--- 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-rand results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mistral-rand This model is a fine-tuned version of [TheBloke/Mistral-7B-v0.1-GPTQ](https://huggingface.co/TheBloke/Mistral-7B-v0.1-GPTQ) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4471 ## 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 | |:-------------:|:-----:|:-----:|:---------------:| | 1.7543 | 0.03 | 50 | 0.9190 | | 0.8445 | 0.05 | 100 | 0.7860 | | 0.7819 | 0.07 | 150 | 0.7460 | | 0.7231 | 0.1 | 200 | 0.7147 | | 0.6985 | 0.12 | 250 | 0.6924 | | 0.6887 | 0.15 | 300 | 0.6823 | | 0.6836 | 0.17 | 350 | 0.6702 | | 0.6624 | 0.2 | 400 | 0.6574 | | 0.6712 | 0.23 | 450 | 0.6507 | | 0.6354 | 0.25 | 500 | 0.6417 | | 0.6089 | 0.28 | 550 | 0.6373 | | 0.6236 | 0.3 | 600 | 0.6284 | | 0.6161 | 0.33 | 650 | 0.6228 | | 0.6367 | 0.35 | 700 | 0.6152 | | 0.6329 | 0.38 | 750 | 0.6097 | | 0.5944 | 0.4 | 800 | 0.6076 | | 0.6036 | 0.42 | 850 | 0.6030 | | 0.5767 | 0.45 | 900 | 0.5989 | | 0.6079 | 0.47 | 950 | 0.5954 | | 0.5915 | 0.5 | 1000 | 0.5916 | | 0.5911 | 0.53 | 1050 | 0.5859 | | 0.5752 | 0.55 | 1100 | 0.5847 | | 0.5698 | 0.57 | 1150 | 0.5802 | | 0.5813 | 0.6 | 1200 | 0.5754 | | 0.5918 | 0.62 | 1250 | 0.5735 | | 0.5587 | 0.65 | 1300 | 0.5677 | | 0.5933 | 0.68 | 1350 | 0.5620 | | 0.5262 | 0.7 | 1400 | 0.5522 | | 0.5455 | 0.72 | 1450 | 0.5457 | | 0.5472 | 0.75 | 1500 | 0.5416 | | 0.536 | 0.78 | 1550 | 0.5400 | | 0.527 | 0.8 | 1600 | 0.5393 | | 0.5516 | 0.82 | 1650 | 0.5350 | | 0.5578 | 0.85 | 1700 | 0.5356 | | 0.5501 | 0.88 | 1750 | 0.5297 | | 0.5316 | 0.9 | 1800 | 0.5288 | | 0.5436 | 0.93 | 1850 | 0.5268 | | 0.514 | 0.95 | 1900 | 0.5295 | | 0.5249 | 0.97 | 1950 | 0.5246 | | 0.538 | 1.0 | 2000 | 0.5226 | | 0.4967 | 1.02 | 2050 | 0.5237 | | 0.4991 | 1.05 | 2100 | 0.5261 | | 0.5142 | 1.07 | 2150 | 0.5203 | | 0.4891 | 1.1 | 2200 | 0.5174 | | 0.5058 | 1.12 | 2250 | 0.5173 | | 0.4895 | 1.15 | 2300 | 0.5182 | | 0.4918 | 1.18 | 2350 | 0.5139 | | 0.485 | 1.2 | 2400 | 0.5091 | | 0.5173 | 1.23 | 2450 | 0.5121 | | 0.5021 | 1.25 | 2500 | 0.5116 | | 0.4834 | 1.27 | 2550 | 0.5097 | | 0.4754 | 1.3 | 2600 | 0.5137 | | 0.4907 | 1.32 | 2650 | 0.5059 | | 0.5155 | 1.35 | 2700 | 0.5051 | | 0.4965 | 1.38 | 2750 | 0.5050 | | 0.5148 | 1.4 | 2800 | 0.5043 | | 0.4709 | 1.43 | 2850 | 0.5032 | | 0.4864 | 1.45 | 2900 | 0.5037 | | 0.4794 | 1.48 | 2950 | 0.5029 | | 0.4803 | 1.5 | 3000 | 0.5012 | | 0.4843 | 1.52 | 