--- task_categories: - visual-question-answering language: - en tags: - Vision - food - recipe configs: - config_name: Recipe1M data_files: - split: test path: food_eval_multitask_v2/data-*.arrow - config_name: Nutrition5K data_files: - split: test path: nutrition50k/data-*.arrow - config_name: Food101 data_files: - split: test path: food101/data-*.arrow - config_name: FoodSeg103 data_files: - split: test path: foodseg103/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.

## 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. Download Data You can load datasets using the `datasets` library: ```python from datasets import load_dataset # Choose the task name from the list of available tasks task_name = 'FoodSeg103' # Options: 'Food101', 'FoodSeg103', 'Nutrition5K', 'Recipe1M' # Load the dataset for the chosen task data = load_dataset('AdaptLLM/food-VQA-benchmark', task_name, split='test') print(list(data)[0]) ``` The mapping between category names and indices for `Food101`, `FoodSeg103`, and `Nutrition5K` datasets is provided in the following files:
Click to expand - Food101: `food101_name_to_label_map.json` - FoodSeg103: `foodSeg103_id2label.json` - Nutrition5K: `nutrition5k_ingredients.py` #### Example Usages: **Food101** ```python import json # Load the mapping file map_path = 'food101_name_to_label_map.json' name_to_label_map = json.load(open(map_path)) name_to_label_map = {key.replace('_', ' '): value for key, value in name_to_label_map.items()} # Reverse mapping: label to name label_to_name_map = {value: key for key, value in name_to_label_map.items()} ``` **FoodSeg103** ```python import json # Load the mapping file map_path = 'foodSeg103_id2label.json' id2name_map = json.load(open(map_path)) # Remove background and irrelevant labels id2name_map.pop("0") # Background id2name_map.pop("103") # Other ingredients # Convert keys to integers id2name_map = {int(key): value for key, value in id2name_map.items()} # Create reverse mapping: name to ID name2id_map = {value: key for key, value in id2name_map.items()} ``` **Nutrition5K** ```python from nutrition5k_ingredients import all_ingredients # Create mappings id2name_map = dict(zip(range(0, len(all_ingredients)), all_ingredients)) name2id_map = {value: key for key, value in id2name_map.items()} ```
## 2. Evaluate Any MLLM Compatible with vLLM on the Food Benchmarks We provide a guide to directly evaluate MLLMs such as LLaVA-v1.6 ([open-source version](https://huggingface.co/Lin-Chen/open-llava-next-llama3-8b)), Qwen2-VL-Instruct, and Llama-3.2-Vision-Instruct. To evaluate other MLLMs, refer to [this guide](https://github.com/vllm-project/vllm/blob/main/examples/offline_inference_vision_language.py) for modifying the `BaseTask` class in the [vllm_inference/utils/task.py](https://github.com/bigai-ai/QA-Synthesizer/blob/main/vllm_inference/utils/task.py) file. Feel free reach out to us for assistance! **The dataset loading script is embedded in the inference code, so you can directly run the following commands to evaluate MLLMs.** ### 1) Setup Install vLLM using `pip` or [from source](https://vllm.readthedocs.io/en/latest/getting_started/installation.html#build-from-source). As recommended in the official vLLM documentation, install vLLM in a **fresh new** conda environment: ```bash conda create -n vllm python=3.10 -y conda activate vllm pip install vllm # Ensure vllm>=0.6.2 for compatibility with Llama-3.2. If Llama-3.2 is not used, vllm==0.6.1 is sufficient. ``` Clone the repository and navigate to the inference directory: ```bash git clone https://github.com/bigai-ai/QA-Synthesizer.git cd QA-Synthesizer/vllm_inference RESULTS_DIR=./eval_results # Directory for saving evaluation scores ``` ### 2) Evaluate Run the following commands: ```bash # Specify the domain: choose from ['food', 'Recipe1M', 'Nutrition5K', 'Food101', 'FoodSeg103'] # 'food' runs inference on all food tasks; others run on individual tasks. DOMAIN='food' # Specify the model type: choose from ['llava', 'qwen2_vl', 'mllama'] # For LLaVA-v1.6, Qwen2-VL, and Llama-3.2-Vision-Instruct, respectively. MODEL_TYPE='qwen2_vl' # Set the model repository ID on Hugging Face. Examples: # "Qwen/Qwen2-VL-2B-Instruct", "AdaptLLM/food-Qwen2-VL-2B-Instruct" for MLLMs based on Qwen2-VL-Instruct. # "meta-llama/Llama-3.2-11B-Vision-Instruct", "AdaptLLM/food-Llama-3.2-11B-Vision-Instruct" for MLLMs based on Llama-3.2-Vision-Instruct. # "AdaptLLM/food-LLaVA-NeXT-Llama3-8B" for MLLMs based on LLaVA-v1.6. MODEL=AdaptLLM/food-Qwen2-VL-2B-Instruct # Set the directory for saving model prediction outputs: OUTPUT_DIR=./output/AdaMLLM-food-Qwen-2B_${DOMAIN} # Run inference with data parallelism; adjust CUDA devices as needed: CUDA_VISIBLE_DEVICES='0,1,2,3,4,5,6,7' bash run_inference.sh ${MODEL} ${DOMAIN} ${MODEL_TYPE} ${OUTPUT_DIR} ${RESULTS_DIR} ``` Detailed scripts to reproduce our results are in [Evaluation.md](https://github.com/bigai-ai/QA-Synthesizer/blob/main/docs/Evaluation.md) ### 3) Results The evaluation results are stored in `./eval_results`, and the model prediction outputs are in `./output`. ## Citation If you find our work helpful, please cite us. [AdaMLLM](https://huggingface.co/papers/2411.19930) ```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} } ``` [Instruction Pre-Training](https://huggingface.co/papers/2406.14491) (EMNLP 2024) ```bibtex @article{cheng2024instruction, title={Instruction Pre-Training: Language Models are Supervised Multitask Learners}, author={Cheng, Daixuan and Gu, Yuxian and Huang, Shaohan and Bi, Junyu and Huang, Minlie and Wei, Furu}, journal={arXiv preprint arXiv:2406.14491}, year={2024} } ``` [Adapt LLM to Domains](https://huggingface.co/papers/2309.09530) (ICLR 2024) ```bibtex @inproceedings{ cheng2024adapting, 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} } ```