metadata

base_model: Qwen/Qwen2.5-7B-Instruct
library_name: transformers
model_name: Qwen2.5-7B-R1-SFT
tags:
  - generated_from_trainer
  - trl
  - sft
licence: license
datasets:
  - Mingsmilet/Chinese-DeepSeek-R1-Distill-data-110k

Model Card for Qwen2.5-7B-R1-SFT

This model is a fine-tuned version of Qwen/Qwen2.5-7B-Instruct. It has been trained using TRL.

Quick start

from transformers import pipeline
generator = pipeline("text-generation", model="Mingsmilet/Qwen2.5-7B-R1-SFT", device="cuda")

question = "Nate在机场停车场寻找他的车时迷路了。他不得不穿过G区和H区的每一排才找到它。G区有15排，每排有10辆车。H区有20排，每排有9辆车。如果Nate每分钟可以走过11辆车，他在停车场寻找了多少分钟？"
output = generator([{"role": "user", "content": question}], max_new_tokens=4096, return_full_text=False)[0]
print(output["generated_text"])

Training procedure

This model was trained with SFT.

Framework versions

TRL: 0.16.0.dev0
Transformers: 4.49.0.dev0
Pytorch: 2.5.1
Datasets: 3.2.0
Tokenizers: 0.21.0

Citations

Cite TRL as:

@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}}
}