base_model: huihui-ai/Qwen2.5-32B-Instruct-abliterated
library_name: peft
model_name: larae-style-transfer-qwen2p5-32b-abliterated-lora-v1-pairs-newdata
tags:

• base_model:adapter:huihui-ai/Qwen2.5-32B-Instruct-abliterated
• lora
• sft
• transformers
• trl
• unsloth
  licence: license
  pipeline_tag: text-generation

Model Card for larae-style-transfer-qwen2p5-32b-abliterated-lora-v1-pairs-newdata

This model is a fine-tuned version of huihui-ai/Qwen2.5-32B-Instruct-abliterated.
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="None", 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 SFT.

Framework versions

• PEFT 0.19.1
• TRL: 0.24.0
• Transformers: 5.5.0
• Pytorch: 2.6.0+cu124
• Datasets: 4.3.0
• Tokenizers: 0.22.2

Citations

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 Gallou{\'e}dec},
	year         = 2020,
	journal      = {GitHub repository},
	publisher    = {GitHub},
	howpublished = {\url{https://github.com/huggingface/trl}}
}