Update README.md

README.md

@@ -34,6 +34,38 @@ The model is intended to be used on segments of **250** characters. Anything els
       
       Emendator Reconstruction:  "Diligam te Domine fortitudo mea Dominus firmamentum meum refugium meum liberator meus"
 
+To use Emendator, you can load it via the Transformers library:
+
+```python
+
+from transformers import T5ForConditionalGeneration, AutoTokenizer
+
+model_path = 'aimgo/Emendator'
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+
+model = T5ForConditionalGeneration.from_pretrained(model_path, torch_dtype=torch.bfloat16).to(device)
+
+model.eval()
+
+enc = tokenizer(texts, return_tensors="pt", padding=True, truncation=True, max_length=256).to(self.device)
+        
+max_input_len = enc["input_ids"].shape[1]
+        
+with torch.cuda.amp.autocast(dtype=torch.bfloat16, enabled=True):
+    outputs = model.generate(
+        enc["input_ids"],
+        attention_mask=enc["attention_mask"],
+        max_new_tokens=max_input_len + 32,
+        num_beams=4,
+        do_sample=False,
+        early_stopping=True,
+        repetition_penalty=1.15,
+        use_cache=True,
+    )
+        
+outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True)
+```
+
 If you use this in your work, please cite: 
 ```
 @misc{mccarthy2026Emendator,