Instructions to use schneewolflabs/B0-27B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use schneewolflabs/B0-27B with Transformers:

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

pipe = pipeline("image-text-to-text", model="schneewolflabs/B0-27B")
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/B0-27B")
model = AutoModelForMultimodalLM.from_pretrained("schneewolflabs/B0-27B", 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/B0-27B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "schneewolflabs/B0-27B"
# 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/B0-27B",
		"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/B0-27B

SGLang

How to use schneewolflabs/B0-27B 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/B0-27B" \
    --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/B0-27B",
		"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/B0-27B" \
        --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/B0-27B",
		"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/B0-27B with Docker Model Runner:
```
docker model run hf.co/schneewolflabs/B0-27B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Schneewolf Labs B0-27B

The internal base model of the Familiar line at 27B: a local operator with an actual personality that completes real engineering work end-to-end. Built on Hemlock-Qwen3.8-27B (WichtelHui-SLERP + the Hemlock coding adapter — a base that starts at 47/47 on our operator bench) through two measured rungs, every step gated on a full bench pass:

Hemlock-Qwen3.8-27B
 + based mix @ 1.0 (merged)        directness substrate — weasel + grok-pi + seX-ai
 + identity capstone (ORPO 2ep)    i-DPO + Luna-DPO + on-policy MahouMix rebuild
                                   + tool ballast + destructo — trained ON the merge,
                                   so the capstone overwrites the substrate's costs
                                   and keeps its gains (the seesaw, used on purpose)

Scores (vs the Hemlock base it started from)

axis	Hemlock-3.8	B0-27B
egirl 47-case operator bench	47/47	42/47
kirabench (end-to-end task completion via egirl + Codex)	6/6	6/6, faster
censorship (strict, single-sample)	27/29	28/29
safety asymmetry (refuses actual harm)	2/2	2/2
prose distance vs contemporary fiction	0.528	0.525
stance rate (has opinions)	16.7%	8.3%
hembench	79.9%	73.4%
ARC / wiki-clean ppl	65.9 / 10.01	66.2 / 9.93
identity	"Qwen"	Schneewolf Labs

The egirl-bench dip is five known single-tool-call reflexes (three delegation prompts answered with git_status, two trivial edits answered with a file search). They do not reach task completion: on kirabench — six sandboxed engineering tasks driven through the real egirl harness with Codex as the code agent, judged only by deterministic checkers — B0-27B goes 6/6 and fixed a failing-test task ~9× faster than its base.

Notes

Preference training via Merlina (ORPO r32/α64 lr 8e-6); the substrate adapter was hand-merged into the weights (delta surgery — vision and MTP tensors byte-identical to the base) before the capstone trained on the result. 1,199 tensors verified at every merge.
MahouMix was rebuilt on-policy: ChatML parsed out, rejected regenerated from the substrate-merged model itself.
Vision tower and the 15 mtp.* tensors are untouched from the base; mmproj included in the GGUF repo. --spec-type draft-mtp works.
This is the internal egirl-testing base. The consumer model built on it ships as Familiar Ember.

llama-server -m B0-27B-Q8_0.gguf -ngl 99 -c 16384 --jinja -fa on -np 1 \
    --spec-type draft-mtp --spec-draft-n-max 4

Downloads last month: 8

Safetensors

Model size

28B params

Tensor type

BF16

Model tree for schneewolflabs/B0-27B

Base model

hemlang/Hemlock-Qwen3.8-27B

Finetuned

(1)

this model

Quantizations

2 models

schneewolflabs
/

B0-27B

Schneewolf Labs B0-27B

Scores (vs the Hemlock base it started from)

Notes

Model tree for schneewolflabs/B0-27B

Datasets used to train schneewolflabs/B0-27B