Update README.md

README.md

@@ -25,7 +25,22 @@ We investigate domain adaptation of MLLMs through post-training, focusing on dat
     <img src="https://cdn-uploads.huggingface.co/production/uploads/650801ced5578ef7e20b33d4/-Jp7pAsCR2Tj4WwfwsbCo.png" width="600">
 </p>
 
-## How to use
+## Resources
+**🤗 We share our data and models with example usages, feel free to open any issues or discussions! 🤗**
+
+| Model                                                                       | Repo ID in HF 🤗                           | Domain       | Base Model              | Training Data                                                                                  | Evaluation Benchmark |
+|:----------------------------------------------------------------------------|:--------------------------------------------|:--------------|:-------------------------|:------------------------------------------------------------------------------------------------|-----------------------|
+| [Visual Instruction Synthesizer](https://huggingface.co/AdaptLLM/visual-instruction-synthesizer) | AdaptLLM/visual-instruction-synthesizer     | -  | open-llava-next-llama3-8b    | VisionFLAN and ALLaVA | -                   |
+| [AdaMLLM-med-2B](https://huggingface.co/AdaptLLM/biomed-Qwen2-VL-2B-Instruct) | AdaptLLM/biomed-Qwen2-VL-2B-Instruct     | Biomedicine  | Qwen2-VL-2B-Instruct    | [biomed-visual-instructions](https://huggingface.co/datasets/AdaptLLM/biomed-visual-instructions) | [biomed-VQA-benchmark](https://huggingface.co/datasets/AdaptLLM/biomed-VQA-benchmark)                   |
+| [AdaMLLM-food-2B](https://huggingface.co/AdaptLLM/food-Qwen2-VL-2B-Instruct) | AdaptLLM/food-Qwen2-VL-2B-Instruct     | Food  | Qwen2-VL-2B-Instruct    | [food-visual-instructions](https://huggingface.co/datasets/AdaptLLM/food-visual-instructions) | [food-VQA-benchmark](https://huggingface.co/datasets/AdaptLLM/food-VQA-benchmark)                   |
+| [AdaMLLM-med-8B](https://huggingface.co/AdaptLLM/biomed-LLaVA-NeXT-Llama3-8B) | AdaptLLM/biomed-LLaVA-NeXT-Llama3-8B     | Biomedicine  | open-llava-next-llama3-8b    | [biomed-visual-instructions](https://huggingface.co/datasets/AdaptLLM/biomed-visual-instructions) | [biomed-VQA-benchmark](https://huggingface.co/datasets/AdaptLLM/biomed-VQA-benchmark)                   |
+| [AdaMLLM-food-8B](https://huggingface.co/AdaptLLM/food-LLaVA-NeXT-Llama3-8B) |AdaptLLM/food-LLaVA-NeXT-Llama3-8B     | Food  | open-llava-next-llama3-8b    | [food-visual-instructions](https://huggingface.co/datasets/AdaptLLM/food-visual-instructions) |  [food-VQA-benchmark](https://huggingface.co/datasets/AdaptLLM/food-VQA-benchmark)                   |
+| [AdaMLLM-med-11B](https://huggingface.co/AdaptLLM/biomed-Llama-3.2-11B-Vision-Instruct) | AdaptLLM/biomed-Llama-3.2-11B-Vision-Instruct     | Biomedicine  | Llama-3.2-11B-Vision-Instruct    | [biomed-visual-instructions](https://huggingface.co/datasets/AdaptLLM/biomed-visual-instructions) | [biomed-VQA-benchmark](https://huggingface.co/datasets/AdaptLLM/biomed-VQA-benchmark)                   |
+| [AdaMLLM-food-11B](https://huggingface.co/AdaptLLM/food-Llama-3.2-11B-Vision-Instruct) | AdaptLLM/food-Llama-3.2-11B-Vision-Instruct     | Food | Llama-3.2-11B-Vision-Instruct    | [food-visual-instructions](https://huggingface.co/datasets/AdaptLLM/food-visual-instructions) |  [food-VQA-benchmark](https://huggingface.co/datasets/AdaptLLM/food-VQA-benchmark)                   |
+
+**Code**: [https://github.com/bigai-ai/QA-Synthesizer](https://github.com/bigai-ai/QA-Synthesizer)
+
+## 1. To Chat with AdaMLLM  
 
 ```python
 from transformers import LlavaNextProcessor, LlavaNextForConditionalGeneration
@@ -40,7 +55,7 @@ image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
 
 instruction = "What's in the image?"
 
-model_path='AdaptLLM/medicine-LLaVA-NeXT-Llama3-8B'
+model_path='AdaptLLM/biomed-LLaVA-NeXT-Llama3-8B'
 
 # =========================== Do NOT need to modify the following ===============================
 # Load the processor
@@ -70,6 +85,10 @@ pred = processor.decode(output[0][answer_start:], skip_special_tokens=True)
 print(pred)
 ```
 
+2. To Evaluate AdaMLLM on the domain-spefic Benchmarks:
+
+See [biomed-VQA-benchmark](https://huggingface.co/datasets/AdaptLLM/biomed-VQA-benchmark) to reproduce our results and evalaute more MLLMs on the domain-specific benchmarks.
+
 ## Citation
 If you find our work helpful, please cite us.