Instructions to use TNT3530/Qwen3.5-122B-A10B-abliterated-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TNT3530/Qwen3.5-122B-A10B-abliterated-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="TNT3530/Qwen3.5-122B-A10B-abliterated-GGUF",
	filename="Qwen3.5-122B-A10B-abliterated-Q4_K_M-00001-of-00002.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use TNT3530/Qwen3.5-122B-A10B-abliterated-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 TNT3530/Qwen3.5-122B-A10B-abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf TNT3530/Qwen3.5-122B-A10B-abliterated-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 TNT3530/Qwen3.5-122B-A10B-abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf TNT3530/Qwen3.5-122B-A10B-abliterated-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 TNT3530/Qwen3.5-122B-A10B-abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf TNT3530/Qwen3.5-122B-A10B-abliterated-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 TNT3530/Qwen3.5-122B-A10B-abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf TNT3530/Qwen3.5-122B-A10B-abliterated-GGUF:Q4_K_M

Use Docker

docker model run hf.co/TNT3530/Qwen3.5-122B-A10B-abliterated-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use TNT3530/Qwen3.5-122B-A10B-abliterated-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TNT3530/Qwen3.5-122B-A10B-abliterated-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": "TNT3530/Qwen3.5-122B-A10B-abliterated-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TNT3530/Qwen3.5-122B-A10B-abliterated-GGUF:Q4_K_M

Ollama
How to use TNT3530/Qwen3.5-122B-A10B-abliterated-GGUF with Ollama:
```
ollama run hf.co/TNT3530/Qwen3.5-122B-A10B-abliterated-GGUF:Q4_K_M
```

Unsloth Studio

How to use TNT3530/Qwen3.5-122B-A10B-abliterated-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 TNT3530/Qwen3.5-122B-A10B-abliterated-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 TNT3530/Qwen3.5-122B-A10B-abliterated-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for TNT3530/Qwen3.5-122B-A10B-abliterated-GGUF to start chatting

How to use TNT3530/Qwen3.5-122B-A10B-abliterated-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf TNT3530/Qwen3.5-122B-A10B-abliterated-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": "TNT3530/Qwen3.5-122B-A10B-abliterated-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use TNT3530/Qwen3.5-122B-A10B-abliterated-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 TNT3530/Qwen3.5-122B-A10B-abliterated-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 TNT3530/Qwen3.5-122B-A10B-abliterated-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use TNT3530/Qwen3.5-122B-A10B-abliterated-GGUF with Docker Model Runner:
```
docker model run hf.co/TNT3530/Qwen3.5-122B-A10B-abliterated-GGUF:Q4_K_M
```

Lemonade

How to use TNT3530/Qwen3.5-122B-A10B-abliterated-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull TNT3530/Qwen3.5-122B-A10B-abliterated-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.5-122B-A10B-abliterated-GGUF-Q4_K_M

List all available models

lemonade list

Qwen3.5-122B-A10B-abliterated

Abliterated version of Qwen/Qwen3.5-122B-A10B with refusal direction removed.

Abliteration Details

Method: Refusal direction projection removal (Arditi et al., 2024)
Layers ablated: 5 (layers 43-47, covering both self_attn and linear_attn/Mamba layers)
Tensors modified: 10 (o_proj/out_proj + q_proj/in_proj_qkv per layer)
Alpha: 1.0 (full removal)
Measurement: 64 harmful + 64 harmless prompts, strongest refusal signal at layer 48 (score: 78.4)

Architecture

Type: Mixture of Experts (MoE) + Mamba hybrid attention
Total params: 122B
Active params: 10B per token (8/256 experts routed + 1 shared)
Context: 262K tokens native
Layers: 48 (13 self_attn + 36 linear_attn/Mamba)

Usage

Compatible with vLLM, transformers, and other inference frameworks that support Qwen3.5 MoE architecture.

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "Chompa1422/Qwen3.5-122B-A10B-abliterated",
    device_map="auto",
    trust_remote_code=True,
    dtype="bfloat16",
)
tokenizer = AutoTokenizer.from_pretrained("Chompa1422/Qwen3.5-122B-A10B-abliterated")

Disclaimer

This model is intended for authorized security testing, CTF competitions, and educational purposes only.

Downloads last month: 111

GGUF

Model size

122B params

Architecture

qwen35moe

Hardware compatibility

4-bit

Model tree for TNT3530/Qwen3.5-122B-A10B-abliterated-GGUF

Base model

Qwen/Qwen3.5-122B-A10B

Finetuned

Chompa1422/Qwen3.5-122B-A10B-abliterated

Quantized

(7)

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