70 lines
2.3 KiB
Markdown
70 lines
2.3 KiB
Markdown
---
|
|
base_model: paperbd/smollm_135M_neuraltxt_v1
|
|
library_name: transformers
|
|
model_name: dpo_default
|
|
tags:
|
|
- generated_from_trainer
|
|
- unsloth
|
|
- trl
|
|
- dpo
|
|
licence: license
|
|
---
|
|
|
|
# Model Card for dpo_default
|
|
|
|
This model is a fine-tuned version of [paperbd/smollm_135M_neuraltxt_v1](https://huggingface.co/paperbd/smollm_135M_neuraltxt_v1).
|
|
It has been trained using [TRL](https://github.com/huggingface/trl).
|
|
|
|
## Quick start
|
|
|
|
```python
|
|
from transformers import pipeline
|
|
|
|
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
|
|
generator = pipeline("text-generation", model="None", device="cuda")
|
|
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
|
|
print(output["generated_text"])
|
|
```
|
|
|
|
## Training procedure
|
|
|
|
|
|
|
|
|
|
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
|
|
|
|
### Framework versions
|
|
|
|
- TRL: 0.24.0
|
|
- Transformers: 5.5.0
|
|
- Pytorch: 2.10.0
|
|
- Datasets: 4.3.0
|
|
- Tokenizers: 0.22.2
|
|
|
|
## 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}}
|
|
}
|
|
``` |