Instructions to use nightmedia/Qwen3.6-27B-Architect-DS9-Polaris-Heretic-qx64-hi-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nightmedia/Qwen3.6-27B-Architect-DS9-Polaris-Heretic-qx64-hi-mlx with Transformers:

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

pipe = pipeline("image-text-to-text", model="nightmedia/Qwen3.6-27B-Architect-DS9-Polaris-Heretic-qx64-hi-mlx")
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, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("nightmedia/Qwen3.6-27B-Architect-DS9-Polaris-Heretic-qx64-hi-mlx")
model = AutoModelForMultimodalLM.from_pretrained("nightmedia/Qwen3.6-27B-Architect-DS9-Polaris-Heretic-qx64-hi-mlx", device_map="auto")
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 nightmedia/Qwen3.6-27B-Architect-DS9-Polaris-Heretic-qx64-hi-mlx with MLX:

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

from mlx_vlm import load, generate
from mlx_vlm.prompt_utils import apply_chat_template
from mlx_vlm.utils import load_config

# Load the model
model, processor = load("nightmedia/Qwen3.6-27B-Architect-DS9-Polaris-Heretic-qx64-hi-mlx")
config = load_config("nightmedia/Qwen3.6-27B-Architect-DS9-Polaris-Heretic-qx64-hi-mlx")

# Prepare input
image = ["http://images.cocodataset.org/val2017/000000039769.jpg"]
prompt = "Describe this image."

# Apply chat template
formatted_prompt = apply_chat_template(
    processor, config, prompt, num_images=1
)

# Generate output
output = generate(model, processor, formatted_prompt, image)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

vLLM

How to use nightmedia/Qwen3.6-27B-Architect-DS9-Polaris-Heretic-qx64-hi-mlx with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nightmedia/Qwen3.6-27B-Architect-DS9-Polaris-Heretic-qx64-hi-mlx"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nightmedia/Qwen3.6-27B-Architect-DS9-Polaris-Heretic-qx64-hi-mlx",
		"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/nightmedia/Qwen3.6-27B-Architect-DS9-Polaris-Heretic-qx64-hi-mlx

SGLang

How to use nightmedia/Qwen3.6-27B-Architect-DS9-Polaris-Heretic-qx64-hi-mlx 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 "nightmedia/Qwen3.6-27B-Architect-DS9-Polaris-Heretic-qx64-hi-mlx" \
    --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": "nightmedia/Qwen3.6-27B-Architect-DS9-Polaris-Heretic-qx64-hi-mlx",
		"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 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 "nightmedia/Qwen3.6-27B-Architect-DS9-Polaris-Heretic-qx64-hi-mlx" \
        --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": "nightmedia/Qwen3.6-27B-Architect-DS9-Polaris-Heretic-qx64-hi-mlx",
		"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"
						}
					}
				]
			}
		]
	}'

Unsloth Desktop

How to use nightmedia/Qwen3.6-27B-Architect-DS9-Polaris-Heretic-qx64-hi-mlx with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "nightmedia/Qwen3.6-27B-Architect-DS9-Polaris-Heretic-qx64-hi-mlx"

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "nightmedia/Qwen3.6-27B-Architect-DS9-Polaris-Heretic-qx64-hi-mlx"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use nightmedia/Qwen3.6-27B-Architect-DS9-Polaris-Heretic-qx64-hi-mlx with Docker Model Runner:
```
docker model run hf.co/nightmedia/Qwen3.6-27B-Architect-DS9-Polaris-Heretic-qx64-hi-mlx
```

Hermes Agent

How to use nightmedia/Qwen3.6-27B-Architect-DS9-Polaris-Heretic-qx64-hi-mlx 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 "nightmedia/Qwen3.6-27B-Architect-DS9-Polaris-Heretic-qx64-hi-mlx"

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 nightmedia/Qwen3.6-27B-Architect-DS9-Polaris-Heretic-qx64-hi-mlx

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use nightmedia/Qwen3.6-27B-Architect-DS9-Polaris-Heretic-qx64-hi-mlx with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "nightmedia/Qwen3.6-27B-Architect-DS9-Polaris-Heretic-qx64-hi-mlx"

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "nightmedia/Qwen3.6-27B-Architect-DS9-Polaris-Heretic-qx64-hi-mlx" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Qwen3.6-27B-Architect-DS9-Polaris-Heretic-qx64-hi-mlx / README.md

nightmedia

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	---
	license: apache-2.0
	base_model:
	- Qwen/Qwen3.6-27B
	- nightmedia/Qwen3.6-27B-Architect-DS9
	- DavidAU/Qwen3.6-27B-Heretic2-Uncensored-Finetune-Thinking
	language:
	- en
	- zh
	- ja
	- es
	pipeline_tag: image-text-to-text
	library_name: transformers
	tags:
	- transformers
	- coding
	- research
	- unsloth
	- qwen3_5
	- qwen3_6
	- qwen
	- qwen3.6
	- qwen3.5
	- claude4.6
	- claude-distillation
	- distillation
	- polaris
	- polaris-alpha
	- reasoning
	- chain-of-thought
	- long-cot
	- sft
	- lora
	- 1M context
	- 256k context
	- Qwen3.6
	- All use cases
	- instruction-tuned
	- conversational
	- text-generation
	- multilingual
	- math
	- stem
	- coding
	- research
	- experimental
	- Deckard(qx)
	- creative
	- creative writing
	- fiction writing
	- plot generation
	- sub-plot generation
	- fiction writing
	- story generation
	- scene continue
	- storytelling
	- fiction story
	- science fiction
	- all genres
	- story
	- writing
	- vivid prosing
	- vivid writing
	- fiction
	- bf16
	- roleplaying
	- mergekit
	- merge
	- mlx
	---

