Instructions to use nightmedia/Qwen3.5-9B-TNG-PKD-Qwopus-Coder-Fable-Polaris-mxfp4-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nightmedia/Qwen3.5-9B-TNG-PKD-Qwopus-Coder-Fable-Polaris-mxfp4-mlx with Transformers:

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

pipe = pipeline("image-text-to-text", model="nightmedia/Qwen3.5-9B-TNG-PKD-Qwopus-Coder-Fable-Polaris-mxfp4-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/Qwen3.5-9B-TNG-PKD-Qwopus-Coder-Fable-Polaris-mxfp4-mlx")
model = AutoModelForMultimodalLM.from_pretrained("nightmedia/Qwen3.5-9B-TNG-PKD-Qwopus-Coder-Fable-Polaris-mxfp4-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/Qwen3.5-9B-TNG-PKD-Qwopus-Coder-Fable-Polaris-mxfp4-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/Qwen3.5-9B-TNG-PKD-Qwopus-Coder-Fable-Polaris-mxfp4-mlx")
config = load_config("nightmedia/Qwen3.5-9B-TNG-PKD-Qwopus-Coder-Fable-Polaris-mxfp4-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/Qwen3.5-9B-TNG-PKD-Qwopus-Coder-Fable-Polaris-mxfp4-mlx with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nightmedia/Qwen3.5-9B-TNG-PKD-Qwopus-Coder-Fable-Polaris-mxfp4-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/Qwen3.5-9B-TNG-PKD-Qwopus-Coder-Fable-Polaris-mxfp4-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/Qwen3.5-9B-TNG-PKD-Qwopus-Coder-Fable-Polaris-mxfp4-mlx

SGLang

How to use nightmedia/Qwen3.5-9B-TNG-PKD-Qwopus-Coder-Fable-Polaris-mxfp4-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/Qwen3.5-9B-TNG-PKD-Qwopus-Coder-Fable-Polaris-mxfp4-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/Qwen3.5-9B-TNG-PKD-Qwopus-Coder-Fable-Polaris-mxfp4-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/Qwen3.5-9B-TNG-PKD-Qwopus-Coder-Fable-Polaris-mxfp4-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/Qwen3.5-9B-TNG-PKD-Qwopus-Coder-Fable-Polaris-mxfp4-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/Qwen3.5-9B-TNG-PKD-Qwopus-Coder-Fable-Polaris-mxfp4-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/Qwen3.5-9B-TNG-PKD-Qwopus-Coder-Fable-Polaris-mxfp4-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/Qwen3.5-9B-TNG-PKD-Qwopus-Coder-Fable-Polaris-mxfp4-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/Qwen3.5-9B-TNG-PKD-Qwopus-Coder-Fable-Polaris-mxfp4-mlx to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="nightmedia/Qwen3.5-9B-TNG-PKD-Qwopus-Coder-Fable-Polaris-mxfp4-mlx",
    max_seq_length=2048,
)

How to use nightmedia/Qwen3.5-9B-TNG-PKD-Qwopus-Coder-Fable-Polaris-mxfp4-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/Qwen3.5-9B-TNG-PKD-Qwopus-Coder-Fable-Polaris-mxfp4-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/Qwen3.5-9B-TNG-PKD-Qwopus-Coder-Fable-Polaris-mxfp4-mlx"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use nightmedia/Qwen3.5-9B-TNG-PKD-Qwopus-Coder-Fable-Polaris-mxfp4-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/Qwen3.5-9B-TNG-PKD-Qwopus-Coder-Fable-Polaris-mxfp4-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/Qwen3.5-9B-TNG-PKD-Qwopus-Coder-Fable-Polaris-mxfp4-mlx

Run Hermes

hermes

OpenClaw new

How to use nightmedia/Qwen3.5-9B-TNG-PKD-Qwopus-Coder-Fable-Polaris-mxfp4-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/Qwen3.5-9B-TNG-PKD-Qwopus-Coder-Fable-Polaris-mxfp4-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/Qwen3.5-9B-TNG-PKD-Qwopus-Coder-Fable-Polaris-mxfp4-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"

Docker Model Runner
How to use nightmedia/Qwen3.5-9B-TNG-PKD-Qwopus-Coder-Fable-Polaris-mxfp4-mlx with Docker Model Runner:
```
docker model run hf.co/nightmedia/Qwen3.5-9B-TNG-PKD-Qwopus-Coder-Fable-Polaris-mxfp4-mlx
```

Qwen3.5-9B-TNG-PKD-Qwopus-Coder-Fable-Polaris-mxfp4-mlx

..what is known is that these workers included not only Irish-Americans and Irish immigrants, but Italians, Scandinavians, Eastern Europeans, Germans, and even Mohawk ironworkers from Canada. (For more than 100 years, Mohawk tribe members have helped build virtually every prominent skyscraper in New York City—Rockefeller Center, the Empire State and Chrysler buildings included.) As a result, people of all different backgrounds from around the world have claimed knowledge of the men in the photo.

https://www.rockefellercenter.com/magazine/arts-culture/lunch-atop-skyscraper-irish-immigrants/

