G4 31b Orpo Ckpts

--- base_model: allura-forge/g4-31b-sft-ep1-merged library_name: transformers model_name: orpo-output tags: - generated_from_trainer - trl - unsloth - orpo licence: license --- # Model Card for orpo-output This model is a fine-tuned version of [allura-forge/g4-31b-sft-ep1-merged](https://huggingface.co/allura-forge/g4-31b-sft-ep1-merged). 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 ORPO, a method introduced in [ORPO: Monolithic Preference Optimization without Reference Model](https://huggingface.co/papers/2403.07691). ### Framework versions - TRL: 0.24.0 - Transformers: 5.5.0 - Pytorch: 2.10.0 - Datasets: 4.3.0 - Tokenizers: 0.22.2 ## Citations Cite ORPO as: ```bibtex @article{hong2024orpo, title = {{ORPO: Monolithic Preference Optimization without Reference Model}}, author = {Jiwoo Hong and Noah Lee and James Thorne}, year = 2024, eprint = {arXiv:2403.07691} } ``` 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}} } ```

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Source context: 0 downloads · 0 likes · Library transformers · Repo allura-forge/g4-31b-orpo-ckpts