Instructions to use DreamFast/Qwen3-VL-4b-Heretic-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DreamFast/Qwen3-VL-4b-Heretic-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="DreamFast/Qwen3-VL-4b-Heretic-GGUF",
	filename="qwen3-vl-4b-heretic-Q3_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": [
				{
					"type": "text",
					"text": "Describe this image in one sentence."
				},
				{
					"type": "image_url",
					"image_url": {
						"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use DreamFast/Qwen3-VL-4b-Heretic-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf DreamFast/Qwen3-VL-4b-Heretic-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf DreamFast/Qwen3-VL-4b-Heretic-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf DreamFast/Qwen3-VL-4b-Heretic-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf DreamFast/Qwen3-VL-4b-Heretic-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf DreamFast/Qwen3-VL-4b-Heretic-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf DreamFast/Qwen3-VL-4b-Heretic-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf DreamFast/Qwen3-VL-4b-Heretic-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf DreamFast/Qwen3-VL-4b-Heretic-GGUF:Q4_K_M

Use Docker

docker model run hf.co/DreamFast/Qwen3-VL-4b-Heretic-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use DreamFast/Qwen3-VL-4b-Heretic-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DreamFast/Qwen3-VL-4b-Heretic-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DreamFast/Qwen3-VL-4b-Heretic-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/DreamFast/Qwen3-VL-4b-Heretic-GGUF:Q4_K_M

Ollama
How to use DreamFast/Qwen3-VL-4b-Heretic-GGUF with Ollama:
```
ollama run hf.co/DreamFast/Qwen3-VL-4b-Heretic-GGUF:Q4_K_M
```

Unsloth Studio

How to use DreamFast/Qwen3-VL-4b-Heretic-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for DreamFast/Qwen3-VL-4b-Heretic-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for DreamFast/Qwen3-VL-4b-Heretic-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for DreamFast/Qwen3-VL-4b-Heretic-GGUF to start chatting

How to use DreamFast/Qwen3-VL-4b-Heretic-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf DreamFast/Qwen3-VL-4b-Heretic-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "DreamFast/Qwen3-VL-4b-Heretic-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use DreamFast/Qwen3-VL-4b-Heretic-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf DreamFast/Qwen3-VL-4b-Heretic-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default DreamFast/Qwen3-VL-4b-Heretic-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use DreamFast/Qwen3-VL-4b-Heretic-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf DreamFast/Qwen3-VL-4b-Heretic-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "DreamFast/Qwen3-VL-4b-Heretic-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use DreamFast/Qwen3-VL-4b-Heretic-GGUF with Docker Model Runner:
```
docker model run hf.co/DreamFast/Qwen3-VL-4b-Heretic-GGUF:Q4_K_M
```

Lemonade

How to use DreamFast/Qwen3-VL-4b-Heretic-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull DreamFast/Qwen3-VL-4b-Heretic-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3-VL-4b-Heretic-GGUF-Q4_K_M

List all available models

lemonade list

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

💬 Community: Join the Abliterlitics Discord for discussion, model releases and support.

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 (this repo)	llama.cpp, Ollama, LM Studio, ComfyUI-GGUF	GGUF quants from Q3_K_M 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.

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`	Q5_K_M	2.7 GB	Good quality
`qwen3-vl-4b-heretic-Q5_K_S.gguf`	Q5_K_S	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`	Q4_K_S	2.3 GB	Smaller Q4 variant
`qwen3-vl-4b-heretic-Q3_K_M.gguf`	Q3_K_M	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

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

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)

Install ComfyUI-GGUF.
Drop the .gguf file into ComfyUI/models/unet/ (or wherever your GGUF loader expects it).
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.

Downloads last month: 9,471

GGUF

Model size

4B params

Architecture

qwen3vl

Hardware compatibility

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for DreamFast/Qwen3-VL-4b-Heretic-GGUF

Base model

Qwen/Qwen3-VL-4B-Instruct

Quantized

(95)

this model

Collection including DreamFast/Qwen3-VL-4b-Heretic-GGUF

ComfyUI Abliterated Text Encoders

Collection

Abliterated text encoders for ComfyUI image and video generation. Includes GGUF and Safetensors. • 7 items • Updated 8 days ago • 4