| | --- |
| | license: mit |
| | base_model: microsoft/phi-2 |
| | tags: |
| | - generated_from_trainer |
| | model-index: |
| | - name: V0417MADP8 |
| | 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. --> |
| |
|
| | # V0417MADP8 |
| |
|
| | This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on an unknown dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.0671 |
| |
|
| | ## 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: 0.0003 |
| | - train_batch_size: 8 |
| | - eval_batch_size: 8 |
| | - seed: 42 |
| | - gradient_accumulation_steps: 16 |
| | - total_train_batch_size: 128 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: cosine_with_restarts |
| | - lr_scheduler_warmup_steps: 60 |
| | - num_epochs: 3 |
| | - mixed_precision_training: Native AMP |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | |
| | |:-------------:|:-----:|:----:|:---------------:| |
| | | 5.4354 | 0.09 | 10 | 2.6496 | |
| | | 4.1578 | 0.18 | 20 | 1.5199 | |
| | | 2.23 | 0.27 | 30 | 0.1826 | |
| | | 0.884 | 0.36 | 40 | 0.1456 | |
| | | 0.1889 | 0.45 | 50 | 0.1308 | |
| | | 0.1501 | 0.54 | 60 | 0.1216 | |
| | | 0.1389 | 0.63 | 70 | 0.1104 | |
| | | 0.1173 | 0.73 | 80 | 0.1018 | |
| | | 0.1086 | 0.82 | 90 | 0.0899 | |
| | | 0.0966 | 0.91 | 100 | 0.0814 | |
| | | 0.098 | 1.0 | 110 | 0.0814 | |
| | | 0.093 | 1.09 | 120 | 0.0846 | |
| | | 0.093 | 1.18 | 130 | 0.0811 | |
| | | 0.091 | 1.27 | 140 | 0.0782 | |
| | | 0.0858 | 1.36 | 150 | 0.0767 | |
| | | 0.0853 | 1.45 | 160 | 0.0817 | |
| | | 0.089 | 1.54 | 170 | 0.0804 | |
| | | 0.0854 | 1.63 | 180 | 0.0751 | |
| | | 0.0841 | 1.72 | 190 | 0.0766 | |
| | | 0.0843 | 1.81 | 200 | 0.0722 | |
| | | 0.0763 | 1.9 | 210 | 0.0706 | |
| | | 0.0778 | 1.99 | 220 | 0.0707 | |
| | | 0.0712 | 2.08 | 230 | 0.0697 | |
| | | 0.066 | 2.18 | 240 | 0.0691 | |
| | | 0.0687 | 2.27 | 250 | 0.0711 | |
| | | 0.0714 | 2.36 | 260 | 0.0695 | |
| | | 0.0685 | 2.45 | 270 | 0.0692 | |
| | | 0.0648 | 2.54 | 280 | 0.0688 | |
| | | 0.0645 | 2.63 | 290 | 0.0675 | |
| | | 0.0668 | 2.72 | 300 | 0.0670 | |
| | | 0.0665 | 2.81 | 310 | 0.0672 | |
| | | 0.0628 | 2.9 | 320 | 0.0671 | |
| | | 0.0736 | 2.99 | 330 | 0.0671 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.36.0.dev0 |
| | - Pytorch 2.2.2+cu121 |
| | - Datasets 2.18.0 |
| | - Tokenizers 0.14.1 |
| |
|
