update the model card

README.md

@@ -1,12 +1,23 @@
-This is the model repository of paper *EDGE: Enhanced Grounded GUI Understanding with Enriched Multi-Granularity Synthetic Data*.
-
-
-The model is fine-tuned based on [*Monkey*](https://github.com/Yuliang-Liu/Monkey). In order to speed up the training, we also made some minor modifications:
-1. Instead of using the Lora Adapters in *Monkey*, the five patches of the raw image are stacked in an extra batch dimension and sent to the image encoder for processing at the same time.
-2. Inside the image encoder, we use [*flash attention*](https://github.com/Dao-AILab/flash-attention) instead of the manually implemented attention.
-3. Separate the step of reading the image from the forward propagation and make it a step of dataset preprocessing to speed up image reading using the `Dataloader` in pytorch.
-
-
-The training dataset (i.e. all training QAs in `.jsonl` format, excluding images) is published in repository [*EDGE-Dataset*](https://huggingface.co/datasets/EDGEwww25/EDGE-Dataset/settings).
-
-The model training and inference scripts are published in anonymous repository [*EDGE*](https://anonymous.4open.science/r/EDGE-1CDB).
+---
+license: mit
+datasets:
+- EDGEwww25/EDGE-Dataset
+- liuhaotian/LLaVA-Instruct-150K
+- echo840/Monkey_Data
+language:
+- en
+base_model:
+- echo840/Monkey-Chat
+---
+This is the model repository of paper *EDGE: Enhanced Grounded GUI Understanding with Enriched Multi-Granularity Synthetic Data*.
+
+
+The model is fine-tuned based on [*Monkey*](https://github.com/Yuliang-Liu/Monkey). In order to speed up the training, we also made some minor modifications:
+1. Instead of using the Lora Adapters in *Monkey*, the five patches of the raw image are stacked in an extra batch dimension and sent to the image encoder for processing at the same time.
+2. Inside the image encoder, we use [*flash attention*](https://github.com/Dao-AILab/flash-attention) instead of the manually implemented attention.
+3. Separate the step of reading the image from the forward propagation and make it a step of dataset preprocessing to speed up image reading using the `Dataloader` in pytorch.
+
+
+The training dataset (i.e. all training QAs in `.jsonl` format, excluding images) is published in repository [*EDGE-Dataset*](https://huggingface.co/datasets/EDGEwww25/EDGE-Dataset).
+
+The model training and inference scripts are published in anonymous repository [*EDGE*](https://anonymous.4open.science/r/EDGE-1CDB).