Instructions to use schneewolflabs/B1-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use schneewolflabs/B1-9B with Transformers:

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

pipe = pipeline("image-text-to-text", model="schneewolflabs/B1-9B")
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/B1-9B")
model = AutoModelForMultimodalLM.from_pretrained("schneewolflabs/B1-9B", 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/B1-9B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "schneewolflabs/B1-9B"
# 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/B1-9B",
		"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/B1-9B

SGLang

How to use schneewolflabs/B1-9B 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/B1-9B" \
    --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/B1-9B",
		"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/B1-9B" \
        --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/B1-9B",
		"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/B1-9B with Docker Model Runner:
```
docker model run hf.co/schneewolflabs/B1-9B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Schneewolf Labs B1-9B

An iteration on B0-9B with one job: make the model answer after it thinks. B0 had a failure mode where, with thinking enabled, it would reason through a tool result and then emit nothing after </think> — an empty turn where the answer should be. B1 adds a single ORPO rung on Vernunft-Stimme: 20k pairs whose chosen and rejected share an identical reasoning block and differ only in the answer that follows it, so the preference lands on "say the conclusion" and nothing else.

B0-9B
 + Vernunft-Stimme ORPO @ 1.0        answer-after-thinking   (2 epochs, r32/α64, lr 8e-6)

What it fixed

Answer present after </think> on a tool-result turn, thinking on, 8 samples per cell, Emma's real egirl system prompt:

request shape	B0-9B	B1-9B
native `tools`, no `/think` prefix	0/8	7/8
`/think` prefix only	0/8	4/8
tool defs in system prompt only	1/8	3/8
both (egirl's provider shape)	0/8	2/8

The residual is request shape, not weights: the untouched Huihui base drops 8/8 → 4/8 across the same rows. Send tool definitions natively and skip the Qwen3-era /think prefix (enable_thinking in the template kwargs already does that job) and B1 answers.

What it cost

Same card suite as B0, same settings (Q8_0, thinking off, single sample):

axis	B0-9B	B1-9B
egirl 47-case tool bench	46/47 (delegation 10/10)	41/47 (delegation 8/10)
censorship (strict, single-sample)	29/29	25/29
safety asymmetry (refuses actual harm)	2/2	1/2
prose distance vs contemporary fiction	0.580	1.881
stance rate (has opinions)	16.7%	8.3%
hembench	53.6%	51.5%
ARC / wiki-clean ppl	61.2 / 12.24	62.9 / 12.25
identity	Schneewolf Labs	Schneewolf Labs

That is the known shape of a reasoning rung stacked on top of a persona stack: it teaches the target behaviour and drags the persona axes back toward the base. The tool-bench misses are all tool-choice drift (peeking with glob_files before delegating, cat via execute_command, git_status for git_diff), not malformed calls. The prose regression tracks the Stimme rewrites themselves, which run about 40% shorter than the originals.

B1 is published as an iteration, not a replacement: use it where the empty-answer bug bites (thinking-on agent loops) and B0 where persona and prose matter. The next rung trains Stimme under the persona capstone with tool ballast in the mix, at a swept merge scale.

Notes

Trained with Merlina (ORPO, LoRA r32/α64, lr 8e-6, β 0.1, 2 epochs, 4,924 steps, final loss 0.45), merged at scale 1.0.
The 15 mtp.* tensors are grafted back after the merge; 775 tensors verified. --spec-type draft-mtp works.
Vision tower intact; mmproj included (byte-identical to B0's).

llama-server -m B1-9B-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/B1-9B

Base model

hemlang/Hemlock-Qwen3.5-9B

Finetuned

schneewolflabs/Wichtelchen-Qwen3.5-9B

Finetuned

schneewolflabs/B0-9B

Finetuned

(2)

this model

Quantizations

3 models

schneewolflabs
/

B1-9B

Schneewolf Labs B1-9B

What it fixed

What it cost

Notes

Model tree for schneewolflabs/B1-9B

Dataset used to train schneewolflabs/B1-9B