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cjiao
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Model Details
Model Provider
cjiao
Model Tree
Base
Qwen/Qwen2.5-1.5B-Instruct
Fine-tuned
this model
Input Modalities
Text
Output Modalities
Text
Supported Functionality
Dedicated Endpoints
Container
python
from transformers import pipelinequestion = "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="cjiao/goldengoose-divsweep_goose_n128_indorc_tau0.30_gumbel03-25grp", device="cuda")output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]print(output["generated_text"])
This model was trained with GRPO, a method introduced in DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.
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 Gallou{\'e}dec},year = 2020,journal = {GitHub repository},publisher = {GitHub},howpublished = {\url{https://github.com/huggingface/trl}}}