AdaptLLM/visual-instruction-synthesizer

Adapting Multimodal Large Language Models to Domains via Post-Training

This repos contains the visual-instruction synthesizer in our paper: On Domain-Specific Post-Training for Multimodal Large Language Models.

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. Basic Usage: Synthesize a task triplet based on a given image-caption pair

To synthesize an "instruction-informative response-precise response" triplet based on the following image-caption pair.

Click to expand

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

# Define your input image-caption pair here:
## image 
url = "https://cdn-uploads.huggingface.co/production/uploads/650801ced5578ef7e20b33d4/mgI_Ayj12_Q_kviWvfAVb.jpeg"
image = Image.open(requests.get(url, stream=True).raw).convert("RGB")

## Caption
caption = "Dish: Strawberry Waffles\n\nSteps to prepare:\na). Preheat and grease a waffle iron according to manufacturer's instructions.\nb). Sift flour, baking powder, and salt together in a bowl. Whisk buttermilk, yogurt, butter, eggs, and sugar together in a separate bowl; stir into flour mixture until batter is smooth. Fold strawberries into batter.\nc). Pour about 1/3 cup batter into preheated waffle iron; cook until lightly browned, 5 to 7 minutes. Repeat with remaining batter.\n\nIngredients you'll need:\n(a). 2 1/2 cups all-purpose flour\n(b). 4 teaspoons baking powder\n(c). 3/4 teaspoon salt\n(d). 2 cups buttermilk\n(e). 1/2 cup vanilla Greek-style yogurt\n(f). 1/2 cup butter, melted\n(g). 2 eggs, beaten\n(h). 1 1/2 tablespoons white sugar\n(i). 3/4 cup chopped strawberries, or more to taste"

# =========================== Do NOT need to modify the following ===============================

# Path to synthesizer
model_path = "AdaptLLM/visual-instruction-synthesizer"

# Prompt Hints
caption_hint = "Describe the image."
precise_hint = "Answer with a precise response.\n"
informative_hint = "Answer with an informative response.\n"

# Function to parse predictions
def parse_pred(pred):
    if not pred.endswith("<|end_of_text|>"):
        return []

    pred = pred[:-len("<|end_of_text|>")]

    QA_str_list = pred.split("<|start_header_id|>user<|end_header_id|>\n\n")
    if not pred.endswith("<|eot_id|>"):
        QA_str_list = QA_str_list[:-1]

    QA_list = []
    for QA_str in QA_str_list:
        try:
            assert "<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n" in QA_str
            Q_str, A_str = QA_str.split("<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n")
            Q_str, A_str = Q_str.strip(), A_str[:-len("<|eot_id|>")].strip()
            assert Q_str and A_str
            QA_list.append({"Q": Q_str, "A": A_str})
        except AssertionError:
            pass  # Skip invalid entries

    conversations = []
    for qa_entry in QA_list:
        conversations.append({"from": "human", "value": qa_entry["Q"]})
        conversations.append({"from": "gpt", "value": qa_entry["A"]})
    return conversations

# Function to extract task triplets
def get_task_triplet(pred):
    pred_QAs = parse_pred(pred)
    precise_QAs = {}
    informative_QAs = {}
    collected_QA = None

    for idx in range(0, len(pred_QAs), 2):  # Iterate over question-answer pairs
        question = pred_QAs[idx]["value"]
        answer = pred_QAs[idx + 1]["value"]
        if question.startswith(precise_hint):
            precise_q = question[len(precise_hint):]
            if precise_q in informative_QAs:
                collected_QA = {
                    "Q": precise_q,
                    "precise_A": answer,
                    "informative_A": informative_QAs[precise_q],
                }
                break
            else:
                precise_QAs[precise_q] = answer
        elif question.startswith(informative_hint):
            informative_q = question[len(informative_hint):]
            if informative_q in precise_QAs:
                collected_QA = {
                    "Q": informative_q,
                    "precise_A": precise_QAs[informative_q],
                    "informative_A": answer,
                }
                break
            else:
                informative_QAs[informative_q] = answer

    return collected_QA

# 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{caption_hint}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
    f"{caption}<|eot_id|><|start_header_id|>user<|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=False)
print(f"## Synthesizer predictions:\n{pred}")

# Extract task triplets
task_triplet = get_task_triplet(pred)
print(f"## Synthesized Task triplet:\n{task_triplet}")

2. Advanced Usage: Convert Image-Caption Pairs into Visual Instructions at Scale

The following steps show how to convert your own data into visual instructions for post-training MLLMs.

We leverage vLLM to accelerate the synthesis process. On a single A100-80GB GPU, it takes about 12.5 hours to convert 100K image-caption pairs.

Click to expand

1) Setup

Install vLLM using pip or from source.

pip install vllm

Clone our code repository and navigate to the inference directory:

git clone https://github.com/bigai-ai/QA-Synthesizer.git
cd QA-Synthesizer/vllm_inference
SYNTHESIZER=AdaptLLM/visual-instruction-synthesizer
CONSISTENCY_CHECKER=meta-llama/Meta-Llama-3-8B  # Language model for consistency checks  

2) Prepare Your Image-Caption Pairs

Format your image_caption_pairs file to match the following structure (similar to ShareGPT), or you can use our data_samples/image_caption_pairs.json for a quick try.

[
  {
    "images": ["image_xxx.jpg"],
    "messages": [
      {
        "content": "<image>instruction",
        "role": "user"
      },
      {
        "content": "response",
        "role": "assistant"
      }
    ]
  },
  ...
]

3) Run Synthesis

The following command generate task triplets using the synthesizer and apply consistency-based filtering to enhance data quality:

IMAGE_CAPTION='../data_samples/image_caption_pairs.json'  # Path to image-caption pairs  
IMAGE_FOLDER='../data_samples/images'  # Path to the image folder  
OUTPUT_DIR='../data_samples/'  # Output directory for synthesized data  

# Run synthesis with data parallelism; adjust CUDA devices as needed:
CUDA_VISIBLE_DEVICES='0,1,2,3,4,5,6,7' bash run_synthesis.sh ${SYNTHESIZER} ${CONSISTENCY_CHECKER} ${IMAGE_CAPTION} ${IMAGE_FOLDER} ${OUTPUT_DIR}  

The synthesized output will be saved at:

${OUTPUT_DIR}/image_caption_and_synthetic_task.json

This output can be directly utilized for single-stage post-training with code repo like LLaMA-Factory.

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}
}