Create README.md

README.md

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+---
+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-Int3
+
+This is an **UNOFFICIAL** GPTQ-Int3 quantized version of the `Qwen2.5-VL` 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**](https://huggingface.co/hfl/Qwen2.5-VL-3B-Instruct-GPTQ-Int4) |   3.2 GB    |     82.56      |   784    |
+| [**Qwen2.5-VL-3B-Instruct-GPTQ-Int3**](https://huggingface.co/hfl/Qwen2.5-VL-3B-Instruct-GPTQ-Int3) |   2.9 GB    |     76.68      |   742    |
+| [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**](https://huggingface.co/hfl/Qwen2.5-VL-7B-Instruct-GPTQ-Int4) |   6.5 GB    |     81.48      |   845    |
+| [**Qwen2.5-VL-7B-Instruct-GPTQ-Int3**](https://huggingface.co/hfl/Qwen2.5-VL-7B-Instruct-GPTQ-Int3) |   5.8 GB    |     78.56      |   823    |
+
+
+#### 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.
+- We recommend use `gptqmodel` instead of `autogptq` library, as `autogptq` is no longer maintained.
+
+### Quick Tour
+
+Install the required libraries:
+```
+pip install git+https://github.com/huggingface/transformers accelerate qwen-vl-utils
+pip install git+https://github.com/huggingface/optimum.git
+pip install gptqmodel 
+```
+
+Optionally, you may need to install:
+
+```
+pip install tokenicer device_smi logbar
+```
+
+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[0])
+```
+
+Response:
+> 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).