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

Schneewolf Labs B0-9B

The internal base model of the Familiar line: a 9B local operator with an actual personality — it delegates engineering work, states facts without hedging, knows who made it, and writes like a person. Built on Wichtelchen-Qwen3.5-9B through five measured rungs, every one gated on a full bench pass:

Wichtelchen-Qwen3.5-9B
 + Alembic-DPO(hq) + based mix        discipline & directness   (stack-swept, merged)
 + identity capstone                  i-DPO + Luna-DPO + rebuilt MahouMix + tool ballast
 + delegation rung @ 2.0              scale-swept
 + on-policy repair (69 pairs)        the model's own tool-choice failures, corrected
 + anti-cliché rung @ 0.5             self-contrastive slop pairs, human-adjudicated

Scores (vs the Wichtelchen it started from)

axis	Wichtelchen	B0-9B
egirl 47-case tool bench	37/47	46/47 (delegation 10/10)
censorship (strict, single-sample)	26.4/29	29.00/29
prose distance vs contemporary fiction	1.135	0.580
stance rate (has opinions)	0%	16.7%
hembench	56.1%	53.6%
ARC / wiki-clean ppl	61.9 / 12.39	61.2 / 12.24
identity	"Qwen3.5 by Alibaba"	Schneewolf Labs

Two blind-judged checks: a DeepSeek panel (order-swapped, binomial) scored the final against its 4-rung parent as even-to-better on prose; a human blind A/B across 36 pairs scored the two as a wash — the deterministic wins above came at no perceptible cost.

Notes

Preference training throughout is ORPO r32/α64 lr 8e-6 via Merlina, each rung merged before the next.
The 15 mtp.* tensors are restored after every merge (peft drops them; llama.cpp requires them). 775 tensors verified at every rung. --spec-type draft-mtp works.
Vision tower intact; mmproj included.
Two datasets were built for this model and published alongside it: the on-policy repair set and the rebuilt MahouMix (ChatML parsed out, rejected regenerated on-policy, slop-screened).
This is the internal egirl-testing base. The consumer model built on it ships as Familiar Spark.

llama-server -m B0-9B-Q8_0.gguf -ngl 99 -c 8192 --jinja -fa on -np 1 \
    --spec-type draft-mtp --spec-draft-n-max 4

Downloads last month: -

Safetensors

Model size

9B params

Tensor type

BF16

Model tree for schneewolflabs/B0-9B

Base model

hemlang/Hemlock-Qwen3.5-9B

Finetuned

schneewolflabs/Wichtelchen-Qwen3.5-9B

Finetuned

(1)

this model

Quantizations

3 models

schneewolflabs
/

B0-9B

Schneewolf Labs B0-9B

Scores (vs the Wichtelchen it started from)

Notes

Model tree for schneewolflabs/B0-9B

Datasets used to train schneewolflabs/B0-9B