Update README.md

README.md

@@ -13,7 +13,7 @@ tags:
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 # Adapting Multimodal Large Language Models to Domains via Post-Training
 
-This repos contains the **biomedicine MLLM developed from Llama-3.2-11B** in our paper: [On Domain-Specific Post-Training for Multimodal Large Language Models](https://huggingface.co/papers/2411.19930). The correspoding training data is in [medicine-visual-instructions](https://huggingface.co/datasets/AdaptLLM/medicine-visual-instructions).
+This repos contains the **biomedicine MLLM developed from Llama-3.2-11B** in our paper: [On Domain-Specific Post-Training for Multimodal Large Language Models](https://huggingface.co/papers/2411.19930). The correspoding training dataset is in [medicine-visual-instructions](https://huggingface.co/datasets/AdaptLLM/medicine-visual-instructions).
 
 The main project page is: [Adapt-MLLM-to-Domains](https://huggingface.co/AdaptLLM/Adapt-MLLM-to-Domains/edit/main/README.md)
 
@@ -30,7 +30,7 @@ We investigate domain adaptation of MLLMs through post-training, focusing on dat
 
 Starting with transformers >= 4.45.0 onward, you can run inference using conversational messages that may include an image you can query about.
 
-Make sure to update your transformers installation via pip install --upgrade transformers.
+Make sure to update your transformers installation via `pip install --upgrade transformers`.
 
 ```bash
 import requests
@@ -68,6 +68,9 @@ output = model.generate(**inputs, max_new_tokens=30)
 print(processor.decode(output[0]))
 ```
 
+Since our model architecture aligns with the base model, you can refer to the official repository of [Llama-3.2-Vision-Instruct](https://huggingface.co/meta-llama/Llama-3.2-11B-Vision-Instruct) for more advanced usage instructions.
+
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 ## Citation
 If you find our work helpful, please cite us.