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ModelInfo
该模型是在Qwen2-VL-2B-Instruct模型的基础上，使用是开源数据中挖掘合成的工业领域多模态数据，经过微调所得，该模型在基本保持通用指标的基础上，有效提升了工业领域的相关指标。
HOW TO USE
模型的使用方案与Qwen2-VL-2B-Instruct一致：https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct#quickstart
# pip install qwen-vl-utils
from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor
from qwen_vl_utils import process_vision_info
# default: Load the model on the available device(s)
model = Qwen2VLForConditionalGeneration.from_pretrained(
"XiaofengAlg/IndusryVL-2B-Instruct", torch_dtype="auto", device_map="auto"
)
# We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios.
# model = Qwen2VLForConditionalGeneration.from_pretrained(
# "XiaofengAlg/IndusryVL-2B-Instruct",
# torch_dtype=torch.bfloat16,
# attn_implementation="flash_attention_2",
# device_map="auto",
# )
# default processer
processor = AutoProcessor.from_pretrained("XiaofengAlg/IndusryVL-2B-Instruct")
# The default range for the number of visual tokens per image in the model is 4-16384. You can set min_pixels and max_pixels according to your needs, such as a token count range of 256-1280, to balance speed and memory usage.
# min_pixels = 256*28*28
# max_pixels = 1280*28*28
# processor = AutoProcessor.from_pretrained("XiaofengAlg/IndusryVL-2B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels)
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("cuda")
# 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)
NOTE
当前版本为一个实验版本，后续版本迭代进行中
Acknowledgements
This work is supported by the National Science and Technology Major Project (No. 2022ZD0116314).
本项目受新一代人工智能国家科技重大专项（No. 2022ZD0116314）支持。
Citation
@misc{shi2025indusryvl,
title = {IndusryVL-2B-Instruct},
author = {Xiaofeng Shi},
year = {2025},
publisher = {Hugging Face},
url = {https://huggingface.co/XiaofengAlg/IndusryVL-2B-Instruct}
}
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