Developed by: KARAKURI Inc.
Model type: Vision-Language Models
Languages: Japanese and English
License: Apache 2.0
Finetuned from model: Qwen/Qwen2.5-VL-32B-Instruct
Contact: For questions and comments about the model, please email karakuri-rd@karakuri.ai
Demo: https://vl.karakuri.cc/

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:

python

from transformers import AutoModelForImageTextToText, AutoProcessor
from qwen_vl_utils import process_vision_info

model_name = "karakuri-ai/karakuri-vl-32b-instruct-2507"
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
text = processor.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True
)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
    text=[text],
    images=image_inputs,
    videos=video_inputs,
    padding=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 20 nodes of an Amazon EC2 trn1.32xlarge instance.
Software: We use code based on neuronx-nemo-megatron.

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_32b_instruct_2507,
    author       = { {KARAKURI} {Inc.} },
    title        = { {KARAKURI} {VL} 32{B} {Instruct} 2507 },
    year         = { 2025 },
    url          = { https://huggingface.co/karakuri-ai/karakuri-vl-32b-instruct-2507 },
    publisher    = { {Hugging Face} },
    journal      = { {Hugging Face} repository }
}