README.md

---
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.

<p align='left'>
    <img src="https://cdn-uploads.huggingface.co/production/uploads/650801ced5578ef7e20b33d4/-Jp7pAsCR2Tj4WwfwsbCo.png" width="600">
</p>

## 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: 
<details>
<summary> Click to expand </summary>

- 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()}
```
</details>  


## 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}
}
```