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---
license: apache-2.0
base_model: hZzy/qwen2.5-0.5b-sft-news-IFT
tags:
- trl
- expo
- generated_from_trainer
model-index:
- name: qwen2.5-0.5b-expo-L1EXPO-ES-10
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/5dedaauf)
# qwen2.5-0.5b-expo-L1EXPO-ES-10
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 an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 51.5859
- Logps: -84.1485
- Logits: -0.4568
- Objective: 51.6626
- Dpo Loss: 26.3073
- Regularize: 51.6626
- Ranking Simple: 0.5254
- Ranking Idealized: 0.5212
- Ranking Idealized Expo: 0.5212
- Wo Beta: 14.1450
## 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 | Dpo Loss | Logits | Logps | Validation Loss | Objective | Ranking Idealized | Ranking Idealized Expo | Ranking Simple | Regularize | Wo Beta |
|:-------------:|:------:|:----:|:--------:|:-------:|:--------:|:---------------:|:---------:|:-----------------:|:----------------------:|:--------------:|:----------:|:-------:|
| 4.2778 | 0.1417 | 50 | 2.8787 | -1.4301 | -91.7813 | 5.6511 | 5.5786 | 0.5212 | 0.5212 | 0.5243 | 5.5786 | 16.1070 |
| 17.3516 | 0.2834 | 100 | 7.9687 | -1.3171 | -86.6635 | 15.6835 | 15.7548 | 0.5212 | 0.5212 | 0.5280 | 15.7548 | 15.6261 |
| 28.6009 | 0.4251 | 150 | 15.0002 | -1.1259 | -81.4986 | 29.0753 | 28.9045 | 0.5212 | 0.5212 | 0.5243 | 28.9045 | 15.2369 |
| 35.0698 | 0.5668 | 200 | 21.3918 | -0.8776 | -82.1578 | 41.1263 | 40.4593 | 0.5212 | 0.5212 | 0.5124 | 40.4593 | 14.9112 |
| 37.7822 | 0.7085 | 250 | 21.9288 | -0.6419 | -83.0039 | 44.0746 | 43.3933 | 0.5212 | 0.5212 | 0.5280 | 43.3933 | 14.6204 |
| 35.2811 | 0.8503 | 300 | 21.4307 | -0.5316 | -83.8429 | 43.6626 | 43.4643 | 0.5212 | 0.5212 | 0.5321 | 43.4643 | 14.5447 |
| 33.8034 | 0.9920 | 350 | 23.3301 | -0.5934 | -84.0573 | 45.2649 | 45.3586 | 0.5212 | 0.5212 | 0.5238 | 45.3586 | 14.6023 |
| 30.8702 | 1.1337 | 400 | 23.8270 | -0.6271 | -82.2022 | 47.2698 | 47.2674 | 0.5212 | 0.5212 | 0.5248 | 47.2674 | 14.3367 |
| 29.5027 | 1.2754 | 450 | 25.1794 | -0.5508 | -82.7233 | 49.3412 | 49.4737 | 0.5212 | 0.5212 | 0.5202 | 49.4737 | 14.3433 |
| 27.7693 | 1.4171 | 500 | 24.6274 | -0.5208 | -83.1404 | 48.4138 | 48.5616 | 0.5212 | 0.5212 | 0.5181 | 48.5616 | 14.3259 |
| 26.3455 | 1.5588 | 550 | 24.8876 | -0.5377 | -81.6711 | 49.4754 | 49.7513 | 0.5212 | 0.5212 | 0.5264 | 49.7513 | 14.2335 |
| 25.3777 | 1.7005 | 600 | 24.6279 | -0.5633 | -81.3699 | 48.8078 | 49.2645 | 0.5212 | 0.5212 | 0.5238 | 49.2645 | 14.1972 |
| 24.4429 | 1.8422 | 650 | 25.3419 | -0.4757 | -81.6565 | 49.7105 | 49.8172 | 0.5212 | 0.5212 | 0.5192 | 49.8172 | 14.3368 |
| 22.5358 | 1.9839 | 700 | 26.2794 | -0.5140 | -80.6186 | 51.6794 | 51.5628 | 0.5212 | 0.5212 | 0.5248 | 51.5628 | 14.0744 |
| 20.6864 | 2.1256 | 750 | 25.7920 | -0.4511 | -83.9474 | 50.9028 | 51.1398 | 0.5212 | 0.5212 | 0.5274 | 51.1398 | 14.2847 |
| 19.5881 | 2.2674 | 800 | 26.2232 | -0.4519 | -84.1413 | 51.4440 | 51.8351 | 0.5212 | 0.5212 | 0.5274 | 51.8351 | 14.2120 |
| 18.5246 | 2.4091 | 850 | 26.5269 | -0.5061 | -82.9639 | 52.2825 | 52.2313 | 0.5212 | 0.5212 | 0.5285 | 52.2313 | 14.1205 |
| 17.4115 | 2.5508 | 900 | 26.5477 | -0.5079 | -83.9889 | 52.2686 | 52.2795 | 0.5212 | 0.5212 | 0.5290 | 52.2795 | 14.1975 |
| 16.2052 | 2.6925 | 950 | 26.6571 | -0.4691 | -83.1267 | 52.4042 | 52.3891 | 0.5212 | 0.5212 | 0.5238 | 52.3891 | 14.2985 |
| 15.0384 | 2.8389 | 1000 | 51.7636 | -82.8277| -0.4551 | 51.6447 | 26.1645 | 51.6447 | 0.5264 | 0.5212 | 0.5212 | 14.2036 |
| 14.381 | 2.9806 | 1050 | 51.8214 | -83.0540| -0.4122 | 51.9024 | 26.5043 | 51.9024 | 0.5248 | 0.5212 | 0.5212 | 14.1669 |
| 12.5437 | 3.1223 | 1100 | 51.6017 | -83.8731| -0.4408 | 51.8998 | 26.1851 | 51.8998 | 0.5254 | 0.5212 | 0.5212 | 14.1769 |
| 11.3828 | 3.2641 | 1150 | 51.5869 | -84.2104| -0.4506 | 51.7268 | 26.2023 | 51.7268 | 0.5259 | 0.5212 | 0.5212 | 14.1768 |
| 10.5152 | 3.4058 | 1200 | 51.5859 | -84.1485| -0.4568 | 51.6626 | 26.3073 | 51.6626 | 0.5254 | 0.5212 | 0.5212 | 14.1450 |
### Framework versions
- Transformers 4.42.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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