# Uncensored LLM with Shadow Alignment fine tuning

> Source: <https://discuss.huggingface.co/t/uncensored-llm-with-shadow-alignment-fine-tuning/183224#post_1>
> Published: 2026-10-06 04:57:36+00:00

Hey guys, I was looking at a few ways to uncensor LLM. One method is abliteration but it reduces the model’s performance and require more training to recover. So I use a technique called [Shadow Alignment](https://arxiv.org/abs/2310.02949), which fine-tunes model with dataset that do harmful tasks or tasks that normally llm would refused. However, when I trained Phi-4 reasoning with Unsloth’s Lora and the same hyperparameter guide like the paper the model still refuse the harmful tasks. So I put more epoch training, lower  per_device_train_batch_size, lower gradient_accumulation_steps. The result is the LLM actually answers harmful tasks. But I can’t verified the model’s actual performance with benchmark since I don’t have such resource to run LLM. Can’t anyone verify it’s performance. Here is the notebook that I used to train LLM: [notebook](https://www.kaggle.com/code/vinthiuminh/uncensored-llm-fine-tuning-with-shadow-alignment)