95 lines
4.3 KiB
Markdown
95 lines
4.3 KiB
Markdown
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---
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license: apache-2.0
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language:
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- zh
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- en
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pipeline_tag: image-text-to-text
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tags:
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- multimodal
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library_name: transformers
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base_model:
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- Qwen/Qwen2.5-VL-3B-Instruct
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---
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# Qwen2.5-VL-3B-Instruct-GPTQ-Int4
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This is an **UNOFFICIAL** GPTQ-Int4 quantized version of the `Qwen2.5-VL` model using `gptqmodel` library.
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The model is compatible with the latest `transformers` library (which can run non-quantized Qwen2.5-VL models).
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### Performance
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| Model | Size (Disk) | ChartQA (test) | OCRBench |
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| ------------------------------------------------------------ | :---------: | :------------: | :------: |
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| [Qwen2.5-VL-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct) | 7.1 GB | 83.48 | 791 |
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| [Qwen2.5-VL-3B-Instruct-AWQ](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct-AWQ) | 3.2 GB | 82.52 | 786 |
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| [**Qwen2.5-VL-3B-Instruct-GPTQ-Int4**](https://huggingface.co/hfl/Qwen2.5-VL-3B-Instruct-GPTQ-Int4) | 3.2 GB | 82.56 | 784 |
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| [**Qwen2.5-VL-3B-Instruct-GPTQ-Int3**](https://huggingface.co/hfl/Qwen2.5-VL-3B-Instruct-GPTQ-Int3) | 2.9 GB | 76.68 | 742 |
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| [Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct) | 16.0 GB | 83.2 | 846 |
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| [Qwen2.5-VL-7B-Instruct-AWQ](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct-AWQ) | 6.5 GB | 79.68 | 837 |
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| [**Qwen2.5-VL-7B-Instruct-GPTQ-Int4**](https://huggingface.co/hfl/Qwen2.5-VL-7B-Instruct-GPTQ-Int4) | 6.5 GB | 81.48 | 845 |
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| [**Qwen2.5-VL-7B-Instruct-GPTQ-Int3**](https://huggingface.co/hfl/Qwen2.5-VL-7B-Instruct-GPTQ-Int3) | 5.8 GB | 78.56 | 823 |
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#### Note
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- Evaluations are performed using [lmms-eval](https://github.com/EvolvingLMMs-Lab/lmms-eval) with default setting.
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- GPTQ models are computationally more effective (fewer VRAM usage, faster inference speed) than AWQ series in these evaluations.
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- We recommend use `gptqmodel` instead of `autogptq` library, as `autogptq` is no longer maintained.
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### Quick Tour
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Install the required libraries:
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```
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pip install git+https://github.com/huggingface/transformers accelerate qwen-vl-utils
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pip install git+https://github.com/huggingface/optimum.git
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pip install gptqmodel
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```
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Optionally, you may need to install:
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```
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pip install tokenicer device_smi logbar
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```
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Sample code:
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```python
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from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
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from qwen_vl_utils import process_vision_info
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model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
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"hfl/Qwen2.5-VL-3B-Instruct-GPTQ-Int4",
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attn_implementation="flash_attention_2",
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device_map="auto"
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)
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processor = AutoProcessor.from_pretrained("hfl/Qwen2.5-VL-3B-Instruct-GPTQ-Int4")
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messages = [{
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"role": "user",
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"content": [
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{"type": "image", "image": "https://raw.githubusercontent.com/ymcui/Chinese-LLaMA-Alpaca-3/refs/heads/main/pics/banner.png"},
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{"type": "text", "text": "请你描述一下这张图片。"},
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],
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}]
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text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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image_inputs, video_inputs = process_vision_info(messages)
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inputs = processor(
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text=[text], images=image_inputs, videos=video_inputs,
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padding=True, return_tensors="pt",
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).to("cuda")
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generated_ids = model.generate(**inputs, max_new_tokens=512)
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generated_ids_trimmed = [out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)]
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output_text = processor.batch_decode(generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False)
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print(output_text[0])
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```
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Response:
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> 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。
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### Disclaimer
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- **This is NOT an official model by Qwen. Use at your own risk.**
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- For detailed usage, please check [Qwen2.5-VL's page](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct).
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