tinymistral-248-DPO
This model is a fine-tuned version of Locutusque/TinyMistral-248M on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3205
- Accuracy: 0.0
- Rewards/chosen: 0.7722
- Rewards/rejected: -0.2727
- Rewards/accuracies: 1.0
- Rewards/margins: 1.0449
- Logps/rejected: -286.5494
- Logps/chosen: -398.5646
- Logits/rejected: -2.3562
- Logits/chosen: -1.8620
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: 0.0002
- train_batch_size: 12
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 12
- total_train_batch_size: 144
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 6
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.5815 | 0.48 | 10 | 0.3205 | 0.0 | 0.7722 | -0.2727 | 1.0 | 1.0449 | -286.5494 | -398.5646 | -2.3562 | -1.8620 |
| 0.3287 | 0.95 | 20 | 0.0970 | 0.0 | 1.0191 | -1.8694 | 1.0 | 2.8886 | -302.5168 | -396.0956 | -2.0547 | -1.5790 |
| 0.2126 | 1.43 | 30 | 0.0414 | 0.0 | 0.3685 | -4.5314 | 1.0 | 4.8999 | -329.1370 | -402.6024 | -1.8100 | -1.4099 |
| 0.1844 | 1.9 | 40 | 0.0260 | 0.0 | 0.9879 | -4.8275 | 1.0 | 5.8153 | -332.0973 | -396.4084 | -1.8704 | -1.4976 |
| 0.1546 | 2.38 | 50 | 0.0190 | 0.0 | 1.1813 | -5.2560 | 1.0 | 6.4373 | -336.3821 | -394.4740 | -1.9098 | -1.5582 |
| 0.1532 | 2.86 | 60 | 0.0140 | 0.0 | 1.0583 | -6.0198 | 1.0 | 7.0780 | -344.0201 | -395.7045 | -1.8920 | -1.5654 |
| 0.1402 | 3.33 | 70 | 0.0112 | 0.0 | 1.0134 | -6.5382 | 1.0 | 7.5517 | -349.2049 | -396.1526 | -1.8823 | -1.5706 |
| 0.1544 | 3.81 | 80 | 0.0089 | 0.0 | 0.8836 | -7.1726 | 1.0 | 8.0562 | -355.5490 | -397.4513 | -1.8518 | -1.5535 |
| 0.1357 | 4.29 | 90 | 0.0072 | 0.0 | 0.7532 | -7.7663 | 1.0 | 8.5195 | -361.4852 | -398.7546 | -1.8193 | -1.5345 |
| 0.1418 | 4.76 | 100 | 0.0061 | 0.0 | 0.6041 | -8.3133 | 1.0 | 8.9174 | -366.9556 | -400.2459 | -1.7889 | -1.5150 |
| 0.1482 | 5.24 | 110 | 0.0051 | 0.0 | 0.4867 | -8.7961 | 1.0 | 9.2828 | -371.7837 | -401.4203 | -1.7611 | -1.4971 |
| 0.141 | 5.71 | 120 | 0.0045 | 0.0 | 0.4212 | -9.1494 | 1.0 | 9.5706 | -375.3166 | -402.0751 | -1.7409 | -1.4842 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
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