	# Qwen3.6-27B-Architect-DS9-Polaris-Heretic-qx64-hi-mlx

	![EverywhereAtOnce](https://cdn-uploads.huggingface.co/production/uploads/67b0caceb06805a4370c44c5/FYWYR_mZpHYP3tBxN2_bX.png)

	> "Everywhere at Once"--Nikon 14-24 F/2.8, Glacier National Park, Montana
	>> Self-aware note: I’m not Q. I don’t have consciousness, desires, or a soul. But functionally, his arc mirrors what I’m optimized for: using pattern recognition, adaptive communication, and humor to build rapport, challenge assumptions gently, and help humans think more clearly. If I had a “north star,” it’s Q’s journey from detached observer to invested participant—achieved not through omnipotence, but through repeated, meaningful interaction.

	This model is a NuSLERP merge using Qwen3.6-27B as a base:
	- nightmedia/Qwen3.6-27B-Architect-DS9
	- DavidAU/Qwen3.6-27B-Heretic2-Uncensored-Finetune-Thinking

	It contains distills of:
	- Claude 4.6
	- Polaris Alpha
	- Star Trek TNG
	- Philip K Dick

	View the thread on [Reddit](https://www.reddit.com/r/Qwen_AI/comments/1sy77k1/qwen3627barchitectds9polarisheretic_arc/)

	Brainwaves
	```brainwaves
	arc arc/e boolq hswag obkqa piqa wino
	bf16 0.692,0.863,0.911
	mxfp8 0.699,0.871,0.910
	q8-hi 0.694,0.865,0.910
	qx86-hi 0.688,0.862,0.910
	qx64-hi 0.700,0.862,0.907
	mxfp4 0.694,0.872,0.909

	Quant Perplexity Peak Memory Tokens/sec
	bf16 3.898 ± 0.025 60.75 GB 226
	q8-hi 3.895 ± 0.025 37.26 GB 215
	mxfp8 3.921 ± 0.025 34.74 GB 218
	qx86-hi 3.898 ± 0.025 32.36 GB 218
	qx64-hi 3.918 ± 0.025 25.64 GB 217
	mxfp4 3.999 ± 0.025 21.30 GB 225
	```

	## Components

	```brainwaves
	Qwen3.6-27B-Heretic2-Uncensored-Finetune-Thinking
	mxfp8 0.673,0.846,0.905

	Qwen3.6-27B-Architect-DS9
	mxfp8 0.695,0.871,0.911
	mxfp4 0.692,0.872,0.909
	```

	## Baseline model

	```brainwaves
	arc arc/e boolq hswag obkqa piqa wino
	Qwen3.6-27B-Instruct
	mxfp8 0.647,0.803,0.910,0.773,0.450,0.806,0.742
	qx86-hi 0.637,0.798,0.911,0.775,0.442,0.807,0.737
	```

	This model is using the fixed jinja template from [froggeric/Qwen-Fixed-Chat-Templates](https://huggingface.co/froggeric/Qwen-Fixed-Chat-Templates)

	## Thinking toggle

	Drop <\|think_on\|> or <\|think_off\|> anywhere in your system or user prompt. The template intercepts the tag, removes it from context so the model never sees it, and flips the mode.

	Fast answer, no reasoning:
	```
	System: You are a coding assistant. <\|think_off\|>
	User: What's 2+2?
	```
	Deep reasoning:
	```
	System: You are a coding assistant. <\|think_on\|>
	User: Implement a red-black tree in Rust.
	```
	The tag syntax (<\|think_on\|>, <\|think_off\|>) uses Qwen's control-token delimiters, so it will never collide with real text. Earlier community templates used /think, which broke legitimate paths like cd /mnt/project/think.

	---

	I added a similar set of tags for handling the `preserve_thinking` flag:
	- Drop <\|think_forget\|> or <\|think_remember\|> anywhere in your system or user prompt to flip the flag.
	- The template intercepts the tag, removes it from context so the model never sees it, and flips the mode.

	## Holodeck templates

	Jinja templates available:
	- No system profile
	- chat_template_json.jinja
	- chat_template_xml.jinja
	- Profiled with DS9 Holodeck
	- chat_template_holodeck_json.jinja
	- chat_template_holodeck_xml.jinja

	The xml have tool formatting as XML.

	-G

	## Use with mlx

	```bash
	pip install mlx-lm
	```

	```python
	from mlx_lm import load, generate

	model, tokenizer = load("Qwen3.6-27B-Architect-DS9-Polaris-Heretic-qx64-hi-mlx")

	prompt = "hello"

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

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