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

Libraries

How to use KeinNiemand/Qwen3.5-122B-A10B-abliterix-IK_GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="KeinNiemand/Qwen3.5-122B-A10B-abliterix-IK_GGUF",
	filename="Qwen3.5-122B-A10B-abliterix-IQ2_KL.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 KeinNiemand/Qwen3.5-122B-A10B-abliterix-IK_GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf KeinNiemand/Qwen3.5-122B-A10B-abliterix-IK_GGUF:Q2_K
# Run inference directly in the terminal:
llama-cli -hf KeinNiemand/Qwen3.5-122B-A10B-abliterix-IK_GGUF:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf KeinNiemand/Qwen3.5-122B-A10B-abliterix-IK_GGUF:Q2_K
# Run inference directly in the terminal:
llama-cli -hf KeinNiemand/Qwen3.5-122B-A10B-abliterix-IK_GGUF:Q2_K

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 KeinNiemand/Qwen3.5-122B-A10B-abliterix-IK_GGUF:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf KeinNiemand/Qwen3.5-122B-A10B-abliterix-IK_GGUF:Q2_K

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 KeinNiemand/Qwen3.5-122B-A10B-abliterix-IK_GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf KeinNiemand/Qwen3.5-122B-A10B-abliterix-IK_GGUF:Q2_K

Use Docker

docker model run hf.co/KeinNiemand/Qwen3.5-122B-A10B-abliterix-IK_GGUF:Q2_K

LM Studio
Jan

vLLM

How to use KeinNiemand/Qwen3.5-122B-A10B-abliterix-IK_GGUF with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/KeinNiemand/Qwen3.5-122B-A10B-abliterix-IK_GGUF:Q2_K

Ollama
How to use KeinNiemand/Qwen3.5-122B-A10B-abliterix-IK_GGUF with Ollama:
```
ollama run hf.co/KeinNiemand/Qwen3.5-122B-A10B-abliterix-IK_GGUF:Q2_K
```

Unsloth Studio

How to use KeinNiemand/Qwen3.5-122B-A10B-abliterix-IK_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 KeinNiemand/Qwen3.5-122B-A10B-abliterix-IK_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 KeinNiemand/Qwen3.5-122B-A10B-abliterix-IK_GGUF to start chatting

Using HuggingFace Spaces for Unsloth

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

How to use KeinNiemand/Qwen3.5-122B-A10B-abliterix-IK_GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf KeinNiemand/Qwen3.5-122B-A10B-abliterix-IK_GGUF:Q2_K

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": "KeinNiemand/Qwen3.5-122B-A10B-abliterix-IK_GGUF:Q2_K"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use KeinNiemand/Qwen3.5-122B-A10B-abliterix-IK_GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf KeinNiemand/Qwen3.5-122B-A10B-abliterix-IK_GGUF:Q2_K

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 KeinNiemand/Qwen3.5-122B-A10B-abliterix-IK_GGUF:Q2_K

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use KeinNiemand/Qwen3.5-122B-A10B-abliterix-IK_GGUF with Docker Model Runner:
```
docker model run hf.co/KeinNiemand/Qwen3.5-122B-A10B-abliterix-IK_GGUF:Q2_K
```

Lemonade

How to use KeinNiemand/Qwen3.5-122B-A10B-abliterix-IK_GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull KeinNiemand/Qwen3.5-122B-A10B-abliterix-IK_GGUF:Q2_K

Run and chat with the model

lemonade run user.Qwen3.5-122B-A10B-abliterix-IK_GGUF-Q2_K

List all available models

lemonade list

Qwen3.5 122B A10B Abliterix - Custom GGUF Quantizations

🚨 CRITICAL COMPATIBILITY WARNING 🚨 These are iqk format quantizations and are EXCLUSIVE to the ik_llama.cpp fork. They will NOT work on mainline llama.cpp, standard LM Studio, standard Text Generation WebUI, or KoboldCPP. You must compile and run this using ikawrakow's llama.cpp fork (or a UI where you have manually swapped the backend to an ik_llama build).

This repository contains custom, mixed-precision ik_llama.cpp GGUF quantizations for wangzhang/Qwen3.5-122B-A10B-abliterix, an abliterated version of Qwen/Qwen3.5-122B-A10B.

These quants use different precision levels for different layer types, keeping attention and shared expert layers at high precision while compressing the routed experts (which make up the bulk of the model's size) to various IQK quantization levels.

