| --- |
| base_model: microsoft/Phi-3-mini-4k-instruct |
| library_name: peft |
| license: mit |
| tags: |
| - trl |
| - sft |
| - generated_from_trainer |
| model-index: |
| - name: phi-3-mini-QLoRA |
| 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. --> |
|
|
| # phi-3-mini-QLoRA |
|
|
| This model is a fine-tuned version of [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 1.0514 |
|
|
| ## 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: 0.0001 |
| - train_batch_size: 8 |
| - eval_batch_size: 4 |
| - seed: 42 |
| - gradient_accumulation_steps: 8 |
| - total_train_batch_size: 64 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - lr_scheduler_warmup_ratio: 0.1 |
| - num_epochs: 2 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | |
| |:-------------:|:------:|:----:|:---------------:| |
| | 2.2314 | 0.0273 | 100 | 2.1230 | |
| | 1.5709 | 0.0546 | 200 | 1.5079 | |
| | 1.4201 | 0.0820 | 300 | 1.4112 | |
| | 1.3689 | 0.1093 | 400 | 1.3759 | |
| | 1.3444 | 0.1366 | 500 | 1.3509 | |
| | 1.3287 | 0.1639 | 600 | 1.3273 | |
| | 1.3019 | 0.1912 | 700 | 1.3038 | |
| | 1.2713 | 0.2185 | 800 | 1.2827 | |
| | 1.2596 | 0.2459 | 900 | 1.2630 | |
| | 1.2433 | 0.2732 | 1000 | 1.2459 | |
| | 1.2233 | 0.3005 | 1100 | 1.2310 | |
| | 1.2197 | 0.3278 | 1200 | 1.2177 | |
| | 1.2033 | 0.3551 | 1300 | 1.2055 | |
| | 1.1902 | 0.3825 | 1400 | 1.1963 | |
| | 1.1927 | 0.4098 | 1500 | 1.1876 | |
| | 1.1771 | 0.4371 | 1600 | 1.1791 | |
| | 1.1615 | 0.4644 | 1700 | 1.1702 | |
| | 1.1655 | 0.4917 | 1800 | 1.1641 | |
| | 1.1725 | 0.5191 | 1900 | 1.1585 | |
| | 1.1348 | 0.5464 | 2000 | 1.1522 | |
| | 1.1429 | 0.5737 | 2100 | 1.1464 | |
| | 1.141 | 0.6010 | 2200 | 1.1413 | |
| | 1.1458 | 0.6283 | 2300 | 1.1362 | |
| | 1.1268 | 0.6556 | 2400 | 1.1314 | |
| | 1.1218 | 0.6830 | 2500 | 1.1272 | |
| | 1.1277 | 0.7103 | 2600 | 1.1226 | |
| | 1.1092 | 0.7376 | 2700 | 1.1198 | |
| | 1.1282 | 0.7649 | 2800 | 1.1156 | |
| | 1.1027 | 0.7922 | 2900 | 1.1116 | |
| | 1.0951 | 0.8196 | 3000 | 1.1084 | |
| | 1.1001 | 0.8469 | 3100 | 1.1057 | |
| | 1.1027 | 0.8742 | 3200 | 1.1021 | |
| | 1.0989 | 0.9015 | 3300 | 1.0987 | |
| | 1.0917 | 0.9288 | 3400 | 1.0966 | |
| | 1.0832 | 0.9562 | 3500 | 1.0939 | |
| | 1.1074 | 0.9835 | 3600 | 1.0915 | |
| | 1.0692 | 1.0108 | 3700 | 1.0891 | |
| | 1.0868 | 1.0381 | 3800 | 1.0872 | |
| | 1.079 | 1.0654 | 3900 | 1.0855 | |
| | 1.0844 | 1.0927 | 4000 | 1.0831 | |
| | 1.0779 | 1.1201 | 4100 | 1.0819 | |
| | 1.0737 | 1.1474 | 4200 | 1.0797 | |
| | 1.0651 | 1.1747 | 4300 | 1.0775 | |
| | 1.0656 | 1.2020 | 4400 | 1.0764 | |
| | 1.0592 | 1.2293 | 4500 | 1.0739 | |
| | 1.07 | 1.2567 | 4600 | 1.0729 | |
| | 1.068 | 1.2840 | 4700 | 1.0719 | |
| | 1.0623 | 1.3113 | 4800 | 1.0701 | |
| | 1.0622 | 1.3386 | 4900 | 1.0691 | |
| | 1.0579 | 1.3659 | 5000 | 1.0678 | |
| | 1.0652 | 1.3933 | 5100 | 1.0667 | |
| | 1.0655 | 1.4206 | 5200 | 1.0654 | |
| | 1.0619 | 1.4479 | 5300 | 1.0642 | |
| | 1.0521 | 1.4752 | 5400 | 1.0635 | |
| | 1.0563 | 1.5025 | 5500 | 1.0625 | |
| | 1.0554 | 1.5298 | 5600 | 1.0611 | |
| | 1.0577 | 1.5572 | 5700 | 1.0599 | |
| | 1.0427 | 1.5845 | 5800 | 1.0590 | |
| | 1.0489 | 1.6118 | 5900 | 1.0583 | |
| | 1.0444 | 1.6391 | 6000 | 1.0578 | |
| | 1.0573 | 1.6664 | 6100 | 1.0562 | |
| | 1.0494 | 1.6938 | 6200 | 1.0555 | |
| | 1.0355 | 1.7211 | 6300 | 1.0551 | |
| | 1.0531 | 1.7484 | 6400 | 1.0544 | |
| | 1.0542 | 1.7757 | 6500 | 1.0540 | |
| | 1.0324 | 1.8030 | 6600 | 1.0535 | |
| | 1.0497 | 1.8304 | 6700 | 1.0532 | |
| | 1.0415 | 1.8577 | 6800 | 1.0529 | |
| | 1.0414 | 1.8850 | 6900 | 1.0522 | |
| | 1.0588 | 1.9123 | 7000 | 1.0520 | |
| | 1.0347 | 1.9396 | 7100 | 1.0519 | |
| | 1.0346 | 1.9669 | 7200 | 1.0516 | |
| | 1.043 | 1.9943 | 7300 | 1.0514 | |
|
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|
|
| ### Framework versions |
|
|
| - PEFT 0.12.0 |
| - Transformers 4.44.0 |
| - Pytorch 2.0.1+cu118 |
| - Datasets 2.21.0 |
| - Tokenizers 0.19.1 |
