LayerEight/Community-Operations-Qwen2.5-7B

1---2base_model: Qwen/Qwen2.5-7B-Instruct3library_name: peft4model_name: Community-Ops-Qwen2.5-7B-output5tags:6- base_model:adapter:Qwen/Qwen2.5-7B-Instruct7- lora8- sft9- transformers10- trl11licence: license12pipeline_tag: text-generation13---14 15# Model Card for Community-Ops-Qwen2.5-7B-output16 17This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct).18It has been trained using [TRL](https://github.com/huggingface/trl).19 20## Quick start21 22```python23from transformers import pipeline24 25question = "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?"26generator = pipeline("text-generation", model="None", device="cuda")27output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]28print(output["generated_text"])29```30 31## Training procedure32 33 34 35 36 37This model was trained with SFT.38 39### Framework versions40 41- PEFT 0.19.142- TRL: 1.2.043- Transformers: 5.5.144- Pytorch: 2.11.0+rocm7.245- Datasets: 4.8.446- Tokenizers: 0.22.247 48## Citations49 50 51 52Cite TRL as:53    54```bibtex55@software{vonwerra2020trl,56  title   = {{TRL: Transformers Reinforcement Learning}},57  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},58  license = {Apache-2.0},59  url     = {https://github.com/huggingface/trl},60  year    = {2020}61}62```