---
library_name: transformers
tags: [KBVQA, Multimodal, Retrieval, Knowledge Retrieval, RAG, FLMR, PreFLMR, ColBERT]
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->



## Model Details

### Model Description

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This is the PreFLMR model 

- **Model type:** PreFLMR is an open-source model for general knowledge retrieval. It is a transformer-based model that uses a combination of text and image inputs to retrieve relevant documents from a large corpus.
- **Language(s) (NLP):** English
- **License:** [More Information Needed]

### Model Sources

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- **Repository:** https://github.com/LinWeizheDragon/FLMR 
- **Paper:** https://arxiv.org/abs/2402.08327
- **Demo:** http://region-3.seetacloud.com:38703/
- **Blog Post:** https://www.jinghong-chen.net/preflmr-sota-open-sourced-multi/
- **Project Page:** https://preflmr.github.io/

## Uses

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### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

This model can be used directly to retrieve documents from a large corpus using a combination of text and image input queries. The retrieval useage can be found in the [official implementation](https://github.com/LinWeizheDragon/FLMR).

### Downstream Use 

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

This model can be used combined with language models to create a retrieval-augmented language model. The useage for Knowledge-based VQA can be found in https://github.com/linweizhedragon/retrieval-augmented-visual-question-answering 

## How to Get Started with the Model

Use the code below to get started with the model.

```python
from transformers import AutoConfig, AutoModel, AutoImageProcessor, AutoTokenizer
import torch

checkpoint_path = "LinWeizheDragon/PreFLMR_ViT-L"
image_processor_name = "openai/clip-vit-large-patch14"
query_tokenizer = AutoTokenizer.from_pretrained(checkpoint_path, subfolder="query_tokenizer", trust_remote_code=True)
context_tokenizer = AutoTokenizer.from_pretrained(checkpoint_path, subfolder="context_tokenizer", trust_remote_code=True)

model = AutoModel.from_pretrained(checkpoint_path,
                                query_tokenizer=query_tokenizer,
                                context_tokenizer=context_tokenizer,
                                trust_remote_code=True,
                                )
image_processor = AutoImageProcessor.from_pretrained(image_processor_name)

Q_encoding = query_tokenizer(["Using the provided image, obtain documents that address the subsequent question: What is the capital of France?", "Extract documents linked to the question provided in conjunction with the image: What is the capital of China?"])
D_encoding = context_tokenizer(["Paris is the capital of France.", "Beijing is the capital of China.",
                            "Paris is the capital of France.", "Beijing is the capital of China."])
Q_pixel_values = torch.zeros(2, 3, 224, 224)
inputs = dict(
    query_input_ids=Q_encoding['input_ids'],
    query_attention_mask=Q_encoding['attention_mask'],
    query_pixel_values=Q_pixel_values,
    context_input_ids=D_encoding['input_ids'],
    context_attention_mask=D_encoding['attention_mask'],
    use_in_batch_negatives=True,
)

res = model.forward(**inputs)
print(res)
```

## Training datasets
The model is trained on a combination of eight image-text datasets and a text-only dataset.
## Citation 


**BibTeX:**
```
@article{Lin_Mei_Chen_Byrne_2024, 
        title={PreFLMR: Scaling Up Fine-Grained Late-Interaction Multi-modal Retrievers}, 
        url={http://arxiv.org/abs/2402.08327}, 
        number={arXiv:2402.08327}, 
        publisher={arXiv}, 
        author={Lin, Weizhe and Mei, Jingbiao and Chen, Jinghong and Byrne, Bill}, 
        year={2024}}
```