---
language:
- en
tags:
- information retrieval
- embedding model
- visual information retrieval
metrics:
- recall
pipeline_tag: feature-extraction
license: apache-2.0
---

# 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.

![Memex Archtechture](images/memex.png)

# News

- 2024-08-18: 👀 We released a new [end-to-end Visual RAG huggingface demo](https://huggingface.co/spaces/bokesyo/MiniCPMV-RAG-PDFQA), 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](https://github.com/RhapsodyAILab/MiniCPM-V-Embedding-v0-Train) 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](https://huggingface.co/spaces/bokesyo/MiniCPM_Visual_Document_Retriever_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](https://huggingface.co/RhapsodyAI/minicpm-visual-embedding-v0/blob/main/pipeline.py).

- 2024-06-27: 🚀 We released our first visual embedding model checkpoint on [huggingface](https://huggingface.co/RhapsodyAI/minicpm-visual-embedding-v0).

- 2024-05-08: 🌍 We [open-sourced](https://github.com/RhapsodyAILab/minicpm-visual-embedding-v0) 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.

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

- Download with huggingface-hub.

```bash
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.

```bash
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.

```bash
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](https://huggingface.co/RhapsodyAI/minicpm-visual-embedding-v0/discussions), we will respond soon.


# For research purpose

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

```python
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

- [x] 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:

```bibtex
@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`.