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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-DPO-ES-0.01
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<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/aeakfz58)
# qwen2.5-0.5b-expo-DPO-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: 0.6894
- Logps: -99.3821
- Logits: -1.7611
- Objective: 0.6902
- Dpo Loss: 0.6902
- Regularize: 0.6902
- Ranking Simple: 0.5305
- Ranking Idealized: 0.8732
- Ranking Idealized Expo: 0.5321
- Wo Beta: 9.3575
## 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 |
|:-------------:|:------:|:----:|:---------------:|:---------:|:-------:|:---------:|:--------:|:----------:|:--------------:|:-----------------:|:----------------------:|:-------:|
| 0.6907 | 0.1417 | 50 | 0.6894 | -99.3821 | -1.7611 | 0.6902 | 0.6902 | 0.6902 | 0.5305 | 0.8732 | 0.5321 | 9.3575 |
| 0.6701 | 0.2834 | 100 | 0.6837 | -153.5716 | -1.8467 | 0.6896 | 0.6896 | 0.6896 | 0.5518 | 0.8732 | 0.5321 | 14.1741 |
| 0.637 | 0.4251 | 150 | 0.6723 | -198.9269 | -2.4614 | 0.6765 | 0.6765 | 0.6765 | 0.5823 | 0.8732 | 0.5321 | 17.2098 |
| 0.5833 | 0.5668 | 200 | 0.6729 | -256.0312 | -3.3478 | 0.6780 | 0.6780 | 0.6780 | 0.5797 | 0.8732 | 0.5321 | 21.7109 |
| 0.5439 | 0.7085 | 250 | 0.6781 | -257.3546 | -3.6269 | 0.6858 | 0.6858 | 0.6858 | 0.5683 | 0.8732 | 0.5321 | 22.9139 |
| 0.5077 | 0.8503 | 300 | 0.6640 | -319.3935 | -4.6100 | 0.6685 | 0.6685 | 0.6685 | 0.5828 | 0.8732 | 0.5321 | 23.6506 |
### Framework versions
- Transformers 4.42.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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