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--- |
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library_name: transformers |
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license: mit |
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base_model: ByteDance/Dolphin |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: ViDolphin-v0 |
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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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# ViDolphin-v0 |
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This model is a fine-tuned version of [ByteDance/Dolphin](https://huggingface.co/ByteDance/Dolphin) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0628 |
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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: 2e-05 |
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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: 4 |
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- total_train_batch_size: 32 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.01 |
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- num_epochs: 15 |
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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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| 0.3233 | 0.2910 | 500 | 0.2670 | |
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| 0.2159 | 0.5821 | 1000 | 0.1814 | |
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| 0.1222 | 1.3099 | 1500 | 0.1093 | |
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| 0.1081 | 1.7464 | 2000 | 0.0942 | |
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| 0.0886 | 2.1825 | 2500 | 0.0865 | |
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| 0.0813 | 2.6189 | 3000 | 0.0811 | |
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| 0.076 | 3.0550 | 3500 | 0.0777 | |
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| 0.0663 | 3.4915 | 4000 | 0.0745 | |
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| 0.0591 | 3.9280 | 4500 | 0.0720 | |
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| 0.0673 | 4.3640 | 5000 | 0.0697 | |
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| 0.0531 | 4.8005 | 5500 | 0.0674 | |
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| 0.0557 | 5.2366 | 6000 | 0.0673 | |
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| 0.0545 | 5.6731 | 6500 | 0.0655 | |
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| 0.0561 | 6.1091 | 7000 | 0.0655 | |
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| 0.0421 | 6.5456 | 7500 | 0.0646 | |
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| 0.044 | 6.9821 | 8000 | 0.0636 | |
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| 0.0398 | 7.4182 | 8500 | 0.0637 | |
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| 0.0448 | 7.8546 | 9000 | 0.0639 | |
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| 0.0355 | 8.2907 | 9500 | 0.0635 | |
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| 0.042 | 8.7272 | 10000 | 0.0631 | |
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| 0.0396 | 9.1632 | 10500 | 0.0635 | |
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| 0.038 | 9.5997 | 11000 | 0.0634 | |
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| 0.0379 | 10.0358 | 11500 | 0.0627 | |
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| 0.0349 | 10.4723 | 12000 | 0.0627 | |
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| 0.0334 | 10.9088 | 12500 | 0.0626 | |
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| 0.0359 | 11.3448 | 13000 | 0.0626 | |
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| 0.035 | 11.7813 | 13500 | 0.0626 | |
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| 0.0305 | 12.2174 | 14000 | 0.0629 | |
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| 0.0293 | 12.6539 | 14500 | 0.0628 | |
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### Framework versions |
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- Transformers 4.56.0.dev0 |
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- Pytorch 2.8.0+cu128 |
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- Datasets 4.0.0 |
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- Tokenizers 0.21.4 |
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