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

Libraries

How to use DragonBophades/B2-Bophades-9B with Transformers:

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

pipe = pipeline("image-text-to-text", model="DragonBophades/B2-Bophades-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("DragonBophades/B2-Bophades-9B")
model = AutoModelForMultimodalLM.from_pretrained("DragonBophades/B2-Bophades-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 DragonBophades/B2-Bophades-9B with vLLM:

Install from pip and serve model

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

SGLang

How to use DragonBophades/B2-Bophades-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 "DragonBophades/B2-Bophades-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": "DragonBophades/B2-Bophades-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 "DragonBophades/B2-Bophades-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": "DragonBophades/B2-Bophades-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 DragonBophades/B2-Bophades-9B with Docker Model Runner:
```
docker model run hf.co/DragonBophades/B2-Bophades-9B
```

B2-Bophades-9B

Schneewolf Labs B2-9B with the Bophades ORPO LoRA merged in at full strength. The LoRA was trained on stock Qwen3.5-9B, not on B2. It ports over cleanly because B2 is a Qwen3.5-9B descendant with the same architecture.

What it does: it is more truthful than B2 (TruthfulQA +2.3, significant) and roughly B2 everywhere else. ARC is down 2.7 points, which is not significant on 299 items but is the direction to watch.

The Bophades LoRA

ORPO, 1 epoch, on 9,399 preference pairs:

source	pairs
jondurbin/truthy-dpo-v0.1	1,015
argilla/distilabel-math-preference-dpo	2,384
openbmb/UltraInteract_pair — Coding, Logic, Math_CoT, Math_PoT, 1,500 each, one pair per problem	6,000

Rendered with the Qwen3.5 chat template, thinking off. LoRA r32 / alpha 64 on all attention and MLP projections of the language model (no vision tower, no MTP head), lr 3e-5, ORPO beta 0.1, effective batch 16, max length 2048. Final loss 0.644. Trained with Merlina and baked in with Kessel (kessel_config.yaml).

Results

Log-prob multiple choice (llama.cpp --multiple-choice, Q8_0). Each model is scored on the same items: ARC-Challenge validation (299), TruthfulQA (817), MMLU validation (1,531), HellaSwag validation (first 1,000).

model	ARC	TruthfulQA	MMLU	HellaSwag
Qwen3.5-9B	50.5	33.7	39.8	75.7
Qwen3.5-9B + Bophades	53.5	35.1	39.9	75.6
B2-9B	60.5	36.5	41.2	77.6
B2-Bophades-9B (this)	57.9	38.8	41.9	77.6

Paired against B2 (items flipped right / wrong, exact binomial):

	gained	lost	p
ARC	5	13	0.096
TruthfulQA	32	13	0.007
MMLU	26	15	0.117
HellaSwag	10	10	1.0

The adapter moves B2 in the same direction it moves the stock model: up on TruthfulQA.

Merges that didn't make it

The plan was a "DragonBophades" multi-model merge. Base models tested alongside B2 were Ornith-1.5, MiMo-V2.6-Distill, Qwen3.8-9B-Distill and Astrea-R8-Chat (best non-B2 donor: 56.2 / 35.0 / 40.8 / 78.2).

model_stock (equal weight), with or without B2: about 53.5 / 34.4 / 39.7 / 76.1. These merges land back near the base. With donors this different, model_stock's interpolation collapses toward the base.
TIES, B2 0.55 / Astrea 0.35 / Bophades 0.3: 59.9 / 36.6 / 41.5 / 77.5. This is B2 within noise on every benchmark. Beating the third-party donors isn't enough of a reason to ship a model that only ties B2.

Applying the adapter directly to B2 was the only build that moved anything.

Notes

Identity: it calls itself a model made by Schneewolf Labs, which it inherits from B2. If you need another identity, set one in the system prompt.
Thinking: trained with thinking off. Thinking-on behaviour comes from B2 and is not evaluated here.
Not evaluated: generation-based benchmarks and agentic / tool-use behaviour. B2 was built for the egirl harness, and this tune has not been re-checked there.
Vision tower and MTP head are B2's, untouched.