---
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-EXPERIMENT
  results: []
---

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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/g2cz8uwi)
# qwen2.5-0.5b-expo-DPO-EXPERIMENT

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: 0.6818
- Logps: -92.0550
- Logits: -1.5636
- Objective: 0.6891
- Dpo Loss: 0.6891
- Regularize: 0.6891
- Ranking Simple: 0.5196
- Ranking Idealized: 0.5888
- Ranking Idealized Expo: 0.5103

## 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: 1e-07
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 6
- gradient_accumulation_steps: 12
- total_train_batch_size: 288
- total_eval_batch_size: 24
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Logps    | Logits  | Objective | Dpo Loss | Regularize | Ranking Simple | Ranking Idealized | Ranking Idealized Expo |
|:-------------:|:------:|:----:|:---------------:|:--------:|:-------:|:---------:|:--------:|:----------:|:--------------:|:-----------------:|:----------------------:|
| 0.6855        | 0.2834 | 50   | 0.6889          | -90.7669 | -1.4343 | 0.6918    | 0.6918   | 0.6918     | 0.5103         | 0.5888            | 0.5103                 |
| 0.6746        | 0.5668 | 100  | 0.6858          | -90.9748 | -1.4764 | 0.6899    | 0.6899   | 0.6899     | 0.5093         | 0.5888            | 0.5103                 |
| 0.6601        | 0.8503 | 150  | 0.6828          | -90.8063 | -1.5179 | 0.6886    | 0.6886   | 0.6886     | 0.5134         | 0.5888            | 0.5103                 |
| 0.6473        | 1.1337 | 200  | 0.6826          | -91.9779 | -1.5427 | 0.6890    | 0.6890   | 0.6890     | 0.5176         | 0.5888            | 0.5103                 |
| 0.6449        | 1.4171 | 250  | 0.6813          | -91.6044 | -1.5537 | 0.6887    | 0.6887   | 0.6887     | 0.5176         | 0.5888            | 0.5103                 |
| 0.6384        | 1.7005 | 300  | 0.6818          | -92.0140 | -1.5627 | 0.6890    | 0.6890   | 0.6890     | 0.5186         | 0.5888            | 0.5103                 |
| 0.6431        | 1.9839 | 350  | 0.6818          | -92.0550 | -1.5636 | 0.6891    | 0.6891   | 0.6891     | 0.5196         | 0.5888            | 0.5103                 |


### Framework versions

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