| | --- |
| | library_name: transformers |
| | license: mit |
| | base_model: ByteDance/Dolphin |
| | tags: |
| | - generated_from_trainer |
| | model-index: |
| | - name: ViDolphin-v1 |
| | 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. --> |
| |
|
| | # ViDolphin-v1 |
| |
|
| | This model is a fine-tuned version of [ByteDance/Dolphin](https://huggingface.co/ByteDance/Dolphin) on the None dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.0640 |
| |
|
| | ## 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: 8 |
| | - eval_batch_size: 8 |
| | - seed: 42 |
| | - gradient_accumulation_steps: 4 |
| | - total_train_batch_size: 32 |
| | - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| | - lr_scheduler_type: linear |
| | - lr_scheduler_warmup_ratio: 0.01 |
| | - num_epochs: 10 |
| | - mixed_precision_training: Native AMP |
| | |
| | ### Training results |
| | |
| | | Training Loss | Epoch | Step | Validation Loss | |
| | |:-------------:|:------:|:-----:|:---------------:| |
| | | 0.2154 | 0.4365 | 500 | 0.1682 | |
| | | 0.1639 | 0.8730 | 1000 | 0.1235 | |
| | | 0.1091 | 1.3090 | 1500 | 0.1027 | |
| | | 0.0988 | 1.7455 | 2000 | 0.0896 | |
| | | 0.0835 | 2.1816 | 2500 | 0.0831 | |
| | | 0.0748 | 2.6181 | 3000 | 0.0785 | |
| | | 0.0735 | 3.0541 | 3500 | 0.0754 | |
| | | 0.0634 | 3.4906 | 4000 | 0.0729 | |
| | | 0.0563 | 3.9271 | 4500 | 0.0708 | |
| | | 0.0659 | 4.3632 | 5000 | 0.0696 | |
| | | 0.0539 | 4.7997 | 5500 | 0.0680 | |
| | | 0.0554 | 5.2357 | 6000 | 0.0676 | |
| | | 0.055 | 5.6722 | 6500 | 0.0660 | |
| | | 0.057 | 6.1082 | 7000 | 0.0660 | |
| | | 0.0447 | 6.5447 | 7500 | 0.0658 | |
| | | 0.0456 | 6.9812 | 8000 | 0.0647 | |
| | | 0.042 | 7.4173 | 8500 | 0.0646 | |
| | | 0.0482 | 7.8538 | 9000 | 0.0646 | |
| | | 0.0386 | 8.2898 | 9500 | 0.0643 | |
| | | 0.046 | 8.7263 | 10000 | 0.0639 | |
| | | 0.0436 | 9.1624 | 10500 | 0.0642 | |
| | | 0.0428 | 9.5989 | 11000 | 0.0640 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.56.0.dev0 |
| | - Pytorch 2.8.0+cu128 |
| | - Datasets 4.0.0 |
| | - Tokenizers 0.21.4 |
| | |
