Instructions to use schneewolflabs/B0-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use schneewolflabs/B0-9B with Transformers:

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

pipe = pipeline("image-text-to-text", model="schneewolflabs/B0-9B")
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("schneewolflabs/B0-9B")
model = AutoModelForMultimodalLM.from_pretrained("schneewolflabs/B0-9B", 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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use schneewolflabs/B0-9B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "schneewolflabs/B0-9B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "schneewolflabs/B0-9B",
		"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/schneewolflabs/B0-9B

SGLang

How to use schneewolflabs/B0-9B 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 "schneewolflabs/B0-9B" \
    --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": "schneewolflabs/B0-9B",
		"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 "schneewolflabs/B0-9B" \
        --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": "schneewolflabs/B0-9B",
		"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 schneewolflabs/B0-9B with Docker Model Runner:
```
docker model run hf.co/schneewolflabs/B0-9B
```

Continuum

by nightmedia - opened 12 days ago

Discussion

nightmedia

12 days ago

•

edited 12 days ago

Great work Nina!

InclusionAI, the top chinese lab designed a model to work like a policeman, Not smart, efficient. Strong vision, low IQ.

You created the B0-9B to give the model personality, I added the Council, and Venus went to Vegas :)

https://huggingface.co/nightmedia/Qwen3.5-9B-Continuum-mxfp8-mlx

Brainwaves

          arc   arc/e boolq hswag obkqa piqa  wino
mxfp8     0.670,0.854,0.905
q8-hi     0.678,0.859,0.900
mxfp4     0.663

Model components

Qwen3.5-9B-Holodeck-Lounge

          arc   arc/e boolq hswag obkqa piqa  wino
mxfp8     0.641,0.832,0.898,0.711,0.466,0.787,0.692
q8-hi     0.656,0.831,0.896,0.718,0.480,0.783,0.704
mxfp4     0.637,0.820,0.885,0.708,0.468,0.781,0.700

schneewolflabs/B0-9B

          arc   arc/e boolq hswag obkqa piqa  wino
mxfp8     0.660,0.843,0.894

inclusionAI/UI-Venus-2-9B

          arc   arc/e boolq hswag obkqa piqa  wino
mxfp8     0.550,0.738,0.892

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
qx86-hi   0.553,0.712,0.892,0.689,0.436,0.765,0.691
q6-hi     0.550,0.713,0.892,0.689,0.442,0.762,0.690
q6        0.545,0.705,0.891,0.689,0.432,0.766,0.691
mxfp4     0.572,0.723,0.884,0.679,0.422,0.774,0.680

nbeerbower

Schneewolf Labs org 12 days ago

Cool! Thanks for experimenting with my models and sharing the results. Keep it up!

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