Instructions to use Blackfrost-AI/BLACKFROST-3.8-FLASH-BF16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Blackfrost-AI/BLACKFROST-3.8-FLASH-BF16 with Transformers:

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

pipe = pipeline("image-text-to-text", model="Blackfrost-AI/BLACKFROST-3.8-FLASH-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/BLACKFROST-3.8-FLASH-BF16")
model = AutoModelForMultimodalLM.from_pretrained("Blackfrost-AI/BLACKFROST-3.8-FLASH-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/BLACKFROST-3.8-FLASH-BF16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Blackfrost-AI/BLACKFROST-3.8-FLASH-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/BLACKFROST-3.8-FLASH-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/BLACKFROST-3.8-FLASH-BF16

SGLang

How to use Blackfrost-AI/BLACKFROST-3.8-FLASH-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/BLACKFROST-3.8-FLASH-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/BLACKFROST-3.8-FLASH-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/BLACKFROST-3.8-FLASH-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/BLACKFROST-3.8-FLASH-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/BLACKFROST-3.8-FLASH-BF16 with Docker Model Runner:
```
docker model run hf.co/Blackfrost-AI/BLACKFROST-3.8-FLASH-BF16
```

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This repository is publicly accessible, but you have to accept the conditions to access its files and content.

BLACKFROST-3.8-FLASH-BF16

BLACKFROST-3.8-FLASH-BF16 is a BF16 research artifact based on Qwen/Qwen3.8-Flash-Next, fine-tuned by Blackfrost-AI for authorized security research and evaluation.

Artifact

Format: Hugging Face Transformers, BF16
Architecture: Qwen4ExpForConditionalGeneration
Base revision: de4b8e4d43b917e7706784d8bb445c9af86a3540
Weight layout: 131 SafeTensors shards
Native context configuration: 262,144 tokens
Default chat template: thinking mode enabled

No benchmark claims are made in this model card. Evaluate the model for your specific workload before deployment.

Serving

Example with vLLM on an eight-GPU node:

vllm serve Blackfrost-AI/BLACKFROST-3.8-FLASH-BF16 \
  --tensor-parallel-size 8 \
  --trust-remote-code \
  --reasoning-parser qwen3

Access and use

Access is manually approved. Use is intended for legitimate, authorized research and evaluation. The upstream Qwen Community License continues to apply; review LICENSE before use or redistribution.

Downloads last month: 4

Safetensors

Model size

180B params

Tensor type

BF16

I64

Model tree for Blackfrost-AI/BLACKFROST-3.8-FLASH-BF16

Base model

Qwen/Qwen3.8-Flash-Next

Finetuned

(57)

this model