3050 | 0.5017 | | 0.4726 | 1.55 | 3100 | 0.4984 | | 0.4773 | 1.57 | 3150 | 0.4968 | | 0.4673 | 1.6 | 3200 | 0.4995 | | 0.4803 | 1.62 | 3250 | 0.4990 | | 0.4926 | 1.65 | 3300 | 0.4965 | | 0.4814 | 1.68 | 3350 | 0.4973 | | 0.4714 | 1.7 | 3400 | 0.4930 | | 0.4797 | 1.73 | 3450 | 0.4903 | | 0.4807 | 1.75 | 3500 | 0.4932 | | 0.4815 | 1.77 | 3550 | 0.4888 | | 0.4852 | 1.8 | 3600 | 0.4874 | | 0.4802 | 1.82 | 3650 | 0.4887 | | 0.4701 | 1.85 | 3700 | 0.4897 | | 0.4572 | 1.88 | 3750 | 0.4873 | | 0.4469 | 1.9 | 3800 | 0.4878 | | 0.478 | 1.93 | 3850 | 0.4885 | | 0.4449 | 1.95 | 3900 | 0.4866 | | 0.4634 | 1.98 | 3950 | 0.4843 | | 0.4718 | 2.0 | 4000 | 0.4838 | | 0.4458 | 2.02 | 4050 | 0.4822 | | 0.461 | 2.05 | 4100 | 0.4801 | | 0.4247 | 2.08 | 4150 | 0.4856 | | 0.4325 | 2.1 | 4200 | 0.4830 | | 0.4354 | 2.12 | 4250 | 0.4827 | | 0.4313 | 2.15 | 4300 | 0.4807 | | 0.4753 | 2.17 | 4350 | 0.4812 | | 0.4442 | 2.2 | 4400 | 0.4833 | | 0.4431 | 2.23 | 4450 | 0.4851 | | 0.4485 | 2.25 | 4500 | 0.4815 | | 0.4416 | 2.27 | 4550 | 0.4813 | | 0.4613 | 2.3 | 4600 | 0.4777 | | 0.4121 | 2.33 | 4650 | 0.4775 | | 0.4311 | 2.35 | 4700 | 0.4768 | | 0.4532 | 2.38 | 4750 | 0.4765 | | 0.4342 | 2.4 | 4800 | 0.4781 | | 0.4189 | 2.42 | 4850 | 0.4743 | | 0.443 | 2.45 | 4900 | 0.4742 | | 0.4596 | 2.48 | 4950 | 0.4734 | | 0.4193 | 2.5 | 5000 | 0.4719 | | 0.4321 | 2.52 | 5050 | 0.4723 | | 0.4456 | 2.55 | 5100 | 0.4713 | | 0.4464 | 2.58 | 5150 | 0.4694 | | 0.4273 | 2.6 | 5200 | 0.4700 | | 0.4239 | 2.62 | 5250 | 0.4701 | | 0.4282 | 2.65 | 5300 | 0.4687 | | 0.4303 | 2.67 | 5350 | 0.4686 | | 0.4559 | 2.7 | 5400 | 0.4695 | | 0.4542 | 2.73 | 5450 | 0.4692 | | 0.4532 | 2.75 | 5500 | 0.4685 | | 0.4505 | 2.77 | 5550 | 0.4663 | | 0.4533 | 2.8 | 5600 | 0.4660 | | 0.4351 | 2.83 | 5650 | 0.4640 | | 0.4354 | 2.85 | 5700 | 0.4651 | | 0.4374 | 2.88 | 5750 | 0.4664 | | 0.4571 | 2.9 | 5800 | 0.4662 | | 0.4663 | 2.92 | 5850 | 0.4636 | | 0.4211 | 2.95 | 5900 | 0.4645 | | 0.4349 | 2.98 | 5950 | 0.4622 | | 0.4167 | 3.0 | 6000 | 0.4634 | | 0.4176 | 3.02 | 6050 | 0.4621 | | 0.4387 | 3.05 | 6100 | 0.4607 | | 0.395 | 3.08 | 6150 | 0.4638 | | 0.4186 | 3.1 | 6200 | 0.4623 | | 0.3993 | 3.12 | 6250 | 0.4622 | | 0.4009 | 3.15 | 6300 | 0.4631 | | 0.4033 | 3.17 | 6350 | 0.4640 | | 0.389 | 3.2 | 6400 | 0.4662 | | 0.4037 | 3.23 | 6450 | 0.4618 | | 0.4287 | 3.25 | 6500 | 0.4617 | | 0.3917 | 3.27 | 6550 | 0.4611 | | 0.3944 | 3.3 | 6600 | 0.4626 | | 0.4088 | 3.33 | 6650 | 0.4622 | | 