Instructions to use karakuri-ai/karakuri-vl-2-8b-thinking-2603 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use karakuri-ai/karakuri-vl-2-8b-thinking-2603 with Transformers:

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

pipe = pipeline("image-text-to-text", model="karakuri-ai/karakuri-vl-2-8b-thinking-2603")
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("karakuri-ai/karakuri-vl-2-8b-thinking-2603")
model = AutoModelForMultimodalLM.from_pretrained("karakuri-ai/karakuri-vl-2-8b-thinking-2603", 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 karakuri-ai/karakuri-vl-2-8b-thinking-2603 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "karakuri-ai/karakuri-vl-2-8b-thinking-2603"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "karakuri-ai/karakuri-vl-2-8b-thinking-2603",
		"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/karakuri-ai/karakuri-vl-2-8b-thinking-2603

SGLang

How to use karakuri-ai/karakuri-vl-2-8b-thinking-2603 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 "karakuri-ai/karakuri-vl-2-8b-thinking-2603" \
    --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": "karakuri-ai/karakuri-vl-2-8b-thinking-2603",
		"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 "karakuri-ai/karakuri-vl-2-8b-thinking-2603" \
        --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": "karakuri-ai/karakuri-vl-2-8b-thinking-2603",
		"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 karakuri-ai/karakuri-vl-2-8b-thinking-2603 with Docker Model Runner:
```
docker model run hf.co/karakuri-ai/karakuri-vl-2-8b-thinking-2603
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

KARAKURI VL 2 8B Thinking 2603

Model Details

Model Description

Developed by: KARAKURI Inc.
Model type: Vision-Language Models
Languages: Japanese and English
License: Apache 2.0
Finetuned from model: Qwen/Qwen3-VL-8B-Thinking
Contact: For questions and comments about the model, please email karakuri-rd@karakuri.ai

Usage

Use in 🤗 Transformers

First, install the required dependencies:

pip install transformers accelerate qwen-vl-utils[decord]==0.0.8

Then, use the following code to load the model and generate responses:

from transformers import AutoModelForImageTextToText, AutoProcessor

model_name = "karakuri-ai/karakuri-vl-2-8b-thinking-2603"
model = AutoModelForImageTextToText.from_pretrained(
    model_name, torch_dtype="auto", device_map="auto"
)
processor = AutoProcessor.from_pretrained(model_name)

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
            },
            {"type": "text", "text": "Describe this image."},
        ],
    }
]

# Preparation for inference
inputs = processor.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_dict=True,
    return_tensors="pt"
)
inputs = inputs.to(model.device)

# Inference: Generation of the output
generated_ids = model.generate(**inputs, max_new_tokens=128)
generated_ids_trimmed = [
    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text)

Training Details

Training Infrastructure

Hardware: The model was trained on Amazon EC2 trn2.48xlarge instances.
Software: We use code based on neuronx-distributed.

Acknowledgments

This work was supported by the Ministry of Economy, Trade and Industry (METI) and the New Energy and Industrial Technology Development Organization (NEDO) through the Generative AI Accelerator Challenge (GENIAC).

Citation

@misc{karakuri_vl_2_8b_thinking_2603,
    author       = { {KARAKURI} {Inc.} },
    title        = { {KARAKURI} {VL} 2 8{B} {Thinking} 2603 },
    year         = { 2026 },
    url          = { https://huggingface.co/karakuri-ai/karakuri-vl-2-8b-thinking-2603 },
    publisher    = { {Hugging Face} },
    journal      = { {Hugging Face} repository }
}

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Safetensors

Model size

9B params

Tensor type

BF16

Model tree for karakuri-ai/karakuri-vl-2-8b-thinking-2603

Base model

Qwen/Qwen3-VL-8B-Thinking

Finetuned

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this model

Quantizations

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