MiniCPM-V-Embedding-preview

MiniCPM-Visual-Embedding: OCR-free Visual Document Embedding Model as Your Personal Librarian

The model only takes images as document-side inputs and produce vectors representing document pages. Memex is trained with over 200k query-visual document pairs, including textual document, visual document, arxiv figures, plots, charts, industry documents, textbooks, ebooks, and openly-available PDFs, etc. Its performance is on a par with our ablation text embedding model on text-oriented documents, and an advantages on visually-intensive documents.

Our model is capable of:

• Help you read a long visually-intensive or text-oriented PDF document and find the pages that answer your question.

• Help you build a personal library and retrieve book pages from a large collection of books.

• It has only 2.8B parameters, and has the potential to run on your PC.

• It works like human: read and comprehend with vision and remember multimodal information in hippocampus.

News

• 2024-08-18: 👀 We released a new end-to-end Visual RAG huggingface demo, which supports both retrieval and generation, which means, you can use our system to answer your questions within a long PDF now! This demo is also locally-deployable, clone the codes in the space and run on your own device.

• 2024-08-17: 👊 We open-sourced cleaned version of training codebase for MiniCPM-Visual-Embedding, which supports deepspeed zero stage 1,2 and large batchsize like 4096 for full-parameter training to turn VLMs into dense retrievers. We also developed methods to filter training datasets and generating queries using unlablled datasets. We supports multi-nodes, multi-GPUs high-efficiency evaluation on large retrieval datasets. With such efforts, we support up to 20B VLM contrastive learning with 4096 batch size. We have tested that one can train a VLM dense retriever with only 1 GPU, but with batch size of 4096.

• 2024-07-14: 🤗 We released online huggingface demo! Try our online demo! This demo is also locally-deployable, clone the codes in the space and run on your own device.

• 2024-07-13: 💻 We released a locally deployable command-line based demo for users to retireve most relavant pages from a given PDF file (could be very long), take a look at pipeline.py.

• 2024-06-27: 🚀 We released our first visual embedding model checkpoint on huggingface.

• 2024-05-08: 🌍 We open-sourced our training code (full-parameter tuning with GradCache and DeepSpeed zero-stage2, supports large batch size across multiple GPUs with zero-stage1) and eval code.

Deploy on your PC

Please make sure you have at least 32GB memory on your PC.

• Apple M1/M2/M3 with 32GB memory.
• x86 CPU with 32GB memory.
• x86 CPU with 32GB memory + Nvidia GPU with 16GB memory.

Install dependencies

Use pip to install all dependencies:

Pillow==10.1.0
timm==0.9.10
torch==2.1.2
torchvision==0.16.2
transformers==4.36.0
sentencepiece==0.1.99
numpy==1.26.0

Download model weights and modeling file

Use one of the following methods:

• Download with git clone.

git lfs install
git clone https://huggingface.co/RhapsodyAI/minicpm-visual-embedding-v0

• Download with huggingface-hub.

pip install huggingface-hub
huggingface-cli download --resume-download RhapsodyAI/minicpm-visual-embedding-v0 --local-dir minicpm-visual-embedding-v0 --local-dir-use-symlinks False

Launch demo

Install gradio first.

pip install gradio

Clone demo source code.

• For retrieval-only demo (without generation), you should clone https://huggingface.co/spaces/bokesyo/MiniCPM_Visual_Document_Retriever_Demo.
• For retrieval and generation (full RAG pipeline), you should clone https://huggingface.co/spaces/bokesyo/MiniCPMV-RAG-PDFQA.

git clone https://huggingface.co/spaces/bokesyo/MiniCPM_Visual_Document_Retriever_Demo
git clone https://huggingface.co/spaces/bokesyo/MiniCPMV-RAG-PDFQA

For retrieval and generation demo, you need to also install flash_attn.

Adapt the code in app.py according to your device.

• For M1/M2/M3 users, please make sure model = model.to(device='mps', dtype=torch.float16) then run PYTORCH_ENABLE_MPS_FALLBACK=1 python app.py.
• For x86 CPU users, please remove model = model.to(device) then run python app.py.
• For x86 CPU + Nvidia GPU users, please make sure model = model.to('cuda') then run python app.py.
• If you encountered an error, please open an issue here, we will respond soon.

For research purpose

To run the model for research purpose, please refer the following code:

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

device = 'cuda:0'

# Load model, be sure to substitute `model_path` by your model path 
model_path = '/local/path/to/model'
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
model = AutoModel.from_pretrained(model_path, trust_remote_code=True)
model.to(device)

# Load image to PIL.Image object
image_1 = Image.open('/local/path/to/images/memex.png').convert('RGB')
image_2 = Image.open('/local/path/to/images/us2020.png').convert('RGB')
image_3 = Image.open('/local/path/to/images/hard_negative.png').convert('RGB')

# User query
query_instruction = 'Represent this query for retrieving relavant document: '
query = 'Who was elected as president of United States in 2020?'
query_full = query_instruction + query

# Embed image documents
with torch.no_grad():
    p_reps = model(text=['', '', ''], image=[image_1, image_2, image_3], tokenizer=tokenizer).reps

# Embed text queries
with torch.no_grad():
    q_reps = model(text=[query_full], image=[None], tokenizer=tokenizer).reps # [B, s, d]

# Calculate similarities
scores = torch.matmul(q_reps, p_reps.T)
print(scores)
# tensor([[-0.0112,  0.3316,  0.2376]], device='cuda:0')

Todos

• Release huggingface space demo.

• Release the evaluation results.

• Release technical report.

Limitations

• This checkpoint is an alpha version, and may not be strong in your tasks, for bad case, please create an issue to let us know, many thanks!

• The modeling script modeling_minicpmv on huggingface is not standard yet, the inference code could be further improved.

• The inference speed is low, because vision encoder uses timm, which does not yet support flash-attn.

• The model performs not well on Chinese and other non-English information retrieval tasks.

Citation

If you find our work useful, please consider cite us:

@misc{RhapsodyEmbedding2024,
  author = {Rhapsody Group, OpenBMB},
  title = {Memex: OCR-free Visual Document Embedding Model as Your Personal Librarian},
  year = {2024},
  howpublished = {\url{https://huggingface.co/RhapsodyAI/minicpm-visual-embedding-v0}},
  note = {Accessed: 2024-06-28}
}

Thanks to MiniCPM-V-2.0 arxiv.org/abs/2408.01800, without which there won't be minicpm-visual-embedding.

中文文档

https://www.53ai.com/news/RAG/2024082047058.html

https://blog.csdn.net/bokesyo/article/details/141335670

https://developer.aliyun.com/article/1590698?spm=a2c6h.13148508.setting.14.b4e94f0eIQp59B

https://cloud.tencent.com/developer/article/2446218