Instructions to use mlx-community/Qwen3.6-27B-AEON-Ultimate-Uncensored-BF16-mlx-8Bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mlx-community/Qwen3.6-27B-AEON-Ultimate-Uncensored-BF16-mlx-8Bit with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="mlx-community/Qwen3.6-27B-AEON-Ultimate-Uncensored-BF16-mlx-8Bit")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("mlx-community/Qwen3.6-27B-AEON-Ultimate-Uncensored-BF16-mlx-8Bit")
model = AutoModelForImageTextToText.from_pretrained("mlx-community/Qwen3.6-27B-AEON-Ultimate-Uncensored-BF16-mlx-8Bit")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

MLX

How to use mlx-community/Qwen3.6-27B-AEON-Ultimate-Uncensored-BF16-mlx-8Bit with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("mlx-community/Qwen3.6-27B-AEON-Ultimate-Uncensored-BF16-mlx-8Bit")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps
LM Studio

vLLM

How to use mlx-community/Qwen3.6-27B-AEON-Ultimate-Uncensored-BF16-mlx-8Bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mlx-community/Qwen3.6-27B-AEON-Ultimate-Uncensored-BF16-mlx-8Bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mlx-community/Qwen3.6-27B-AEON-Ultimate-Uncensored-BF16-mlx-8Bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mlx-community/Qwen3.6-27B-AEON-Ultimate-Uncensored-BF16-mlx-8Bit

SGLang

How to use mlx-community/Qwen3.6-27B-AEON-Ultimate-Uncensored-BF16-mlx-8Bit with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "mlx-community/Qwen3.6-27B-AEON-Ultimate-Uncensored-BF16-mlx-8Bit" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mlx-community/Qwen3.6-27B-AEON-Ultimate-Uncensored-BF16-mlx-8Bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "mlx-community/Qwen3.6-27B-AEON-Ultimate-Uncensored-BF16-mlx-8Bit" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mlx-community/Qwen3.6-27B-AEON-Ultimate-Uncensored-BF16-mlx-8Bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

How to use mlx-community/Qwen3.6-27B-AEON-Ultimate-Uncensored-BF16-mlx-8Bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "mlx-community/Qwen3.6-27B-AEON-Ultimate-Uncensored-BF16-mlx-8Bit"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "mlx-community/Qwen3.6-27B-AEON-Ultimate-Uncensored-BF16-mlx-8Bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use mlx-community/Qwen3.6-27B-AEON-Ultimate-Uncensored-BF16-mlx-8Bit with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "mlx-community/Qwen3.6-27B-AEON-Ultimate-Uncensored-BF16-mlx-8Bit"

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 mlx-community/Qwen3.6-27B-AEON-Ultimate-Uncensored-BF16-mlx-8Bit

Run Hermes

hermes

MLX LM

How to use mlx-community/Qwen3.6-27B-AEON-Ultimate-Uncensored-BF16-mlx-8Bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "mlx-community/Qwen3.6-27B-AEON-Ultimate-Uncensored-BF16-mlx-8Bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "mlx-community/Qwen3.6-27B-AEON-Ultimate-Uncensored-BF16-mlx-8Bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "mlx-community/Qwen3.6-27B-AEON-Ultimate-Uncensored-BF16-mlx-8Bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Docker Model Runner
How to use mlx-community/Qwen3.6-27B-AEON-Ultimate-Uncensored-BF16-mlx-8Bit with Docker Model Runner:
```
docker model run hf.co/mlx-community/Qwen3.6-27B-AEON-Ultimate-Uncensored-BF16-mlx-8Bit
```

You need to agree to share your contact information to access this model

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

usermma/Qwen3.6-27B-AEON-Ultimate-Uncensored-BF16-mlx-8Bit

The Model mlx-community/Qwen3.6-27B-AEON-Ultimate-Uncensored-BF16-mlx-8Bit was converted to MLX format from AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-BF16 using mlx-lm version 0.31.2.

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("mlx-community/Qwen3.6-27B-AEON-Ultimate-Uncensored-BF16-mlx-8Bit")

prompt="hello"

if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, tokenize=False, add_generation_prompt=True
    )

response = generate(model, tokenizer, prompt=prompt, verbose=True)