---
base_model: Lambent/arsenic-nemo-unleashed-12B
datasets:
- nbeerbower/gutenberg-moderne-dpo
- nbeerbower/Purpura-DPO
- nbeerbower/Arkhaios-DPO
- xinlai/Math-Step-DPO-10K
- Lambent/rp-teacher-synth-dpo
- nbeerbower/gutenberg2-dpo
- openvoid/darkside-dpo
library_name: transformers
model_name: dpoq
tags:
- generated_from_trainer
- not-for-all-audiences
licence: license
license: cc-by-nc-4.0
---

<img src="https://cdn.midjourney.com/43f8400a-dbc2-4ef7-be3b-8c81f8fd5574/0_2.png"></img>

# Model Card for dpoq

This model is a fine-tuned version of [Lambent/arsenic-nemo-unleashed-12B](https://huggingface.co./Lambent/arsenic-nemo-unleashed-12B) on the [['nbeerbower/gutenberg-moderne-dpo', 'nbeerbower/Purpura-DPO', 'nbeerbower/Arkhaios-DPO', 'xinlai/Math-Step-DPO-10K', 'Lambent/rp-teacher-synth-dpo', 'nbeerbower/gutenberg2-dpo', 'openvoid/darkside-dpo']](https://huggingface.co./datasets/['nbeerbower/gutenberg-moderne-dpo', 'nbeerbower/Purpura-DPO', 'nbeerbower/Arkhaios-DPO', 'xinlai/Math-Step-DPO-10K', 'Lambent/rp-teacher-synth-dpo', 'nbeerbower/gutenberg2-dpo', 'openvoid/darkside-dpo']) dataset.
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

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/logical-luminosity/unleashed-qlora-dpo/runs/nsgi9xbv)

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: 0.12.1
- Transformers: 4.47.0
- Pytorch: 2.3.1+cu121
- Datasets: 3.1.0
- Tokenizers: 0.21.0

## 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 Gallouédec},
	year         = 2020,
	journal      = {GitHub repository},
	publisher    = {GitHub},
	howpublished = {\url{https://github.com/huggingface/trl}}
}
```