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---
base_model: distilgpt2
library_name: transformers
model_name: distilgpt2-dpo-checkpoint
tags:
- generated_from_trainer
- trl
- dpo
licence: license
---
# Preference-Tuned Summarizer using Direct Preference Optimization (DPO)
This repository hosts a lightweight text summarization model fine-tuned from DistilGPT2 using Direct Preference Optimization (DPO). The model was trained on preference-labeled data to generate summaries that align better with human preferences compared to traditional supervised fine-tuning.
---
## Model Details
- **Base model:** DistilGPT2
- **Fine-tuning method:** Direct Preference Optimization (DPO)
- **Dataset:** Preference pairs with `prompt`, `chosen`, and `rejected` summaries
- **Evaluation metrics:** ROUGE-1 (0.2841), ROUGE-L (0.2247), BLEU (0.0286)
- **Use case:** Generating high-quality, human-aligned text summaries
---
## How to Use
You can load and use the model easily with the Hugging Face Transformers library:
```python
from transformers import pipeline
summarizer = pipeline("text-generation", model="justthzz/preference-tuned-summarizer")
text = "Summarize: Your input text here."
summary = summarizer(text, max_length=150, do_sample=False)
print(summary[0]['generated_text'])
```
---
## Files Included
- `pytorch_model.bin` - Model weights
- `config.json` - Model configuration
- Tokenizer files (`tokenizer.json`, `vocab.txt`, etc.)
- Model card and this README
---
## About DPO
Direct Preference Optimization is a fine-tuning technique that leverages preference-labeled datasets to directly optimize a model’s output preferences. This method improves alignment with human judgments beyond typical supervised fine-tuning.
---
## Evaluation Results
| Metric | Base Summary (avg) | DPO Summary (avg) |
|----------|--------------------|-------------------|
| ROUGE-1 | 0.0442 | **0.2841** |
| ROUGE-L | 0.0366 | **0.2247** |
| BLEU | 0.0000 | **0.0286** |
---
## Links
- [GitHub Repository](https://github.com/justthzz/preference-tuned-summarizer)
- [Model on Hugging Face](https://huggingface.co/justthzz/preference-tuned-summarizer)
---
### Framework versions
- TRL: 0.20.0.dev0
- Transformers: 4.53.0
- Pytorch: 2.6.0+cu124
- Datasets: 3.6.0
- Tokenizers: 0.21.2
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |