license: apache-2.0
datasets:
- FreedomIntelligence/ALLaVA-4V
- Vision-Flan/vision-flan_191-task_1k
language:
- en
base_model:
- Lin-Chen/open-llava-next-llama3-8b
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.
How to use
To synthesize an "instruction-informative response-precise response" triplet based on the following image-caption pair.
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"
# Path to synthesizer
model_path = "AdaptLLM/visual-instruction-synthesizer"
# =========================== Do NOT need to modify the following ===============================
# 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}")
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}
}