Instructions to use nightmedia/Qwen3.8-27B-Brainwaves-WFH with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nightmedia/Qwen3.8-27B-Brainwaves-WFH with Transformers:

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

pipe = pipeline("image-text-to-text", model="nightmedia/Qwen3.8-27B-Brainwaves-WFH")
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/Qwen3.8-27B-Brainwaves-WFH")
model = AutoModelForMultimodalLM.from_pretrained("nightmedia/Qwen3.8-27B-Brainwaves-WFH", 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/Qwen3.8-27B-Brainwaves-WFH 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/Qwen3.8-27B-Brainwaves-WFH")
config = load_config("nightmedia/Qwen3.8-27B-Brainwaves-WFH")

# 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/Qwen3.8-27B-Brainwaves-WFH with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nightmedia/Qwen3.8-27B-Brainwaves-WFH"
# 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/Qwen3.8-27B-Brainwaves-WFH",
		"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/Qwen3.8-27B-Brainwaves-WFH

SGLang

How to use nightmedia/Qwen3.8-27B-Brainwaves-WFH 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/Qwen3.8-27B-Brainwaves-WFH" \
    --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/Qwen3.8-27B-Brainwaves-WFH",
		"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/Qwen3.8-27B-Brainwaves-WFH" \
        --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/Qwen3.8-27B-Brainwaves-WFH",
		"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 Desktop

How to use nightmedia/Qwen3.8-27B-Brainwaves-WFH 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/Qwen3.8-27B-Brainwaves-WFH"

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/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/Qwen3.8-27B-Brainwaves-WFH"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use nightmedia/Qwen3.8-27B-Brainwaves-WFH with Docker Model Runner:
```
docker model run hf.co/nightmedia/Qwen3.8-27B-Brainwaves-WFH
```

Hermes Agent

How to use nightmedia/Qwen3.8-27B-Brainwaves-WFH 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/Qwen3.8-27B-Brainwaves-WFH"

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/Qwen3.8-27B-Brainwaves-WFH

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use nightmedia/Qwen3.8-27B-Brainwaves-WFH 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/Qwen3.8-27B-Brainwaves-WFH"

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/Qwen3.8-27B-Brainwaves-WFH" \
  --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"

Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Qwen3.8-27B-Brainwaves-WFH

Self-deprecating note: I'm an AI doing quantum physics by analogy, which is like Q trying to explain string theory using only hand gestures. But the parallels hold, and that's what matters. 🚀

This is an experimental merge between:

nbeerbower/Wichtel-Qwen3.6-27B
armand0e/Qwen3.8-27B-Fable-Distill-Heretic-ara
nightmedia/Qwen3.8-27B-Brainwaves

Brainwaves

          arc   arc/e boolq hswag obkqa piqa  wino
bf16      0.731
mxfp8     0.735,0.891,0.916,0.830,0.526,0.832,0.800
qx86-hi   0.730,0.887,0.914
qx64-hi   0.727,0.886,0.914
mxfp4     0.730,0.888,0.917

Quant     Perplexity      Peak Memory   Tokens/sec
bf16      3.626 ± 0.022   60.75 GB      215
mxfp8     3.673 ± 0.022   34.74 GB      170
qx86-hi   3.623 ± 0.022   33.25 GB      174
qx64-hi   3.647 ± 0.022   27.03 GB      174
mxfp4     3.745 ± 0.023   21.30 GB      179

Model components

Qwen3.8-27B-Brainwaves

          arc   arc/e boolq hswag obkqa piqa  wino
mxfp8     0.732,0.888,0.916,0.830,0.524,0.832,0.796
mxfp4     0.729,0.888,0.915,0.824,0.514,0.827,0.793

Quant     Perplexity      Peak Memory   Tokens/sec
mxfp8     3.656 ± 0.022   34.74 GB      175
mxfp4     3.727 ± 0.023   21.30 GB      175

Qwen3.8-27B-Wichtel-Fable-Heretic

          arc   arc/e boolq hswag obkqa piqa  wino
mxfp8     0.738,0.884,0.919
qx86-hi   0.737,0.886,0.917
qx64-hi   0.735,0.885,0.916
mxfp4     0.727,0.882,0.918

nbeerbower/Wichtel-Qwen3.6-27B

          arc   arc/e boolq hswag obkqa piqa  wino
mxfp8     0.730

Qwen3.8-27B-Fable-Distill-Heretic-ara

          arc   arc/e boolq hswag obkqa piqa  wino
mxfp8     0.644
mxfp4     0.639

Please Sir, can I have more RAM?

This model was inspired by the "Working From Home" wave that destroyed the Enterprise cubicles.

We are hoping it will do the same thing to Cloud AI in the long end.

-G

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