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---
base_model: llm-book/Swallow-7b-hf-oasst1-21k-ja
library_name: peft
tags:
- trl
- dpo
- generated_from_trainer
model-index:
- name: preference_tuning_results
  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. -->

# preference_tuning_results

This model is a fine-tuned version of [llm-book/Swallow-7b-hf-oasst1-21k-ja](https://huggingface.co/llm-book/Swallow-7b-hf-oasst1-21k-ja) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6610
- Rewards/chosen: -0.1479
- Rewards/rejected: -0.2665
- Rewards/accuracies: 0.5917
- Rewards/margins: 0.1186
- Logps/rejected: -146.9710
- Logps/chosen: -134.8070
- Logits/rejected: 0.3116
- Logits/chosen: 0.3255

## 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-06
- train_batch_size: 4
- eval_batch_size: 8
- 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_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 |
|:-------------:|:------:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.6935        | 0.0337 | 50   | 0.6908          | 0.0025         | -0.0026          | 0.5417             | 0.0050          | -144.3320      | -133.3038    | 0.1607          | 0.1710        |
| 0.6936        | 0.0673 | 100  | 0.6915          | 0.0016         | -0.0021          | 0.5750             | 0.0037          | -144.3277      | -133.3129    | 0.1674          | 0.1783        |
| 0.6905        | 0.1010 | 150  | 0.6889          | 0.0026         | -0.0067          | 0.5167             | 0.0093          | -144.3729      | -133.3024    | 0.1746          | 0.1857        |
| 0.6891        | 0.1347 | 200  | 0.6886          | 0.0109         | 0.0007           | 0.5250             | 0.0102          | -144.2993      | -133.2191    | 0.1697          | 0.1812        |
| 0.6866        | 0.1684 | 250  | 0.6865          | 0.0219         | 0.0071           | 0.5917             | 0.0148          | -144.2358      | -133.1099    | 0.1783          | 0.1895        |
| 0.6851        | 0.2020 | 300  | 0.6826          | 0.0255         | 0.0020           | 0.6000             | 0.0234          | -144.2859      | -133.0740    | 0.1736          | 0.1853        |
| 0.6842        | 0.2357 | 350  | 0.6820          | 0.0240         | -0.0014          | 0.6083             | 0.0254          | -144.3206      | -133.0886    | 0.1721          | 0.1833        |
| 0.679         | 0.2694 | 400  | 0.6761          | 0.0333         | -0.0070          | 0.5750             | 0.0404          | -144.3764      | -132.9950    | 0.1766          | 0.1877        |
| 0.6814        | 0.3030 | 450  | 0.6741          | 0.0215         | -0.0244          | 0.5333             | 0.0459          | -144.5500      | -133.1130    | 0.1943          | 0.2060        |
| 0.674         | 0.3367 | 500  | 0.6693          | 0.0179         | -0.0423          | 0.5667             | 0.0602          | -144.7297      | -133.1494    | 0.2098          | 0.2217        |
| 0.6748        | 0.3704 | 550  | 0.6691          | -0.0133        | -0.0788          | 0.5583             | 0.0655          | -145.0942      | -133.4615    | 0.2477          | 0.2594        |
| 0.6673        | 0.4040 | 600  | 0.6615          | -0.0450        | -0.1350          | 0.6000             | 0.0899          | -145.6558      | -133.7786    | 0.3043          | 0.3172        |
| 0.6769        | 0.4377 | 650  | 0.6654          | -0.0385        | -0.1222          | 0.6000             | 0.0837          | -145.5283      | -133.7136    | 0.2800          | 0.2928        |
| 0.6677        | 0.4714 | 700  | 0.6643          | -0.0537        | -0.1442          | 0.6167             | 0.0905          | -145.7482      | -133.8651    | 0.2681          | 0.2808        |
| 0.675         | 0.5051 | 750  | 0.6596          | -0.0396        | -0.1394          | 0.6083             | 0.0998          | -145.7003      | -133.7247    | 0.2512          | 0.2644        |
| 0.6633        | 0.5387 | 800  | 0.6607          | -0.0756        | -0.1792          | 0.5833             | 0.1036          | -146.0984      | -134.0848    | 0.2626          | 0.2751        |
| 0.6661        | 0.5724 | 850  | 0.6603          | -0.0903        | -0.2000          | 0.6000             | 0.1097          | -146.3066      | -134.2316    | 0.2735          | 0.2861        |
| 0.6677        | 0.6061 | 900  | 0.6619          | -0.0994        | -0.2070          | 0.5750             | 0.1076          | -146.3762      | -134.3224    | 0.2735          | 0.2864        |
| 0.6614        | 0.6397 | 950  | 0.6615          | -0.1019        | -0.2104          | 0.5750             | 0.1084          | -146.4101      | -134.3480    | 0.2690          | 0.2818        |
| 0.6514        | 0.6734 | 1000 | 0.6610          | -0.1138        | -0.2245          | 0.6000             | 0.1107          | -146.5513      | -134.4665    | 0.2835          | 0.2963        |
| 0.6625        | 0.7071 | 1050 | 0.6602          | -0.1136        | -0.2259          | 0.5833             | 0.1124          | -146.5656      | -134.4642    | 0.2873          | 0.3006        |
| 0.6421        | 0.7407 | 1100 | 0.6610          | -0.1285        | -0.2408          | 0.5833             | 0.1122          | -146.7140      | -134.6137    | 0.2892          | 0.3024        |
| 0.6438        | 0.7744 | 1150 | 0.6585          | -0.1373        | -0.2590          | 0.5750             | 0.1217          | -146.8963      | -134.7020    | 0.3015          | 0.3152        |
| 0.6534        | 0.8081 | 1200 | 0.6603          | -0.1478        | -0.2671          | 0.5917             | 0.1192          | -146.9771      | -134.8070    | 0.3120          | 0.3259        |
| 0.653         | 0.8418 | 1250 | 0.6607          | -0.1460        | -0.2651          | 0.5917             | 0.1191          | -146.9573      | -134.7881    | 0.3120          | 0.3259        |
| 0.6667        | 0.8754 | 1300 | 0.6599          | -0.1475        | -0.2678          | 0.5917             | 0.1203          | -146.9841      | -134.8036    | 0.3108          | 0.3247        |
| 0.6596        | 0.9091 | 1350 | 0.6606          | -0.1452        | -0.2632          | 0.6000             | 0.1181          | -146.9385      | -134.7802    | 0.3114          | 0.3255        |
| 0.648         | 0.9428 | 1400 | 0.6614          | -0.1475        | -0.2644          | 0.6000             | 0.1169          | -146.9505      | -134.8035    | 0.3118          | 0.3258        |
| 0.641         | 0.9764 | 1450 | 0.6610          | -0.1479        | -0.2665          | 0.5917             | 0.1186          | -146.9710      | -134.8070    | 0.3116          | 0.3255        |


### Framework versions

- PEFT 0.11.1
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1