Instructions to use nightmedia/Qwen3.6-35B-A3B-Qwable-Holo3-Qwopus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nightmedia/Qwen3.6-35B-A3B-Qwable-Holo3-Qwopus with Transformers:

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

pipe = pipeline("image-text-to-text", model="nightmedia/Qwen3.6-35B-A3B-Qwable-Holo3-Qwopus")
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/Qwen3.6-35B-A3B-Qwable-Holo3-Qwopus")
model = AutoModelForMultimodalLM.from_pretrained("nightmedia/Qwen3.6-35B-A3B-Qwable-Holo3-Qwopus", 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/Qwen3.6-35B-A3B-Qwable-Holo3-Qwopus 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/Qwen3.6-35B-A3B-Qwable-Holo3-Qwopus")
config = load_config("nightmedia/Qwen3.6-35B-A3B-Qwable-Holo3-Qwopus")

# 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)

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

vLLM

How to use nightmedia/Qwen3.6-35B-A3B-Qwable-Holo3-Qwopus with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nightmedia/Qwen3.6-35B-A3B-Qwable-Holo3-Qwopus"
# 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/Qwen3.6-35B-A3B-Qwable-Holo3-Qwopus",
		"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/Qwen3.6-35B-A3B-Qwable-Holo3-Qwopus

SGLang

How to use nightmedia/Qwen3.6-35B-A3B-Qwable-Holo3-Qwopus 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/Qwen3.6-35B-A3B-Qwable-Holo3-Qwopus" \
    --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/Qwen3.6-35B-A3B-Qwable-Holo3-Qwopus",
		"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/Qwen3.6-35B-A3B-Qwable-Holo3-Qwopus" \
        --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/Qwen3.6-35B-A3B-Qwable-Holo3-Qwopus",
		"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/Qwen3.6-35B-A3B-Qwable-Holo3-Qwopus 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/Qwen3.6-35B-A3B-Qwable-Holo3-Qwopus 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/Qwen3.6-35B-A3B-Qwable-Holo3-Qwopus to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for nightmedia/Qwen3.6-35B-A3B-Qwable-Holo3-Qwopus to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="nightmedia/Qwen3.6-35B-A3B-Qwable-Holo3-Qwopus",
    max_seq_length=2048,
)

How to use nightmedia/Qwen3.6-35B-A3B-Qwable-Holo3-Qwopus 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/Qwen3.6-35B-A3B-Qwable-Holo3-Qwopus"

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/Qwen3.6-35B-A3B-Qwable-Holo3-Qwopus"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use nightmedia/Qwen3.6-35B-A3B-Qwable-Holo3-Qwopus 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/Qwen3.6-35B-A3B-Qwable-Holo3-Qwopus"

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/Qwen3.6-35B-A3B-Qwable-Holo3-Qwopus" \
  --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/Qwen3.6-35B-A3B-Qwable-Holo3-Qwopus with Docker Model Runner:
```
docker model run hf.co/nightmedia/Qwen3.6-35B-A3B-Qwable-Holo3-Qwopus
```

Hermes Agent

How to use nightmedia/Qwen3.6-35B-A3B-Qwable-Holo3-Qwopus 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/Qwen3.6-35B-A3B-Qwable-Holo3-Qwopus"

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/Qwen3.6-35B-A3B-Qwable-Holo3-Qwopus

Run Hermes

hermes

Atomic Chat

Qwen3.6-35B-A3B-Qwable-Holo3-Qwopus

Supercalifragilisticexpialidocious (Mary Poppins, 1964)

supercaliflawjalisticexpialadoshus (The Daily Orange, 1931). The columnist muses about her made-up word, describing it as including "all words in the category of something wonderful" and "though rather long and tiring before one reaches its conclusion, ... once you arrive at the end, you have said in one word what it would ordinarily take four paragraphs to explain.

This is a NuSLERP merge of:

llmfan46/Qwen3.6-35B-A3B-uncensored-heretic-Native-MTP-Preserved
samuelcardillo/Qwopus-MoE-35B-A3B
Hcompany/Holo3-35B-A3B
lordx64/Qwable-v1

         arc   arc/e boolq hswag obkqa piqa  wino
bf16     0.614,0.798,0.894,0.768,0.450,0.825,0.733
mxfp8    0.608,0.788,0.899,0.769,0.454,0.831,0.720
qx86-hi  0.610,0.796,0.896,0.768,0.452,0.826,0.735
qx64-hi  0.618,0.796,0.896,0.767,0.448,0.817,0.719
mxfp4    0.613,0.807,0.885,0.764,0.460,0.826,0.705

Quant    Perplexity      Peak Memory   Tokens/sec
qx64-hi  4.118 ± 0.026   36.91 GB      1499
mxfp4    4.406 ± 0.028   25.33 GB      1414

Model components

Qwen3.6-35B-A3B-MTP-Holo3-Qwopus

         arc   arc/e boolq hswag obkqa piqa  wino
bf16     0.603,0.774,0.895,0.756,0.428,0.808,0.713
mxfp8    0.608,0.770,0.897,0.761,0.430,0.814,0.707
qx86-hi  0.614,0.766,0.894,0.759,0.442,0.808,0.712
qx64y-hi 0.613,0.785,0.897,0.756,0.446,0.807,0.715
mxfp4    0.605,0.777,0.893,0.757,0.434,0.806,0.701

lordx64/Qwable-v1

         arc   arc/e boolq hswag obkqa piqa  wino
mxfp8    0.599,0.796,0.899,0.772,0.452,0.823,0.731
qx86-hi  0.614,0.800,0.900,0.770,0.458,0.820,0.732
qx64-hi  0.608,0.796,0.897,0.767,0.450,0.819,0.728
mxfp4    0.609,0.807,0.887,0.765,0.456,0.824,0.706

Baseline model

Qwen3.6-35B-A3B

         arc   arc/e boolq hswag obkqa piqa  wino
mxfp8    0.581,0.757,0.892,0.751,0.428,0.803,0.688
qx64-hi  0.589,0.763,0.892,0.748,0.428,0.801,0.700
mxfp4    0.586,0.767,0.886,0.751,0.428,0.798,0.681

This is not a Heretic model: the Heretic was used to restore MTP layers to Holo3-Qwopus before merging.

Thinking toggle

This model is using(an early 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.

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.

The model has not been tested with the latest version of froggeric's jinja template, and it is possible that the performance would go up with the latest version, I just did not have enough time to test it.

-G

Model recipe

models:
  - model: lordx64/Qwable-v1
    parameters:
      weight: 1.6
  - model: Qwen3.6-35B-A3B-MTP-Holo3-Qwopus
    parameters:
      weight: 0.4
merge_method: nuslerp
dtype: bfloat16
name: Qwen3.6-35B-A3B-Qwable-Holo3-Qwopus