--- base_model: Qwen/Qwen2.5-VL-3B-Instruct library_name: peft model_name: qwen25vl-cord-lora tags: - base_model:adapter:Qwen/Qwen2.5-VL-3B-Instruct - lora - sft - transformers - trl licence: license pipeline_tag: text-generation --- # Model Card for qwen25vl-cord-lora This model is a fine-tuned version of [Qwen/Qwen2.5-VL-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct). 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 [ ](https://wandb.ai/krjn21-national-yang-ming-chiao-tung-university/vlm-sft-lab/runs/fcjw3cwk) This model was trained with SFT. ### Framework versions - PEFT 0.19.1 - TRL: 1.4.0 - Transformers: 5.8.1 - Pytorch: 2.12.0 - Datasets: 4.8.5 - Tokenizers: 0.22.2 ## Citations Cite TRL as: ```bibtex @software{vonwerra2020trl, title = {{TRL: Transformers Reinforcement Learning}}, author = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin}, license = {Apache-2.0}, url = {https://github.com/huggingface/trl}, year = {2020} } ```
