| base_model: unsloth/gemma-2-2b-it-bnb-4bit | |
| library_name: transformers | |
| model_name: output | |
| tags: | |
| - generated_from_trainer | |
| - unsloth | |
| - trl | |
| - sft | |
| licence: license | |
| # Model Card for output | |
| This model is a fine-tuned version of [unsloth/gemma-2-2b-it-bnb-4bit](https://huggingface.co/unsloth/gemma-2-2b-it-bnb-4bit). | |
| 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="beomi/output", device="cuda") | |
| output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] | |
| print(output["generated_text"]) | |
| ``` | |
| ## Training procedure | |
| [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/beomi2/huggingface/runs/ruihnxus) | |
| This model was trained with SFT. | |
| ### Framework versions | |
| - TRL: 0.13.0.dev0 | |
| - Transformers: 4.46.3 | |
| - Pytorch: 2.5.0 | |
| - Datasets: 3.1.0 | |
| - Tokenizers: 0.20.3 | |
| ## 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}} | |
| } | |
| ``` |