Update README.md

README.md

@@ -23,7 +23,29 @@ We investigate domain adaptation of MLLMs through post-training, focusing on dat
     <img src="https://cdn-uploads.huggingface.co/production/uploads/650801ced5578ef7e20b33d4/bRu85CWwP9129bSCRzos2.png" width="1000">
 </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  
+
+Our model architecture aligns with the base model: Llama-3.2-Vision-Instruct. We provide a usage example below, and you may refer to the official [Llama-3.2-Vision-Instruct Repository](https://huggingface.co/meta-llama/Llama-3.2-11B-Vision-Instruct) for more advanced usage instructions, 
+
+**Note:** For AdaMLLM, always place the image at the beginning of the input instruction in the messages.  
+
+<details>
+<summary> Click to expand </summary>
 
 Starting with transformers >= 4.45.0 onward, you can run inference using conversational messages that may include an image you can query about.
 
@@ -47,6 +69,7 @@ processor = AutoProcessor.from_pretrained(model_id)
 url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/0052a70beed5bf71b92610a43a52df6d286cd5f3/diffusers/rabbit.jpg"
 image = Image.open(requests.get(url, stream=True).raw)
 
+# NOTE: For AdaMLLM, always place the image at the beginning of the input instruction in the messages.  
 messages = [
     {"role": "user", "content": [
         {"type": "image"},
@@ -65,7 +88,12 @@ 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.
+</details>  
+
+## 2. To Evaluate AdaMLLM on Domain-Specific Benchmarks  
+
+Refer to the [food-VQA-benchmark](https://huggingface.co/datasets/AdaptLLM/food-VQA-benchmark) to reproduce our results and evaluate many other MLLMs on domain-specific benchmarks.  
+
 
 ## Citation
 If you find our work helpful, please cite us.