---
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-EXPERIMENT-0.1-5e6
  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/mk0r3xjl)
# qwen2.5-0.5b-expo-L2EXPO-EXPERIMENT-0.1-5e6

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: 0.5835
- Logps: -80.5388
- Logits: -0.6811
- Objective: 0.5760
- Dpo Loss: 0.7150
- Regularize: 0.5760
- Ranking Simple: 0.5248
- 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: 5e-06
- 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: 5

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Logps    | Logits  | Objective | Dpo Loss | Regularize | Ranking Simple | Ranking Idealized | Ranking Idealized Expo |
|:-------------:|:------:|:----:|:---------------:|:--------:|:-------:|:---------:|:--------:|:----------:|:--------------:|:-----------------:|:----------------------:|
| 0.3559        | 0.2834 | 50   | 0.4230          | -96.0254 | -1.4739 | 0.4318    | 0.6921   | 0.4318     | 0.5186         | 0.5888            | 0.5103                 |
| 0.3834        | 0.5668 | 100  | 0.4808          | -85.0174 | -1.1784 | 0.4771    | 0.6952   | 0.4771     | 0.5155         | 0.5888            | 0.5103                 |
| 0.3746        | 0.8503 | 150  | 0.5245          | -81.6313 | -1.0148 | 0.5243    | 0.7075   | 0.5243     | 0.5165         | 0.5888            | 0.5103                 |
| 0.3365        | 1.1337 | 200  | 0.5510          | -80.3085 | -1.0812 | 0.5435    | 0.7045   | 0.5435     | 0.5134         | 0.5888            | 0.5103                 |
| 0.2986        | 1.4171 | 250  | 0.5600          | -79.8608 | -0.9740 | 0.5590    | 0.7114   | 0.5590     | 0.5217         | 0.5888            | 0.5103                 |
| 0.2571        | 1.7005 | 300  | 0.5774          | -77.6594 | -0.8023 | 0.5724    | 0.7149   | 0.5724     | 0.5217         | 0.5888            | 0.5103                 |
| 0.2355        | 1.9839 | 350  | 0.5797          | -79.4555 | -0.7278 | 0.5736    | 0.7176   | 0.5736     | 0.5186         | 0.5888            | 0.5103                 |
| 0.1974        | 2.2674 | 400  | 0.5802          | -81.3670 | -0.7596 | 0.5785    | 0.7156   | 0.5785     | 0.5279         | 0.5888            | 0.5103                 |
| 0.1787        | 2.5508 | 450  | 0.5830          | -80.8003 | -0.7106 | 0.5799    | 0.7161   | 0.5799     | 0.5227         | 0.5888            | 0.5103                 |
| 0.1582        | 2.8342 | 500  | 0.5836          | -80.3096 | -0.7272 | 0.5800    | 0.7177   | 0.5800     | 0.5176         | 0.5888            | 0.5103                 |
| 0.1257        | 3.1176 | 550  | 0.5853          | -80.8767 | -0.6681 | 0.5816    | 0.7178   | 0.5816     | 0.5238         | 0.5888            | 0.5103                 |
| 0.1018        | 3.4010 | 600  | 0.5870          | -80.2631 | -0.6520 | 0.5793    | 0.7155   | 0.5793     | 0.5227         | 0.5888            | 0.5103                 |
| 0.0908        | 3.6845 | 650  | 0.5846          | -80.0938 | -0.6950 | 0.5751    | 0.7142   | 0.5751     | 0.5310         | 0.5888            | 0.5103                 |
| 0.0782        | 3.9679 | 700  | 0.5832          | -80.5798 | -0.6694 | 0.5775    | 0.7158   | 0.5775     | 0.5217         | 0.5888            | 0.5103                 |
| 0.0573        | 4.2513 | 750  | 0.5847          | -80.5919 | -0.6764 | 0.5775    | 0.7156   | 0.5775     | 0.5238         | 0.5888            | 0.5103                 |
| 0.0513        | 4.5347 | 800  | 0.5835          | -80.5038 | -0.6806 | 0.5758    | 0.7149   | 0.5758     | 0.5248         | 0.5888            | 0.5103                 |
| 0.0447        | 4.8181 | 850  | 0.5835          | -80.5460 | -0.6807 | 0.5761    | 0.7150   | 0.5761     | 0.5248         | 0.5888            | 0.5103                 |


### Framework versions

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