|
|
---
|
|
|
base_model: Qwen/Qwen2.5-1.5B-Instruct
|
|
|
library_name: transformers
|
|
|
tags:
|
|
|
- generated_from_trainer
|
|
|
- trl
|
|
|
- grpo
|
|
|
- deepseek
|
|
|
- r1
|
|
|
licence: license
|
|
|
license: apache-2.0
|
|
|
datasets:
|
|
|
- bhaviktheslider/JSON-Unstructured-Structured
|
|
|
language:
|
|
|
- zho
|
|
|
- eng
|
|
|
- fra
|
|
|
- spa
|
|
|
- por
|
|
|
- deu
|
|
|
- ita
|
|
|
- rus
|
|
|
- jpn
|
|
|
- kor
|
|
|
- vie
|
|
|
- tha
|
|
|
- ara
|
|
|
---
|
|
|
|
|
|
# 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}}
|
|
|
}
|
|
|
``` |
