---
license: apache-2.0
language:
- zh
- en
pipeline_tag: image-text-to-text
tags:
- multimodal
library_name: transformers
base_model:
- Qwen/Qwen2.5-VL-3B-Instruct
---

# Qwen2.5-VL-3B-Instruct-GPTQ-Int4

This is an **UNOFFICIAL** GPTQ-Int4 quantized version of the `Qwen2.5-VL-3B-Instruct` model using `gptqmodel` library. 

The model is compatible with the latest `transformers` library (which can run non-quantized Qwen2.5-VL models).

### Performance

| Model                                                        | Size (Disk) | ChartQA (test) | OCRBench |
| ------------------------------------------------------------ | :---------: | :------------: | :------: |
| [Qwen2.5-VL-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct) |   7.1 GB    |     83.48      |   791    |
| [Qwen2.5-VL-3B-Instruct-AWQ](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct-AWQ) |   3.2 GB    |     82.52      |   786    |
| **Qwen2.5-VL-3B-Instruct-GPTQ-Int4**                         |   3.2 GB    |     82.56      |   784    |
| [Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct) |   16.0 GB   |      83.2      |   846    |
| [Qwen2.5-VL-7B-Instruct-AWQ](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct-AWQ) |   6.5 GB    |     79.68      |   837    |
| **Qwen2.5-VL-7B-Instruct-GPTQ-Int4**                         |   6.5 GB    |     81.48      |   845    |


#### Note

- Evaluations are performed using [lmms-eval](https://github.com/EvolvingLMMs-Lab/lmms-eval) with default setting. 
- GPTQ models are computationally more effective (fewer VRAM usage, faster inference speed) than AWQ series in these evaluations.

### Quick Tour

Install the required libraries:
```
pip install git+https://github.com/huggingface/transformers accelerate qwen-vl-utils
pip install gptqmodel tokenicer # optional
```

Sample code:
```python
from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
from qwen_vl_utils import process_vision_info

model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    "hfl/Qwen2.5-VL-3B-Instruct-GPTQ-Int4", 
    attn_implementation="flash_attention_2",
    device_map="auto"
)
processor = AutoProcessor.from_pretrained("hfl/Qwen2.5-VL-3B-Instruct-GPTQ-Int4")

messages = [{
    "role": "user",
    "content": [
        {"type": "image", "image": "https://raw.githubusercontent.com/ymcui/Chinese-LLaMA-Alpaca-3/refs/heads/main/pics/banner.png"},
        {"type": "text", "text": "请你描述一下这张图片。"},
    ],
}]

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",
).to("cuda")

generated_ids = model.generate(**inputs, max_new_tokens=512)
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)
```

Results:
```
['这张图片展示了一个中文和英文的标志，内容为“中文LLaMA & Alpaca大模型”和“Chinese LLaMA & Alpaca Large Language Models”。标志左侧有两个卡通形象，一个是红色围巾的羊驼，另一个是白色毛发的羊驼，背景是一个绿色的草地和一座红色屋顶的建筑。标志右侧有一个数字3，旁边有一些电路图案。整体设计简洁明了，使用了明亮的颜色和可爱的卡通形象来吸引注意力。']
```


### Disclaimer
- **This is NOT an official model by Qwen. Use at your own risk.**
- For detailed usage, please check [Qwen2.5-VL's page](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct).



