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
- Transformers
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") 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 Studio
How to use nightmedia/Qwen3.6-27B-Architect-DS9-Polaris-Heretic-qx64-hi-mlx 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 nightmedia/Qwen3.6-27B-Architect-DS9-Polaris-Heretic-qx64-hi-mlx 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 nightmedia/Qwen3.6-27B-Architect-DS9-Polaris-Heretic-qx64-hi-mlx to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for nightmedia/Qwen3.6-27B-Architect-DS9-Polaris-Heretic-qx64-hi-mlx to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="nightmedia/Qwen3.6-27B-Architect-DS9-Polaris-Heretic-qx64-hi-mlx", max_seq_length=2048, ) - Pi
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 @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": "nightmedia/Qwen3.6-27B-Architect-DS9-Polaris-Heretic-qx64-hi-mlx" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
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
- 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
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)
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Model tree for nightmedia/Qwen3.6-27B-Architect-DS9-Polaris-Heretic-qx64-hi-mlx
Base model
trohrbaugh/Qwen3.6-27B-heretic-ara