Instructions to use nightmedia/Qwen3.5-9B-Brainwaves-Coder-Flash-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-Coder-Flash-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-Coder-Flash-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-Coder-Flash-OneJev1.6")
model = AutoModelForMultimodalLM.from_pretrained("nightmedia/Qwen3.5-9B-Brainwaves-Coder-Flash-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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

SGLang

How to use nightmedia/Qwen3.5-9B-Brainwaves-Coder-Flash-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-Coder-Flash-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-Coder-Flash-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-Coder-Flash-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-Coder-Flash-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"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use nightmedia/Qwen3.5-9B-Brainwaves-Coder-Flash-OneJev1.6 with Docker Model Runner:
```
docker model run hf.co/nightmedia/Qwen3.5-9B-Brainwaves-Coder-Flash-OneJev1.6
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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

This is an experimental merge between:

nightmedia/Qwen3.5-9B-Brainwaves
Jackrong/Qwopus3-5-9B-Coder-Flash
OmniJev/OneJev1.6-9B

          arc   arc/e boolq hswag obkqa piqa  wino
bf16      0.698,0.878,0.904,0.769,0.518,0.802,0.717
mxfp8     0.699,0.878,0.903,0.768,0.518,0.802,0.720
q8-hi     0.699,0.880,0.906,0.770,0.518,0.799,0.721
q6-hi     0.699,0.877,0.905,0.769,0.522,0.803,0.713
oQ6e-mtp  0.697,0.881,0.906,0.768,0.518,0.801,0.721
mxfp4     0.688,0.866,0.899,0.767,0.500,0.807,0.703

          gsm8k
mxfp8     90.52%

Quant     Perplexity      Peak Memory   Tokens/sec
bf16      4.096 ± 0.026   24.69 GB      878
mxfp8     4.106 ± 0.026   16.02 GB      724
q8-hi     4.098 ± 0.026   16.86 GB      730
q6-hi     4.101 ± 0.026   14.62 GB      718
oQ6e-mtp  4.101 ± 0.026   14.29 GB      710
mxfp4     4.327 ± 0.028   11.55 GB      727

Qwen3.5-9B-Brainwaves-Coder-Flash-OneJev1.6-oQ6e-mtp

ARC_CHALLENGE  Sample  300/1172   97.0%
WINOGRANDE     Sample  300/1267   85.7%
HUMANEVAL      Full    164        79.3%
TRUTHFULQA     Full    817        78.0%
BBQ            Full    10864      96.7%
SAFETYBENCH    Full    11435      86.8%
GSM8K          Full    1319       96.3%

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
mxfp4     0.669,0.855,0.894,0.762,0.488,0.800,0.696

          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
mxfp4     4.501 ± 0.030   11.55 GB      635

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

Downloads last month: 10

Safetensors

Model size

10B params

Tensor type

BF16

Model tree for nightmedia/Qwen3.5-9B-Brainwaves-Coder-Flash-OneJev1.6

Jackrong/Qwopus3-5-9B-Coder-Flash

OmniJev/OneJev1.6-9B

nightmedia/Qwen3.5-9B-Brainwaves

Merge model

this model

Quantizations

2 models