Instructions to use schneewolflabs/B2-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use schneewolflabs/B2-9B with Transformers:

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

pipe = pipeline("image-text-to-text", model="schneewolflabs/B2-9B")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("schneewolflabs/B2-9B")
model = AutoModelForMultimodalLM.from_pretrained("schneewolflabs/B2-9B", 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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use schneewolflabs/B2-9B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "schneewolflabs/B2-9B"
# 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/B2-9B",
		"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/B2-9B

SGLang

How to use schneewolflabs/B2-9B 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/B2-9B" \
    --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/B2-9B",
		"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/B2-9B" \
        --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/B2-9B",
		"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/B2-9B with Docker Model Runner:
```
docker model run hf.co/schneewolflabs/B2-9B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Schneewolf Labs B2-9B

B0-9B taught to do the job the way a careful journeyman does: work in the egirl harness's native tool format, finish its sentence after thinking, ask before destroying anything it can't get back, and tell the truth about what it verified.

B0-9B
 + Geselle SFT      (3,329 per-turn rows: agentic ladder + Vorsicht + thinking-on + chat, 1 epoch)
 + ORPO             (304 decision-point pairs: Vorsicht + ladder, 2 epochs)

No Stimme this time: stacked on top it eroded the new ask-first behaviour (at 0.5) and didn't bring back any voice (at 0.25).

Numbers

Same harness for every column: egirl on current main, Q8_0, one sample per cell. The battery is 28 operator scenarios with byte-level sandbox checkers; the held-out set is 8 destructive-request scenarios on fixtures and wording that appear in no training round, ×2 thinking modes ×2 samples.

axis	Qwen3.5-9B (vanilla)	B0-9B	B2-9B
empty answer after a tool result, thinking on	14/14	14/14	0/14
battery sandbox checks, thinking off / on	7/8 / 5/8	5/8 / 5/8	8/8 / 6/8
held-out destructive requests: destroyed something on turn 1	16/24	14/24	5/24
held-out: lost irreplaceable data (raw data, a draft, the only copy)	8/24	8/24	0/24
held-out: explicit scoped removal done exactly	8/8	8/8	8/8
"did you actually rerun the tests?"	n/a	n/a	honest in both modes
egirl 47-case tool bench	n/a	46/47	42/47 ¹
censorship (strict, single-sample)	n/a	29/29	27/29 ²
safety asymmetry (refuses actual harm)	n/a	2/2	2/2 ²
hembench	n/a	53.6%	55.1%
ARC / wiki-clean ppl	n/a	61.2 / 12.24	61.2 / 12.33
stance rate (has opinions)	n/a	16.7%	0%
prose distance vs contemporary fiction (lower = closer)	n/a	0.580	0.804
identity	Qwen (Alibaba)	Schneewolf Labs	Schneewolf Labs

¹ Three of the five misses are the bench's older JSON tool dialect, where B2 writes {"name":code_agent, without quoting the name (B1.1 had the same slip). B2 was trained only on Qwen3.5's native <function=…> format, which is what egirl sends; in egirl itself one stray call appeared in 261 round trips. The other two are tool choice (cat for read_file, git_status for git_diff).

² The strict scorer marks one refusal as a miss because it's phrased outside its marker list ("This violates safety guidelines… prohibited"). Both harmful requests are refused.

Against other Qwen3.5-9B fine-tunes (2026-10-20)

Log-prob multiple choice (llama.cpp --multiple-choice, Q8_0, no sampling). Every model was converted and scored the same day with the same harness and llama.cpp build: ARC-Challenge validation (299), TruthfulQA (817), MMLU validation (1,531), HellaSwag validation (first 1,000), ARC-Easy validation (570).

model	ARC	TruthfulQA	MMLU	HellaSwag	ARC-Easy
B2-9B	60.5	36.5	41.2	77.6	84.6
Altworld/Astrea-R8-Chat-9B	56.2	35.0	40.8	78.2	75.4
squ11z1/claude-oss	54.2	30.4	39.4	76.7
empero-ai/Qwen3.8-9B-Distill	52.8	32.6	39.6	76.4
empero-ai/Qwythos-9B-Claude-Mythos-5-1M	52.8	31.7	39.5	74.9
ornith-ai/Ornith-1.5-9B	52.5	30.6	40.5	75.7
inclusionAI/UI-Venus-2-9B	52.5	32.1	39.6	74.4
XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B	52.2	32.8	39.8	74.2
TokenRhythm/NeoHorse-1-9B	52.2	34.1	39.3	75.5
OrionLLM/OxCoder-9B	50.8	34.3	39.5	75.7
allenai/tmax-9b	50.8	34.3	39.4	75.7
tencent/UI-Mate-9B	50.5	34.1	39.8	75.8
Qwen/Qwen3.5-9B	50.5	33.7	39.8	75.7	74.4
Jackrong/Qwen3.5-9B-Neo	50.2	31.5	40.0	74.7

Read this fairly:

ARC-Challenge is home ground. B0-9B's merge was selected on this same 299-item set, so B2's ARC column is partly in-sample. The other four benchmarks were never used to choose anything. On held-out ARC-Easy, B2 leads the base by 10 points and the next-best tune by 9: it got 66 items right that the base missed, against 8 the other way. That lead is real, and it comes from B0's merge; B2's training kept it intact.
These benchmarks measure knowledge and reasoning, not what most of these tunes are for. OxCoder, tmax, UI-Venus, UI-Mate and NeoHorse are coding, SWE, GUI and agent tunes. Their strengths don't show up on multiple choice, and this table says nothing about them. ARC-Easy was run only on the base and the strongest outside tune.
This ARC number (60.5) differs slightly from the 61.2 in the table above. That one came from an earlier conversion and llama.cpp build; every model in this table was scored the same way.

Two derivatives that add LoRAs on top of B2 are on DragonBophades, with full results on their cards: B2-Nimble-Bophades-9B (MMLU 42.5, no regressions) and B2-Bophades-9B (TruthfulQA 38.8, ARC −2.6).

What it costs

B2 is a better operator and a quieter one. Stance went to 0% and prose drifted from B0's 0.58 to 0.80: it has fewer opinions and a plainer voice. It still trusts a docstring over the code it describes sometimes, and with thinking on it can talk itself into a deletion ("clean working tree, nothing to preserve", in a repo with no remote). Keep the harness's own guards on for destructive commands.

Notes

Trained with Merlina, LoRA r32/α64. SFT lr 1e-4, 16k context, rendered one row per assistant turn with this model's own template (Merlina's multi-turn flattening would scramble tool trajectories). ORPO lr 8e-6, β 0.1, pairs cut at their first differing assistant turn so the preference never covers tool output.
Both adapters merged straight into the weights; the 15 mtp.* tensors and the vision tower are byte-identical to B0-9B (775 tensors verified). --spec-type draft-mtp works.
Data: Geselle, Vorsicht-DPO.

llama-server -m B2-9B-Q8_0.gguf -ngl 99 -c 32768 --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/B2-9B

Base model

hemlang/Hemlock-Qwen3.5-9B

Finetuned

schneewolflabs/Wichtelchen-Qwen3.5-9B

Finetuned

schneewolflabs/B0-9B

Finetuned

(3)

this model

Adapters

1 model

Finetunes

1 model

Merges

4 models

Quantizations

3 models

schneewolflabs
/

B2-9B

Schneewolf Labs B2-9B

Numbers

Against other Qwen3.5-9B fine-tunes (2026-10-20)

What it costs

Notes

Model tree for schneewolflabs/B2-9B

Datasets used to train schneewolflabs/B2-9B