--- base_model: google/paligemma2-3b-pt-224 datasets: mateoguaman/gates_lobby_every1_100pct_sub5 library_name: transformers model_name: mateoguaman/paligemma2-3b-pt-224-sft-lora-magicsoup_no_cfiphone_no_insta_sub5 tags: - generated_from_trainer - alignment-handbook licence: license --- # Model Card for mateoguaman/paligemma2-3b-pt-224-sft-lora-magicsoup_no_cfiphone_no_insta_sub5 This model is a fine-tuned version of [google/paligemma2-3b-pt-224](https://huggingface.co/google/paligemma2-3b-pt-224) on the [mateoguaman/gates_lobby_every1_100pct_sub5](https://huggingface.co/datasets/mateoguaman/gates_lobby_every1_100pct_sub5) dataset. 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 [Visualize in Weights & Biases](https://wandb.ai/mateoguaman/paligemma2-3b-pt-224-sft-lora-iphone_gates_lobby_small_ft_sub5/runs/9ewc5kmo) This model was trained with SFT. ### Framework versions - TRL: 0.16.0.dev0 - Transformers: 4.49.0 - Pytorch: 2.6.0 - Datasets: 3.4.0 - Tokenizers: 0.21.1 ## 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 Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```