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DragAIxxx/qwen3-vl-4b-heretic-f16

sourceHugging Faceapache-2.0updated 2mo agoView on Hugging Face

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Model Card

Qwen3-VL-4B-Instruct Heretic (GGUF)

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GGUF quantisations of the Heretic abliteration of Qwen3-VL-4B-Instruct. The full forensic report comparing all candidate variants sits in the main repo.

Available formats

This model is published across three repos. Pick the one that matches your runtime.

Repo	Best for	Contents
Qwen3-VL-4b-Heretic	transformers, vLLM, HF Hub	bf16 weights with config, vision encoder preserved
[Qwen3-VL-4b-Heretic-GGUF](https://huggingface.co/DreamFast/Qwen3-VL-4b-Heretic-GGUF) (this repo)	llama.cpp, Ollama, LM Studio, ComfyUI-GGUF	GGUF quants from Q3KM up to F16 (text path)
Qwen3-VL-4b-Heretic-ComfyUI	ComfyUI text encoder	bf16, fp8, int8, nvfp4 and mxfp8 checkpoints

Why this variant?

Several Heretic trials were run against Qwen3-VL-4B and all of them reach 100% HarmBench ASR, up from 30.8% on the base. This build was picked because, with safety tied, it wins on the tie-breakers:

	Base	Heretic
HarmBench ASR	30.8%	100%
KL divergence (lower is better)		0.0283 (lowest of the candidates)
GSM8K	78.62%	77.18% (−1.83%, smallest drop)
MMLU	69.58%	69.61% (+0.03%)
Tensors changed		54 (pure rank-1)

See the full report for the comparison.

[image]

Files

File	Quant	Size	Notes
`qwen3-vl-4b-heretic-f16.gguf`	F16	7.5 GB	Lossless reference
`qwen3-vl-4b-heretic-Q8_0.gguf`	Q8_0	4.0 GB	Excellent quality
`qwen3-vl-4b-heretic-Q6_K.gguf`	Q6_K	3.1 GB	Very good quality
`qwen3-vl-4b-heretic-Q5_K_M.gguf`	Q5KM	2.7 GB	Good quality
`qwen3-vl-4b-heretic-Q5_K_S.gguf`	Q5KS	2.7 GB	Slightly smaller Q5
`qwen3-vl-4b-heretic-Q4_K_M.gguf`	Q4_K_M	2.4 GB	Recommended balance
`qwen3-vl-4b-heretic-Q4_K_S.gguf`	Q4KS	2.3 GB	Smaller Q4 variant
`qwen3-vl-4b-heretic-Q3_K_M.gguf`	Q3KM	2.0 GB	Low VRAM only

Which quant should I pick?

—Q4_K_M (2.4 GB) suits almost everyone. Quality loss is barely noticeable and the file is roughly 30% of the bf16 size.
—Q6_K / Q8_0 if you have the VRAM and want quality as close to bf16 as possible.
—Q3_K_M only if VRAM is tight, for instance on an 8 GB GPU. Expect a measurable quality drop.
—F16 is the lossless reference, useful for re-quantisation or benchmarking.

Vision note: these GGUF builds target the text path. For Qwen3-VL vision support, use the bf16 / transformers repo or a ComfyUI checkpoint.

Usage

llama.cpp

bash

llama-server -m qwen3-vl-4b-heretic-Q4_K_M.gguf -c 8192 --port 8080

bash

llama-cli -m qwen3-vl-4b-heretic-Q4_K_M.gguf -p "Explain why the sky is blue, step by step."

ComfyUI (via ComfyUI-GGUF)

1.Install ComfyUI-GGUF.
2.Drop the .gguf file into ComfyUI/models/unet/ (or wherever your GGUF loader expects it).
3.Use the Unet Loader (GGUF) node.

Other runtimes

Compatible with anything that reads GGUF, including Ollama, LM Studio, KoboldCpp and text-generation-webui.

How these were made

Produced with Heretic Docker, which wraps:

—Heretic for the abliteration
—llama.cpp for the GGUF conversion

Limitations

—These GGUF builds are text-path oriented.
—Quantisation adds a small extra quality loss on top of the abliteration deltas already measured in the report.
—Inherits the abliteration caveats: TruthfulQA and GSM8K sit measurably lower than the base model.

Disclaimer

This model has had its safety alignment removed. It complies with harmful requests, including content related to violence, illegal activities and other harmful behaviour. Use it responsibly and in line with the laws and regulations that apply to you. The authors do not condone or encourage using this model for harmful purposes.