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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-L1EXPO
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/cdsh593u)
# qwen2.5-0.5b-expo-L1EXPO
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: 0.0088
- Logps: -98.4573
- Logits: -1.9894
- Objective: 0.0088
- Dpo Loss: 0.6929
- Regularize: 0.0088
- Ranking Simple: 0.5180
- Ranking Idealized: 0.6022
- Ranking Idealized Expo: 0.5207
## 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: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 12
- total_train_batch_size: 48
- total_eval_batch_size: 4
- 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:-------:|:---------:|:--------:|:----------:|:--------------:|:-----------------:|:----------------------:|
| 0.006 | 0.0472 | 50 | 0.0060 | -98.6152 | -1.9958 | 0.0061 | 0.6930 | 0.0061 | 0.5180 | 0.6022 | 0.5207 |
| 0.0092 | 0.0945 | 100 | 0.0073 | -98.7889 | -1.9954 | 0.0073 | 0.6929 | 0.0073 | 0.5186 | 0.6022 | 0.5207 |
| 0.0142 | 0.1417 | 150 | 0.0092 | -98.6620 | -1.9986 | 0.0093 | 0.6930 | 0.0093 | 0.5186 | 0.6022 | 0.5207 |
| 0.0173 | 0.1890 | 200 | 0.0097 | -98.7946 | -1.9957 | 0.0098 | 0.6929 | 0.0098 | 0.5173 | 0.6022 | 0.5207 |
| 0.0245 | 0.2362 | 250 | 0.0121 | -98.6416 | -1.9951 | 0.0121 | 0.6929 | 0.0121 | 0.5186 | 0.6022 | 0.5207 |
| 0.0234 | 0.2834 | 300 | 0.0136 | -98.3321 | -1.9940 | 0.0140 | 0.6932 | 0.0140 | 0.5166 | 0.6022 | 0.5207 |
| 0.0262 | 0.3307 | 350 | 0.0178 | -98.3457 | -1.9947 | 0.0181 | 0.6926 | 0.0181 | 0.5200 | 0.6022 | 0.5207 |
| 0.0315 | 0.3779 | 400 | 0.0165 | -98.1128 | -1.9941 | 0.0164 | 0.6926 | 0.0164 | 0.5200 | 0.6022 | 0.5207 |
| 0.0294 | 0.4252 | 450 | 0.0145 | -98.3787 | -1.9950 | 0.0148 | 0.6924 | 0.0148 | 0.5186 | 0.6022 | 0.5207 |
| 0.032 | 0.4724 | 500 | 0.0139 | -98.6457 | -1.9920 | 0.0139 | 0.6925 | 0.0139 | 0.5193 | 0.6022 | 0.5207 |
| 0.0314 | 0.5196 | 550 | 0.0136 | -98.9689 | -1.9943 | 0.0135 | 0.6927 | 0.0135 | 0.5186 | 0.6022 | 0.5207 |
| 0.0311 | 0.5669 | 600 | 0.0142 | -98.1223 | -1.9968 | 0.0144 | 0.6925 | 0.0144 | 0.5186 | 0.6022 | 0.5207 |
| 0.0333 | 0.6141 | 650 | 0.0145 | -98.6917 | -1.9935 | 0.0146 | 0.6926 | 0.0146 | 0.5180 | 0.6022 | 0.5207 |
| 0.028 | 0.6614 | 700 | 0.0138 | -98.6777 | -1.9953 | 0.0140 | 0.6930 | 0.0140 | 0.5193 | 0.6022 | 0.5207 |
| 0.0319 | 0.7086 | 750 | 0.0147 | -98.7712 | -1.9952 | 0.0145 | 0.6926 | 0.0145 | 0.5180 | 0.6022 | 0.5207 |
| 0.0297 | 0.7558 | 800 | 0.0157 | -98.1348 | -1.9950 | 0.0163 | 0.6929 | 0.0163 | 0.5186 | 0.6022 | 0.5207 |
| 0.0286 | 0.8031 | 850 | 0.0124 | -98.5940 | -1.9954 | 0.0125 | 0.6928 | 0.0125 | 0.5173 | 0.6022 | 0.5207 |
| 0.0285 | 0.8503 | 900 | 0.0117 | -98.9422 | -1.9931 | 0.0118 | 0.6929 | 0.0118 | 0.5166 | 0.6022 | 0.5207 |
