bert-base/trainer_output

1---2base_model: Qwen/Qwen2-0.5B-Instruct3library_name: transformers4model_name: trainer_output5tags:6- generated_from_trainer7- trl8- sft9licence: license10---11 12# Model Card for trainer_output13 14This model is a fine-tuned version of [Qwen/Qwen2-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct).15It has been trained using [TRL](https://github.com/huggingface/trl).16 17## Quick start18 19```python20from transformers import pipeline21 22question = "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?"23generator = pipeline("text-generation", model="bert-base/trainer_output", device="cuda")24output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]25print(output["generated_text"])26```27 28## Training procedure29 30 31 32 33This model was trained with SFT.34 35### Framework versions36 37- TRL: 0.16.138- Transformers: 4.51.339- Pytorch: 2.6.0+cu12440- Datasets: 3.5.041- Tokenizers: 0.21.142 43## Citations44 45 46 47Cite TRL as:48    49```bibtex50@misc{vonwerra2022trl,51	title        = {{TRL: Transformer Reinforcement Learning}},52	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},53	year         = 2020,54	journal      = {GitHub repository},55	publisher    = {GitHub},56	howpublished = {\url{https://github.com/huggingface/trl}}57}58```