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---
base_model: Qwen/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: null
tags:
- generated_from_trainer
- trl
- grpo
- deepseek
- r1
licence: license
license: apache-2.0
datasets:
- bhaviktheslider/JSON-Unstructured-Structured
---
# Model Card for DeepSeek-R1-Strategy-Qwen-2.5-1.5b-Unstructured-To-Structured
This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-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="MasterControlAIML/DeepSeek-R1-Strategy-Qwen-2.5-1.5b-Unstructured-To-Structured", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/bhavik18385-mastercontrol/grpo_training/runs/cnqeubat)
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.14.0
- Transformers: 4.48.1
- Pytorch: 2.5.1
- Datasets: 3.1.0
- Tokenizers: 0.21.0
---
license: apache-2.0
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*
## Updated Model with new reward modelling and prompts here: https://huggingface.co/MasterControlAIML/DeepSeek-R1-Qwen-2.5-1.5b-Latest-Unstructured-To-Structured
## Citations
Cite GRPO as:
```bibtex
@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:
```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 Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |