Update README.md

README.md

@@ -24,7 +24,7 @@ You can access the fine-tuning code [here](https://colab.research.google.com/dri
 
 Trained with NVIDIA L4 with 150 steps, took around 8 minutes.
 
-## Example Usage
+## Example Usages
 You can use the adapter model with PEFT.
 ```py
 from peft import PeftModel, PeftConfig
@@ -55,6 +55,35 @@ inputs = tokenizer([
 outputs = model.generate(**inputs, max_new_tokens=256)
 print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 ```
+
+You can use it from Transformers:
+```py
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("myzens/llama3-8b-tr-finetuned")
+model = AutoModelForCausalLM.from_pretrained("myzens/llama3-8b-tr-finetuned")
+
+alpaca_prompt = """
+Instruction:
+{}
+
+Input:
+{}
+
+Response:
+{}"""
+
+inputs = tokenizer([
+    alpaca_prompt.format(
+        "",
+        "Ankara'da gezilebilecek 3 yeri söyle ve ne olduklarını kısaca açıkla.",
+        "",
+)], return_tensors = "pt").to("cuda")
+
+
+outputs = model.generate(**inputs, max_new_tokens=192)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
 Output:
 ```
 Instruction: