--- 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.

## 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.
Click to expand 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])) ```
## 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](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} } ```