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---
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
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