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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metadata

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

"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

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

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

         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

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

pip install mlx-lm

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)