Instructions to use nightmedia/Qwen3.6-27B-Architect-Polaris2-Fable-B-F451-Tess with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nightmedia/Qwen3.6-27B-Architect-Polaris2-Fable-B-F451-Tess 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-Polaris2-Fable-B-F451-Tess")
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-Polaris2-Fable-B-F451-Tess")
model = AutoModelForMultimodalLM.from_pretrained("nightmedia/Qwen3.6-27B-Architect-Polaris2-Fable-B-F451-Tess", 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-Polaris2-Fable-B-F451-Tess 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-Polaris2-Fable-B-F451-Tess")
config = load_config("nightmedia/Qwen3.6-27B-Architect-Polaris2-Fable-B-F451-Tess")

# 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-Polaris2-Fable-B-F451-Tess 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-Polaris2-Fable-B-F451-Tess"
# 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-Polaris2-Fable-B-F451-Tess",
		"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-Polaris2-Fable-B-F451-Tess

SGLang

How to use nightmedia/Qwen3.6-27B-Architect-Polaris2-Fable-B-F451-Tess 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-Polaris2-Fable-B-F451-Tess" \
    --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-Polaris2-Fable-B-F451-Tess",
		"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-Polaris2-Fable-B-F451-Tess" \
        --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-Polaris2-Fable-B-F451-Tess",
		"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-Polaris2-Fable-B-F451-Tess 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-Polaris2-Fable-B-F451-Tess 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-Polaris2-Fable-B-F451-Tess 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-Polaris2-Fable-B-F451-Tess 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-Polaris2-Fable-B-F451-Tess",
    max_seq_length=2048,
)

How to use nightmedia/Qwen3.6-27B-Architect-Polaris2-Fable-B-F451-Tess 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-Polaris2-Fable-B-F451-Tess"

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-Polaris2-Fable-B-F451-Tess"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use nightmedia/Qwen3.6-27B-Architect-Polaris2-Fable-B-F451-Tess 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-Polaris2-Fable-B-F451-Tess"

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-Polaris2-Fable-B-F451-Tess

Run Hermes

hermes

OpenClaw new

How to use nightmedia/Qwen3.6-27B-Architect-Polaris2-Fable-B-F451-Tess 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-Polaris2-Fable-B-F451-Tess"

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-Polaris2-Fable-B-F451-Tess" \
  --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"

