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sft-trl-claim-128-llama2-13b-chat-hf

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metadata
tags:
  - generated_from_trainer
datasets:
  - facebook/anli
base_model: meta-llama/Llama-2-13b-chat-hf
model-index:
  - name: sft-trl-claim-128-llama2-13b-chat-hf
    results: []

sft-trl-claim-128-llama2-13b-chat-hf

This model is a fine-tuned version of meta-llama/Llama-2-13b-chat-hf on the anli dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4327

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: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • num_epochs: 3.0

Training results

Training Loss Epoch Step Validation Loss
1.2823 0.02 1000 1.5153
1.1442 0.03 2000 1.4907
1.1578 0.05 3000 1.4674
1.1452 0.06 4000 1.4707
1.175 0.08 5000 1.4579
1.1516 0.1 6000 1.4382
1.2068 0.11 7000 1.4134
1.2737 0.13 8000 1.3936
1.3977 0.15 9000 1.4009
1.0338 0.16 10000 1.3899
1.0997 0.18 11000 1.3971
1.1361 0.19 12000 1.3488
1.012 0.21 13000 1.3628
1.0694 0.23 14000 1.3532
1.067 0.24 15000 1.3531
1.2775 0.26 16000 1.3492
1.1953 0.28 17000 1.3598
1.0807 0.29 18000 1.3294
1.2402 0.31 19000 1.2770
1.1454 0.32 20000 1.2803
1.0369 0.34 21000 1.2688
0.9862 0.36 22000 1.2651
1.151 0.37 23000 1.2371
1.1605 0.39 24000 1.2199
1.1367 0.41 25000 1.2268
1.011 0.42 26000 1.2123
1.1792 0.44 27000 1.1763
0.8839 0.45 28000 1.1784
1.166 0.47 29000 1.1999
1.2947 0.49 30000 1.2064
0.7324 0.5 31000 1.2174
0.9919 0.52 32000 1.1837
1.3371 0.54 33000 1.1761
0.9874 0.55 34000 1.1966
1.0411 0.57 35000 1.1885
1.1669 0.58 36000 1.1789
1.231 0.6 37000 1.1616
0.9799 0.62 38000 1.1003
1.0801 0.63 39000 1.0844
1.0984 0.65 40000 1.1037
1.1046 0.67 41000 1.0683
1.1537 0.68 42000 1.0569
1.0744 0.7 43000 1.0661
1.1451 0.71 44000 1.0460
0.9822 0.73 45000 1.0559
1.0439 0.75 46000 1.0473
0.8704 0.76 47000 1.0721
0.9934 0.78 48000 0.9789
1.134 0.79 49000 0.9800
1.0722 0.81 50000 1.0122
1.0894 0.83 51000 0.9872
0.822 0.84 52000 1.0067
1.0764 0.86 53000 0.9941
1.1809 0.88 54000 0.9966
1.0228 0.89 55000 0.9657
0.9944 0.91 56000 0.9778
0.7453 0.92 57000 0.9442
1.1951 0.94 58000 0.9621
0.7633 0.96 59000 0.9271
0.5546 0.97 60000 0.9493
1.2136 0.99 61000 0.9211
0.7377 1.01 62000 0.8821
0.8892 1.02 63000 0.8635
0.9491 1.04 64000 0.8695
0.9627 1.05 65000 0.8808
0.7405 1.07 66000 0.8450
1.1451 1.09 67000 0.8496
0.9935 1.1 68000 0.8708
1.2591 1.12 69000 0.8246
1.1467 1.14 70000 0.8376
0.8296 1.15 71000 0.8411
0.9733 1.17 72000 0.8356
1.1116 1.18 73000 0.8564
0.9909 1.2 74000 0.8420
1.0602 1.22 75000 0.8398
1.0284 1.23 76000 0.8429
0.9611 1.25 77000 0.8288
0.9866 1.27 78000 0.8432
0.6751 1.28 79000 0.8109
0.9637 1.3 80000 0.8039
1.2506 1.31 81000 0.8088
1.1821 1.33 82000 0.8080