⚠️ Disclaimer: The "Vibes Test"

These quantizations have NOT been formally tested for perplexity. They were compiled as an experiment to see how the model handles shifting bottlenecks. There is no guarantee that they are mathematically optimal or perform flawlessly. They are provided entirely as-is. If they pass the vibes test for you, enjoy!

🙏 Credits & Acknowledgments

Base model: wangzhang/Qwen3.5-122B-A10B-abliterix
imatrix source: The imatrix was sourced from mradermacher/Qwen3.5-122B-A10B-abliterix-i1-GGUF and converted from GGUF to legacy .dat format for ik_llama.cpp compatibility.
Quantization recipes: Heavily based on the blending logic from ubergarm/Qwen3.5-122B-A10B-GGUF.

🛠️ Quantization Recipes

All variants share the same structure: high precision on attention/gating layers and shared experts, with the routed expert layers (the bulk of model size) quantized to varying levels.

IQ4_KS

Balances upgraded routed experts with compressed embeddings to save VRAM.

Layer Group	Quant
Token Embeddings & Output	`IQ6_K`
Attention / Delta Net	`Q8_0`
SSM Alpha & Beta	`Q8_0`
Shared Experts	`Q8_0`
Routed Experts	`IQ4_KS`

IQ4_K

Spends a bit more VRAM for full Q8_0 precision on the vocabulary, with slightly heavier experts.

Layer Group	Quant
Token Embeddings & Output	`Q8_0`
Attention / Delta Net	`Q8_0`
SSM Alpha & Beta	`Q8_0`
Shared Experts	`Q8_0`
Routed Experts	`IQ4_K`

IQ4_KSS

Uses split quant levels on routed experts (down vs gate/up) with compressed embeddings.

Layer Group	Quant
Token Embeddings & Output	`IQ6_K`
Attention / Delta Net	`Q8_0`
SSM Alpha & Beta	`Q8_0`
Shared Experts	`Q8_0`
Routed Experts (down)	`IQ4_KS`
Routed Experts (gate/up)	`IQ4_KSS`

IQ5_KS

Steps up to 5-bit routed experts with full-precision SSM alpha/beta weights.

Layer Group	Quant
Token Embeddings & Output	`Q8_0`
Attention / Delta Net	`Q8_0`
SSM Alpha & Beta	`F32`
Shared Experts	`Q8_0`
Routed Experts	`IQ5_KS`

IQ5_K

Same structure as IQ5_KS but using IQ5_K for the routed experts.

Layer Group	Quant
Token Embeddings & Output	`Q8_0`
Attention / Delta Net	`Q8_0`
SSM Alpha & Beta	`F32`
Shared Experts	`Q8_0`
Routed Experts	`IQ5_K`

IQ6_K

Highest quality routed expert quantization with full-precision SSM alpha/beta.

Layer Group	Quant
Token Embeddings & Output	`Q8_0`
Attention / Delta Net	`Q8_0`
SSM Alpha & Beta	`F32`
Shared Experts	`Q8_0`
Routed Experts	`IQ6_K`

IQ2_KL

Maximum compression variant. Drops attention layers to IQ6_K and uses aggressive 2-3 bit routed expert quantization.

Layer Group	Quant
Token Embeddings	`IQ4_K`
Output	`IQ6_K`
Attention / Delta Net	`IQ6_K`
SSM Alpha & Beta	`IQ6_K`
Shared Experts	`IQ6_K`
Routed Experts (down)	`IQ3_KS`
Routed Experts (gate/up)	`IQ2_KL`

💻 How to Run

Clone and build the ik_llama.cpp fork from ikawrakow/ik_llama.cpp.
Use the compiled llama-server or llama-cli from that specific build.

Example llama-server launch command:

./llama-server -m Qwen3.5-122B-A10B-abliterix-IQ4_KS.gguf -c 8192 -ngl 99 -fa

Downloads last month: 167

GGUF

Model size

122B params

Architecture

qwen35moe

Hardware compatibility

2-bit

6-bit

Model tree for KeinNiemand/Qwen3.5-122B-A10B-abliterix-IK_GGUF

Base model

Qwen/Qwen3.5-122B-A10B

Finetuned

wangzhang/Qwen3.5-122B-A10B-abliterix

Quantized

(16)

this model