Update README.md

README.md

@@ -42,8 +42,7 @@ We investigate domain adaptation of MLLMs through post-training, focusing on dat
 **Code**: [https://github.com/bigai-ai/QA-Synthesizer](https://github.com/bigai-ai/QA-Synthesizer)
 
 ## 1. To Chat with AdaMLLM  
-
-Our model architecture aligns with the base model: Qwen-2-VL-Instruct. Below, we provide a usage example. For more advanced usage instructions, please refer to the official [Qwen-2-VL-Instruct repository](https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct/edit/main/README.md).  
+Our model architecture aligns with the base model: Qwen-2-VL-Instruct. We provide a usage example below, and you may refer to the official [Qwen-2-VL-Instruct repository](https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct/edit/main/README.md) for more advanced usage instructions.
 
 **Note:** For AdaMLLM, always place the image at the beginning of the input instruction in the messages.  
 
@@ -61,24 +60,24 @@ from qwen_vl_utils import process_vision_info
 
 # default: Load the model on the available device(s)
 model = Qwen2VLForConditionalGeneration.from_pretrained(
-    "AdaptLLM/medicine-Qwen2-VL-2B-Instruct", torch_dtype="auto", device_map="auto"
+    "AdaptLLM/biomed-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(
-#     "AdaptLLM/medicine-Qwen2-VL-2B-Instruct",
+#     "AdaptLLM/biomed-Qwen2-VL-2B-Instruct",
 #     torch_dtype=torch.bfloat16,
 #     attn_implementation="flash_attention_2",
 #     device_map="auto",
 # )
 
 # default processer
-processor = AutoProcessor.from_pretrained("AdaptLLM/medicine-Qwen2-VL-2B-Instruct")
+processor = AutoProcessor.from_pretrained("AdaptLLM/biomed-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("AdaptLLM/medicine-Qwen2-VL-2B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels)
+# processor = AutoProcessor.from_pretrained("AdaptLLM/biomed-Qwen2-VL-2B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels)
 
 
 # NOTE: For AdaMLLM, always place the image at the beginning of the input instruction in the messages.