Update README.md

README.md

@@ -17,15 +17,6 @@ This repos contains the **biomedicine MLLM developed from Llama-3.2-11B-Vision-I
 
 The main project page is: [Adapt-MLLM-to-Domains](https://huggingface.co/AdaptLLM/Adapt-MLLM-to-Domains/edit/main/README.md)
 
-We investigate domain adaptation of MLLMs through post-training, focusing on data synthesis, training pipelines, and task evaluation. 
-**(1) Data Synthesis**: Using open-source models, we develop a visual instruction synthesizer that effectively generates diverse visual instruction tasks from domain-specific image-caption pairs. **Our synthetic tasks surpass those generated by manual rules, GPT-4, and GPT-4V in enhancing the domain-specific performance of MLLMs.** 
-**(2) Training Pipeline**: While the two-stage training--initially on image-caption pairs followed by visual instruction tasks--is commonly adopted for developing general MLLMs, we apply a single-stage training pipeline to enhance task diversity for domain-specific post-training. 
-**(3) Task Evaluation**: We conduct experiments in two domains, biomedicine and food, by post-training MLLMs of different sources and scales (e.g., Qwen2-VL-2B, LLaVA-v1.6-8B, Llama-3.2-11B), and then evaluating MLLM performance on various domain-specific tasks.
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 ## 1. To Chat with AdaMLLM  
 
 Our model architecture aligns with the base model: Llama-3.2-Vision-Instruct. We provide a usage example below, and you may refer to the official [Llama-3.2-Vision-Instruct Repository](https://huggingface.co/meta-llama/Llama-3.2-11B-Vision-Instruct) for more advanced usage instructions,