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README.md

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 # Adapting Multimodal Large Language Models to Domains via Post-Training
 
-This repository provides an implementation preview of our paper, **On Domain-Specific Post-Training for Multimodal Large Language Models**.
+This repository provides an implementation preview of our paper: [On Domain-Specific Post-Training for Multimodal Large Language Models](https://huggingface.co/papers/2411.19930).
 
 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.** 
@@ -37,7 +37,7 @@ AdaMLLM represents our latest advancement in building domain-specific foundation
 - **[AdaptLLM](https://huggingface.co/papers/2309.09530): Adapt LLM to domains**  
   We employ rule-based methods to extract tasks from domain-specific corpora, reformatting them into reading comprehension tasks for continued pre-training. Our 7B finance model outperforms domain-specific models of much larger scales, such as BloombergGPT-50B.
 
-- **AdaMLLM: Adapt Multimodal LLM to domains**  
+- **[AdaMLLM](https://huggingface.co/papers/2411.19930): Adapt Multimodal LLM to domains**  
   We extend supervised task synthesis to multimodality, introducing a unified visual instruction synthesizer to extract instruction-response pairs from domain-specific image-caption pairs. Our synthetic tasks outperform those generated by manual rules, GPT-4, and GPT-4V in improving domain-specific performance for MLLMs.