update README with code on how to load and use

README.md

@@ -18,7 +18,11 @@ model_type: t5
 
 # **Text-to-API Command Model**
 
-This repository contains a fine-tuned T5-Small model trained to convert natural language commands into standardized API commands. The model is designed for use cases where human-written instructions need to be translated into machine-readable commands for home automation systems or other API-driven platforms.
+This repository contains a fine-tuned T5-Small model trained to convert natural language commands into 
+standardized API commands. The model is designed for use cases where human-written instructions need
+to be translated into machine-readable commands for home automation systems or other API-driven platforms.
+
+---
 
 ## **Model Details**
 
@@ -73,3 +77,39 @@ This model is designed for:
 - Ambiguous or overly complex inputs may produce unexpected outputs.
 - Fine-tuning on domain-specific data is recommended for specialized use cases.
 
+
+## **How to Use the Model**
+
+### **Loading the Model**
+
+### **Step 1: Install Required Libraries**
+
+To use this model, first install the required libraries:
+
+```bash
+pip install transformers torch
+```
+
+### **Step 2: Load and Use the Model**
+
+You can use the following Python code to generate API commands from natural language inputs:
+
+```python
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+
+# Load the tokenizer and model
+tokenizer = T5Tokenizer.from_pretrained('vincenthuynh/SLM_CS576')
+model = T5ForConditionalGeneration.from_pretrained('vincenthuynh/SLM_CS576')
+
+# Function to generate API commands
+def generate_api_command(model, tokenizer, text, device='cpu', max_length=50):
+    input_ids = tokenizer.encode(text, return_tensors='pt').to(device)
+    with torch.no_grad():
+        generated_ids = model.generate(input_ids=input_ids, max_length=max_length, num_beams=5, early_stopping=True)
+    return tokenizer.decode(generated_ids[0], skip_special_tokens=True)
+
+# Example usage
+command = "Please turn off the kitchen lights"
+api_command = generate_api_command(model, tokenizer, command)
+print(api_command)
+