---
library_name: transformers
license: mit
base_model: ByteDance/Dolphin
tags:
- generated_from_trainer
model-index:
- name: ViDolphin-v0
  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-v0

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: 0.0628

## 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: 15
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step  | Validation Loss |
|:-------------:|:-------:|:-----:|:---------------:|
| 0.3233        | 0.2910  | 500   | 0.2670          |
| 0.2159        | 0.5821  | 1000  | 0.1814          |
| 0.1222        | 1.3099  | 1500  | 0.1093          |
| 0.1081        | 1.7464  | 2000  | 0.0942          |
| 0.0886        | 2.1825  | 2500  | 0.0865          |
| 0.0813        | 2.6189  | 3000  | 0.0811          |
| 0.076         | 3.0550  | 3500  | 0.0777          |
| 0.0663        | 3.4915  | 4000  | 0.0745          |
| 0.0591        | 3.9280  | 4500  | 0.0720          |
| 0.0673        | 4.3640  | 5000  | 0.0697          |
| 0.0531        | 4.8005  | 5500  | 0.0674          |
| 0.0557        | 5.2366  | 6000  | 0.0673          |
| 0.0545        | 5.6731  | 6500  | 0.0655          |
| 0.0561        | 6.1091  | 7000  | 0.0655          |
| 0.0421        | 6.5456  | 7500  | 0.0646          |
| 0.044         | 6.9821  | 8000  | 0.0636          |
| 0.0398        | 7.4182  | 8500  | 0.0637          |
| 0.0448        | 7.8546  | 9000  | 0.0639          |
| 0.0355        | 8.2907  | 9500  | 0.0635          |
| 0.042         | 8.7272  | 10000 | 0.0631          |
| 0.0396        | 9.1632  | 10500 | 0.0635          |
| 0.038         | 9.5997  | 11000 | 0.0634          |
| 0.0379        | 10.0358 | 11500 | 0.0627          |
| 0.0349        | 10.4723 | 12000 | 0.0627          |
| 0.0334        | 10.9088 | 12500 | 0.0626          |
| 0.0359        | 11.3448 | 13000 | 0.0626          |
| 0.035         | 11.7813 | 13500 | 0.0626          |
| 0.0305        | 12.2174 | 14000 | 0.0629          |
| 0.0293        | 12.6539 | 14500 | 0.0628          |


### Framework versions

- Transformers 4.56.0.dev0
- Pytorch 2.8.0+cu128
- Datasets 4.0.0
- Tokenizers 0.21.4