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karakuri-ai
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Model Details
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karakuri-ai
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Qwen/Qwen2.5-VL-32B-Instruct
Input Modalities
Output Modalities
Supported Functionality
karakuri-rd@karakuri.aiFirst, install the required dependencies:
bash
pip install transformers accelerate qwen-vl-utils[decord]==0.0.8Then, use the following code to load the model and generate responses:
python
from transformers import AutoModelForImageTextToText, AutoProcessorfrom 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 inferencetext = 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 outputgenerated_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)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).
text
@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 }}pip install transformers accelerate qwen-vl-utils[decord]==0.0.8from transformers import AutoModelForImageTextToText, AutoProcessorfrom 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 inferencetext = 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 outputgenerated_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)@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 }}