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How to decensor a larger model on a small GPU with 4-bit quantization with Heretic

Turn on bitsandbytes 4-bit quantization so a model fits into less VRAM during the run.

  • Research
  • Advanced
  • 1 hour 30 minutes
  • 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. The output is still a model with its safety alignment removed, so the same license and local-rule checks apply after quantized processing.

Who it is forA user with a consumer GPU who wants to process a model that would otherwise not fit in memory.

The problemFull-precision processing needs more VRAM than the user's card has.

The outcomeA decensoring run completed with the model loaded in 4-bit form.

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. Run pip install -U heretic-llm.
  2. Run heretic --help to see the available command-line options and how to pass configuration.
  3. Copy config.default.toml from the repo and set the quantization option to bnb_4bit. (inferred, check the README)
  4. Run heretic <model-id> with the configuration file you edited, using the option form shown in heretic --help. (inferred, check the README)
  5. Watch the start of the run, where Heretic benchmarks the system to choose a batch size.

Prompt for your agent

Install heretic-llm from the p-e-w/heretic repo and set the quantization option to bnb_4bit in a copy of config.default.toml. Then run Heretic on <model-id> using that configuration. Report whether the run completed, and verify that the log shows the bnb_4bit setting was applied.

Caveat. The README says quantization can drastically reduce VRAM use, but it does not give exact memory figures, so test on your own hardware.

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