---
license: apache-2.0
base_model: hZzy/qwen2.5-0.5b-sft-news-IFT
tags:
- alignment-handbook
- ndcg
- trl
- expo
- generated_from_trainer
- trl
- expo
- generated_from_trainer
datasets:
- hZzy/train_pairwise
model-index:
- name: qwen2.5-0.5b-expo-DPO-ES-100
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/zhiyuzha-university-of-florida/huggingface/runs/tkmcvc2n)
# qwen2.5-0.5b-expo-DPO-ES-100

This model is a fine-tuned version of [hZzy/qwen2.5-0.5b-sft-news-IFT](https://huggingface.co/hZzy/qwen2.5-0.5b-sft-news-IFT) on the hZzy/train_pairwise dataset.
It achieves the following results on the evaluation set:
- Loss: 226.3468
- Logps: -80.2667
- Logits: -0.6269
- Objective: 213.3031
- Dpo Loss: 213.3031
- Regularize: 213.3031
- Ranking Simple: 0.5399
- Ranking Idealized: 0.5212
- Ranking Idealized Expo: 0.5212
- Wo Beta: 6.6215

## 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: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 3
- gradient_accumulation_steps: 12
- total_train_batch_size: 144
- total_eval_batch_size: 12
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Logps    | Logits  | Objective | Dpo Loss | Regularize | Ranking Simple | Ranking Idealized | Ranking Idealized Expo | Wo Beta |
|:-------------:|:------:|:----:|:---------------:|:--------:|:-------:|:---------:|:--------:|:----------:|:--------------:|:-----------------:|:----------------------:|:-------:|
| 17.3723       | 0.1417 | 50   | 32.1125         | -90.9520 | -1.4391 | 31.4854   | 31.4854  | 31.4854    | 0.5264         | 0.5212            | 0.5212                 | 7.6851  |
| 60.4454       | 0.2834 | 100  | 70.9968         | -86.6000 | -1.4386 | 70.7719   | 70.7719  | 70.7719    | 0.5305         | 0.5212            | 0.5212                 | 7.5289  |
| 100.2237      | 0.4251 | 150  | 129.8928        | -85.7303 | -1.2892 | 126.8845  | 126.8845 | 126.8845   | 0.5321         | 0.5212            | 0.5212                 | 7.4641  |
| 120.8284      | 0.5668 | 200  | 164.0152        | -75.5542 | -1.3195 | 159.5013  | 159.5013 | 159.5013   | 0.5357         | 0.5212            | 0.5212                 | 7.1836  |
| 134.8217      | 0.7085 | 250  | 195.7212        | -79.3891 | -1.2058 | 190.8510  | 190.8510 | 190.8510   | 0.5285         | 0.5212            | 0.5212                 | 7.2711  |
| 119.0273      | 0.8503 | 300  | 192.5231        | -84.2971 | -0.9945 | 188.0580  | 188.0580 | 188.0580   | 0.5357         | 0.5212            | 0.5212                 | 6.9382  |
| 114.0792      | 0.9920 | 350  | 205.7797        | -82.1125 | -1.0045 | 192.3920  | 192.3920 | 192.3920   | 0.5409         | 0.5212            | 0.5212                 | 6.9235  |
| 72.4145       | 1.1337 | 400  | 212.6613        | -82.8156 | -0.7120 | 204.8122  | 204.8122 | 204.8122   | 0.5409         | 0.5212            | 0.5212                 | 7.0485  |
| 76.9668       | 1.2754 | 450  | 210.2291        | -82.4190 | -0.7807 | 203.0261  | 203.0261 | 203.0261   | 0.5383         | 0.5212            | 0.5212                 | 6.9244  |
| 77.9261       | 1.4171 | 500  | 211.3156        | -81.3728 | -0.7438 | 202.1569  | 202.1569 | 202.1569   | 0.5362         | 0.5212            | 0.5212                 | 6.8863  |
| 70.5755       | 1.5588 | 550  | 212.6468        | -82.3296 | -0.6838 | 200.1410  | 200.1410 | 200.1410   | 0.5430         | 0.5212            | 0.5212                 | 6.7241  |
| 69.6026       | 1.7005 | 600  | 212.0254        | -80.7129 | -0.5569 | 196.9669  | 196.9669 | 196.9669   | 0.5419         | 0.5212            | 0.5212                 | 6.6975  |
| 69.7829       | 1.8422 | 650  | 222.2766        | -79.4968 | -0.7062 | 209.6782  | 209.6782 | 209.6782   | 0.5404         | 0.5212            | 0.5212                 | 6.6541  |
| 62.7864       | 1.9839 | 700  | 226.3468        | -80.2667 | -0.6269 | 213.3031  | 213.3031 | 213.3031   | 0.5399         | 0.5212            | 0.5212                 | 6.6215  |
| 37.3326       | 2.1256 | 750  | 219.7785        | -80.5665 | -0.7007 | 208.8723  | 208.8723 | 208.8723   | 0.5440         | 0.5212            | 0.5212                 | 6.7265  |
| 33.2099       | 2.2674 | 800  | 221.8786        | -81.8901 | -0.5673 | 207.6881  | 207.6881 | 207.6881   | 0.5450         | 0.5212            | 0.5212                 | 6.6717  |
| 33.915        | 2.4091 | 850  | 217.6955        | -81.9134 | -0.5178 | 205.0515  | 205.0515 | 205.0515   | 0.5424         | 0.5212            | 0.5212                 | 6.7249  |
| 35.3572       | 2.5508 | 900  | 224.5402        | -81.5880 | -0.4729 | 214.5052  | 214.5052 | 214.5052   | 0.5435         | 0.5212            | 0.5212                 | 6.8278  |
| 31.032        | 2.6925 | 950  | 225.2907        | -80.3480 | -0.5542 | 216.5803  | 216.5803 | 216.5803   | 0.5419         | 0.5212            | 0.5212                 | 6.8429  |


### Framework versions

- Transformers 4.42.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1