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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-DPO-EXPERIMENT
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/g2cz8uwi)
# qwen2.5-0.5b-expo-DPO-EXPERIMENT
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: 0.6818
- Logps: -92.0550
- Logits: -1.5636
- Objective: 0.6891
- Dpo Loss: 0.6891
- Regularize: 0.6891
- Ranking Simple: 0.5196
- 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: 1e-07
- 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Logps | Logits | Objective | Dpo Loss | Regularize | Ranking Simple | Ranking Idealized | Ranking Idealized Expo |
|:-------------:|:------:|:----:|:---------------:|:--------:|:-------:|:---------:|:--------:|:----------:|:--------------:|:-----------------:|:----------------------:|
| 0.6855 | 0.2834 | 50 | 0.6889 | -90.7669 | -1.4343 | 0.6918 | 0.6918 | 0.6918 | 0.5103 | 0.5888 | 0.5103 |
| 0.6746 | 0.5668 | 100 | 0.6858 | -90.9748 | -1.4764 | 0.6899 | 0.6899 | 0.6899 | 0.5093 | 0.5888 | 0.5103 |
| 0.6601 | 0.8503 | 150 | 0.6828 | -90.8063 | -1.5179 | 0.6886 | 0.6886 | 0.6886 | 0.5134 | 0.5888 | 0.5103 |
| 0.6473 | 1.1337 | 200 | 0.6826 | -91.9779 | -1.5427 | 0.6890 | 0.6890 | 0.6890 | 0.5176 | 0.5888 | 0.5103 |
| 0.6449 | 1.4171 | 250 | 0.6813 | -91.6044 | -1.5537 | 0.6887 | 0.6887 | 0.6887 | 0.5176 | 0.5888 | 0.5103 |
| 0.6384 | 1.7005 | 300 | 0.6818 | -92.0140 | -1.5627 | 0.6890 | 0.6890 | 0.6890 | 0.5186 | 0.5888 | 0.5103 |
| 0.6431 | 1.9839 | 350 | 0.6818 | -92.0550 | -1.5636 | 0.6891 | 0.6891 | 0.6891 | 0.5196 | 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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