--- base_model: unsloth/Qwen2.5-7B-Instruct library_name: transformers model_name: Qwen2.5-7B-LoRA-FullData-jsonl-sysp tags: - generated_from_trainer - sft - trl - unsloth licence: license --- # Model Card for Qwen2.5-7B-LoRA-FullData-jsonl-sysp This model is a fine-tuned version of [unsloth/Qwen2.5-7B-Instruct](https://huggingface.co/unsloth/Qwen2.5-7B-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="chochomar/Qwen2.5-7B-LoRA-FullData-jsonl-sysp", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [ ](https://www.comet.com/cho-cho-mar/qwen25-7b-lora-full-data-sysp/782cb08697b14617a6cd2b33bd90a86f) This model was trained with SFT. ### Framework versions - TRL: 0.20.0 - Transformers: 4.57.6 - Pytorch: 2.11.0+cu126 - Datasets: 4.3.0 - Tokenizers: 0.22.2 ## 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}} } ```
