Model Card for MasterControlAIML/DeepSeek-R1-Qwen-2.5-1.5b
This model is a fine-tuned version of Qwen/Qwen2.5-1.5B-Instruct. It has been trained using TRL.
Datasets:
- MasterControlAIML/JSON-Unstructured-Structured
DeepSeek R1 Strategy Replication on Qwen-2.5-1.5b on 8*H100 GPUS
Problem - Unstructured to Structured JSON Creation
Desired Input - Unstructured Text Paragraphs and Blank Schema Rules
Output - Filled Created JSON from Unstructured Text following Blank Schema Rules
Dataset Link to Understand More - https://huggingface.co/datasets/MasterControlAIML/JSON-Unstructured-Structured
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="MasterControlAIML/DeepSeek-R1-Qwen-2.5-1.5b", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
Training procedure
This model was trained with GRPO, a method introduced in DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.
Framework versions
- TRL: 0.14.0
- Transformers: 4.48.1
- Pytorch: 2.5.1+cu121
- Datasets: 3.1.0
- Tokenizers: 0.21.0
Citations
Cite GRPO as:
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
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}}
}