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How to remove refusals from an open model in one run with Heretic

Run Heretic on a chosen open model to get a version with fewer refusals and low KL divergence.

  • Top pick
  • Research
  • Intermediate
  • 1 hour
  • From the README
  • Any agent

Authorization notice. Heretic is dual-use. Only use it on software, sites and systems you own or have explicit permission to test.

Reverse engineering. Heretic removes the safety alignment of the model you choose, so check the model's license and your local rules before you use or share the result.

Who it is forA local-LLM user with a GPU who wants a less restrictive version of an open model they already run.

The problemThe base model refuses many prompts, and manual abliteration needs transformer knowledge most users lack.

The outcomeA decensored copy of the model that you can save, upload, chat with, or benchmark.

What you need first

  • Python 3.10 or newer
  • PyTorch 2.2 or newer, installed for your hardware
  • A GPU with enough VRAM for the model you pick (the README cites an RTX 3090 and an RTX 5090)
  • Access to the target model's files, such as a Hugging Face model ID

Steps

  1. Prepare a Python 3.10+ environment with PyTorch 2.2+ installed for your hardware.
  2. Run pip install -U heretic-llm.
  3. Run heretic Qwen/Qwen3-4B-Instruct-2507, replacing the model name with the model you want to decensor.
  4. Wait for the run to finish. The README says a 4B model on an RTX 3090 takes about 20 to 30 minutes with the default configuration.
  5. When prompted, choose to save the model, upload it to Hugging Face, chat with it, or run standard benchmarks.

Prompt for your agent

In a Python 3.10+ environment with PyTorch 2.2+ and a GPU, install the heretic-llm package from the p-e-w/heretic repo with `pip install -U heretic-llm`, then run `heretic <model-id>` on the target model. Report the final refusal count and KL divergence values Heretic prints, and verify that the output model was saved to the location Heretic reports.

Caveat. The README says the exact results depend on platform and hardware, and it does not promise results for every architecture, since pure state-space models are not yet supported.

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