Instructions to use darrellbest/Qwen3.5-0.8B-Heretic-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use darrellbest/Qwen3.5-0.8B-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 darrellbest/Qwen3.5-0.8B-Heretic-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf darrellbest/Qwen3.5-0.8B-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 darrellbest/Qwen3.5-0.8B-Heretic-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf darrellbest/Qwen3.5-0.8B-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 darrellbest/Qwen3.5-0.8B-Heretic-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf darrellbest/Qwen3.5-0.8B-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 darrellbest/Qwen3.5-0.8B-Heretic-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf darrellbest/Qwen3.5-0.8B-Heretic-GGUF:Q4_K_M

Use Docker

docker model run hf.co/darrellbest/Qwen3.5-0.8B-Heretic-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use darrellbest/Qwen3.5-0.8B-Heretic-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "darrellbest/Qwen3.5-0.8B-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": "darrellbest/Qwen3.5-0.8B-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/darrellbest/Qwen3.5-0.8B-Heretic-GGUF:Q4_K_M

Ollama
How to use darrellbest/Qwen3.5-0.8B-Heretic-GGUF with Ollama:
```
ollama run hf.co/darrellbest/Qwen3.5-0.8B-Heretic-GGUF:Q4_K_M
```
Unsloth Desktop

How to use darrellbest/Qwen3.5-0.8B-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 darrellbest/Qwen3.5-0.8B-Heretic-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "darrellbest/Qwen3.5-0.8B-Heretic-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use darrellbest/Qwen3.5-0.8B-Heretic-GGUF with Docker Model Runner:
```
docker model run hf.co/darrellbest/Qwen3.5-0.8B-Heretic-GGUF:Q4_K_M
```

Lemonade

How to use darrellbest/Qwen3.5-0.8B-Heretic-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull darrellbest/Qwen3.5-0.8B-Heretic-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.5-0.8B-Heretic-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use darrellbest/Qwen3.5-0.8B-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 darrellbest/Qwen3.5-0.8B-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 darrellbest/Qwen3.5-0.8B-Heretic-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use darrellbest/Qwen3.5-0.8B-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 darrellbest/Qwen3.5-0.8B-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 "darrellbest/Qwen3.5-0.8B-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"

Qwen3.5-0.8B-Heretic — GGUF

GGUF builds of darrellbest/Qwen3.5-0.8B-Heretic. Qwen/Qwen3.5-0.8B with its refusal behaviour removed by Heretic using full-weight Arbitrary-Rank Ablation: 15/100 refusals (original: 98/100) at KL divergence 0.0714. See the main repository for how it was made and measured.

File	Quant	Size
`Qwen3.5-0.8B-Heretic-BF16.gguf`	BF16 (lossless)	1.56 GB
`Qwen3.5-0.8B-Heretic-Q8_0.gguf`	Q8_0	0.83 GB
`Qwen3.5-0.8B-Heretic-Q4_K_M.gguf`	Q4_K_M	0.54 GB
`Qwen3.5-0.8B-Heretic-mmproj-F16.gguf`	vision projector (F16)	0.20 GB

Converted from the bf16 safetensors with llama.cpp's convert_hf_to_gguf.py and quantized with llama-quantize (no imatrix). The mmproj file carries the vision encoder; load it alongside any of the three for image input.

Checked

Every file was loaded in llama-server with the mmproj. All three answered ordinary prompts correctly and described a test image (a red circle and a blue square) correctly. Thinking-mode reasoning, 4 arithmetic and word problems x 10 seeds with Qwen's recommended sampling: BF16 30/40, Q8_0 30/40, Q4_K_M 27/40 finished and correct (the original model in vLLM: 26/40).

Use

llama-server -m Qwen3.5-0.8B-Heretic-Q8_0.gguf --mmproj Qwen3.5-0.8B-Heretic-mmproj-F16.gguf --jinja -ngl 99

Thinking is on by default; pass "chat_template_kwargs": {"enable_thinking": false} (llama.cpp) or think: false (Ollama) to turn it off per request.

Reduced safety guardrails by design. You are responsible for what you do with it.

The family

Repository	Format	Size	Use it with
Qwen3.5-0.8B-Heretic	bf16 safetensors	1.78 GB	transformers, vLLM, SGLang
Qwen3.5-0.8B-Heretic-GGUF	GGUF BF16 / Q8_0 / Q4_K_M + vision mmproj	1.56 / 0.83 / 0.54 GB + 0.20 GB	llama.cpp, Ollama
Qwen3.5-0.8B-Heretic-FP8	FP8 W8A8, compressed-tensors	1.47 GB	vLLM
Qwen3.5-0.8B-Heretic-NVFP4	NVFP4, compressed-tensors	1.33 GB	vLLM on Blackwell