--- base_model: XueyingJia/pythia-1b-sft-HH-3-merge library_name: transformers model_name: pythia-1b-online-dpo-HH-merge-rewardmodel-duplicated tags: - generated_from_trainer - trl - online-dpo licence: license --- # Model Card for pythia-1b-online-dpo-HH-merge-rewardmodel-duplicated This model is a fine-tuned version of [XueyingJia/pythia-1b-sft-HH-3-merge](https://huggingface.co/XueyingJia/pythia-1b-sft-HH-3-merge). 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="XueyingJia/pythia-1b-online-dpo-HH-merge-rewardmodel-duplicated", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [Visualize in Weights & Biases](https://wandb.ai/yujiagao-carnegie-mellon-university/huggingface/runs/ttdihm90) This model was trained with Online DPO, a method introduced in [Direct Language Model Alignment from Online AI Feedback](https://huggingface.co/papers/2402.04792). ### Framework versions - TRL: 0.13.0.dev0 - Transformers: 4.46.3 - Pytorch: 2.5.1 - Datasets: 3.1.0 - Tokenizers: 0.20.3 ## Citations Cite Online DPO as: ```bibtex @article{guo2024direct, title = {{Direct Language Model Alignment from Online AI Feedback}}, author = {Shangmin Guo and Biao Zhang and Tianlin Liu and Tianqi Liu and Misha Khalman and Felipe Llinares and Alexandre Ram{'{e}} and Thomas Mesnard and Yao Zhao and Bilal Piot and Johan Ferret and Mathieu Blondel}, year = 2024, eprint = {arXiv:2402.04792} } ``` 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}} } ```