Update README.md

README.md

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 ---
 library_name: transformers
-tags: []
+license: mit
+datasets:
+- lmms-lab/DocVQA
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
+## 1 Introduction
+DIVE-Doc is a VLM architecture built as a trade-off between end-to-end lightweight architectures and LVLMs for the DocVQA task. 
+Without relying on external tools such as OCR, it processes the inputs in an end-to-end way.
+It takes an image document and a question as input and returns an answer. <br>
+- **Repository:** [GitHub](https://github.com/JayRay5/DIVE-Doc)
 - **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
 
-### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+## 2 Model Summary
+DIVE-Doc is built as a trade-off between end-to-end lightweight architectures and LVLMs.
+Where the first category has both a lightweight visual encoder and a language decoder, and LVLMs have both a large visual encoder and a large decoder, 
+DIVE-Doc contains a small visual encoder in combination with a large decoder in order to balance model size and performance.
+It is built by distilling the [SigLIP-400m](https://arxiv.org/abs/2303.15343) visual encoder of [PaliGEMMA](https://arxiv.org/abs/2407.07726) into a small hierarchical [Swin transformer](https://openaccess.thecvf.com/content/ICCV2021/html/Liu_Swin_Transformer_Hierarchical_Vision_Transformer_Using_Shifted_Windows_ICCV_2021_paper) initialized with the weights of [Donut](https://link.springer.com/chapter/10.1007/978-3-031-19815-1_29), while reusing the original [GEMMA](https://arxiv.org/abs/2403.08295) decoder. 
+This enables DIVE‑Doc to reduce its visual encoder’s parameter count by 80%.
+Moreover, the model is finetuned using LoRA adapters, which have been merged into the base model using [merge_and_unload](https://huggingface.co/docs/peft/main/en/package_reference/lora#peft.LoraModel.merge_and_unload).
+Trained on the [DocVQA dataset](https://openaccess.thecvf.com/content/WACV2021/html/Mathew_DocVQA_A_Dataset_for_VQA_on_Document_Images_WACV_2021_paper.html) for both the distillation and finetuning steps, this strategy allows DIVE-Doc to be competitive with LVLMs while outperforming ligthweight architectures.
+
+
+## 3 Quick Start
+
+### Installation
+```bash
+git clone https://github.com/JayRay5/DIVE-Doc.git
+cd DIVE-Doc
+conda create -n dive-doc-env python=3.11.5
+conda activate dive-doc-env
+pip install -r requirements.txt
+```
+### Inference example using the model repository and gradio
+In app.py, modify the path variable to "JayRay5/DIVE-Doc-ARD-LRes":
+```bash
+if __name__ == "__main__":
+    path = "JayRay5/DIVE-Doc-ARD-LRes"
+    app(path) 
+```
+Then run:
+```bash
+python app.py
+```
+This will start a [gradio](https://www.gradio.app/) web interface where you can use the model.
+## Notification
 
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
 
+### Direct Use
 
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
+This model is designed to answer a question from a single-page image document and is mostly trained on industrial documents [DocVQA dataset](https://openaccess.thecvf.com/content/WACV2021/html/Mathew_DocVQA_A_Dataset_for_VQA_on_Document_Images_WACV_2021_paper.html). 
 
-[More Information Needed]
 
-#### Hardware
+### Downstream Use 
 
-[More Information Needed]
+This model can be finetuned on other DocVQA datasets such as [InfoGraphVQA](https://openaccess.thecvf.com/content/WACV2022/html/Mathew_InfographicVQA_WACV_2022_paper.html) to improve its performance on infographic documents.
 
-#### Software
 
-[More Information Needed]
 
 ## Citation [optional]