Update README.md

README.md

@@ -2,10 +2,51 @@
 ## Description
 - This is the baseline implementation of AI Text Steganography for our final project in Software Designing and Applied Information Security courses in HCMUS-VNU.
 - Our project focuses on hiding data inside a text sequence generated by LLMs (e.g. GPT-2).
-- We took inspiration from [Kirchenbauer et al](https://arxiv.org/abs/2301.10226).
+- We took inspiration from [Kirchenbauer et al.](https://arxiv.org/abs/2301.10226).
 ## Members
 - Tran Nam Khanh
 - Phan Le Dac Phu
+## Installation
+1. Clone this repository:
+```Bash
+git clone https://github.com/trnKhanh/ai-text-steganography.git
+cd ai-text-steganography
+```
+2. (Optional) Create new conda environment:
+```Bash
+conda create -n ai-text-steganography python=3.10
+conda activate ai-text-steganography
+```
+3. Install requirements:
+```Bash
+pip install -r requirements.txt
+```
+## Usage
+- Gradio demo:
+```Bash
+python demo.py
+```
+- RestAPI:
+```Bash
+python api.py
+```
+- See help message of the Command Line Interface by:
+```Bash
+python main.py -h
+```
+## Configuration
+- `config.ini` is the config file of the project. We use the modified syntax of the `configparser` package. Every key-value pair follows the syntax: `key = type:value`. Currently, `type` can only be `int`, `float` or `str`.
+- Details on config:
+	- `server`: parameters for the RestAPI:
+	- `models.names`: names of LLMs allowed. Note that this follows the name defined on [Hugging Face](https://huggingface.co/models).
+	- `models.params`: parameters used to load LLMs.
+	- `encrypt.default`: default parameters for encryption algorithm.
+	- `decrypt.default`: default parameters for decryption algorithm.
+## Notes on implementation
+- Because of the limited resources, we load multiple models on the same machine (implementation is in `model_factory.py`):
+	- Each model is first loaded to the `load_device` (e.g. cpu).
+	- If there is a request to use a specific model, it is loaded to the `run_device` (e.g. gpu) for inference. 
+- Therefore, only one model can be used for inference at a time. As a result, we could optimize the limited resources we have to allow users to choose different LLMs, but it forces the API to be synchronous instead.
 ## TODO lists
 - [x] Baseline code.
 - [x] CLI.
@@ -16,3 +57,4 @@
 - [ ] Attack strategies
     - [ ] White-box
     - [ ] Black-box
+