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PengxiangLi/
MAT

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pipeline_tag: image-text-to-text datasets:

openbmb/RLAIF-V-Dataset library_name: transformers language:
multilingual tags:
minicpm-v
vision
ocr
multi-image
video
custom_code

Multi-modal Agent Tuning: Building a VLM-Driven Agent for Efficient Tool Usage

GitHub | Project

MAT-MiniCPM-V 2.6

This model is a fine-tuned version of the MiniCPM V2.6 7B model on the MM-traj dataset. On GTA and GAIA benchmarks, it achieved improvements of 18.59% and 7.78% respectively compared to the non-fine-tuned baseline.

Usage

Our model inherits the inference architecture from MiniCPM-V-2.6. The following implementation is adapted from their original inference code with full compatibility.

Requirements (tested on Python 3.10):

Pillow==10.1.0
torch==2.1.2
torchvision==0.16.2
transformers==4.40.0
sentencepiece==0.1.99
decord

Basic Inference

python

# test.py
import torch
from PIL import Image
from transformers import AutoModel, AutoTokenizer

# Load our fine-tuned model (based on MiniCPM-V-2.6 architecture)
model = AutoModel.from_pretrained('PengxiangLi/MAT', trust_remote_code=True,
    attn_implementation='sdpa', torch_dtype=torch.bfloat16)  # Maintain original implementation choices
model = model.eval().cuda()
tokenizer = AutoTokenizer.from_pretrained('PengxiangLi/MAT', trust_remote_code=True)

image = Image.open('xx.jpg').convert('RGB')
question = 'What is in the image?'
msgs = [{'role': 'user', 'content': [image, question]}]

# The chat interface follows MiniCPM's original implementation
response = model.chat(
    image=None,
    msgs=msgs,
    tokenizer=tokenizer
)
print(response)

## Streaming output (inherited from MiniCPM's implementation)
response_stream = model.chat(
    image=None,
    msgs=msgs,
    tokenizer=tokenizer,
    sampling=True,
    stream=True
)

generated_text = ""
for new_text in response_stream:
    generated_text += new_text
    print(new_text, flush=True, end='')

Multi-image Chat

Implementation adapted from MiniCPM's original multi-image handling

python

import torch
from PIL import Image
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained('PengxiangLi/MAT', trust_remote_code=True,
    attn_implementation='sdpa', torch_dtype=torch.bfloat16)
model = model.eval().cuda()
tokenizer = AutoTokenizer.from_pretrained('PengxiangLi/MAT', trust_remote_code=True)

# The message format follows MiniCPM's original schema
image1 = Image.open('image1.jpg').convert('RGB')
image2 = Image.open('image2.jpg').convert('RGB')
question = 'Compare the two images...'

msgs = [{'role': 'user', 'content': [image1, image2, question]}]

# Using the original chat interface design
answer = model.chat(
    image=None,
    msgs=msgs,
    tokenizer=tokenizer
)
print(answer)

Few-shot Learning

Adapted from MiniCPM's few-shot implementation

python

import torch
from PIL import Image
from transformers import AutoModel, AutoTokenizer

# Maintain original model loading parameters
model = AutoModel.from_pretrained('PengxiangLi/MAT', trust_remote_code=True,
    attn_implementation='sdpa', torch_dtype=torch.bfloat16)
model = model.eval().cuda()
tokenizer = AutoTokenizer.from_pretrained('PengxiangLi/MAT', trust_remote_code=True)

# Following MiniCPM's message structure
question = "production date" 
image1 = Image.open('example1.jpg').convert('RGB')
answer1 = "2023.08.04"
image2 = Image.open('example2.jpg').convert('RGB')
answer2 = "2007.04.24"
image_test = Image.open('test.jpg').convert('RGB')

msgs = [
    {'role': 'user', 'content': [image1, question]}, 
    {'role': 'assistant', 'content': [answer1]},
    {'role': 'user', 'content': [image2, question]}, 
    {'role': 'assistant', 'content': [answer2]},
    {'role': 'user', 'content': [image_test, question]}
]

# Using the unmodified chat interface from original implementation
answer = model.chat(
    image=None,
    msgs=msgs,
    tokenizer=tokenizer
)
print(answer)

Implementation Notes:

All core inference logic is directly inherited from MiniCPM-V-2.6
The chat() interface remains unchanged from the original implementation
Model loading parameters maintain compatibility with the base architecture
Message formatting follows MiniCPM's original schema

License

Model License

The code in this repository is licensed under the Apache-2.0 License.
Usage of our fine-tuned MiniCPM-based model weights must strictly adhere to the MiniCPM Model License.

Usage Terms

Academic Research: The model weights are freely available for academic use without restrictions.
Commercial Use:
- After completing the official registration questionnaire and obtaining authorization, the MiniCPM-V 2.6 based weights (including our fine-tuned version) are available for commercial use free of charge.
- Commercial users must maintain compliance with all terms outlined in the MiniCPM Model License.

Inheritance Clause

As a derivative work of MiniCPM, our model inherits and is bound by all original licensing requirements from the base model. Users are responsible for ensuring compliance with both our terms and the upstream MiniCPM license terms.

Citation

If you find our work helpful, please consider citing our papers 📝 and liking this project ❤️！

bib

@article{gao2024multi,
  title={Multi-modal Agent Tuning: Building a VLM-Driven Agent for Efficient Tool Usage},
  author={Gao, Zhi and Zhang, Bofei and Li, Pengxiang and Ma, Xiaojian and Yuan, Tao and Fan, Yue and Wu, Yuwei and Jia, Yunde and Zhu, Song-Chun and Li, Qing},
  journal={arXiv preprint arXiv:2412.15606},
  year={2024}
}