README.md

---
license: llama3.2
language:
- en
base_model:
- meta-llama/Llama-3.2-11B-Vision-Instruct
tags:
- food
- recipe
---
# Adapting Multimodal Large Language Models to Domains via Post-Training

This repos contains the **food MLLM developed from Llama-3.2-11B-Vision-Instruct** 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/bRu85CWwP9129bSCRzos2.png" width="1000">
</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. 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.

Make sure to update your transformers installation via `pip install --upgrade transformers`.

```bash
import requests
import torch
from PIL import Image
from transformers import MllamaForConditionalGeneration, AutoProcessor

model_id = "AdaptLLM/food-Llama-3.2-11B-Vision-Instruct"

model = MllamaForConditionalGeneration.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
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"},
        {"type": "text", "text": "If I had to write a haiku for this one, it would be: "}
    ]}
]
input_text = processor.apply_chat_template(messages, add_generation_prompt=True)
inputs = processor(
    image,
    input_text,
    add_special_tokens=False,
    return_tensors="pt"
).to(model.device)

output = model.generate(**inputs, max_new_tokens=30)
print(processor.decode(output[0]))
```

</details>  

## 2. To Evaluate Any MLLM 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.

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}
}
```