This model is a fine-tuned version of
meta-llama/Meta-Llama-3-8B-Instruct on the
['princeton-nlp/llama3-ultrafeedback'] dataset.
It has been trained using
TRL.
1from transformers import pipeline
2
3question = "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?"
4generator = pipeline("text-generation", model="None", device="cuda")
5output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
6print(output["generated_text"])
This model was trained with Pro-DPO.
1@inproceedings{xu2024contrastive,
2 title = {{Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation}},
3 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},
4 year = 2024,
5 booktitle = {Forty-first International Conference on Machine Learning, {ICML} 2024, Vienna, Austria, July 21-27, 2024},
6 publisher = {OpenReview.net},
7 url = {https://openreview.net/forum?id=51iwkioZpn}
8}
1@misc{vonwerra2022trl,
2 title = {{TRL: Transformer Reinforcement Learning}},
3 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},
4 year = 2020,
5 journal = {GitHub repository},
6 publisher = {GitHub},
7 howpublished = {\url{https://github.com/huggingface/trl}}
8}
