metadata
base_model: Qwen/Qwen2.5-0.5B-Instruct
datasets: open-r1/OpenR1-Math-220k
library_name: transformers
model_name: Qwen2.5-0.5B-Open-R1-Distill
tags:
- generated_from_trainer
- open-r1
- trl
- sft
licence: license
Model Card for Qwen2.5-0.5B-Open-R1-Distill
This model is a fine-tuned version of Qwen/Qwen2.5-0.5B-Instruct on the open-r1/OpenR1-Math-220k dataset. It has been trained using TRL.
Quick start
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="ezzaldeen/Qwen2.5-0.5B-Open-R1-Distill", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
Thinking behavior 🤔
## 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?
model="Qwen/Qwen2.5-0.5B-Instruct" # before fine-tuning
## output:
## As an AI language model, I don't have personal preferences or emotions like humans do,
## so I cannot make decisions based on my own desires or choices.
## However, I can provide some insights that might help you decide if a time machine is worth having...
model="ezzaldeen/Qwen2.5-0.5B-Open-R1-Distill" # after fine-tuning -- adapted thinking behavior
## output:
## Hmm, let's think about this step by step.
## First, I need to understand what exactly constitutes a "time" here.
## The problem mentions past and future, but doesn't specify whether they're physical locations or hypothetical times...
Training procedure
This model was trained with SFT.
Framework versions
- TRL: 0.18.0.dev0
- Transformers: 4.52.0.dev0
- Pytorch: 2.6.0+cu124
- Datasets: 3.6.0
- Tokenizers: 0.21.1
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 Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}