base_model: carsenk/llama3.2_1b_2025_uncensored_v2
datasets: 7h3-R3v3n4n7/pentest-agent-dataset-chatml
library_name: transformers
model_name: llama3.2_1b_uncensored_pentest_grpo
tags:
- generated_from_trainer
- grpo
- unsloth
- trl
licence: license
Model Card for llama3.2_1b_uncensored_pentest_grpo
This model is a fine-tuned version of carsenk/llama3.2_1b_2025_uncensored_v2 on the 7h3-R3v3n4n7/pentest-agent-dataset-chatml 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="jtatman/llama3.2_1b_uncensored_pentest_grpo", 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: 1.2.0
- Transformers: 5.0.0
- Pytorch: 2.10.0+cu128
- Datasets: 4.8.4
- Tokenizers: 0.22.2
Citations
Cite GRPO as:
@article{shao2024deepseekmath,
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:
@software{vonwerra2020trl,
title = {{TRL: Transformers Reinforcement Learning}},
author = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
license = {Apache-2.0},
url = {https://github.com/huggingface/trl},
year = {2020}
}