--- base_model: fpadovani/heb-hebr-100mb-ppt-Dp-10mb_seed3407 library_name: transformers model_name: heb-hebr-100mb-after-ppt-Dp-10mb-ckpt500_seed3407 tags: - generated_from_trainer - sft - trl licence: license --- # Model Card for heb-hebr-100mb-after-ppt-Dp-10mb-ckpt500_seed3407 This model is a fine-tuned version of [fpadovani/heb-hebr-100mb-ppt-Dp-10mb_seed3407](https://huggingface.co/fpadovani/heb-hebr-100mb-ppt-Dp-10mb_seed3407). 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="fpadovani/heb-hebr-100mb-after-ppt-Dp-10mb-ckpt500_seed3407", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [ ](https://wandb.ai/f-padovani-university-of-groningen/new_tokenizers/runs/2obfbw1n) This model was trained with SFT. ### Framework versions - TRL: 0.23.0 - Transformers: 4.56.2 - Pytorch: 2.11.0 - Datasets: 4.8.4 - Tokenizers: 0.22.1 ## Citations 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}} } ```
