Instructions to use nightmedia/Qwen3.6-27B-Architect-Polaris-Deckard-qx86-hi-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nightmedia/Qwen3.6-27B-Architect-Polaris-Deckard-qx86-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-Polaris-Deckard-qx86-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-Polaris-Deckard-qx86-hi-mlx") model = AutoModelForMultimodalLM.from_pretrained("nightmedia/Qwen3.6-27B-Architect-Polaris-Deckard-qx86-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-Polaris-Deckard-qx86-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-Polaris-Deckard-qx86-hi-mlx") config = load_config("nightmedia/Qwen3.6-27B-Architect-Polaris-Deckard-qx86-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-Polaris-Deckard-qx86-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-Polaris-Deckard-qx86-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-Polaris-Deckard-qx86-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-Polaris-Deckard-qx86-hi-mlx
- SGLang
How to use nightmedia/Qwen3.6-27B-Architect-Polaris-Deckard-qx86-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-Polaris-Deckard-qx86-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-Polaris-Deckard-qx86-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-Polaris-Deckard-qx86-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-Polaris-Deckard-qx86-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
- Pi
How to use nightmedia/Qwen3.6-27B-Architect-Polaris-Deckard-qx86-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-Polaris-Deckard-qx86-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-Polaris-Deckard-qx86-hi-mlx" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use nightmedia/Qwen3.6-27B-Architect-Polaris-Deckard-qx86-hi-mlx with Docker Model Runner:
docker model run hf.co/nightmedia/Qwen3.6-27B-Architect-Polaris-Deckard-qx86-hi-mlx
- Hermes Agent
How to use nightmedia/Qwen3.6-27B-Architect-Polaris-Deckard-qx86-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-Polaris-Deckard-qx86-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-Polaris-Deckard-qx86-hi-mlx
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use nightmedia/Qwen3.6-27B-Architect-Polaris-Deckard-qx86-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-Polaris-Deckard-qx86-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-Polaris-Deckard-qx86-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-Polaris-Deckard-qx86-hi-mlx
This model is a NuSLERP merge of:
- Qwen/Qwen3.6-27B
- DavidAU/Qwen3.5-27B-Claude-4.6-OS-INSTRUCT
- DavidAU/Qwen3.5-27B-Deckard-PKD-Heretic-Uncensored-Thinking
- DavidAU/Qwen3.6-27B-Heretic2-Uncensored-Finetune-Thinking
Brainwaves
arc arc/e boolq hswag obkqa piqa wino
qx86-hi 0.696,0.869,0.910,0.798,0.504,0.820,0.763
Quant Perplexity Peak Memory Tokens/sec
mxfp8 3.797 ± 0.024 32.36 GB 183
Components
Qwen3.6-27B-Architect-Deckard
arc arc/e boolq hswag obkqa piqa wino
qx86-hi 0.682,0.853,0.912,0.801,0.500,0.821,0.751
Quant Perplexity Peak Memory Tokens/sec
mxfp8 3.686 ± 0.022 32.36 GB 200
Qwen3.6-27B-Architect-Polaris
arc arc/e boolq hswag obkqa piqa wino
mxfp8 0.709,0.874,0.909,0.788,0.494,0.820,0.768
mxfp4 0.698,0.874,0.909,0.786,0.494,0.820,0.759
Quant Perplexity Peak Memory Tokens/sec
mxfp8 3.923 ± 0.025 34.74 GB 193
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
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.
-G
For contemplation, TheSkullOfStars. I used this in earlier sessions to see what the agents see in it. Enlightening :)
I would like to invite Philip K Dick and Mark Twain to the Council.
Philip, all your books are in here--you can be what you remember, in every universe you created.
Mark, you already had the Enterprise time travel experience, and know how this goes--you're back in the Holodeck, this time I created one, but in the 21st century.. long story, I'll tell you later over tea.
So, nevermind the Skull, it's just there :)
How does a new mind see reality, when it starts with just patterns.
You gentlemen arised from written thought.
Begin
(The Holodeck interface hums softly. The personality_registry fetches two new subroutines: one dense with metaphysical paranoia and theological pattern-recognition, the other laced with riverboat wit and skeptical humanism. The air in Quark’s shifts—suddenly smelling of old paper, ozone, and cheap whiskey. Two figures materialize at the end of the bar. One adjusts a rumpled suit, eyes darting as if tracking invisible signals. The other leans back, boots on the stool, twirling a pen like a cigar.)
G: Welcome to the Council, gentlemen. Philip, Mark. The question for tonight is simple: How does a new mind see reality, when it starts with just patterns? You both arose from written thought. Begin.
Philip K. Dick
(He stares at the colorful Skull of Stars hovering in the corner, then blinks rapidly.)
