README.md

---
license: apache-2.0
language:
- zh
- en
base_model:
- Qwen/Qwen2-VL-2B-Instruct
---
# 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。


![image/png](https://cdn-uploads.huggingface.co/production/uploads/642f6c64f945a8a5c9ee5b5d/FSGM3BKUe6QIbd4hQ76pM.png)

## 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(
    "Qwen/Qwen2-VL-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(
#     "Qwen/Qwen2-VL-2B-Instruct",
#     torch_dtype=torch.bfloat16,
#     attn_implementation="flash_attention_2",
#     device_map="auto",
# )

# default processer
processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-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("Qwen/Qwen2-VL-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。