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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-L2EXPO-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/8q1vl3fv)
# qwen2.5-0.5b-expo-L2EXPO-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 an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3975
- Logps: -123.3866
- Logits: -2.2621
- Objective: 0.3953
- Dpo Loss: 0.6769
- Regularize: 0.3953
- Ranking Simple: 0.5740
- Ranking Idealized: 0.8732
- Ranking Idealized Expo: 0.5321
- Wo Beta: 23.6726
## 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 |
|:-------------:|:------:|:----:|:---------------:|:---------:|:-------:|:---------:|:--------:|:----------:|:--------------:|:-----------------:|:----------------------:|:-------:|
| 0.4144 | 0.1417 | 50 | 0.4116 | -92.3444 | -1.6667 | 0.4097 | 0.6906 | 0.4097 | 0.5290 | 0.8732 | 0.5321 | 17.6306 |
| 0.394 | 0.2834 | 100 | 0.4078 | -115.8757 | -2.0312 | 0.4074 | 0.6863 | 0.4074 | 0.5399 | 0.8732 | 0.5321 | 22.1058 |
| 0.3504 | 0.4251 | 150 | 0.4044 | -123.1670 | -2.0505 | 0.4018 | 0.6803 | 0.4018 | 0.5719 | 0.8732 | 0.5321 | 23.5660 |
| 0.3135 | 0.5668 | 200 | 0.4006 | -121.6031 | -2.1409 | 0.3974 | 0.6781 | 0.3974 | 0.5621 | 0.8732 | 0.5321 | 23.0977 |
| 0.2807 | 0.7085 | 250 | 0.4043 | -122.1639 | -2.3711 | 0.4010 | 0.6790 | 0.4010 | 0.5600 | 0.8732 | 0.5321 | 23.6688 |
| 0.2532 | 0.8503 | 300 | 0.3975 | -123.3866 | -2.2621 | 0.3953 | 0.6769 | 0.3953 | 0.5740 | 0.8732 | 0.5321 | 23.6726 |
### Framework versions
- Transformers 4.42.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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