Update README.md

README.md

@@ -1,3 +1,79 @@
----
-license: apache-2.0
----
+---
+task_categories:
+- visual-question-answering
+language:
+- en
+tags:
+- Vision
+- food
+- recipe
+configs:
+- config_name: Food101
+  data_files:
+  - split: test
+    path: food101/data-*.arrow
+- config_name: FoodSeg103
+  data_files:
+  - split: test
+    path: foodseg103/data-*.arrow
+- config_name: Nutrition5K
+  data_files:
+  - split: test
+    path: nutriton50k/data-*.arrow
+- config_name: Recipe1M
+  data_files:
+  - split: test
+    path: food_eval_multitask_v2/data-*.arrow
+---
+
+# Adapting Multimodal Large Language Models to Domains via Post-Training
+
+This repos contains the **food visual instruction tasks for evaluating MLLMs** in our paper: [On Domain-Specific Post-Training for Multimodal Large Language Models](https://huggingface.co/papers/2411.19930).
+
+The main project page is: [Adapt-MLLM-to-Domains](https://huggingface.co/AdaptLLM/Adapt-MLLM-to-Domains/edit/main/README.md)
+
+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.** 
+**(2) Training Pipeline**: While the two-stage training--initially on image-caption pairs followed by visual instruction tasks--is commonly adopted for developing general MLLMs, we apply a single-stage training pipeline to enhance task diversity for domain-specific post-training. 
+**(3) Task Evaluation**: We conduct experiments in two domains, biomedicine and food, by post-training MLLMs of different sources and scales (e.g., Qwen2-VL-2B, LLaVA-v1.6-8B, Llama-3.2-11B), and then evaluating MLLM performance on various domain-specific tasks.
+
+<p align='left'>
+    <img src="https://cdn-uploads.huggingface.co/production/uploads/650801ced5578ef7e20b33d4/-Jp7pAsCR2Tj4WwfwsbCo.png" width="600">
+</p>
+
+## How to use
+
+```python
+from datasets import load_dataset
+
+# Choose the task name from the list of available tasks
+task_name = 'Food101'  # Options: 'Food101', 'FoodSeg103', 'Nutrition5K', 'Recipe1M'
+
+# Load the dataset for the chosen task
+data = load_dataset('AdaptLLM/food-vision-language-tasks', task_name, split='test')
+```
+
+## Citation
+If you find our work helpful, please cite us.
+
+AdaMLLM
+```bibtex
+@article{adamllm,
+  title={On Domain-Specific Post-Training for Multimodal Large Language Models},
+  author={Cheng, Daixuan and Huang, Shaohan and Zhu, Ziyu and Zhang, Xintong and Zhao, Wayne Xin and Luan, Zhongzhi and Dai, Bo and Zhang, Zhenliang},
+  journal={arXiv preprint arXiv:2411.19930},
+  year={2024}
+}
+```
+
+[AdaptLLM](https://huggingface.co/papers/2309.09530) (ICLR 2024)
+```bibtex
+@inproceedings{
+adaptllm,
+title={Adapting Large Language Models via Reading Comprehension},
+author={Daixuan Cheng and Shaohan Huang and Furu Wei},
+booktitle={The Twelfth International Conference on Learning Representations},
+year={2024},
+url={https://openreview.net/forum?id=y886UXPEZ0}
+}
+```