Update README.md

README.md

@@ -65,5 +65,44 @@ print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 
 ---
 
+## 🔧 Example Usage (with repetition control)
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_id = "yasserrmd/RSCaLM-138M-LLaMA"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
+
+prompt = "when a man goes to fishing"
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+# Generation settings to reduce repetition
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=100,        # Limit length of output
+    temperature=0.7,           # Lower temperature = more focused
+    top_p=0.9,                  # Nucleus sampling
+    top_k=50,                   # Top-K filtering
+    repetition_penalty=1.2,     # Penalize repeating tokens
+    no_repeat_ngram_size=3,     # Prevent repeating trigrams
+    eos_token_id=tokenizer.eos_token_id,  # End generation at EOS
+)
+
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+---
+
+### 💡 Tips for controlling repetition:
+
+1. **`repetition_penalty`** – Increase slightly above `1.0` (e.g., `1.2–1.5`) to discourage repeated phrases.
+2. **`no_repeat_ngram_size`** – Set to `3` or `4` to avoid repeated n-grams.
+3. **`top_k` + `top_p`** – Combine both for better randomness control.
+4. **Lower `temperature`** – Keeps outputs focused and less chaotic.
+5. **Stop sequences** – Add specific words/phrases to halt generation early if needed.
+
+---
+
 ## 📜 License
 apache-2.0