0.9813 1.35 83000 0.8003
1.175 1.36 84000 0.7962
0.8377 1.38 85000 0.7913
1.114 1.4 86000 0.7977
0.9089 1.41 87000 0.7779
0.9896 1.43 88000 0.7665
0.7499 1.44 89000 0.7693
1.0132 1.46 90000 0.7420
0.6964 1.48 91000 0.7405
0.9243 1.49 92000 0.7396
0.8555 1.51 93000 0.7448
0.9978 1.52 94000 0.7449
1.2293 1.54 95000 0.7324
0.8886 1.56 96000 0.7519
0.8325 1.57 97000 0.7625
0.5212 1.59 98000 0.7554
0.8564 1.61 99000 0.7211
0.7141 1.62 100000 0.7227
0.5702 1.64 101000 0.6725
1.0332 1.65 102000 0.6611
1.0074 1.67 103000 0.6633
0.8829 1.69 104000 0.6682
0.5767 1.7 105000 0.6641
0.6375 1.72 106000 0.6652
0.7965 1.74 107000 0.6728
1.1104 1.75 108000 0.6564
0.5071 1.77 109000 0.6273
0.9045 1.78 110000 0.6298
0.7056 1.8 111000 0.6223
1.013 1.82 112000 0.6290
0.943 1.83 113000 0.6281
0.6309 1.85 114000 0.6231
0.7388 1.87 115000 0.6069
0.5067 1.88 116000 0.5786
1.144 1.9 117000 0.5723
1.1245 1.91 118000 0.5761
0.9093 1.93 119000 0.5832
1.1755 1.95 120000 0.5798
0.4415 1.96 121000 0.5789
0.8623 1.98 122000 0.5793
0.7605 2.0 123000 0.5529
0.9743 2.01 124000 0.5542
1.0476 2.03 125000 0.5585
1.0112 2.04 126000 0.5673
0.9344 2.06 127000 0.5644
0.976 2.08 128000 0.5771
1.0471 2.09 129000 0.5615
0.626 2.11 130000 0.5597
0.6866 2.13 131000 0.5520
0.7723 2.14 132000 0.5416
0.7605 2.16 133000 0.5407
0.8413 2.17 134000 0.5452
1.1015 2.19 135000 0.5506
0.7203 2.21 136000 0.5470
0.7008 2.22 137000 0.5535
1.035 2.24 138000 0.5404
0.8432 2.25 139000 0.5459
0.7886 2.27 140000 0.5403
1.1197 2.29 141000 0.5533
0.8474 2.3 142000 0.5237
0.8785 2.32 143000 0.5325
1.1119 2.34 144000 0.5105
1.2089 2.35 145000 0.5133
0.8626 2.37 146000 0.5097
1.106 2.38 147000 0.5120
0.9681 2.4 148000 0.5116
1.0139 2.42 149000 0.5102
0.6389 2.43 150000 0.5152
0.86 2.45 151000 0.5141
1.041 2.47 152000 0.5102
0.984 2.48 153000 0.4967
0.7444 2.5 154000 0.4792
0.6543 2.51 155000 0.4693
0.6596 2.53 156000 0.4511
1.2077 2.55 157000 0.4506
0.9068 2.56 158000 0.4580
0.9328 2.58 159000 0.4602
0.8111 2.6 160000 0.4607
0.8047 2.61 161000 0.4475
0.7448 2.63 162000 0.4532
1.0685 2.64 163000 0.4628
0.9309 2.66 164000 0.4569
0.8024 2.68 165000 0.4571
0.9119 2.69 166000 0.4572
0.5736 2.71 167000 0.4567
0.7446 2.73 168000 0.4481
1.1721 2.74 169000 0.4449
0.7552 2.76 170000 0.4429
0.7927 2.77 171000 0.4358
1.2197 2.79 172000 0.4301
0.771 2.81 173000 0.4388
0.7656 2.82 174000 0.4385
1.0673 2.84 175000 0.4432
1.0228 2.86 176000 0.4361
0.7585 2.87 177000 0.4380
0.63 2.89 178000 0.4371
1.0722 2.9 179000 0.4314
0.7093 2.92 180000 0.4337
0.468 2.94 181000 0.4341
0.7841 2.95 182000 0.4313
0.779 2.97 183000 0.4312
0.4624 2.98 184000 0.4327

Framework versions

  • Transformers 4.33.0.dev0
  • Pytorch 2.0.1+cu117
  • Datasets 2.13.1
  • Tokenizers 0.13.3