Mistral2_1000_STEPS_05beta_1e7rate_CDPOSFT
This model is a fine-tuned version of tsavage68/mistralit2_1000_STEPS_5e7_SFT on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.3136
- Rewards/chosen: -2.3904
- Rewards/rejected: -7.1332
- Rewards/accuracies: 0.6593
- Rewards/margins: 4.7427
- Logps/rejected: -40.8232
- Logps/chosen: -28.4526
- Logits/rejected: -1.9252
- Logits/chosen: -1.9252
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: 5e-07
- train_batch_size: 4
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 1000
Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.5792 | 0.0977 | 50 | 0.5909 | -2.0186 | -2.6003 | 0.6220 | 0.5817 | -31.7574 | -27.7089 | -2.2531 | -2.2527 |
| 0.6303 | 0.1953 | 100 | 0.6667 | 3.3013 | 1.7992 | 0.6549 | 1.5021 | -22.9585 | -17.0691 | -2.2458 | -2.2455 |
| 0.5825 | 0.2930 | 150 | 0.7920 | 2.4778 | 0.7375 | 0.6505 | 1.7403 | -25.0818 | -18.7161 | -2.1900 | -2.1897 |
| 0.6114 | 0.3906 | 200 | 0.7379 | 2.2312 | 0.7238 | 0.6659 | 1.5074 | -25.1093 | -19.2092 | -2.3138 | -2.3135 |
| 0.6456 | 0.4883 | 250 | 0.8073 | 3.3802 | 1.8907 | 0.6220 | 1.4894 | -22.7754 | -16.9114 | -2.1555 | -2.1552 |
| 0.6342 | 0.5859 | 300 | 0.8059 | 3.1536 | 1.5241 | 0.6286 | 1.6295 | -23.5086 | -17.3644 | -2.2658 | -2.2655 |
| 0.6242 | 0.6836 | 350 | 0.8249 | 1.4081 | -0.6396 | 0.6659 | 2.0477 | -27.8361 | -20.8555 | -2.2305 | -2.2303 |
| 0.7214 | 0.7812 | 400 | 0.8283 | 2.4761 | 0.6640 | 0.6418 | 1.8121 | -25.2289 | -18.7195 | -2.3316 | -2.3314 |
| 0.7045 | 0.8789 | 450 | 0.8201 | 1.8174 | -0.1276 | 0.6352 | 1.9451 | -26.8121 | -20.0369 | -2.1939 | -2.1937 |
| 0.479 | 0.9766 | 500 | 0.7489 | 2.6325 | 1.0003 | 0.6593 | 1.6322 | -24.5563 | -18.4067 | -2.3131 | -2.3129 |
| 0.0869 | 1.0742 | 550 | 0.9388 | 0.3435 | -2.9890 | 0.6681 | 3.3325 | -32.5349 | -22.9847 | -2.0092 | -2.0092 |
| 0.2298 | 1.1719 | 600 | 1.1052 | -0.7335 | -4.5697 | 0.6593 | 3.8362 | -35.6963 | -25.1386 | -1.9647 | -1.9647 |
| 0.2182 | 1.2695 | 650 | 1.2321 | -1.9830 | -6.2540 | 0.6593 | 4.2711 | -39.0649 | -27.6376 | -1.9426 | -1.9426 |
| 0.0774 | 1.3672 | 700 | 1.2775 | -2.3773 | -6.9288 | 0.6615 | 4.5515 | -40.4144 | -28.4262 | -1.9328 | -1.9328 |
| 0.1026 | 1.4648 | 750 | 1.3159 | -2.4992 | -7.2166 | 0.6615 | 4.7174 | -40.9900 | -28.6701 | -1.9244 | -1.9244 |
| 0.0987 | 1.5625 | 800 | 1.3118 | -2.4534 | -7.2109 | 0.6593 | 4.7575 | -40.9786 | -28.5784 | -1.9248 | -1.9248 |
| 0.2393 | 1.6602 | 850 | 1.3108 | -2.3855 | -7.1139 | 0.6637 | 4.7283 | -40.7846 | -28.4428 | -1.9255 | -1.9255 |
| 0.2495 | 1.7578 | 900 | 1.3100 | -2.3926 | -7.1330 | 0.6637 | 4.7404 | -40.8229 | -28.4569 | -1.9264 | -1.9264 |
| 0.1851 | 1.8555 | 950 | 1.3120 | -2.4001 | -7.1405 | 0.6637 | 4.7404 | -40.8378 | -28.4718 | -1.9253 | -1.9253 |
| 0.0934 | 1.9531 | 1000 | 1.3136 | -2.3904 | -7.1332 | 0.6593 | 4.7427 | -40.8232 | -28.4526 | -1.9252 | -1.9252 |
Framework versions
- Transformers 4.40.1
- Pytorch 2.0.0+cu117
- Datasets 2.19.0
- Tokenizers 0.19.1
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