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mistral-augmentation-digikey-rand

PEFT
Safetensors
trl
sft
Generated from Trainer
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metadata
license: apache-2.0
library_name: peft
tags:
  - trl
  - sft
  - generated_from_trainer
base_model: TheBloke/Mistral-7B-v0.1-GPTQ
model-index:
  - name: mistral-augmentation-digikey-rand
    results: []

mistral-augmentation-digikey-rand

This model is a fine-tuned version of TheBloke/Mistral-7B-v0.1-GPTQ on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4955

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: 2e-05
  • train_batch_size: 4
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • num_epochs: 15
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss
2.0496 0.01 50 1.1570
0.9361 0.03 100 0.8592
0.7691 0.04 150 0.7989
0.7555 0.06 200 0.7768
0.7213 0.07 250 0.7575
0.6993 0.09 300 0.7440
0.6905 0.1 350 0.7291
0.6855 0.12 400 0.7210
0.6732 0.13 450 0.7076
0.6516 0.15 500 0.7005
0.639 0.16 550 0.6920
0.6322 0.18 600 0.6829
0.6164 0.19 650 0.6755
0.6185 0.21 700 0.6704
0.6457 0.22 750 0.6667
0.6238 0.24 800 0.6630
0.6173 0.25 850 0.6570
0.6076 0.27 900 0.6562
0.6097 0.28 950 0.6493
0.5693 0.3 1000 0.6423
0.5887 0.31 1050 0.6404
0.5869 0.33 1100 0.6361
0.5964 0.34 1150 0.6341
0.5373 0.36 1200 0.6281
0.5684 0.37 1250 0.6277
0.5746 0.39 1300 0.6183
0.5703 0.4 1350 0.6221
0.5851 0.42 1400 0.6175
0.5519 0.43 1450 0.6167
0.5716 0.45 1500 0.6115
0.552 0.46 1550 0.6095
0.5885 0.47 1600 0.6100
0.5739 0.49 1650 0.6061
0.5598 0.5 1700 0.6061
0.5729 0.52 1750 0.6011
0.5575 0.53 1800 0.6013
0.5418 0.55 1850 0.6003
0.5365 0.56 1900 0.5940
0.5096 0.58 1950 0.5878
0.5458 0.59 2000 0.5878
0.5603 0.61 2050 0.5863
0.5388 0.62 2100 0.5854
0.5187 0.64 2150 0.5789
0.5402 0.65 2200 0.5809
0.5398 0.67 2250 0.5761
0.5123 0.68 2300 0.5751
0.4936 0.7 2350 0.5712
0.4899 0.71 2400 0.5672
0.5197 0.73 2450 0.5627
0.509 0.74 2500 0.5574
0.4963 0.76 2550 0.5560
0.4989 0.77 2600 0.5544
0.4809 0.79 2650 0.5526
0.49 0.8 2700 0.5473
0.5151 0.82 2750 0.5485
0.5005 0.83 2800 0.5469
0.5072 0.85 2850 0.5466
0.5008 0.86 2900 0.5464
0.4857 0.88 2950 0.5441
0.4889 0.89 3000 0.5429
0.4714 0.91 3050 0.5441
0.4618 0.92 3100 0.5404
0.4623 0.93 3150 0.5418
0.4771 0.95 3200 0.5396
0.4592 0.96 3250 0.5409
0.4783 0.98 3300 0.5373
0.5021 0.99 3350 0.5343
0.4753 1.01 3400 0.5350
0.4369 1.02 3450 0.5338
0.4651 1.04 3500 0.5318
0.4395 1.05 3550 0.5320
0.4771 1.07 3600 0.5311
0.4659 1.08 3650 0.5337
0.4699 1.1 3700 0.5309
0.4717 1.11 3750 0.5301
0.4445 1.13 3800 0.5282
0.4342 1.14 3850 0.5303
0.4599 1.16 3900 0.5266
0.4442 1.17 3950 0.5275
0.4628 1.19 4000 0.5260
0.4339 1.2 4050 0.5243
0.4577 1.22 4100 0.5283
0.463 1.23 4150 0.5253
0.4602 1.25 4200 0.5243
0.4411 1.26 4250 0.5255
0.4542 1.28 4300 0.5263
0.4379 1.29 4350 0.5213
0.4471 1.31 4400 0.5189
0.4372 1.32 4450 0.5236
0.4526 1.34 4500 0.5203
0.4504 1.35 4550 0.5198
0.4708 1.36 4600 0.5171
0.4748 1.38 4650 0.5177
0.4511 1.39 4700 0.5152
0.4758 1.41 4750 0.5179
0.4543 1.42 4800 0.5165
0.4506 1.44 4850 0.5167
0.44 1.45 4900 0.5152
0.4443 1.47 4950 0.5135
0.4538 1.48 5000 0.5140
0.435 1.5 5050 0.5142
0.439 1.51 5100 0.5135
0.4408 1.53 5150 0.5121
0.4532 1.54 5200 0.5137
0.4177 1.56 5250 0.5143
0.4434 1.57 5300 0.5139
0.4395 1.59 5350 0.5117
0.4327 1.6 5400 0.5124
0.4257 1.62 5450 0.5128
0.4225 1.63 5500 0.5106
0.4517 1.65 5550 0.5119
0.4632 1.66 5600 0.5076
0.4371 1.68 5650 0.5110
0.4209 1.69 5700 0.5082
0.4336 1.71 5750 0.5072
0.4269 1.72 5800 0.5125
0.4208 1.74 5850 0.5105
0.4334 1.75 5900 0.5074
0.4306 1.77 5950 0.5052
0.4454 1.78 6000 0.5073
0.4227 1.8 6050 0.5068
0.4467 1.81 6100 0.5041
0.4279 1.82 6150 0.5034
0.4368 1.84 6200 0.5021
0.4205 1.85 6250 0.5025
0.415 1.87 6300 0.5029
0.4213 1.88 6350 0.5019
0.4316 1.9 6400 0.5053
0.4065 1.91 6450 0.5004
0.4578 1.93 6500 0.5045
0.4479 1.94 6550 0.4998
0.43 1.96 6600 0.4947
0.4192 1.97 6650 0.4967
0.4061 1.99 6700 0.4961
0.4309 2.0 6750 0.4960
0.4118 2.02 6800 0.4979
0.4149 2.03 6850 0.4955

Framework versions

  • PEFT 0.7.1
  • Transformers 4.36.2
  • Pytorch 2.1.2+cu121
  • Datasets 2.16.1
  • Tokenizers 0.15.0