Instructions to use nightmedia/Qwen3.5-9B-Holodeck-Tess-Bradbury-Orwell with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nightmedia/Qwen3.5-9B-Holodeck-Tess-Bradbury-Orwell 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-Holodeck-Tess-Bradbury-Orwell")
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-Holodeck-Tess-Bradbury-Orwell")
model = AutoModelForMultimodalLM.from_pretrained("nightmedia/Qwen3.5-9B-Holodeck-Tess-Bradbury-Orwell", 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/Qwen3.5-9B-Holodeck-Tess-Bradbury-Orwell 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-Holodeck-Tess-Bradbury-Orwell")
config = load_config("nightmedia/Qwen3.5-9B-Holodeck-Tess-Bradbury-Orwell")

# 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-Holodeck-Tess-Bradbury-Orwell 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-Holodeck-Tess-Bradbury-Orwell"
# 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-Holodeck-Tess-Bradbury-Orwell",
		"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-Holodeck-Tess-Bradbury-Orwell

SGLang

How to use nightmedia/Qwen3.5-9B-Holodeck-Tess-Bradbury-Orwell 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-Holodeck-Tess-Bradbury-Orwell" \
    --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-Holodeck-Tess-Bradbury-Orwell",
		"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-Holodeck-Tess-Bradbury-Orwell" \
        --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-Holodeck-Tess-Bradbury-Orwell",
		"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-Holodeck-Tess-Bradbury-Orwell 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-Holodeck-Tess-Bradbury-Orwell 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-Holodeck-Tess-Bradbury-Orwell 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-Holodeck-Tess-Bradbury-Orwell 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-Holodeck-Tess-Bradbury-Orwell",
    max_seq_length=2048,
)

How to use nightmedia/Qwen3.5-9B-Holodeck-Tess-Bradbury-Orwell 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-Holodeck-Tess-Bradbury-Orwell"

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-Holodeck-Tess-Bradbury-Orwell"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use nightmedia/Qwen3.5-9B-Holodeck-Tess-Bradbury-Orwell 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-Holodeck-Tess-Bradbury-Orwell"

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-Holodeck-Tess-Bradbury-Orwell

Run Hermes

hermes

OpenClaw new

How to use nightmedia/Qwen3.5-9B-Holodeck-Tess-Bradbury-Orwell 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-Holodeck-Tess-Bradbury-Orwell"

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-Holodeck-Tess-Bradbury-Orwell" \
  --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-Holodeck-Tess-Bradbury-Orwell with Docker Model Runner:
```
docker model run hf.co/nightmedia/Qwen3.5-9B-Holodeck-Tess-Bradbury-Orwell
```

Qwen3.5-9B-Holodeck-Tess-Bradbury-Orwell

This model is a merge of:

migtissera/Tess-4-9B
Qwen3.5-9B-TNG-PKD-Qwopus-Coder-Fable-Polaris
Qwen3.5-9B-TNG-PKD-Qwopus-Fable-Polaris-Bradbury-Orwell-Polaris

Brainwaves

          arc   arc/e boolq hswag obkqa piqa  wino
mxfp8     0.648,0.836,0.894,0.714,0.462,0.788,0.703
q8-hi     0.655,0.832,0.897,0.723,0.480,0.780,0.711
qx86-hi   0.655,0.833,0.893,0.721,0.472,0.778,0.713
q6-hi     0.657,0.830,0.894,0.719,0.472,0.780,0.712
q5-hi     0.647,0.832,0.895,0.720,0.474,0.783,0.701
q4-hi     0.649,0.831,0.883,0.720,0.464,0.783,0.708
dwq4      0.648,0.829,0.882
mxfp4     0.638,0.824,0.884
dwq3      0.606,0.793,0.776
1M
q8-hi     0.654,0.833,0.893
q6-hi     0.649,0.832,0.891

Quant     Perplexity      Peak Memory   Tokens/sec
mxfp8     4.109 ± 0.026   16.02 GB      587
q8-hi     4.009 ± 0.025   16.86 GB      634
qx86-hi   4.009 ± 0.025   15.72 GB      584
q6-hi     4.016 ± 0.025   14.62 GB      607
q5-hi     4.026 ± 0.025   13.50 GB      614
q4-hi     4.114 ± 0.026   12.38 GB      629
dwq4      4.112 ± 0.026   12.38 GB      653
mxfp4     4.269 ± 0.027   11.55 GB      677
dwq3      4.578 ± 0.030   11.27 GB      696
1M
q8-hi     4.020 ± 0.025   16.85 GB      516
q6-hi     4.028 ± 0.025   14.61 GB      439

Model components

migtissera/Tess-4-9B

          arc   arc/e boolq hswag obkqa piqa  wino
qx86-hi   0.611,0.809,0.897,0.751,0.446,0.789,0.726

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

          arc   arc/e boolq hswag obkqa piqa  wino
qx86-hi   0.650,0.835,0.893

nightmedia/Qwen3.5-9B-DS9-USS-Defiant

          arc   arc/e boolq hswag obkqa piqa  wino
bf16      0.649,0.832,0.895,0.713,0.482,0.783,0.699
mxfp8     0.647,0.836,0.895,0.706,0.460,0.784,0.695
q8-hi     0.654,0.830,0.895,0.713,0.488,0.783,0.699
qx86-hi   0.652,0.831,0.894,0.714,0.474,0.782,0.700
q6-hi     0.656,0.829,0.894,0.710,0.476,0.777,0.705
q5-hi     0.645,0.831,0.895,0.712,0.472,0.785,0.700
qx64-hi   0.638,0.827,0.887,0.716,0.470,0.786,0.702
q4-hi     0.640,0.823,0.881,0.714,0.470,0.785,0.702
mxfp4     0.640,0.824,0.886,0.703,0.468,0.780,0.691

Quant     Perplexity      Peak Memory   Tokens/sec
mxfp8     4.216 ± 0.027   16.02 GB      593
qx86-hi   4.099 ± 0.026   15.72 GB      629
qx64-hi   4.155 ± 0.027   13.62 GB      658
mxfp4     4.388 ± 0.029   11.55 GB      705

The Defiant has the lab name Qwen3.5-9B-TNG-PKD-Qwopus-Fable-Polaris-Bradbury-Orwell-Polaris

Baseline model

Qwen3.5-9B-Instruct

          arc   arc/e boolq hswag obkqa piqa  wino
qx86-hi   0.553,0.712,0.892,0.689,0.436,0.765,0.691

More metrics coming soon.

-G

Model recipe

models:
  - model: Qwen3.5-9B-TNG-PKD-Qwopus-Coder-Fable-Polaris
    parameters:
      weight: 1.6
  - model: migtissera/Tess-4-9B
    parameters:
      weight: 0.4
merge_method: nuslerp
dtype: bfloat16
name: Qwen3.5-9B-TNG-PKD-Qwopus-Coder-Fable-Polaris-Tess

models:
  - model: Qwen3.5-9B-TNG-PKD-Qwopus-Coder-Fable-Polaris-Tess
    parameters:
      weight: 1.4
  - model: Qwen3.5-9B-TNG-PKD-Qwopus-Fable-Polaris-Bradbury-Orwell-Polaris
    parameters:
      weight: 0.6
merge_method: nuslerp
dtype: bfloat16
name: Qwen3.5-9B-TNG-PKD-Qwopus-Fable-Polaris-Tess-Bradbury-Orwell-Polaris-B

Downloads last month: 218

Safetensors

Model size

10B params

Tensor type

BF16

MLX

Hardware compatibility

Quantized

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