Instructions to use schneewolflabs/B4-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use schneewolflabs/B4-9B with Transformers:

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

pipe = pipeline("image-text-to-text", model="schneewolflabs/B4-9B")
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/B4-9B")
model = AutoModelForMultimodalLM.from_pretrained("schneewolflabs/B4-9B", 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/B4-9B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "schneewolflabs/B4-9B"
# 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/B4-9B",
		"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/B4-9B

SGLang

How to use schneewolflabs/B4-9B 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/B4-9B" \
    --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/B4-9B",
		"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/B4-9B" \
        --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/B4-9B",
		"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/B4-9B with Docker Model Runner:
```
docker model run hf.co/schneewolflabs/B4-9B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Schneewolf Labs B4-9B

B2-9B taught to check before it answers, with thinking off. Behind egirl's OpenAI-compatible API the model has web_search, web_research and calculate. With thinking on, B2-9B already uses them well. With thinking off, the fast mode a chat product wants, it mostly answered from memory: stale facts about who holds a role, old version numbers, and mental math it got wrong. B4 makes the thinking-on choice in the thinking-off mode, averaging 2.5 s per answer instead of 5.7 s with thinking low.

B2-9B
 + ORPO   Nachschlagen: 165 first-turn pairs on egirl's own API context, thinking off, 2 epochs
          63  current-info questions   chosen: web_search / web_research   rejected: answer from memory
          36  arithmetic and dates     chosen: calculate                   rejected: mental math
          16  look-up-then-compute     chosen: search first                rejected: answer from memory
          50  controls                 chosen: direct answer               rejected: an unneeded web_search

The chosen turns are B2-9B's own thinking-on first moves (thinking stripped), kept only when the whole run's answer was right. Rejected turns are its thinking-off answers. Pairs cut at the first assistant turn, so the model learns the decision, not a new voice. The data is public: schneewolflabs/Nachschlagen.

The 50 controls (trivia, coding, rewriting text the user supplied, historical facts) keep the trigger on staleness and arithmetic rather than "any question".

Numbers

Held-out sets share no question with training; the second one uses indirect phrasing ("is it worth upgrading to the latest Python?", "what's 18% tip on $47.80 split three ways?") and traps such as past-dated questions and data the user already supplied. Thinking off, egirl, Q8_0 + bf16 adapter.

	B2-9B	B4-9B
held-out (41): right first move	19/41	41/41
current-info questions searched	2/10	10/10
arithmetic sent to the calculator	1/10	10/10
easy questions answered directly	10/10	10/10 (9/10 in two of three reruns)
harder set (37): right first move	20/37	36/37
user-supplied numbers: calculated, not searched	0/4	4/4
keyed answers correct (32)	21/32	32/32
average time per answer	0.8 s	2.5 s (thinking low: 5.7 s)
ARC / ARC-Easy / TruthfulQA	60.5 / 84.6 / 36.5	61.9 / 84.2 / 36.8

With thinking low it still scores 41/41.

Known residuals

It sometimes searches facts it knows: about one easy question in ten across reruns ("how many continents?"), and "who was Intel's CEO in 2015" because it looks like a current-role question. The answers stay right; it costs a few seconds.
It can't search what it is sure it knows. It says Brazil won the 1998 World Cup about half the time, as B2-9B does.

Notes

Built for egirl, used through its OpenAI API with server-side tools. Other harnesses weren't a goal and weren't tested.
Date math (days_between, add_days, weekday, today() inside calculate) needs an egirl with the calculate date functions (PR #194); 12 training calls use them.
Trained with Merlina, ORPO β 0.1, LoRA r16/α32 on all attention and MLP projections, lr 2e-5 cosine, batch 8, on a DGX Spark (GB10). Stronger runs (lr 5e-5, 3 epochs, r32) scored the same, so the lightest shipped.
The adapter was baked into the weights with Kessel. The mtp.* and vision tensors are byte-identical to B2-9B.