metadata

license: apache-2.0
datasets:
  - AdaptLLM/biomed-visual-instructions
language:
  - en
base_model:
  - Lin-Chen/open-llava-next-llama3-8b
tags:
  - medical
  - biology

Adapting Multimodal Large Language Models to Domains via Post-Training

This repo contains the biomedicine MLLM developed from LLaVA-NeXT-Llama3-8B in our paper: On Domain-Specific Post-Training for Multimodal Large Language Models. The correspoding training dataset is in medicine-visual-instructions.

The main project page is: Adapt-MLLM-to-Domains

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	AdaptLLM/visual-instruction-synthesizer	-	open-llava-next-llama3-8b	VisionFLAN and ALLaVA	-
AdaMLLM-med-2B	AdaptLLM/biomed-Qwen2-VL-2B-Instruct	Biomedicine	Qwen2-VL-2B-Instruct	biomed-visual-instructions	biomed-VQA-benchmark
AdaMLLM-food-2B	AdaptLLM/food-Qwen2-VL-2B-Instruct	Food	Qwen2-VL-2B-Instruct	food-visual-instructions	food-VQA-benchmark
AdaMLLM-med-8B	AdaptLLM/biomed-LLaVA-NeXT-Llama3-8B	Biomedicine	open-llava-next-llama3-8b	biomed-visual-instructions	biomed-VQA-benchmark
AdaMLLM-food-8B	AdaptLLM/food-LLaVA-NeXT-Llama3-8B	Food	open-llava-next-llama3-8b	food-visual-instructions	food-VQA-benchmark
AdaMLLM-med-11B	AdaptLLM/biomed-Llama-3.2-11B-Vision-Instruct	Biomedicine	Llama-3.2-11B-Vision-Instruct	biomed-visual-instructions	biomed-VQA-benchmark
AdaMLLM-food-11B	AdaptLLM/food-Llama-3.2-11B-Vision-Instruct	Food	Llama-3.2-11B-Vision-Instruct	food-visual-instructions	food-VQA-benchmark

Code: https://github.com/bigai-ai/QA-Synthesizer

1. To Chat with AdaMLLM

from transformers import LlavaNextProcessor, LlavaNextForConditionalGeneration
import torch
from PIL import Image
import requests

# Define your input image and instruction here:
## image 
url = "https://cdn-uploads.huggingface.co/production/uploads/650801ced5578ef7e20b33d4/bRu85CWwP9129bSCRzos2.png"
image = Image.open(requests.get(url, stream=True).raw).convert("RGB")

instruction = "What's in the image?"

model_path='AdaptLLM/biomed-LLaVA-NeXT-Llama3-8B'

# =========================== Do NOT need to modify the following ===============================
# Load the processor
processor = LlavaNextProcessor.from_pretrained(model_path)

# Define image token
image_token = "<|reserved_special_token_4|>"

# Format the prompt
prompt = (
    f"<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n"
    f"You are a helpful language and vision assistant. You are able to understand the visual content that the user provides, and assist the user with a variety of tasks using natural language."
    f"<|eot_id|><|start_header_id|>user<|end_header_id|>\n\n"
    f"{image_token}\n{instruction}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
)

# Load the model
model = LlavaNextForConditionalGeneration.from_pretrained(model_path, torch_dtype=torch.float16, device_map="auto")

# Prepare inputs and generate output
inputs = processor(images=image, text=prompt, return_tensors="pt").to(model.device)
answer_start = int(inputs["input_ids"].shape[-1])
output = model.generate(**inputs, max_new_tokens=512)

# Decode predictions
pred = processor.decode(output[0][answer_start:], skip_special_tokens=True)
print(pred)

2. To Evaluate Any MLLM on Domain-Specific Benchmarks

Refer to the biomed-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

@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 (EMNLP 2024)

@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 (ICLR 2024)

@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}
}