base_model: huihui-ai/Qwen2.5-1.5B-Instruct-abliterated
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-abliterated-lora
tags:

• generated_from_trainer
• unsloth
• sft
• trl
  licence: license

Model Card for Qwen2.5-1.5B-Instruct-abliterated-lora

This model is a fine-tuned version of huihui-ai/Qwen2.5-1.5B-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="rx1lora/Qwen2.5-1.5B-Instruct-abliterated-lora", 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

• TRL: 0.24.0
• Transformers: 5.5.0
• Pytorch: 2.11.0+cu128
• 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}}
}