0.4205 | 3.35 | 6700 | 0.4604 | | 0.4273 | 3.38 | 6750 | 0.4608 | | 0.4139 | 3.4 | 6800 | 0.4607 | | 0.3888 | 3.42 | 6850 | 0.4603 | | 0.4353 | 3.45 | 6900 | 0.4573 | | 0.4222 | 3.48 | 6950 | 0.4577 | | 0.4083 | 3.5 | 7000 | 0.4571 | | 0.4161 | 3.52 | 7050 | 0.4560 | | 0.3879 | 3.55 | 7100 | 0.4540 | | 0.3819 | 3.58 | 7150 | 0.4570 | | 0.4345 | 3.6 | 7200 | 0.4551 | | 0.4101 | 3.62 | 7250 | 0.4569 | | 0.4194 | 3.65 | 7300 | 0.4543 | | 0.4066 | 3.67 | 7350 | 0.4563 | | 0.4144 | 3.7 | 7400 | 0.4553 | | 0.4134 | 3.73 | 7450 | 0.4566 | | 0.3906 | 3.75 | 7500 | 0.4550 | | 0.4128 | 3.77 | 7550 | 0.4546 | | 0.4227 | 3.8 | 7600 | 0.4535 | | 0.4069 | 3.83 | 7650 | 0.4517 | | 0.3927 | 3.85 | 7700 | 0.4548 | | 0.3977 | 3.88 | 7750 | 0.4521 | | 0.4184 | 3.9 | 7800 | 0.4516 | | 0.3854 | 3.92 | 7850 | 0.4513 | | 0.4129 | 3.95 | 7900 | 0.4524 | | 0.3998 | 3.98 | 7950 | 0.4548 | | 0.4227 | 4.0 | 8000 | 0.4534 | | 0.3788 | 4.03 | 8050 | 0.4520 | | 0.3732 | 4.05 | 8100 | 0.4501 | | 0.375 | 4.08 | 8150 | 0.4565 | | 0.3845 | 4.1 | 8200 | 0.4515 | | 0.378 | 4.12 | 8250 | 0.4492 | | 0.3874 | 4.15 | 8300 | 0.4508 | | 0.3802 | 4.17 | 8350 | 0.4510 | | 0.3596 | 4.2 | 8400 | 0.4524 | | 0.4009 | 4.22 | 8450 | 0.4549 | | 0.4105 | 4.25 | 8500 | 0.4515 | | 0.3716 | 4.28 | 8550 | 0.4508 | | 0.3673 | 4.3 | 8600 | 0.4497 | | 0.3882 | 4.33 | 8650 | 0.4513 | | 0.375 | 4.35 | 8700 | 0.4524 | | 0.3654 | 4.38 | 8750 | 0.4503 | | 0.3983 | 4.4 | 8800 | 0.4509 | | 0.4067 | 4.42 | 8850 | 0.4487 | | 0.3966 | 4.45 | 8900 | 0.4519 | | 0.378 | 4.47 | 8950 | 0.4505 | | 0.3755 | 4.5 | 9000 | 0.4508 | | 0.3855 | 4.53 | 9050 | 0.4500 | | 0.3938 | 4.55 | 9100 | 0.4527 | | 0.3946 | 4.58 | 9150 | 0.4531 | | 0.3752 | 4.6 | 9200 | 0.4506 | | 0.3723 | 4.62 | 9250 | 0.4459 | | 0.3704 | 4.65 | 9300 | 0.4467 | | 0.3861 | 4.67 | 9350 | 0.4484 | | 0.3965 | 4.7 | 9400 | 0.4481 | | 0.3972 | 4.72 | 9450 | 0.4482 | | 0.3917 | 4.75 | 9500 | 0.4447 | | 0.3688 | 4.78 | 9550 | 0.4473 | | 0.3861 | 4.8 | 9600 | 0.4491 | | 0.3593 | 4.83 | 9650 | 0.4491 | | 0.3916 | 4.85 | 9700 | 0.4432 | | 0.3748 | 4.88 | 9750 | 0.4432 | | 0.3921 | 4.9 | 9800 | 0.4459 | | 0.3745 | 4.92 | 9850 | 0.4457 | | 0.4002 | 4.95 | 9900 | 0.4443 | | 0.3767 | 4.97 | 9950 | 0.4430 | | 0.3537 | 5.0 | 10000 | 0.4470 | | 0.3673 | 5.03 | 10050 | 0.4531 | | 0.3506 | 5.05 | 10100 | 0.4474 | | 0.3506 | 5.08 | 10150 | 0.4497 | | 0.3622 | 5.1 | 10200 | 0.4471 | ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0