This is a merge between:

nightmedia/Qwen3.5-9B-TNG-PKD-Qwopus-Coder-Fable
DavidAU/Qwen3.5-9B-Polaris-PolarisQwen-3NMDST2

Brainwaves

          arc   arc/e boolq hswag obkqa piqa  wino
bf16      0.653,0.830,0.893,0.716,0.466,0.779,0.706
mxfp8     0.653,0.832,0.896,0.707,0.454,0.787,0.691
qx86-hi   0.648,0.831,0.892,0.715,0.466,0.779,0.705
qx64-hi   0.643,0.827,0.884,0.715,0.448,0.782,0.705
mxfp4     0.633,0.822,0.884,0.708,0.462,0.782,0.686

Quant    Perplexity      Peak Memory   Tokens/sec
bf16     4.175 ± 0.027   24.69 GB      645
mxfp8    4.291 ± 0.028   16.02 GB      574
qx86-hi  4.174 ± 0.027   15.72 GB      482
qx64-hi  4.231 ± 0.027   13.62 GB      488
mxfp4    4.461 ± 0.029   11.55 GB      610

Model components

Qwen3.5-9B-TNG-PKD-Qwopus-Coder-Fable

          arc   arc/e boolq hswag obkqa piqa  wino
bf16      0.643,0.822,0.898,0.718,0.468,0.783,0.707
mxfp8     0.637,0.819,0.892

Qwen3.5-9B-Polaris-PolarisQwen-3NMDST2

          arc   arc/e boolq hswag obkqa piqa  wino
bf16      0.638,0.829,0.875,0.677,0.450,0.763,0.664
mxfp8     0.628,0.828,0.874,0.668,0.436,0.755,0.672
qx86-hi   0.638,0.832,0.876,0.677,0.446,0.762,0.658

Baseline model

Qwen3.5-9B-Instruct

          arc   arc/e boolq hswag obkqa piqa  wino
mxfp8     0.571,0.719,0.895,0.683,0.426,0.770,0.671

This last merge brings a set of TNG-infused coding sessions recorded in the DS9 Holodeck.

The lessons include:

Golang coding with Worf and Dax, Nog
Python coding with Odo and Julian
Haskell coding with Spock and Data

In all sessions Sisko, Kira, Garak, Quark, Q, and some other DS9 characters provided expert commentary, both in the think tag and in the response.

Along with the lessons, some of the Polaris Alpha questions in the original set were distilled from nightmedia/Qwen3.6-35B-A3B-Holo3-Qwopus-qx86-hi-mlx

The curriculum is exclusively in backend engineering, LLM design, and Holodeck operations(including Quark's Bar and Latinum economy), and was designed with the help of Google Gemini.

-G

Model lineage

Qwen3.5-9B-TNG-PKD-Qwopus-Coder-Fable

nightmedia/Qwen3.5-9B-TNG-PKD-Qwopus-Coder
armand0e/Qwen3.5-9B-Fable-5-v1

Bringing outstanding Fable traces with tool use and agentic in a very well trained model. Since Fable is XML-friendly, this model reasons better with an XML template--best is the one from DavidAU. Tools work better with json, the current template.

Qwen3.5-9B-TNG-PKD-Qwopus-Coder

nightmedia/Qwen3.5-9B-Claude-GBO-Fire-Deckard-Agent-Qwopus3.5-Coder-Heretic
DavidAU/Qwen3.5-9B-Haskell-Rust-Python

This is a set of TNG-infused coding traces, exclusively profiled for Holodeck use, AI training, LLM design, in-memory ops.

Qwen3.5-9B-Claude-GBO-Fire-Deckard-Agent-Qwopus3.5-Coder-Heretic

nightmedia/Qwen3.5-9B-Claude-GBO-Fire-Deckard-Agent-Heretic
nightmedia/Qwen3.5-9B-Claude-GBO-Fire-Deckard-Qwopus3.5-Coder-Heretic

Coalesces the previous two models, giving the voice to Agent

Qwen3.5-9B-Claude-GBO-Fire-Deckard-Qwopus3.5-Coder-Heretic

nightmedia/Qwen3.5-9B-Claude-GBO-Fire-Deckard-Heretic-Thinking
Jackrong/Qwopus3.5-9B-Coder

Jack managed to build a good model, and since it's different than how the other traces are trained it will raise IQ

Qwen3.5-9B-Claude-GBO-Fire-Deckard-Agent-Heretic

nightmedia/Qwen3.5-9B-Claude-GBO-Fire-Deckard-Heretic-Thinking
armand0e/Qwen3.5-9B-Agent

Agent is a collection of quality traces from all over the world, curated by armand0e

armand0e/badlogicgames-pi-mono-opus-filtered - Pi traces from Claude Opus (mainly 4.5)
armand0e/kimi-k2.6-claude-code-traces - Claude Code traces from kimi k2.6
armand0e/kimi-k2.6-agent - Codex traces from kimi k2.6
armand0e/minimax-m2.7-agent - Pi traces from minimax m2.7
TeichAI/Claude-Opus-4.6-Reasoning-887x (Downsampled to 200 examples, only present to stabilize chat behavior)

Qwen3.5-9B-Claude-GBO-Fire-Deckard-Heretic-Thinking

nightmedia/Qwen3.5-9B-Claude-GBO-Fire-Heretic-Thinking
DavidAU/Qwen3.5-9B-Deckard-Uncensored-Heretic-Thinking

Philip K Dick joins the merge. From here on, there be dragons.

