Update README.md

README.md

@@ -72,20 +72,9 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 
 ### Direct Use
 
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
-
-tokenizer = AutoTokenizer.from_pretrained("shkna1368/v1-Kurdana")
-model = AutoModelForSeq2SeqLM.from_pretrained("shkna1368/v1-Kurdana")
-
-input_ids =  tokenizer.encode(question, return_tensors="pt")
 
-output_ids = model.generate(input_ids, max_length=1200, num_beams=200, early_stopping=False)
 
-answer = tokenizer.decode(output_ids[0], skip_special_tokens=True)
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
 
 ### Downstream Use [optional]
 
@@ -113,9 +102,16 @@ Users (both direct and downstream) should be made aware of the risks, biases and
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 
-[More Information Needed]
+tokenizer = AutoTokenizer.from_pretrained("shkna1368/v1-Kurdana")
+model = AutoModelForSeq2SeqLM.from_pretrained("shkna1368/v1-Kurdana")
+
+input_ids =  tokenizer.encode(question, return_tensors="pt")
+
+output_ids = model.generate(input_ids, max_length=1200, num_beams=200, early_stopping=False)
+
+answer = tokenizer.decode(output_ids[0], skip_special_tokens=True)
 
 ## Training Details