| 0.0248 | 0.8976 | 950 | 0.0156 | -98.6447 | -1.9902 | 0.0155 | 0.6932 | 0.0155 | 0.5173 | 0.6022 | 0.5207 |
| 0.0272 | 0.9448 | 1000 | 0.0126 | -98.1242 | -1.9906 | 0.0128 | 0.6931 | 0.0128 | 0.5180 | 0.6022 | 0.5207 |
| 0.0215 | 0.9920 | 1050 | 0.0133 | -98.3357 | -1.9911 | 0.0135 | 0.6927 | 0.0135 | 0.5180 | 0.6022 | 0.5207 |
| 0.0242 | 1.0393 | 1100 | 0.0128 | -98.5121 | -1.9881 | 0.0127 | 0.6927 | 0.0127 | 0.5180 | 0.6022 | 0.5207 |
| 0.0248 | 1.0865 | 1150 | 0.0121 | -98.3740 | -1.9900 | 0.0124 | 0.6929 | 0.0124 | 0.5180 | 0.6022 | 0.5207 |
| 0.0238 | 1.1338 | 1200 | 0.0131 | -98.6523 | -1.9881 | 0.0132 | 0.6931 | 0.0132 | 0.5186 | 0.6022 | 0.5207 |
| 0.0213 | 1.1810 | 1250 | 0.0116 | -98.3820 | -1.9892 | 0.0118 | 0.6929 | 0.0118 | 0.5186 | 0.6022 | 0.5207 |
| 0.0213 | 1.2282 | 1300 | 0.0101 | -98.3519 | -1.9901 | 0.0103 | 0.6930 | 0.0103 | 0.5180 | 0.6022 | 0.5207 |
| 0.0191 | 1.2755 | 1350 | 0.0105 | -98.1708 | -1.9895 | 0.0107 | 0.6929 | 0.0107 | 0.5186 | 0.6022 | 0.5207 |
| 0.0183 | 1.3227 | 1400 | 0.0098 | -98.2989 | -1.9896 | 0.0099 | 0.6928 | 0.0099 | 0.5180 | 0.6022 | 0.5207 |
| 0.0173 | 1.3700 | 1450 | 0.0120 | -98.4475 | -1.9888 | 0.0120 | 0.6929 | 0.0120 | 0.5193 | 0.6022 | 0.5207 |
| 0.0171 | 1.4172 | 1500 | 0.0093 | -98.4978 | -1.9892 | 0.0093 | 0.6929 | 0.0093 | 0.5186 | 0.6022 | 0.5207 |
| 0.0164 | 1.4645 | 1550 | 0.0100 | -98.4887 | -1.9898 | 0.0101 | 0.6928 | 0.0101 | 0.5180 | 0.6022 | 0.5207 |
| 0.0165 | 1.5117 | 1600 | 0.0097 | -98.4418 | -1.9892 | 0.0096 | 0.6929 | 0.0096 | 0.5186 | 0.6022 | 0.5207 |
| 0.0128 | 1.5589 | 1650 | 0.0100 | -98.3605 | -1.9889 | 0.0101 | 0.6927 | 0.0101 | 0.5180 | 0.6022 | 0.5207 |
| 0.0132 | 1.6062 | 1700 | 0.0090 | -98.4055 | -1.9891 | 0.0089 | 0.6928 | 0.0089 | 0.5180 | 0.6022 | 0.5207 |
| 0.0133 | 1.6534 | 1750 | 0.0094 | -98.4174 | -1.9885 | 0.0094 | 0.6928 | 0.0094 | 0.5180 | 0.6022 | 0.5207 |
| 0.0138 | 1.7007 | 1800 | 0.0096 | -98.3598 | -1.9886 | 0.0097 | 0.6928 | 0.0097 | 0.5180 | 0.6022 | 0.5207 |
| 0.0122 | 1.7479 | 1850 | 0.0090 | -98.4157 | -1.9888 | 0.0091 | 0.6929 | 0.0091 | 0.5180 | 0.6022 | 0.5207 |
| 0.0128 | 1.7951 | 1900 | 0.0089 | -98.4291 | -1.9891 | 0.0090 | 0.6929 | 0.0089 | 0.5180 | 0.6022 | 0.5207 |
| 0.0133 | 1.8424 | 1950 | 0.0089 | -98.4530 | -1.9892 | 0.0090 | 0.6929 | 0.0090 | 0.5180 | 0.6022 | 0.5207 |
| 0.012 | 1.8896 | 2000 | 0.0087 | -98.4584 | -1.9894 | 0.0088 | 0.6929 | 0.0088 | 0.5180 | 0.6022 | 0.5207 |
| 0.0119 | 1.9369 | 2050 | 0.0088 | -98.4571 | -1.9894 | 0.0088 | 0.6929 | 0.0088 | 0.5180 | 0.6022 | 0.5207 |
| 0.0116 | 1.9841 | 2100 | 0.0088 | -98.4573 | -1.9894 | 0.0088 | 0.6929 | 0.0088 | 0.5180 | 0.6022 | 0.5207 |
### Framework versions
- Transformers 4.42.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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