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This is the HuatuoGPT-Vision model based on the Qwen2.5-VL architecture. It was trained on the PubMedVision dataset using the Qwen2.5-VL framework. For details, please refer to the training code at HuatuoGPT-Vision Code.
transformers and qwen_vl_utils:1from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
2from qwen_vl_utils import process_vision_info
3
4# default processer
5processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct")
6
7messages = [
8 {
9 "role": "user",
10 "content": [
11 {
12 "type": "image",
13 "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
14 },
15 {"type": "text", "text": "Describe this image."},
16 ],
17 }
18]
19
20text = processor.apply_chat_template(
21 messages, tokenize=False, add_generation_prompt=True
22)
23image_inputs, video_inputs = process_vision_info(messages)
24inputs = processor(
25 text=[text],
26 images=image_inputs,
27 videos=video_inputs,
28 padding=True,
29 return_tensors="pt",
30)
31inputs = inputs.to("cuda")
32
33# Inference: Generation of the output
34generated_ids = model.generate(**inputs, max_new_tokens=128)
35generated_ids_trimmed = [
36 out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
37]
38output_text = processor.batch_decode(
39 generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
40)
41print(output_text)@misc{chen2024huatuogptvisioninjectingmedicalvisual,
title={HuatuoGPT-Vision, Towards Injecting Medical Visual Knowledge into Multimodal LLMs at Scale},
author={Junying Chen and Ruyi Ouyang and Anningzhe Gao and Shunian Chen and Guiming Hardy Chen and Xidong Wang and Ruifei Zhang and Zhenyang Cai and Ke Ji and Guangjun Yu and Xiang Wan and Benyou Wang},
year={2024},
eprint={2406.19280},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2406.19280},
}