Instructions to use noctrex/Huihui-Qwen3.5-35B-A3B-abliterated-MXFP4_MOE-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use noctrex/Huihui-Qwen3.5-35B-A3B-abliterated-MXFP4_MOE-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="noctrex/Huihui-Qwen3.5-35B-A3B-abliterated-MXFP4_MOE-GGUF",
	filename="Huihui-Qwen3.5-35B-A3B-abliterated-MXFP4_MOE.gguf",
)

llm.create_chat_completion(
	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"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use noctrex/Huihui-Qwen3.5-35B-A3B-abliterated-MXFP4_MOE-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 noctrex/Huihui-Qwen3.5-35B-A3B-abliterated-MXFP4_MOE-GGUF:MXFP4_MOE
# Run inference directly in the terminal:
llama cli -hf noctrex/Huihui-Qwen3.5-35B-A3B-abliterated-MXFP4_MOE-GGUF:MXFP4_MOE

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf noctrex/Huihui-Qwen3.5-35B-A3B-abliterated-MXFP4_MOE-GGUF:MXFP4_MOE
# Run inference directly in the terminal:
llama cli -hf noctrex/Huihui-Qwen3.5-35B-A3B-abliterated-MXFP4_MOE-GGUF:MXFP4_MOE

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 noctrex/Huihui-Qwen3.5-35B-A3B-abliterated-MXFP4_MOE-GGUF:MXFP4_MOE
# Run inference directly in the terminal:
./llama-cli -hf noctrex/Huihui-Qwen3.5-35B-A3B-abliterated-MXFP4_MOE-GGUF:MXFP4_MOE

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 noctrex/Huihui-Qwen3.5-35B-A3B-abliterated-MXFP4_MOE-GGUF:MXFP4_MOE
# Run inference directly in the terminal:
./build/bin/llama-cli -hf noctrex/Huihui-Qwen3.5-35B-A3B-abliterated-MXFP4_MOE-GGUF:MXFP4_MOE

Use Docker

docker model run hf.co/noctrex/Huihui-Qwen3.5-35B-A3B-abliterated-MXFP4_MOE-GGUF:MXFP4_MOE

LM Studio
Jan

vLLM

How to use noctrex/Huihui-Qwen3.5-35B-A3B-abliterated-MXFP4_MOE-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "noctrex/Huihui-Qwen3.5-35B-A3B-abliterated-MXFP4_MOE-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": "noctrex/Huihui-Qwen3.5-35B-A3B-abliterated-MXFP4_MOE-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/noctrex/Huihui-Qwen3.5-35B-A3B-abliterated-MXFP4_MOE-GGUF:MXFP4_MOE

Ollama
How to use noctrex/Huihui-Qwen3.5-35B-A3B-abliterated-MXFP4_MOE-GGUF with Ollama:
```
ollama run hf.co/noctrex/Huihui-Qwen3.5-35B-A3B-abliterated-MXFP4_MOE-GGUF:MXFP4_MOE
```

Unsloth Studio

How to use noctrex/Huihui-Qwen3.5-35B-A3B-abliterated-MXFP4_MOE-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 noctrex/Huihui-Qwen3.5-35B-A3B-abliterated-MXFP4_MOE-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 noctrex/Huihui-Qwen3.5-35B-A3B-abliterated-MXFP4_MOE-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for noctrex/Huihui-Qwen3.5-35B-A3B-abliterated-MXFP4_MOE-GGUF to start chatting

How to use noctrex/Huihui-Qwen3.5-35B-A3B-abliterated-MXFP4_MOE-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf noctrex/Huihui-Qwen3.5-35B-A3B-abliterated-MXFP4_MOE-GGUF:MXFP4_MOE

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": "noctrex/Huihui-Qwen3.5-35B-A3B-abliterated-MXFP4_MOE-GGUF:MXFP4_MOE"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use noctrex/Huihui-Qwen3.5-35B-A3B-abliterated-MXFP4_MOE-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 noctrex/Huihui-Qwen3.5-35B-A3B-abliterated-MXFP4_MOE-GGUF:MXFP4_MOE

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 noctrex/Huihui-Qwen3.5-35B-A3B-abliterated-MXFP4_MOE-GGUF:MXFP4_MOE

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use noctrex/Huihui-Qwen3.5-35B-A3B-abliterated-MXFP4_MOE-GGUF with Docker Model Runner:
```
docker model run hf.co/noctrex/Huihui-Qwen3.5-35B-A3B-abliterated-MXFP4_MOE-GGUF:MXFP4_MOE
```

Lemonade

How to use noctrex/Huihui-Qwen3.5-35B-A3B-abliterated-MXFP4_MOE-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull noctrex/Huihui-Qwen3.5-35B-A3B-abliterated-MXFP4_MOE-GGUF:MXFP4_MOE

Run and chat with the model

lemonade run user.Huihui-Qwen3.5-35B-A3B-abliterated-MXFP4_MOE-GGUF-MXFP4_MOE

List all available models

lemonade list

These are quantizations of the model Huihui-Qwen3.5-35B-A3B-abliterated

Download the latest llama.cpp to use these quantizations.
For the mmproj file, the F32 version is recommended for best results.
Order of quality: F32 > BF16 > F16

Read the guide from unsloth in order to set up the model's recommended settings:
Qwen3.5 - How to Run Locally Guide

The mainline standard is to use MXFP4 for the MoE tensors, and Q8 for the rest.
So I created a new variant, where the other tensors are BF16 instead of Q8.
On some architectures BF16 will be slower, but its the highest quality, essentialy its the original tensors from the model copied over unquantized.

As of 2026-03-03 the chat template has been updated, and is using the fixed version from unsloth.
If you don't want to download the model again, you can just update the chat template.

Downloads last month: 520

GGUF

Model size

35B params

Architecture

qwen35moe

Hardware compatibility

4-bit

Model tree for noctrex/Huihui-Qwen3.5-35B-A3B-abliterated-MXFP4_MOE-GGUF

Base model

Qwen/Qwen3.5-35B-A3B-Base

Finetuned

Qwen/Qwen3.5-35B-A3B

Finetuned

huihui-ai/Huihui-Qwen3.5-35B-A3B-abliterated

Quantized

(20)

this model