Docker Model Runner
How to use nightmedia/Qwen3.6-27B-Architect-Polaris2-Fable-B-F451-Tess with Docker Model Runner:
```
docker model run hf.co/nightmedia/Qwen3.6-27B-Architect-Polaris2-Fable-B-F451-Tess
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Qwen3.6-27B-Architect-Polaris2-Fable-B-F451-Tess

This model is a NuSLERP merge of:

migtissera/Tess-4-27B
nightmedia/Qwen3.6-27B-Architect-Polaris-Fable-F451

Contributing models:

migtissera/Tess-4-27B
armand0e/Qwen3.6-27B-Fable-5-Experimental
DavidAU/Qwen3.5-27B-Claude-4.6-OS-INSTRUCT
DavidAU/Qwen3.5-27B-Polar-Rev1-Uncensored-Heretic
DavidAU/Qwen3.6-27B-Heretic2-Uncensored-Finetune-Thinking
DavidAU/Qwen3.6-27B-F451-AND-TRI-Polar-Ultra-Pro-Writer-Uncensored-Heretic

Brainwaves

          arc   arc/e boolq hswag obkqa piqa  wino
mxfp8     0.712,0.879,0.911,0.792,0.508,0.823,0.764
qx86-hi   0.701,0.877,0.911,0.794,0.518,0.823,0.758
qx64-hi   0.706,0.873,0.909,0.795,0.512,0.823,0.752
mxfp4     0.706,0.873,0.910,0.790,0.496,0.817,0.761
1M
mxfp8     0.703,0.877,0.909
qx86-hi   0.702,0.873,0.911,0.793,0.508,0.824,0.765
qx64-hi   0.706,0.873,0.909,0.795,0.512,0.823,0.752
mxfp4     0.701,0.874,0.912,0.789,0.500,0.817,0.759

Quant     Perplexity      Peak Memory   Tokens/sec
mxfp8     3.797 ± 0.024   34.74 GB      183
qx86-hi   3.756 ± 0.023   33.25 GB      170
qx64-hi   3.765 ± 0.023   27.03 GB      181
mxfp4     3.876 ± 0.024   21.26 GB      176
1M
mxfp8     3.803 ± 0.024   34.70 GB      169
qx86-hi   3.758 ± 0.023   33.21 GB      175
qx64-hi   3.769 ± 0.023   26.99 GB      172
mxfp4     3.876 ± 0.024   21.26 GB      176

Model components

Qwen3.6-27B-Architect-Polaris2-Fable-B-F451

          arc   arc/e boolq hswag obkqa piqa  wino
mxfp8     0.711,0.879,0.910,0.790,0.514,0.823,0.763
qx86-hi   0.696,0.876,0.912,0.791,0.518,0.824,0.760
qx64-hi   0.702,0.873,0.909,0.794,0.514,0.822,0.750
mxfp4     0.701,0.873,0.909,0.786,0.488,0.813,0.759

Quant     Perplexity      Peak Memory   Tokens/sec
mxfp8     3.783 ± 0.023   34.74 GB      203
qx86-hi   3.735 ± 0.023   33.25 GB      183
qx64-hi   3.747 ± 0.023   27.03 GB      194
mxfp4     3.854 ± 0.024   21.30 GB      197

Qwen3.6-27B-Architect-Polaris2-Fable-B-Tess

          arc   arc/e boolq hswag obkqa piqa  wino
mxfp8     0.708,0.879,0.911

Quant     Perplexity      Peak Memory   Tokens/sec
mxfp8     3.870 ± 0.024   34.74 GB      188

Qwen3.6-27B-Architect-Polaris2-Fable-B

          arc   arc/e boolq hswag obkqa piqa  wino
mxfp8     0.706,0.875,0.911,0.788,0.516,0.821,0.769
qx86-hi   0.701,0.873,0.911,0.787,0.506,0.823,0.758

Quant     Perplexity      Peak Memory   Tokens/sec
mxfp8     3.794 ± 0.024   34.74 GB      171
qx86-hi   3.750 ± 0.023   33.25 GB      181
qx64-hi   3.769 ± 0.023   27.03 GB      163
mxfp4     3.871 ± 0.024   21.30 GB      158

migtissera/Tess-4-27B

          arc   arc/e boolq hswag obkqa piqa  wino
mxfp8     0.648,0.817,0.910

Contribute to NightmediaAI

Nightmedia is an independent AI lab located in Montana, USA.

Our lab is one Macbook Pro 128GB and a few memory cards.

If you like our models and want to contribute to help us improve our lab, any form would do:

ETH:0x6b6633606995BC180925c47d4249ED624aB7b2A5 USDC:0x19e6bDDCBa47BB09a9Bc153Bb6479fc57284421a BTC:36d7U1n3MFaXgnNRAaEL3Pa3Hy6oFhM7XY BCH:15dNMzhJ87XJSTU89VCBsDHj747QvBQaap

My models and I thank you :)

-G

Model recipe

models:
  - model: Qwen3.6-27B-Claude-4.6-OS
    parameters:
      weight: 1.4
  - model: DavidAU/Qwen3.6-27B-Heretic2-Uncensored-Finetune-Thinking
    parameters:
      weight: 0.6
merge_method: nuslerp
dtype: bfloat16
name: Qwen3.6-27B-Architect-Polaris

models:
  - model: Qwen3.6-27B-Architect-Polaris
    parameters:
      weight: 1.6
  - model: armand0e/Qwen3.6-27B-Fable-5-Experimental
    parameters:
      weight: 0.4
merge_method: nuslerp
dtype: bfloat16
name: Qwen3.6-27B-Architect-Polaris-Fable

models:
  - model: Qwen3.6-27B-Architect-Polaris-Fable
    parameters:
      weight: 1.4
  - model: DavidAU/Qwen3.5-27B-Polar-Rev1-Uncensored-Heretic
    parameters:
      weight: 0.6
merge_method: nuslerp
dtype: bfloat16
name: Qwen3.6-27B-Architect-Polaris2-Fable-B

models:
  - model: Qwen3.6-27B-Architect-Polaris-Fable
    parameters:
      weight: 1.4
  - model: DavidAU/Qwen3.6-27B-F451-AND-TRI-Polar-Ultra-Pro-Writer-Uncensored-Heretic
    parameters:
      weight: 0.6
merge_method: nuslerp
dtype: bfloat16
name: Qwen3.6-27B-Architect-Polaris-Fable-F451

models:
  - model: Qwen3.6-27B-Architect-Polaris2-Fable-B
    parameters:
      weight: 1.4
  - model: migtissera/Tess-4-27B
    parameters:
      weight: 0.6
merge_method: nuslerp
dtype: bfloat16
name: Qwen3.6-27B-Architect-Polaris2-Fable-B-Tess

models:
  - model: Qwen3.6-27B-Architect-Polaris2-Fable-B-F451
    parameters:
      weight: 1.4
  - model: Qwen3.6-27B-Architect-Polaris2-Fable-B-Tess
    parameters:
      weight: 0.6
merge_method: nuslerp
dtype: bfloat16
name: Qwen3.6-27B-Architect-Polaris2-Fable-B-F451-Tess

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Safetensors

Model size

28B params

Tensor type

BF16

MLX

Hardware compatibility

Quantized

Model tree for nightmedia/Qwen3.6-27B-Architect-Polaris2-Fable-B-F451-Tess

Base model

Qwen/Qwen3.5-27B

Finetuned

DavidAU/Qwen3.5-27B-Claude-4.6-OS-INSTRUCT

Adapter

(24)

this model

Quantizations

1 model