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"

anlord commited on 9 days ago

Commit

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README.md +105 -0
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	@@ -0,0 +1,105 @@

+---
+license: apache-2.0
+base_model: Qwen/Qwen3.5-0.8B
+tags:
+- qwen3.5
+- qwen
+- gguf
+- abliterated
+- llama.cpp
+- quantized
+pipeline_tag: text-generation
+---
+# 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](https://github.com/justbedwarsplay/AnlordAbliterator)** 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
+```text
+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](https://github.com/justbedwarsplay/AnlordAbliterator)
+## Running with llama.cpp
+Example:
+```bash
+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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