400 0.5048
0.4951 0.58 16600 0.5017
0.3621 0.59 16800 0.4971
0.5166 0.59 17000 0.4935
0.5464 0.6 17200 0.4896
0.5093 0.61 17400 0.4858
0.443 0.62 17600 0.4828
0.4323 0.62 17800 0.4787
0.5066 0.63 18000 0.4754
0.4388 0.64 18200 0.4717
0.5436 0.64 18400 0.4682
0.3881 0.65 18600 0.4649
0.6051 0.66 18800 0.4623
0.5628 0.66 19000 0.4589
0.4372 0.67 19200 0.4560
0.4748 0.68 19400 0.4529
0.5461 0.69 19600 0.4499
0.4313 0.69 19800 0.4471
0.4353 0.7 20000 0.4445
0.4988 0.71 20200 0.4419
0.4037 0.71 20400 0.4394
0.446 0.72 20600 0.4368
0.4381 0.73 20800 0.4347
0.4849 0.73 21000 0.4324
0.4726 0.74 21200 0.4303
0.4842 0.75 21400 0.4279
0.3508 0.76 21600 0.4259
0.4452 0.76 21800 0.4236
0.3565 0.77 22000 0.4216
0.4634 0.78 22200 0.4196
0.3925 0.78 22400 0.4179
0.4086 0.79 22600 0.4164
0.4149 0.8 22800 0.4145
0.3856 0.8 23000 0.4128
0.4053 0.81 23200 0.4113
0.47 0.82 23400 0.4099
0.3918 0.83 23600 0.4086
0.4021 0.83 23800 0.4074
0.376 0.84 24000 0.4063
0.5067 0.85 24200 0.4052
0.4721 0.85 24400 0.4041
0.4015 0.86 24600 0.4031
0.3576 0.87 24800 0.4021
0.3975 0.87 25000 0.4015
0.3898 0.88 25200 0.4006
0.4235 0.89 25400 0.4000
0.3808 0.9 25600 0.3992
0.3811 0.9 25800 0.3989
0.4011 0.91 26000 0.3983
0.4219 0.92 26200 0.3981
0.3764 0.92 26400 0.3977
0.4046 0.93 26600 0.3974
0.4342 0.94 26800 0.3972
0.4209 0.94 27000 0.3969
0.4549 0.95 27200 0.3967
0.3316 0.96 27400 0.3967
0.4648 0.97 27600 0.3967
0.4657 0.97 27800 0.3965
0.3959 0.98 28000 0.3964
0.3666 0.99 28200 0.3964
0.3973 0.99 28400 0.3963

Framework versions

  • Transformers 4.35.0.dev0
  • Pytorch 2.1.0+cu118
  • Datasets 2.14.5
  • Tokenizers 0.14.1