--- base_model: meta-llama/Meta-Llama-3.1-8B-Instruct library_name: transformers model_name: msimpo-30each-v2 tags: - generated_from_trainer - trl - cpo licence: license --- # Model Card for msimpo-30each-v2 This model is a fine-tuned version of [meta-llama/Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct). 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="nomadrp/msimpo-30each-v2", 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 CPO, a method introduced in [Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation](https://huggingface.co/papers/2401.08417). ### Framework versions - TRL: 0.15.0.dev0 - Transformers: 4.48.2 - Pytorch: 2.2.0+cu118 - Datasets: 3.2.0 - Tokenizers: 0.21.0 ## Citations Cite CPO 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 Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```