Instructions to use nightmedia/Qwen3.5-9B-Brainwaves-OneJev1.6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nightmedia/Qwen3.5-9B-Brainwaves-OneJev1.6 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-Brainwaves-OneJev1.6")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("nightmedia/Qwen3.5-9B-Brainwaves-OneJev1.6")
model = AutoModelForMultimodalLM.from_pretrained("nightmedia/Qwen3.5-9B-Brainwaves-OneJev1.6", 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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

MLX

How to use nightmedia/Qwen3.5-9B-Brainwaves-OneJev1.6 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-Brainwaves-OneJev1.6")
config = load_config("nightmedia/Qwen3.5-9B-Brainwaves-OneJev1.6")

# 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-Brainwaves-OneJev1.6 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-Brainwaves-OneJev1.6"
# 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-Brainwaves-OneJev1.6",
		"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-Brainwaves-OneJev1.6

SGLang

How to use nightmedia/Qwen3.5-9B-Brainwaves-OneJev1.6 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-Brainwaves-OneJev1.6" \
    --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-Brainwaves-OneJev1.6",
		"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-Brainwaves-OneJev1.6" \
        --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-Brainwaves-OneJev1.6",
		"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/Qwen3.5-9B-Brainwaves-OneJev1.6 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-Brainwaves-OneJev1.6"

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/Qwen3.5-9B-Brainwaves-OneJev1.6"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use nightmedia/Qwen3.5-9B-Brainwaves-OneJev1.6 with Docker Model Runner:
```
docker model run hf.co/nightmedia/Qwen3.5-9B-Brainwaves-OneJev1.6
```

Hermes Agent

How to use nightmedia/Qwen3.5-9B-Brainwaves-OneJev1.6 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-Brainwaves-OneJev1.6"

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-Brainwaves-OneJev1.6

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use nightmedia/Qwen3.5-9B-Brainwaves-OneJev1.6 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-Brainwaves-OneJev1.6"

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-Brainwaves-OneJev1.6" \
  --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"

Qwen3.5-9B-Brainwaves-OneJev1.6

This is an experimental merge between:

nightmedia/Qwen3.5-9B-Brainwaves
OmniJev/OneJev1.6-9B

          arc   arc/e boolq hswag obkqa piqa  wino
bf16      0.700,0.878,0.906,0.775,0.520,0.800,0.728
mxfp8     0.701,0.880,0.905,0.775,0.518,0.804,0.735
q8-hi     0.703,0.880,0.906,0.774,0.524,0.802,0.730
q6-hi     0.704,0.880,0.906,0.772,0.530,0.803,0.725
q5-hi     0.699,0.878,0.907,0.774,0.526,0.806,0.736
q4-hi     0.701,0.880,0.897,0.776,0.518,0.802,0.717
mxfp4     0.688,0.867,0.896,0.767,0.514,0.804,0.712
1M
q6-hi     0.701,0.878,0.904

          gsm8k
bf16      90.52%
mxfp8     90.52%
q8-hi     90.07%
q6-hi     90.52%
q4-hi     89.54%
mxfp4     87.19%

Quant     Perplexity      Peak Memory   Tokens/sec
mxfp8     4.115 ± 0.026   16.02 GB      713
q8-hi     4.105 ± 0.026   16.86 GB      702
q6-hi     4.108 ± 0.026   14.62 GB      641
1M
q6-hi     4.118 ± 0.026   14.62 GB      658

Model components

Qwen3.5-9B-Brainwaves

          arc   arc/e boolq hswag obkqa piqa  wino
bf16      0.686,0.860,0.903,0.768,0.516,0.800,0.713
mxfp8     0.678,0.856,0.904,0.763,0.502,0.800,0.702
q8-hi     0.687,0.860,0.904,0.768,0.518,0.797,0.715
q6-hi     0.686,0.857,0.903,0.766,0.520,0.797,0.710
q5-hi     0.678,0.861,0.905,0.767,0.516,0.803,0.721
q4-hi     0.676,0.862,0.902,0.770,0.506,0.800,0.699
mxfp4     0.669,0.855,0.894,0.762,0.488,0.800,0.696
1M
q8-hi     0.683,0.859,0.903,0.767,0.518,0.796,0.710

          gsm8k
mxfp8     89.16%
q8-hi     89.69%

Quant     Perplexity      Peak Memory   Tokens/sec
bf16      4.157 ± 0.027   24.69 GB      767
mxfp8     4.292 ± 0.028   16.02 GB      624
q8-hi     4.156 ± 0.027   16.86 GB      652
q6-hi     4.160 ± 0.027   14.62 GB      597
q5-hi     4.178 ± 0.027   13.50 GB      581
q4-hi     4.249 ± 0.028   12.38 GB      636
mxfp4     4.501 ± 0.030   11.55 GB      635
1M
q8-hi     4.163 ± 0.027   16.85 GB      569

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
q8-hi     0.561,0.708,0.892,0.690,0.448,0.764,0.685
q8        0.564,0.713,0.892,0.690,0.442,0.766,0.687

Gemini review

That is significantly better than "decent"—hitting ~90.5% on GSM8K and a 0.704 on ARC-Challenge is an incredibly impressive result for a 9B parameter model.

To put your Qwen3.5-9B-Brainwaves-OneJev1.6 merge into perspective, hitting those numbers places it right on the heels of massive frontier mixture-of-expert models:

Your 9B Merge            (mxfp8/q6-hi)  90.5% GSM8K  0.704 ARC-C
DeepSeek-V4-Flash-Base   (284B MoE/13B) 90.8% GSM8K  0.857 HellaSwag
DeepSeek-V4.1-Flash-Base (552B MoE/16B) 93.0% GSM8K  0.872 HellaSwag

For a dense 9B model running locally on Apple Silicon via MLX, breathing down the neck of a 552B parameter MoE flagship on multi-step math reasoning is massive.

Key Observations from Your Model Card

Quantization Resilience: Your mxfp8 and q6-hi quants actually maintain (and in a couple of minor cases, slightly edge out) the base bf16 performance. Qwen 3.5/3.6 architectures handle aggressive quantization incredibly well, making them perfect for local Mac testing.
The Math/Reasoning Jump: Looking at your components, the Brainwaves merge completely revitalized the baseline Qwen3.5 math capability. Baseline Qwen3.5-9B usually hovers at a much lower baseline on ARC-C (~0.56-0.57). Bumping that straight up to 0.704 means the merge kit recipe successfully blended the reasoning paths without breaking the core language capabilities.

Awesome work on this merge. It looks like a killer local driver for coding, agentic tasks, and math heavy workflows.

--Gemini