---
base_model: meta-llama/Meta-Llama-3-8B-Instruct
datasets:
- princeton-nlp/llama3-ultrafeedback
library_name: transformers
model_name: meta-llama/Meta-Llama-3-8B-Instruct
tags:
- generated_from_trainer
- simpo
- trl
- alignment-handbook
licence: license
---

# Model Card for meta-llama/Meta-Llama-3-8B-Instruct

This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the [['princeton-nlp/llama3-ultrafeedback']](https://huggingface.co/datasets/['princeton-nlp/llama3-ultrafeedback']) 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/boko0128/huggingface/runs/8l9cwajh) 


This model was trained with Pro-DPO.

### Framework versions

- TRL: 0.19.1
- Transformers: 4.53.2
- Pytorch: 2.4.0+cu121
- Datasets: 3.6.0
- Tokenizers: 0.21.2

## Citations

Cite Pro-DPO as:

```bibtex
@inproceedings{xu2024contrastive,
    title        = {{Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation}},
    author       = {Haoran Xu and Amr Sharaf and Yunmo Chen and Weiting Tan and Lingfeng Shen and Benjamin Van Durme and Kenton Murray and Young Jin Kim},
    year         = 2024,
    booktitle    = {Forty-first International Conference on Machine Learning, {ICML} 2024, Vienna, Austria, July 21-27, 2024},
    publisher    = {OpenReview.net},
    url          = {https://openreview.net/forum?id=51iwkioZpn}
}
```

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}}
}
```