mistral-dpo
This model is a fine-tuned version of TheBloke/Mistral-7B-v0.1-GPTQ on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0000
- Rewards/chosen: -2.0502
- Rewards/rejected: -28.3632
- Rewards/accuracies: 1.0
- Rewards/margins: 26.3129
- Logps/rejected: -399.8283
- Logps/chosen: -35.7179
- Logits/rejected: -2.1171
- Logits/chosen: -1.8480
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: 1
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2
- training_steps: 250
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.6453 | 0.2 | 10 | 0.4086 | 0.1393 | -0.7001 | 1.0 | 0.8394 | -123.1976 | -13.8225 | -2.5461 | -2.5162 |
| 0.1759 | 0.4 | 20 | 0.0051 | 0.3963 | -6.4413 | 1.0 | 6.8376 | -180.6101 | -11.2527 | -2.5253 | -2.4045 |
| 0.0015 | 0.6 | 30 | 0.0000 | 0.2885 | -20.7441 | 1.0 | 21.0326 | -323.6376 | -12.3309 | -2.2440 | -1.8851 |
| 0.0 | 0.8 | 40 | 0.0000 | -0.6913 | -26.5964 | 1.0 | 25.9051 | -382.1607 | -22.1282 | -1.9054 | -1.5507 |
| 0.0 | 1.0 | 50 | 0.0000 | -1.6661 | -28.8376 | 1.0 | 27.1715 | -404.5731 | -31.8766 | -1.7581 | -1.4145 |
| 0.0 | 1.2 | 60 | 0.0000 | -2.1659 | -29.6823 | 1.0 | 27.5164 | -413.0200 | -36.8745 | -1.7071 | -1.3649 |
| 0.0 | 1.4 | 70 | 0.0000 | -2.0973 | -30.0476 | 1.0 | 27.9503 | -416.6729 | -36.1886 | -1.6955 | -1.3541 |
| 0.0 | 1.6 | 80 | 0.0000 | -2.0065 | -30.1726 | 1.0 | 28.1661 | -417.9230 | -35.2805 | -1.6941 | -1.3519 |
| 0.0 | 1.8 | 90 | 0.0000 | -1.9541 | -30.2266 | 1.0 | 28.2724 | -418.4622 | -34.7568 | -1.6935 | -1.3518 |
| 0.0023 | 2.0 | 100 | 0.0000 | -0.7061 | -30.2814 | 1.0 | 29.5753 | -419.0107 | -22.2763 | -1.7664 | -1.4215 |
| 0.0 | 2.2 | 110 | 0.0000 | -1.6234 | -29.4682 | 1.0 | 27.8448 | -410.8783 | -31.4494 | -2.0371 | -1.7164 |
| 0.0 | 2.4 | 120 | 0.0000 | -1.9528 | -28.6154 | 1.0 | 26.6626 | -402.3507 | -34.7431 | -2.0991 | -1.8126 |
| 0.0 | 2.6 | 130 | 0.0000 | -2.0210 | -28.3739 | 1.0 | 26.3529 | -399.9358 | -35.4253 | -2.1141 | -1.8394 |
| 0.0 | 2.8 | 140 | 0.0000 | -2.0443 | -28.2878 | 1.0 | 26.2435 | -399.0752 | -35.6588 | -2.1185 | -1.8487 |
| 0.0 | 3.0 | 150 | 0.0000 | -2.0504 | -28.2651 | 1.0 | 26.2147 | -398.8474 | -35.7192 | -2.1201 | -1.8510 |
| 0.0 | 3.2 | 160 | 0.0000 | -2.0500 | -28.2657 | 1.0 | 26.2157 | -398.8541 | -35.7157 | -2.1202 | -1.8519 |
| 0.0 | 3.4 | 170 | 0.0000 | -2.0530 | -28.2687 | 1.0 | 26.2157 | -398.8837 | -35.7460 | -2.1205 | -1.8521 |
| 0.0 | 3.6 | 180 | 0.0000 | -2.0529 | -28.2660 | 1.0 | 26.2131 | -398.8570 | -35.7444 | -2.1202 | -1.8515 |
| 0.0 | 3.8 | 190 | 0.0000 | -2.0531 | -28.2649 | 1.0 | 26.2119 | -398.8461 | -35.7464 | -2.1202 | -1.8519 |
| 0.0 | 4.0 | 200 | 0.0000 | -2.0579 | -28.3150 | 1.0 | 26.2571 | -399.3466 | -35.7943 | -2.1191 | -1.8507 |
| 0.0 | 4.2 | 210 | 0.0000 | -2.0509 | -28.3341 | 1.0 | 26.2832 | -399.5381 | -35.7246 | -2.1178 | -1.8487 |
| 0.0 | 4.4 | 220 | 0.0000 | -2.0516 | -28.3405 | 1.0 | 26.2889 | -399.6018 | -35.7316 | -2.1178 | -1.8490 |
| 0.0 | 4.6 | 230 | 0.0000 | -2.0516 | -28.3495 | 1.0 | 26.2979 | -399.6917 | -35.7317 | -2.1176 | -1.8489 |
| 0.0 | 4.8 | 240 | 0.0000 | -2.0508 | -28.3684 | 1.0 | 26.3176 | -399.8806 | -35.7236 | -2.1173 | -1.8488 |
| 0.0 | 5.0 | 250 | 0.0000 | -2.0502 | -28.3632 | 1.0 | 26.3129 | -399.8283 | -35.7179 | -2.1171 | -1.8480 |
Framework versions
- PEFT 0.7.1
- Transformers 4.36.2
- Pytorch 2.0.1+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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