license: apache-2.0
datasets:
- AdaptLLM/medicine-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.
How to use
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/medicine-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)
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}
}