vicuna-adv-robust-ul15-sft-lora
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
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
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 16
- total_train_batch_size: 512
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 15
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|
| 1.2291 | 0.57 | 14 | 1.1108 |
| 1.1237 | 1.59 | 29 | 1.0651 |
| 1.0918 | 2.6 | 44 | 1.0472 |
| 1.0711 | 3.57 | 58 | 1.0371 |
| 1.0498 | 4.58 | 73 | 1.0299 |
| 1.0255 | 5.6 | 88 | 1.0247 |
| 1.0131 | 6.57 | 102 | 1.0210 |
| 1.0047 | 7.58 | 117 | 1.0181 |
| 1.004 | 8.59 | 132 | 1.0160 |
| 1.0007 | 9.57 | 146 | 1.0145 |
| 0.9938 | 10.58 | 161 | 1.0132 |
| 0.9916 | 11.59 | 176 | 1.0122 |
| 0.9884 | 12.56 | 190 | 1.0115 |
| 0.9881 | 13.58 | 205 | 1.0109 |
| 0.9856 | 14.59 | 220 | 1.0104 |
Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0a0+32f93b1
- Datasets 2.14.6
- Tokenizers 0.14.1
