---
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-L2EXPO-ES-0.01
  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/8q1vl3fv)
# qwen2.5-0.5b-expo-L2EXPO-ES-0.01

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: 0.4116
- Logps: -92.3444
- Logits: -1.6667
- Objective: 0.4097
- Dpo Loss: 0.6906
- Regularize: 0.4097
- Ranking Simple: 0.5290
- Ranking Idealized: 0.8732
- Ranking Idealized Expo: 0.5321
- Wo Beta: 17.6306

## 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 |
|:-------------:|:------:|:----:|:---------------:|:---------:|:-------:|:---------:|:--------:|:----------:|:--------------:|:-----------------:|:----------------------:|:-------:|
| 0.4144        | 0.1417 | 50   | 0.4116          | -92.3444  | -1.6667 | 0.4097    | 0.6906   | 0.4097     | 0.5290         | 0.8732            | 0.5321                 | 17.6306 |
| 0.394         | 0.2834 | 100  | 0.4078          | -115.8757 | -2.0312 | 0.4074    | 0.6863   | 0.4074     | 0.5399         | 0.8732            | 0.5321                 | 22.1058 |
| 0.3504        | 0.4251 | 150  | 0.4044          | -123.1670 | -2.0505 | 0.4018    | 0.6803   | 0.4018     | 0.5719         | 0.8732            | 0.5321                 | 23.5660 |
| 0.3135        | 0.5668 | 200  | 0.4006          | -121.6031 | -2.1409 | 0.3974    | 0.6781   | 0.3974     | 0.5621         | 0.8732            | 0.5321                 | 23.0977 |
| 0.2807        | 0.7085 | 250  | 0.4043          | -122.1639 | -2.3711 | 0.4010    | 0.6790   | 0.4010     | 0.5600         | 0.8732            | 0.5321                 | 23.6688 |
| 0.2532        | 0.8503 | 300  | 0.3975          | -123.3866 | -2.2621 | 0.3953    | 0.6769   | 0.3953     | 0.5740         | 0.8732            | 0.5321                 | 23.6726 |


### Framework versions

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