File size: 4,689 Bytes
59c7c4d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 |
---
tags:
- trl
- dpo
- generated_from_trainer
model-index:
- name: dpo-selective-longerrun
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# dpo-selective-longerrun
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4916
- Rewards/chosen: -0.6959
- Rewards/rejected: -2.0431
- Rewards/accuracies: 0.7579
- Rewards/margins: 1.3472
- Logps/rejected: -312.5994
- Logps/chosen: -310.2374
- Logits/rejected: -2.3498
- Logits/chosen: -2.3901
## 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: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 1500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.6163 | 0.1 | 100 | 0.6145 | 0.0147 | -0.2611 | 0.7024 | 0.2758 | -276.9589 | -296.0254 | -2.3069 | -2.3542 |
| 0.5608 | 0.21 | 200 | 0.5507 | -0.0898 | -0.8075 | 0.7401 | 0.7176 | -287.8870 | -298.1169 | -2.3806 | -2.4286 |
| 0.4934 | 0.31 | 300 | 0.5225 | -0.1646 | -1.0392 | 0.7579 | 0.8746 | -292.5221 | -299.6117 | -2.3416 | -2.3850 |
| 0.4812 | 0.42 | 400 | 0.5148 | -0.2130 | -1.1798 | 0.7599 | 0.9668 | -295.3333 | -300.5795 | -2.3285 | -2.3697 |
| 0.5217 | 0.52 | 500 | 0.5094 | -0.1747 | -1.1571 | 0.7599 | 0.9824 | -294.8788 | -299.8136 | -2.3074 | -2.3432 |
| 0.5069 | 0.63 | 600 | 0.5037 | -0.0404 | -1.0494 | 0.7659 | 1.0090 | -292.7251 | -297.1272 | -2.2444 | -2.2854 |
| 0.4582 | 0.73 | 700 | 0.5003 | -0.6338 | -1.7232 | 0.7599 | 1.0894 | -306.2008 | -308.9958 | -2.2469 | -2.2897 |
| 0.457 | 0.84 | 800 | 0.4907 | -0.4901 | -1.6054 | 0.7639 | 1.1153 | -303.8464 | -306.1228 | -2.2928 | -2.3342 |
| 0.4723 | 0.94 | 900 | 0.4933 | -0.4418 | -1.5567 | 0.7659 | 1.1149 | -302.8719 | -305.1562 | -2.3355 | -2.3762 |
| 0.3094 | 1.05 | 1000 | 0.4922 | -0.8030 | -2.0474 | 0.7639 | 1.2444 | -312.6856 | -312.3804 | -2.3698 | -2.4094 |
| 0.2725 | 1.15 | 1100 | 0.4921 | -0.5635 | -1.8640 | 0.7460 | 1.3005 | -309.0183 | -307.5903 | -2.3382 | -2.3785 |
| 0.2932 | 1.26 | 1200 | 0.4924 | -0.6522 | -2.0030 | 0.7579 | 1.3509 | -311.7977 | -309.3632 | -2.3511 | -2.3915 |
| 0.275 | 1.36 | 1300 | 0.4916 | -0.6366 | -1.9750 | 0.7599 | 1.3383 | -311.2369 | -309.0526 | -2.3531 | -2.3934 |
| 0.2768 | 1.47 | 1400 | 0.4922 | -0.7011 | -2.0464 | 0.7579 | 1.3453 | -312.6646 | -310.3419 | -2.3505 | -2.3908 |
| 0.2863 | 1.57 | 1500 | 0.4916 | -0.6959 | -2.0431 | 0.7579 | 1.3472 | -312.5994 | -310.2374 | -2.3498 | -2.3901 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2
- Datasets 2.14.6
- Tokenizers 0.15.0
|