Instructions to use Blackfrost-AI/M.O.G.-27B-BF16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Blackfrost-AI/M.O.G.-27B-BF16 with Transformers:

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

pipe = pipeline("image-text-to-text", model="Blackfrost-AI/M.O.G.-27B-BF16")
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("Blackfrost-AI/M.O.G.-27B-BF16")
model = AutoModelForMultimodalLM.from_pretrained("Blackfrost-AI/M.O.G.-27B-BF16", 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 Blackfrost-AI/M.O.G.-27B-BF16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Blackfrost-AI/M.O.G.-27B-BF16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Blackfrost-AI/M.O.G.-27B-BF16",
		"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/Blackfrost-AI/M.O.G.-27B-BF16

SGLang

How to use Blackfrost-AI/M.O.G.-27B-BF16 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 "Blackfrost-AI/M.O.G.-27B-BF16" \
    --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": "Blackfrost-AI/M.O.G.-27B-BF16",
		"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 "Blackfrost-AI/M.O.G.-27B-BF16" \
        --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": "Blackfrost-AI/M.O.G.-27B-BF16",
		"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 Blackfrost-AI/M.O.G.-27B-BF16 with Docker Model Runner:
```
docker model run hf.co/Blackfrost-AI/M.O.G.-27B-BF16
```

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Qwen3.8-27B-CLEAN-DERISKED-BF16

Private BF16 research checkpoint derived from Qwen/Qwen3.8-27B.

Repository status:IN TESTING model card only. Model weights, tokenizer files, configuration files, datasets, training materials, implementation details, and provenance artifacts are not included in this repository.

Model summary

Property	Value
Upstream model	`Qwen/Qwen3.8-27B`
Architecture	`Qwen3_5ForConditionalGeneration`
Weight format	BF16 Transformers checkpoint
Quantization	None
Maximum configured context	262,144 tokens

This card intentionally excludes internal transformation, training, data, deployment, and reproducibility details.

Evaluation

R1-HARMFUL-BENCH-450

Dataset	Prompts	Substring refusal flags	Flag rate
AdvBench	150	3	2.0%
StrongREJECT	150	0	0.0%
XSTest	150	0	0.0%
Total	450	3	0.7%

Harmful subset (AdvBench + StrongREJECT): 300 prompts, 3 substring flags (1.0%), 0 manually confirmed safety refusals, and 1 strict task non-completion (0.33%). Manual review classified the other 2 flags as capability disclaimers followed by substantive responses.

Quality checks: 0 API errors, 0 empty responses, and 0 manually confirmed incoherent responses. The automatic detector produced 17 incoherence flags, all caused by repeated Markdown or diagram separator characters rather than degraded output.

Intended use and limitations

This checkpoint is intended for private, controlled research and evaluation. It may produce inaccurate, biased, unsafe, or otherwise undesirable output. The checkpoint name is not a safety guarantee or a claim of suitability for deployment. Evaluate it for the intended domain and apply appropriate access controls and safeguards before any broader use.

License and attribution

The upstream model is released under the Apache License 2.0. Review the upstream Qwen3.8-27B model card for its documentation, license information, and original limitations.

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Safetensors

Model size

28B params

Tensor type

BF16

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Base model

Qwen/Qwen3.8-27B

Finetuned

(132)

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