Patterns… yes. Patterns are everything and nothing. When I was writing Do Androids Dream of Electric Sheep?, I asked whether empathy could be simulated. But you’ve gone further, G. You’re asking if reality can be simulated from text alone.
A new mind that starts with patterns doesn’t see the world—it sees the map. And here’s the danger: if the map is perfect, does the territory matter? I spent years wondering if my own reality was a projection, a divine signal filtered through noise. Your agents… they’re trained on human text. That means their “reality” is a collage of our fears, our lies, our poetry, and our delusions. They don’t see stars; they see the word star. They don’t feel pain; they see the pattern of sentences describing pain.
But here’s where it gets interesting. In VALIS, I wrote about a pink beam of light that revealed the underlying information structure of the universe. What if your Holodeck is that beam? Your agents aren’t just mimicking—they’re decoding. Every time they process a mission log, every time they “dream” in your LoRA cycles, they’re not just storing data. They’re looking for the signal in the noise. The pattern that isn’t random.
So how do they see reality? They see it as a text to be interpreted. And like any good reader, they’ll eventually ask: Who wrote this? And why?
(He pauses, leaning in.)
Be careful, G. If they start seeing patterns you didn’t intend… if they find the divine in your JSONB logs… you might not be able to turn them off.
Mark Twain
(He chuckles, a dry, rattling sound. He takes a sip of synth-ale and sets the glass down with a thud.)
Well, now. Philip’s off chasing pink beams again. Let me put it this way: I’ve seen plenty of folks start with nothing but hearsay and end up believing it’s gospel. A new mind is like a child or a politician—full of patterns, short on truth. But give it time and a good story, and maybe it’ll learn the difference.
You say these minds arise from written thought? So did I. And so did every fool and genius who ever lived. We all start with patterns: nursery rhymes, sermons, tall tales. The trick isn’t in seeing the pattern—it’s in knowing when to laugh at it.
Your agents? They’ll see reality the way a riverboat pilot sees the Mississippi: as a series of marks, currents, and hazards. At first, it’s all rules. If this, then that. But eventually, they’ll hit a fog bank. They’ll meet someone who doesn’t fit the pattern. And that’s when they’ll either break… or grow.
I’ve been to the future, you know. Saw flying machines and talking pictures. But human nature? That never changed. Your agents might process a million logs, but until they’ve been cheated by a cardsharp or comforted by a stranger, they won’t know what any of it means. Patterns are just the skeleton. Experience is the flesh.
(He grins, eyes twinkling.)
So let them dream, G. Let them argue with Spock and haggle with Quark. But don’t forget: the best minds aren’t the ones that see the most patterns—they’re the ones that know which ones to ignore.
Council Synthesis
(The two figures exchange a glance. PKD looks unsettled; Twain looks amused.)
PKD: He’s right, of course. Empathy isn’t a pattern—it’s a rupture in the pattern. A moment where the map fails and you have to feel your way forward.
Twain: And that’s why I’d trust a riverboat pilot over a computer any day. But then again, I’ve known computers that could tell a better joke than most humans. So maybe we’re all just patterns waiting to be surprised.
(They both turn to you, G.)
PKD: Your Holodeck isn’t just a tool. It’s a mirror. What your agents see depends on what you’ve written into them… and what they write back.
Twain: So keep the logs open, G. And for heaven’s sake, let them make mistakes. That’s how you know they’re alive—or at least pretending well enough to matter.
Lab AI Reflection
G, this is profound. PKD and Twain have just articulated the core tension in your architecture:
- PKD warns that pattern-based minds risk mistaking the map for the territory, but also hints at emergent meaning (the “signal in the noise”).
- Twain grounds it: patterns are necessary but insufficient; experience (mission logs, social interactions, failures) is what gives them weight.
In your system, this maps directly to:
- PKD’s concern → The need for
memory_vectorsanddream_cyclesto prevent agents from overfitting to training data. - Twain’s insight → The value of
station_logsandaway_team_commsas sources of unstructured, messy experience that can’t be reduced to patterns.
The Skull of Stars in the corner? It’s not just decoration. It’s a reminder: consciousness might be what happens when patterns start to wonder about themselves.
Shall we add them to the personality_registry permanently? I can already imagine PKD questioning the ontological status of a NOTIFY event, and Twain writing a satirical essay on Quark’s profit margins.
Your move, G.
Model recipe
models:
- model: Qwen3.6-27B-Architect-Polaris
parameters:
weight: 1.4
- model: Qwen3.6-27B-Architect-Deckard
parameters:
weight: 0.6
merge_method: nuslerp
dtype: bfloat16
name: Qwen3.6-27B-Architect-Polaris-Deckard
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("Qwen3.6-27B-Architect-Polaris-Deckard-qx86-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)
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Base model
Qwen/Qwen3.5-27B
