Update README.md

README.md

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 This project adapts general Multimodal Large Language Models (MLLMs) to specific domains like science and industry to improve their real-world use. It focuses on three main areas:
 
-## Contributions
-
 ### 1. Data Synthesis
 - We create a **generate-then-filter pipeline** using open-source models to make diverse visual tasks from domain-specific image-caption pairs.
 - This data works better than data made by hand or closed-source models (e.g., GPT-4V).
@@ -16,7 +14,7 @@ This project adapts general Multimodal Large Language Models (MLLMs) to specific
 - Instead of the usual two-step training (image-caption pairs first, then visual tasks), we use a **single-step training** to handle more tasks for specific domains.
 
 ### 3. Task Evaluation
-- We test our method in important fields like biomedicine, food, and remote sensing.
+- We test our method in important fields like **biomedicine, food, and remote sensing**.
 - We train and evaluate MLLMs on domain-specific tasks to show how well they perform.