Update README.md

README.md

@@ -46,15 +46,15 @@ The LLM was trained on a subset for 5000 samples of the bjoernp/tagesschau-2018-
 
 
 
-## How to Get Started with the Model
-
 # Load model directly
+```
 from transformers import AutoTokenizer, AutoModelForCausalLM
 
 tokenizer = AutoTokenizer.from_pretrained("Kamilatr/Ueberschriftengenerator_LEOLM", trust_remote_code=True)
 model = AutoModelForCausalLM.from_pretrained("Kamilatr/Ueberschriftengenerator_LEOLM", trust_remote_code=True)
-
+```
 # Use a pipeline as a high-level helper
+```
 from transformers import pipeline
 
 pipe = pipeline("text-generation", model="Kamilatr/Ueberschriftengenerator_LEOLM", trust_remote_code=True)
@@ -64,10 +64,10 @@ pipe = pipeline("text-generation", model="Kamilatr/Ueberschriftengenerator_LEOLM
 
 The LeoLM Model was fine tuned with LoRA. 
 
-
+```
 
 #### Speeds, Sizes, Times
-
+```
 training_arguments = TrainingArguments(
         output_dir="./results",
         evaluation_strategy="epoch", 
@@ -83,7 +83,7 @@ training_arguments = TrainingArguments(
         warmup_steps=100,
         lr_scheduler_type="constant",
 )
-
+```
 
 
 ## Evaluation and Testing