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--- |
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license: mit |
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base_model: microsoft/Phi-3-mini-4k-instruct |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: Phi0503HMA8 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# Phi0503HMA8 |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0686 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0003 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 16 |
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- total_train_batch_size: 128 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine_with_restarts |
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- lr_scheduler_warmup_steps: 100 |
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- num_epochs: 3 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 4.3865 | 0.09 | 10 | 1.2753 | |
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| 0.5539 | 0.18 | 20 | 0.2766 | |
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| 0.5947 | 0.27 | 30 | 0.2953 | |
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| 0.2568 | 0.36 | 40 | 0.2363 | |
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| 0.2491 | 0.45 | 50 | 0.2147 | |
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| 0.2056 | 0.54 | 60 | 0.2216 | |
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| 0.1891 | 0.63 | 70 | 0.1671 | |
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| 0.1675 | 0.73 | 80 | 0.1412 | |
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| 0.1048 | 0.82 | 90 | 0.0875 | |
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| 0.0832 | 0.91 | 100 | 0.0893 | |
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| 0.1 | 1.0 | 110 | 0.0979 | |
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| 0.0777 | 1.09 | 120 | 0.0755 | |
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| 0.0726 | 1.18 | 130 | 0.0886 | |
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| 0.1565 | 1.27 | 140 | 0.0863 | |
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| 0.0881 | 1.36 | 150 | 0.0741 | |
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| 0.0792 | 1.45 | 160 | 0.0784 | |
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| 0.0742 | 1.54 | 170 | 0.0716 | |
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| 0.0673 | 1.63 | 180 | 0.0688 | |
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| 0.0644 | 1.72 | 190 | 0.0674 | |
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| 0.0687 | 1.81 | 200 | 0.0684 | |
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| 0.0644 | 1.9 | 210 | 0.0695 | |
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| 0.0641 | 1.99 | 220 | 0.0694 | |
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| 0.039 | 2.08 | 230 | 0.0703 | |
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| 0.0375 | 2.18 | 240 | 0.0849 | |
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| 0.0345 | 2.27 | 250 | 0.0772 | |
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| 0.0324 | 2.36 | 260 | 0.0694 | |
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| 0.0386 | 2.45 | 270 | 0.0736 | |
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| 0.0336 | 2.54 | 280 | 0.0731 | |
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| 0.0321 | 2.63 | 290 | 0.0704 | |
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| 0.0365 | 2.72 | 300 | 0.0705 | |
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| 0.0394 | 2.81 | 310 | 0.0697 | |
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| 0.0357 | 2.9 | 320 | 0.0687 | |
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| 0.0379 | 2.99 | 330 | 0.0686 | |
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### Framework versions |
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- Transformers 4.36.0.dev0 |
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- Pytorch 2.1.2+cu121 |
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- Datasets 2.14.6 |
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- Tokenizers 0.14.0 |
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