vicuna-adv-robust-u15-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 |
|---|
| No log | 0 | 0 | 2.4952 |
| 2.5615 | 1.09 | 1 | 2.5270 |
| 2.5615 | 1.09 | 1 | 2.5362 |
| 2.5615 | 3.03 | 2 | 2.5338 |
| 2.5615 | 4.12 | 3 | 2.2734 |
| 2.5615 | 4.12 | 3 | 2.3209 |
| 2.5615 | 6.06 | 4 | 2.1026 |
| 2.3634 | 7.15 | 5 | 2.0141 |
| 2.3634 | 7.15 | 5 | 2.0772 |
| 2.3634 | 9.09 | 6 | 1.9678 |
| 2.3634 | 9.09 | 6 | 1.8940 |
| 2.3634 | 11.03 | 7 | 1.8120 |
| 2.3634 | 12.12 | 8 | 1.8152 |
| 2.3634 | 12.12 | 8 | 1.7991 |
| 2.3634 | 14.06 | 9 | 1.7646 |
Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0a0+32f93b1
- Datasets 2.14.6
- Tokenizers 0.14.1
