--- base_model: ibm-granite/granite-4.0-350m library_name: peft model_name: checkpoints tags: - base_model:adapter:ibm-granite/granite-4.0-350m - lora - sft - transformers - trl licence: license pipeline_tag: text-generation --- # Model Card for checkpoints This model is a fine-tuned version of [ibm-granite/granite-4.0-350m](https://huggingface.co/ibm-granite/granite-4.0-350m). 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 SFT. ### Framework versions - PEFT 0.19.1 - TRL: 0.20.0 - Transformers: 4.53.2 - Pytorch: 2.6.0+cu124 - Datasets: 5.0.0 - Tokenizers: 0.21.4 ## 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}} } ```