A PKD base will allow multiple realities to develop in a merge.

PKD provides the neural wiring that allows completely different new models to keep joining the NuSLERP.

Since the model is on an ablit base, PKD is at full strength.

Qwen3.5-9B-Claude-GBO-Fire-Heretic-Thinking

DavidAU/Qwen3.5-9B-Claude-4.6-OS-Auto-Variable-HERETIC-UNCENSORED-THINKING-X8b
DavidAU/Qwen3.5-9B-GBO-Fire-HERETIC-UNCENSORED-THINKING-X8

Excellent Claude trained model joined with a special mix of GLM/Polaris Alpha

Thinking toggle

This model is using an earlier version of the fixed jinja template from froggeric/Qwen-Fixed-Chat-Templates

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.

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.

Model recipe

models:
  - model: DavidAU/Qwen3.5-9B-Claude-4.6-OS-Auto-Variable-HERETIC-UNCENSORED-THINKING-X8b
    parameters:
      weight: 1.4
  - model: DavidAU/Qwen3.5-9B-GBO-Fire-HERETIC-UNCENSORED-THINKING-X8
    parameters:
      weight: 0.6
merge_method: nuslerp
dtype: bfloat16
name: Qwen3.5-9B-Claude-GBO-Fire-Heretic-Thinking


models:
  - model: Qwen3.5-9B-Claude-GBO-Fire-Heretic-Thinking
    parameters:
      weight: 1.3
  - model: Qwen3.5-9B-Deckard-Uncensored-Heretic-Thinking
    parameters:
      weight: 0.7
merge_method: nuslerp
dtype: bfloat16
name: Qwen3.5-9B-Claude-GBO-Fire-Deckard-Heretic-Thinking


models:
  - model: Qwen3.5-9B-Claude-GBO-Fire-Deckard-Heretic-Thinking
    parameters:
      weight: 1.6
  - model: armand0e/Qwen3.5-9B-Agent
    parameters:
      weight: 0.4
merge_method: nuslerp
dtype: bfloat16
name: Qwen3.5-9B-Claude-GBO-Fire-Deckard-Agent-Heretic


models:
  - model: Qwen3.5-9B-Claude-GBO-Fire-Deckard-Heretic-Thinking
    parameters:
      weight: 1.6
  - model: Jackrong/Qwopus3.5-9B-Coder
    parameters:
      weight: 0.4
merge_method: nuslerp
dtype: bfloat16
name: Qwen3.5-9B-Claude-GBO-Fire-Deckard-Qwopus3.5-Coder-Heretic


models:
  - model: Qwen3.5-9B-Claude-GBO-Fire-Deckard-Agent-Heretic
    parameters:
      weight: 1.6
  - model: Qwen3.5-9B-Claude-GBO-Fire-Deckard-Qwopus3.5-Coder-Heretic
    parameters:
      weight: 0.4
merge_method: nuslerp
dtype: bfloat16
name: Qwen3.5-9B-Claude-GBO-Fire-Deckard-Agent-Qwopus3.5-Coder-Heretic

models:
  - model: Qwen3.5-9B-Claude-GBO-Fire-Deckard-Agent-Qwopus3.5-Coder-Heretic
    parameters:
      weight: 1.6
  - model: DavidAU/Qwen3.5-9B-Haskell-Rust-Python
    parameters:
      weight: 0.4
merge_method: nuslerp
dtype: bfloat16
name: Qwen3.5-9B-TNG-PKD-Qwopus-Coder

models:
  - model: Qwen3.5-9B-TNG-PKD-Qwopus-Coder
    parameters:
      weight: 1.6
  - model: armand0e/Qwen3.5-9B-Fable-5-v1
    parameters:
      weight: 0.4
merge_method: nuslerp
dtype: bfloat16
name: Qwen3.5-9B-TNG-PKD-Qwopus-Coder-Fable


models:
  - model: Qwen3.5-9B-TNG-PKD-Qwopus-Coder-Fable
    parameters:
      weight: 1.6
  - model: DavidAU/Qwen3.5-9B-Polaris-PolarisQwen-3NMDST2
    parameters:
      weight: 0.4
merge_method: nuslerp
dtype: bfloat16

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("Qwen3.5-9B-TNG-PKD-Qwopus-Coder-Fable-Polaris-mxfp4-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)

Downloads last month: 1,383

Safetensors

Model size

2B params

Tensor type

U32

BF16

MLX

Hardware compatibility

4-bit

Model tree for nightmedia/Qwen3.5-9B-TNG-PKD-Qwopus-Coder-Fable-Polaris-mxfp4-mlx

Jackrong/Qwopus3.5-9B-Coder

TeichAI/Qwen3.5-9B-Fable-5-v1

armand0e/Qwen3.5-9B-Coder

Merge model

this model