Update README.md

README.md

@@ -2,11 +2,7 @@
 
 This repository provides an implementation preview of our paper, **On Domain-Specific Post-Training for Multimodal Large Language Models**.
 
-Building on our previous work, [AdaptLLM](https://huggingface.co/papers/2309.09530), which develops domain-specific LLMs through continued training on domain-specific corpora, we introduce **AdaMLLM**. This framework explores 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 (Qwen2-VL-2B, LLaVA-v1.6-8B, Llama-3.2-11B), and then evaluating MLLM performance on various domain-specific tasks. 
+Building on our previous work, [AdaptLLM](https://huggingface.co/papers/2309.09530), which develops domain-specific LLMs through continued training on domain-specific corpora, we introduce **AdaMLLM**. This work explores domain adaptation of MLLMs through post-training, focusing on data synthesis, training pipelines, and task evaluation.
 
 <p align='center'>
     <img src="https://cdn-uploads.huggingface.co/production/uploads/650801ced5578ef7e20b33d4/iklQIKW_6TyCT13BMq5-d.png" width="600">