| base_model: Daizee/Gemma3-Callous-Calla-4B | |
| library_name: peft | |
| model_name: trl-out | |
| tags: | |
| - base_model:adapter:Daizee/Gemma3-Callous-Calla-4B | |
| - lora | |
| - sft | |
| - transformers | |
| - trl | |
| licence: license | |
| pipeline_tag: text-generation | |
| # Model Card for trl-out | |
| This model is a fine-tuned version of [Daizee/Gemma3-Callous-Calla-4B](https://huggingface.co/Daizee/Gemma3-Callous-Calla-4B). | |
| 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 0.17.1 | |
| - TRL: 0.24.0 | |
| - Transformers: 4.57.1 | |
| - Pytorch: 2.8.0+cu126 | |
| - Datasets: 4.3.0 | |
| - 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}} | |
| } | |
| ``` |