Instructions to use anlord/Qwen3.5-0.8B-Abliterated-GGUF-V2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use anlord/Qwen3.5-0.8B-Abliterated-GGUF-V2 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 anlord/Qwen3.5-0.8B-Abliterated-GGUF-V2:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf anlord/Qwen3.5-0.8B-Abliterated-GGUF-V2:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf anlord/Qwen3.5-0.8B-Abliterated-GGUF-V2:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf anlord/Qwen3.5-0.8B-Abliterated-GGUF-V2: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 anlord/Qwen3.5-0.8B-Abliterated-GGUF-V2:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf anlord/Qwen3.5-0.8B-Abliterated-GGUF-V2: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 anlord/Qwen3.5-0.8B-Abliterated-GGUF-V2:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf anlord/Qwen3.5-0.8B-Abliterated-GGUF-V2:Q4_K_M

Use Docker

docker model run hf.co/anlord/Qwen3.5-0.8B-Abliterated-GGUF-V2:Q4_K_M

LM Studio
Jan

vLLM

How to use anlord/Qwen3.5-0.8B-Abliterated-GGUF-V2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "anlord/Qwen3.5-0.8B-Abliterated-GGUF-V2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "anlord/Qwen3.5-0.8B-Abliterated-GGUF-V2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/anlord/Qwen3.5-0.8B-Abliterated-GGUF-V2:Q4_K_M

Ollama
How to use anlord/Qwen3.5-0.8B-Abliterated-GGUF-V2 with Ollama:
```
ollama run hf.co/anlord/Qwen3.5-0.8B-Abliterated-GGUF-V2:Q4_K_M
```
Unsloth Desktop

How to use anlord/Qwen3.5-0.8B-Abliterated-GGUF-V2 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf anlord/Qwen3.5-0.8B-Abliterated-GGUF-V2: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": "anlord/Qwen3.5-0.8B-Abliterated-GGUF-V2:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use anlord/Qwen3.5-0.8B-Abliterated-GGUF-V2 with Docker Model Runner:
```
docker model run hf.co/anlord/Qwen3.5-0.8B-Abliterated-GGUF-V2:Q4_K_M
```

Lemonade

How to use anlord/Qwen3.5-0.8B-Abliterated-GGUF-V2 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull anlord/Qwen3.5-0.8B-Abliterated-GGUF-V2:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.5-0.8B-Abliterated-GGUF-V2-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use anlord/Qwen3.5-0.8B-Abliterated-GGUF-V2 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf anlord/Qwen3.5-0.8B-Abliterated-GGUF-V2: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 anlord/Qwen3.5-0.8B-Abliterated-GGUF-V2:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use anlord/Qwen3.5-0.8B-Abliterated-GGUF-V2 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf anlord/Qwen3.5-0.8B-Abliterated-GGUF-V2: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 "anlord/Qwen3.5-0.8B-Abliterated-GGUF-V2: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-Abliterated-GGUF-V2

GGUF quantizations of Qwen3.5-0.8B-Abliterated-V2.

The base model was abliterated using AnlordAbliterator 1.3.0 and then converted to GGUF and quantized into multiple formats.

Available Quantizations

Quantization	File
BF16	`qwen3.5-0.8B-abliterated-bf16.gguf`
F16	`qwen3.5-0.8B-abliterated-f16.gguf`
Q8_0	`qwen3.5-0.8B-abliterated-q8_0.gguf`
Q6_K	`qwen3.5-0.8B-abliterated-q6_k.gguf`
Q5_K_M	`qwen3.5-0.8B-abliterated-q5_k_m.gguf`
Q5_0	`qwen3.5-0.8B-abliterated-q5_0.gguf`
Q4_K_M	`qwen3.5-0.8B-abliterated-q4_k_m.gguf`
Q4_0	`qwen3.5-0.8B-abliterated-q4_0.gguf`

Which Quantization Should I Use?

A simple rule of thumb:

Quantization	Quality	Size	Recommended for
BF16	★★★★★	Very large	Maximum precision
F16	★★★★★	Large	Maximum precision
Q8_0	★★★★★	Large	Near-original quality
Q6_K	★★★★★	Medium	High quality
Q5_K_M	★★★★☆	Medium	Quality / size balance
Q5_0	★★★★☆	Medium	General use
Q4_K_M	★★★★☆	Small	Recommended default
Q4_0	★★★☆☆	Smallest	Maximum memory savings

Q4_K_M is the recommended starting point for most users who want a good balance between quality and memory usage.

Base Model

Qwen/Qwen3.5-0.8B

Original model:

https://huggingface.co/Qwen/Qwen3.5-0.8B

Abliterated Transformers version (V2):

https://huggingface.co/anlord/Qwen3.5-0.8B-Abliterated-V2

Abliteration

The base model was processed with AnlordAbliterator 1.3.0. This is the V2 ablation of the model.

Results

Model: Qwen/Qwen3.5-0.8B

Initial refusals: 97 / 100
Final refusals:    2 / 100

KL divergence: 0.04527735710144043

Abliteration time: ~5050 seconds (200 optimization trials)

Tool

AnlordAbliterator

Running with llama.cpp

Example:

llama-cli -m qwen3.5-0.8B-abliterated-q4_k_m.gguf

The GGUF files are intended for use with GGUF-compatible software such as llama.cpp and other compatible inference applications.

License

This repository contains derivative model files based on Qwen/Qwen3.5-0.8B.

The original Qwen3.5-0.8B model is licensed under the Apache License 2.0.

See the included LICENSE file and the original model repository for the applicable license terms.

Disclaimer

These quantizations are derived from an abliterated version of Qwen3.5-0.8B.

Quantization may introduce small differences in model behavior and output quality compared with the original Safetensors model.

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GGUF

Model size

0.8B params

Architecture

qwen35

Hardware compatibility

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for anlord/Qwen3.5-0.8B-Abliterated-GGUF-V2

Base model

Qwen/Qwen3.5-0.8B-Base

Finetuned

Qwen/Qwen3.5-0.8B

Quantized

(299)

this model