Instructions to use Hyphonical/Qwen3.6-35B-A3B-abliterated-MAX-APEX-i-nano-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Hyphonical/Qwen3.6-35B-A3B-abliterated-MAX-APEX-i-nano-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Hyphonical/Qwen3.6-35B-A3B-abliterated-MAX-APEX-i-nano-GGUF",
	filename="Qwen3.6-35B-A3B-abliterated-MAX-APEX-i-nano.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 Hyphonical/Qwen3.6-35B-A3B-abliterated-MAX-APEX-i-nano-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Hyphonical/Qwen3.6-35B-A3B-abliterated-MAX-APEX-i-nano-GGUF:Q8_0
# Run inference directly in the terminal:
llama-cli -hf Hyphonical/Qwen3.6-35B-A3B-abliterated-MAX-APEX-i-nano-GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Hyphonical/Qwen3.6-35B-A3B-abliterated-MAX-APEX-i-nano-GGUF:Q8_0
# Run inference directly in the terminal:
llama-cli -hf Hyphonical/Qwen3.6-35B-A3B-abliterated-MAX-APEX-i-nano-GGUF:Q8_0

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 Hyphonical/Qwen3.6-35B-A3B-abliterated-MAX-APEX-i-nano-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf Hyphonical/Qwen3.6-35B-A3B-abliterated-MAX-APEX-i-nano-GGUF:Q8_0

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 Hyphonical/Qwen3.6-35B-A3B-abliterated-MAX-APEX-i-nano-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Hyphonical/Qwen3.6-35B-A3B-abliterated-MAX-APEX-i-nano-GGUF:Q8_0

Use Docker

docker model run hf.co/Hyphonical/Qwen3.6-35B-A3B-abliterated-MAX-APEX-i-nano-GGUF:Q8_0

LM Studio
Jan

vLLM

How to use Hyphonical/Qwen3.6-35B-A3B-abliterated-MAX-APEX-i-nano-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Hyphonical/Qwen3.6-35B-A3B-abliterated-MAX-APEX-i-nano-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": "Hyphonical/Qwen3.6-35B-A3B-abliterated-MAX-APEX-i-nano-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Hyphonical/Qwen3.6-35B-A3B-abliterated-MAX-APEX-i-nano-GGUF:Q8_0

Ollama
How to use Hyphonical/Qwen3.6-35B-A3B-abliterated-MAX-APEX-i-nano-GGUF with Ollama:
```
ollama run hf.co/Hyphonical/Qwen3.6-35B-A3B-abliterated-MAX-APEX-i-nano-GGUF:Q8_0
```

Unsloth Studio

How to use Hyphonical/Qwen3.6-35B-A3B-abliterated-MAX-APEX-i-nano-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 Hyphonical/Qwen3.6-35B-A3B-abliterated-MAX-APEX-i-nano-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 Hyphonical/Qwen3.6-35B-A3B-abliterated-MAX-APEX-i-nano-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Hyphonical/Qwen3.6-35B-A3B-abliterated-MAX-APEX-i-nano-GGUF to start chatting

How to use Hyphonical/Qwen3.6-35B-A3B-abliterated-MAX-APEX-i-nano-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Hyphonical/Qwen3.6-35B-A3B-abliterated-MAX-APEX-i-nano-GGUF:Q8_0

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": "Hyphonical/Qwen3.6-35B-A3B-abliterated-MAX-APEX-i-nano-GGUF:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Hyphonical/Qwen3.6-35B-A3B-abliterated-MAX-APEX-i-nano-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 Hyphonical/Qwen3.6-35B-A3B-abliterated-MAX-APEX-i-nano-GGUF:Q8_0

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 Hyphonical/Qwen3.6-35B-A3B-abliterated-MAX-APEX-i-nano-GGUF:Q8_0

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use Hyphonical/Qwen3.6-35B-A3B-abliterated-MAX-APEX-i-nano-GGUF with Docker Model Runner:
```
docker model run hf.co/Hyphonical/Qwen3.6-35B-A3B-abliterated-MAX-APEX-i-nano-GGUF:Q8_0
```

Lemonade

How to use Hyphonical/Qwen3.6-35B-A3B-abliterated-MAX-APEX-i-nano-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Hyphonical/Qwen3.6-35B-A3B-abliterated-MAX-APEX-i-nano-GGUF:Q8_0

Run and chat with the model

lemonade run user.Qwen3.6-35B-A3B-abliterated-MAX-APEX-i-nano-GGUF-Q8_0

List all available models

lemonade list

Qwen3.6-35B-A3B-abliterated-MAX | APEX i-nano (2.72 BPW)

This model was quantized using apex-quant with the i-nano profile and an importance matrix calibrated on a diverse code/math/reasoning dataset.

Quantization Details

Property	Value
Base Model	prithivMLmods/Qwen3.6-35B-A3B-abliterated-MAX
Quantizer	mudler/apex-quant
Profile	`i-nano` (importance-matrix calibrated)
BPW	2.72
File Size	~11 GB
Layers	40
Calibration Data	tomngdev/imatrix-calibration-data

What is APEX Quantization?

APEX applies a per-layer, per-tensor quantization gradient _ higher precision on edge layers (first and last ~5), aggressive quantization on the middle layers, with separate handling for routed experts, shared experts, attention weights, and SSM weights. The i-nano variant uses importance matrix calibration to enable very low-bit formats (IQ2_S, IQ2_XXS) on middle-layer expert weights while preserving output quality.

Usage

Run with any recent llama.cpp build, no custom fork or patches required:

# CLI
./llama-cli -m Qwen3.6-35B-A3B-abliterated-MAX-APEX-i-nano.gguf -p "Your prompt here"

# Server
./llama-server -m Qwen3.6-35B-A3B-abliterated-MAX-APEX-i-nano.gguf--host 0.0.0.0 --port 8080

Files

File	Description
`Qwen3.6-35B-A3B-abliterated-MAX-APEX-i-nano.gguf`	The quantized model (~11 GB)
`imatrix.dat`	Importance matrix used for calibration

Downloads last month: 415

GGUF

Model size

35B params

Architecture

qwen35moe

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Model tree for Hyphonical/Qwen3.6-35B-A3B-abliterated-MAX-APEX-i-nano-GGUF

Base model

Qwen/Qwen3.6-35B-A3B

Finetuned

prithivMLmods/Qwen3.6-35B-A3B-abliterated-MAX

Quantized

(4)

this model