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nids_med

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metadata
base_model: meta-llama/Llama-2-7b-hf
tags:
  - generated_from_trainer
model-index:
  - name: mid-nids
    results: []

Built with Axolotl

mid-nids

This model is a fine-tuned version of meta-llama/Llama-2-7b-hf on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0342

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: 1
  • eval_batch_size: 1
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 4
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 8
  • total_eval_batch_size: 4
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_steps: 10
  • num_epochs: 3

Training results

Training Loss Epoch Step Validation Loss
0.0682 0.03 20 0.0982
0.0895 0.06 40 0.0792
0.015 0.09 60 0.0405
0.0376 0.11 80 0.0357
0.0196 0.14 100 0.0342
0.0219 0.17 120 0.0334
0.0188 0.2 140 0.0317
0.0147 0.23 160 0.0365
0.0224 0.26 180 0.0388
0.0116 0.28 200 0.0504
0.0158 0.31 220 0.0692
0.0193 0.34 240 0.0407
0.0181 0.37 260 0.0443
0.0124 0.4 280 0.0482
0.0094 0.43 300 0.0549
0.0081 0.46 320 0.0341
0.0188 0.48 340 0.0401
0.021 0.51 360 0.0508
0.0125 0.54 380 0.0409
0.0071 0.57 400 0.0424
0.0165 0.6 420 0.0566
0.0075 0.63 440 0.0537
0.0096 0.65 460 0.0338
0.012 0.68 480 0.0489
0.0041 0.71 500 0.0442
0.0012 0.74 520 0.0439
0.0096 0.77 540 0.0381
0.005 0.8 560 0.0449
0.0239 0.83 580 0.0452
0.0166 0.85 600 0.0383
0.0081 0.88 620 0.0249
0.0166 0.91 640 0.0442
0.0106 0.94 660 0.0327
0.0161 0.97 680 0.0386
0.0038 1.0 700 0.0377
0.0029 1.02 720 0.0367
0.0164 1.05 740 0.0276
0.0128 1.08 760 0.0259
0.0108 1.11 780 0.0294
0.026 1.14 800 0.0285
0.0104 1.17 820 0.0297
0.0102 1.19 840 0.0271
0.0111 1.22 860 0.0293
0.0088 1.25 880 0.0305
0.0116 1.28 900 0.0250
0.0066 1.31 920 0.0442
0.0061 1.34 940 0.0309
0.0173 1.37 960 0.0231
0.0032 1.39 980 0.0230
0.0119 1.42 1000 0.0401
0.0083 1.45 1020 0.0274
0.0047 1.48 1040 0.0359
0.0221 1.51 1060 0.0301
0.0038 1.54 1080 0.0280
0.0052 1.56 1100 0.0235
0.0084 1.59 1120 0.0323
0.012 1.62 1140 0.0320
0.0019 1.65 1160 0.0256
0.0175 1.68 1180 0.0300
0.0078 1.71 1200 0.0362
0.0088 1.74 1220 0.0310
0.0065 1.76 1240 0.0301
0.0059 1.79 1260 0.0348
0.0066 1.82 1280 0.0341
0.0015 1.85 1300 0.0280
0.0091 1.88 1320 0.0266
0.0053 1.91 1340 0.0350
0.0077 1.93 1360 0.0333
0.0081 1.96 1380 0.0320
0.0129 1.99 1400 0.0391
0.0082 2.02 1420 0.0388
0.008 2.05 1440 0.0212
0.0025 2.08 1460 0.0362
0.0006 2.11 1480 0.0289
0.0034 2.13 1500 0.0347
0.0115 2.16 1520 0.0313
0.0061 2.19 1540 0.0297
0.0065 2.22 1560 0.0335
0.0144 2.25 1580 0.0379
0.0075 2.28 1600 0.0300
0.0093 2.3 1620 0.0322
0.0091 2.33 1640 0.0313
0.0051 2.36 1660 0.0278
0.0046 2.39 1680 0.0294
0.0004 2.42 1700 0.0283
0.0054 2.45 1720 0.0296
0.0034 2.48 1740 0.0337
0.0065 2.5 1760 0.0341
0.0034 2.53 1780 0.0345
0.0114 2.56 1800 0.0371
0.0044 2.59 1820 0.0377
0.0086 2.62 1840 0.0344
0.0065 2.65 1860 0.0332
0.0051 2.67 1880 0.0344
0.008 2.7 1900 0.0355
0.0035 2.73 1920 0.0351
0.0065 2.76 1940 0.0352
0.0097 2.79 1960 0.0347
0.0034 2.82 1980 0.0347
0.0054 2.84 2000 0.0348
0.0045 2.87 2020 0.0344
0.0032 2.9 2040 0.0343
0.0072 2.93 2060 0.0342
0.0074 2.96 2080 0.0344
0.0111 2.99 2100 0.0342

Framework versions

  • Transformers 4.34.1
  • Pytorch 2.0.1+cu117
  • Datasets 2.14.6
  • Tokenizers 0.14.1