Update README.md

README.md

@@ -13,7 +13,7 @@ base_model:
 
 # Qwen2.5-VL-7B-Instruct-GPTQ-Int4
 
-This is an **UNOFFICIAL** GPTQ-Int4 quantized version of the `Qwen2.5-VL-7B-Instruct` model using `gptqmodel` library. 
+This is an **UNOFFICIAL** GPTQ-Int4 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).
 
@@ -23,36 +23,47 @@ The model is compatible with the latest `transformers` library (which can run no
 | ------------------------------------------------------------ | :---------: | :------------: | :------: |
 | [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-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**                         |   6.5 GB    |     81.48      |   845    |
+| [**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+- We recommend use `gptqmodel` instead of `autogptq` library, as `autogptq` is no longer maintained.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 gptqmodel tokenicer # optional
+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-7B-Instruct-GPTQ-Int4", 
+    "hfl/Qwen2.5-VL-3B-Instruct-GPTQ-Int4", 
     attn_implementation="flash_attention_2",
     device_map="auto"
 )
-processor = AutoProcessor.from_pretrained("hfl/Qwen2.5-VL-7B-Instruct-GPTQ-Int4")
+processor = AutoProcessor.from_pretrained("hfl/Qwen2.5-VL-3B-Instruct-GPTQ-Int4")
 
 messages = [{
     "role": "user",
@@ -72,17 +83,12 @@ inputs = processor(
 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)
+print(output_text[0])
 ```
 
-Results:
-```
-['这张图片展示了一个标志或图标，包含以下内容：\n\n1. 左侧有一个圆形的图标，里面有一幅插画，描绘了两只羊驼（Alpaca），背景中有树木和一座亭子。\n2. 中间部分用中文写着“中文LLaMA & Alpaca大模型”，意思是“Chinese LLaMA & Alpaca Large Language Models”。\n3. 右侧有一个黑色的数字“3”，旁边有一些电路板的图案。\n\n整体来看，这个标志可能与中文的大型语言模型（LLaMA和Alpaca）有关，可能是一个项目、平台或产品的名称。']
-```
+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-7B-Instruct).
-
-
-
+- For detailed usage, please check [Qwen2.5-VL's page](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct).