Update README.md

README.md

@@ -28,5 +28,40 @@ that facilitates the fine-tuning of various well-known LLMs on custom data.
 Parameter-efficient fine-tuning is achieved via the QLoRA method [Dettmers et al., 2023](https://proceedings.neurips.cc/paper_files/paper/2023/file/1feb87871436031bdc0f2beaa62a049b-Paper-Conference.pdf).
 
 
-## Usage
+## Usage Guide
 
+This project was executed on an Ubuntu 22.04.3 system running Linux kernel 6.8.0-40-generic.
+
+### Installation
+
+To get started, you first need to set up the environment using the **LLaMA-Factory** project. Please refer to the official [LLaMA-Factory repository](https://github.com/hiyouga/LLaMA-Factory) for more details.
+
+You can install the project by running the following commands:
+
+```bash
+git clone --depth 1 https://github.com/hiyouga/LLaMA-Factory.git
+cd LLaMA-Factory
+pip install -e ".[torch,metrics]"
+```
+
+### Execution
+In the DeMINT project, the model was utilized to create a REST API. Below is an example of how to configure and run it.
+
+**Setting Server Configuration**
+
+To specify the port and server address, use the following environment variables:
+
+To set the port and the address of the server:
+```bash
+# Default 8000
+export KIND_TEACHER_PORT=8000
+# Default localhost
+export KIND_TEACHER_HOST="localhost"
+```
+
+**Running the Program**
+
+Once the environment is configured, you can execute the program by running the following command:
+```bash
+llamafactory-cli api run_api_inference_1.yaml
+```