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---
tags:
- trl
- dpo
- generated_from_trainer
model-index:
- name: pythia-1.4b-dpo-full
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. -->
# pythia-1.4b-dpo-full
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6403
- Rewards/chosen: 0.6094
- Rewards/rejected: 0.4102
- Rewards/accuracies: 0.5893
- Rewards/margins: 0.2002
- Logps/rejected: -2024.0
- Logps/chosen: -2320.0
- Logits/rejected: -0.6719
- Logits/chosen: -0.6172
## 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: 5
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 6
- total_train_batch_size: 30
- total_eval_batch_size: 48
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.684 | 0.05 | 100 | 0.6768 | 0.2314 | 0.1904 | 0.4494 | 0.0405 | -2048.0 | -2352.0 | -0.7227 | -0.6641 |
| 0.663 | 0.1 | 200 | 0.6566 | 0.5977 | 0.4883 | 0.4940 | 0.1108 | -2016.0 | -2320.0 | -0.7266 | -0.6680 |
| 0.6529 | 0.15 | 300 | 0.6513 | 0.625 | 0.4941 | 0.5149 | 0.1279 | -2016.0 | -2320.0 | -0.7188 | -0.6562 |
| 0.6371 | 0.2 | 400 | 0.6491 | 0.6562 | 0.5 | 0.5595 | 0.1523 | -2016.0 | -2304.0 | -0.7266 | -0.6680 |
| 0.6206 | 0.25 | 500 | 0.6466 | 0.5391 | 0.3945 | 0.5952 | 0.1445 | -2024.0 | -2320.0 | -0.7148 | -0.6562 |
| 0.686 | 0.29 | 600 | 0.6446 | 0.5781 | 0.4180 | 0.5714 | 0.1592 | -2024.0 | -2320.0 | -0.7188 | -0.6602 |
| 0.6459 | 0.34 | 700 | 0.6449 | 0.5508 | 0.3633 | 0.6012 | 0.1885 | -2032.0 | -2320.0 | -0.6875 | -0.6289 |
| 0.6458 | 0.39 | 800 | 0.6421 | 0.5586 | 0.3867 | 0.5774 | 0.1709 | -2024.0 | -2320.0 | -0.6953 | -0.6406 |
| 0.6451 | 0.44 | 900 | 0.6398 | 0.7109 | 0.5039 | 0.5685 | 0.2070 | -2016.0 | -2304.0 | -0.6719 | -0.6133 |
| 0.6213 | 0.49 | 1000 | 0.6407 | 0.7734 | 0.5742 | 0.5714 | 0.2012 | -2008.0 | -2304.0 | -0.6602 | -0.6016 |
| 0.6313 | 0.54 | 1100 | 0.6387 | 0.5391 | 0.3555 | 0.5893 | 0.1807 | -2032.0 | -2320.0 | -0.6680 | -0.6094 |
| 0.6298 | 0.59 | 1200 | 0.6380 | 0.6953 | 0.4922 | 0.6042 | 0.2031 | -2016.0 | -2304.0 | -0.6523 | -0.5977 |
| 0.6461 | 0.64 | 1300 | 0.6396 | 0.5586 | 0.3613 | 0.5863 | 0.1963 | -2032.0 | -2320.0 | -0.6914 | -0.6367 |
| 0.6258 | 0.69 | 1400 | 0.6360 | 0.6914 | 0.4727 | 0.5923 | 0.2207 | -2016.0 | -2304.0 | -0.6758 | -0.6172 |
| 0.6347 | 0.74 | 1500 | 0.6375 | 0.625 | 0.4141 | 0.5893 | 0.2100 | -2024.0 | -2320.0 | -0.6641 | -0.6094 |
| 0.6185 | 0.79 | 1600 | 0.6382 | 0.5977 | 0.3926 | 0.6042 | 0.2051 | -2032.0 | -2320.0 | -0.6797 | -0.625 |
| 0.6408 | 0.83 | 1700 | 0.6374 | 0.5977 | 0.3926 | 0.5952 | 0.2041 | -2024.0 | -2320.0 | -0.6719 | -0.6172 |
| 0.662 | 0.88 | 1800 | 0.6355 | 0.6094 | 0.3984 | 0.6012 | 0.2119 | -2024.0 | -2320.0 | -0.6836 | -0.6289 |
| 0.6385 | 0.93 | 1900 | 0.6379 | 0.6055 | 0.3926 | 0.625 | 0.2129 | -2024.0 | -2320.0 | -0.6758 | -0.6211 |
| 0.6154 | 0.98 | 2000 | 0.6381 | 0.6094 | 0.4043 | 0.6012 | 0.2041 | -2024.0 | -2320.0 | -0.6758 | -0.6211 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1
- Datasets 2.14.6
- Tokenizers 0.15.2
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