---
library_name: transformers
license: other
base_model: Qwen/Qwen2-7B
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: qwen-2-7b-2e-5-paged_adamw_32bit
  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-paged_adamw_32bit

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 [Qwen/Qwen2-7B](https://huggingface.co/Qwen/Qwen2-7B) on the ultrachat_200k_train dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8906

## 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: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1.0

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.9206        | 0.1232 | 200  | 0.9238          |
| 0.9521        | 0.2463 | 400  | 0.9254          |
| 0.9654        | 0.3695 | 600  | 0.9204          |
| 0.9188        | 0.4926 | 800  | 0.9126          |
| 0.967         | 0.6158 | 1000 | 0.9037          |
| 0.8783        | 0.7389 | 1200 | 0.8964          |
| 0.8915        | 0.8621 | 1400 | 0.8918          |
| 0.9246        | 0.9852 | 1600 | 0.8906          |


### Framework versions

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