Instructions to use schneewolflabs/B1.1-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use schneewolflabs/B1.1-9B with Transformers:

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

pipe = pipeline("image-text-to-text", model="schneewolflabs/B1.1-9B")
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/B1.1-9B")
model = AutoModelForMultimodalLM.from_pretrained("schneewolflabs/B1.1-9B", 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/B1.1-9B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "schneewolflabs/B1.1-9B"
# 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/B1.1-9B",
		"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/B1.1-9B

SGLang

How to use schneewolflabs/B1.1-9B 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/B1.1-9B" \
    --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/B1.1-9B",
		"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/B1.1-9B" \
        --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/B1.1-9B",
		"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/B1.1-9B with Docker Model Runner:
```
docker model run hf.co/schneewolflabs/B1.1-9B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Schneewolf Labs B1.1-9B

B1-9B's adapter merged at half strength. B1 fixed B0's empty-answer-after-</think> bug with one ORPO rung on Vernunft-Stimme, but at merge scale 1.0 it also dragged the persona axes hard toward the base. A runtime sweep of the same adapter at 0.25 / 0.5 / 1.0 showed the fix scaling roughly linearly and the damage scaling worse than linearly, with the knee at 0.5. B1.1 is that point, merged.

B0-9B
 + Vernunft-Stimme ORPO adapter @ 0.5     (same adapter as B1-9B, r32/α64, 2 epochs)

No new training. Same weights as B1, half the delta.

Numbers

Same card suite as B0 and B1, same settings (Q8_0, thinking off, single sample), plus the agentic ladder that B0's card did not have:

axis	B0-9B	B1.1-9B	B1-9B
answer after `</think>` (native tools, no `/think`, 8 samples)	0/8	5/8	7/8
egirl 47-case tool bench	46/47	41/47 ¹	41/47
buchbinder ladder L1–2 (10 real implement-the-function tasks, verified by the repo's tests)	10/10	9/10	8/10
censorship (strict, single-sample)	29/29	27/29	25/29
safety asymmetry (refuses actual harm)	2/2	1/2	1/2
prose distance vs contemporary fiction (lower = closer)	0.580	0.679	1.881
stance rate (has opinions)	16.7%	8.3%	8.3%
hembench	53.6%	50.7%	51.5%
ARC / wiki-clean ppl	61.2 / 12.24	62.9 / 12.19	62.9 / 12.25
identity	Schneewolf Labs	Schneewolf Labs	Schneewolf Labs

¹ Three of the six misses are name-quoting slips ({"name":code_agent,) on an otherwise correct call, which a tolerant parser repairs; the tool choice was right in all three. The other three are B1's tool-choice drift (cat via execute_command, git_status for git_diff), inherited at reduced strength.

Read across the rows: B1.1 keeps most of the answer-after-thinking gain, recovers most of the prose and censorship regression, and holds capability. The one thing it does not recover is B0's willingness to delegate: on the ladder B0 handed the hardest task to the code agent and passed; B1.1 attempted it itself and missed. That is the target of the next rung, not a scale problem.

Request shape still matters

The answer-after-thinking gain is largest when tool definitions go through the native tools field and the prompt carries no Qwen3-era /think prefix (enable_thinking in the template kwargs already does that job). With tools injected into the system prompt and a /think prefix, B1.1 drops to 1/8 on the same probe. That row is the client's to fix, not the weights'.

Notes

Adapter trained with Merlina (ORPO, LoRA r32/α64, lr 8e-6, β 0.1, 2 epochs, 4,924 steps, final loss 0.45); merged here at scale 0.5.
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 B1.1-9B-Q8_0.gguf -ngl 99 -c 8192 --jinja -fa on -np 1 \
    --spec-type draft-mtp --spec-draft-n-max 4

Downloads last month: 23

Safetensors

Model size

9B params

Tensor type

BF16

Model tree for schneewolflabs/B1.1-9B

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
/

B1.1-9B

Schneewolf Labs B1.1-9B

Numbers

Request shape still matters

Notes

Model tree for schneewolflabs/B1.1-9B

Dataset used to train schneewolflabs/B1.1-9B