---
library_name: transformers
base_model: NoManDeRY/DPO-Shift-Qwen-2-7B-UltraChat200K-SFT
tags:
- alignment-handbook
- generated_from_trainer
datasets:
- HuggingFaceH4/ultrafeedback_binarized
model-index:
- name: qwen-2-7b-dpo-ultrafeedback-5e-7-SFTed-paged_adamw_32bit-fixed-0.95
  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. -->

# qwen-2-7b-dpo-ultrafeedback-5e-7-SFTed-paged_adamw_32bit-fixed-0.95

This is a model released from the preprint: [DPO-Shift: Shifting the Distribution of Direct Preference Optimization](https://arxiv.org/abs/2502.07599). Please refer to our [repository](https://github.com/Meaquadddd/DPO-Shift) for more details.


This model is a fine-tuned version of [NoManDeRY/DPO-Shift-Qwen-2-7B-UltraChat200K-SFT](https://huggingface.co/NoManDeRY/DPO-Shift-Qwen-2-7B-UltraChat200K-SFT) on the HuggingFaceH4/ultrafeedback_binarized dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5890
- Rewards/chosen: -0.0170
- Rewards/rejected: -0.3976
- Dpo Lambda: 0.9500
- Rewards/accuracies: 0.7302
- Rewards/margins: 0.3806
- Logps/rejected: -346.4959
- Logps/chosen: -335.0127
- Logits/rejected: -1.2190
- Logits/chosen: -1.1200

## 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: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- 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
- num_epochs: 1

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Dpo Lambda | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:------:|:----:|:---------------:|:--------------:|:----------------:|:----------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.6842        | 0.1047 | 50   | 0.6827          | 0.1496         | 0.1349           | 0.9500     | 0.6865             | 0.0147          | -293.2452      | -318.3515    | -1.2886         | -1.1643       |
| 0.6498        | 0.2093 | 100  | 0.6609          | 0.2769         | 0.2144           | 0.9500     | 0.7381             | 0.0625          | -285.2926      | -305.6219    | -1.2589         | -1.1266       |
| 0.6549        | 0.3140 | 150  | 0.6408          | 0.2288         | 0.0982           | 0.9500     | 0.7341             | 0.1307          | -296.9194      | -310.4279    | -1.3148         | -1.1846       |
| 0.6413        | 0.4186 | 200  | 0.6250          | 0.1619         | -0.0318          | 0.9500     | 0.7381             | 0.1938          | -309.9195      | -317.1192    | -1.2956         | -1.1761       |
| 0.6069        | 0.5233 | 250  | 0.6114          | 0.0886         | -0.1684          | 0.9500     | 0.7302             | 0.2570          | -323.5783      | -324.4538    | -1.2827         | -1.1695       |
| 0.611         | 0.6279 | 300  | 0.5997          | 0.0461         | -0.2674          | 0.9500     | 0.7381             | 0.3135          | -333.4765      | -328.6992    | -1.2575         | -1.1528       |
| 0.6151        | 0.7326 | 350  | 0.5924          | -0.0016        | -0.3586          | 0.9500     | 0.7222             | 0.3570          | -342.5963      | -333.4674    | -1.2391         | -1.1370       |
| 0.5997        | 0.8373 | 400  | 0.5898          | -0.0127        | -0.3884          | 0.9500     | 0.7222             | 0.3758          | -345.5813      | -334.5772    | -1.2248         | -1.1256       |
| 0.5708        | 0.9419 | 450  | 0.5890          | -0.0170        | -0.3976          | 0.9500     | 0.7302             | 0.3806          | -346.4959      | -335.0127    | -1.2190         | -1.1200       |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1