Update README.md

README.md

@@ -29,6 +29,7 @@ This is a finetuned version of Code-Llama-70B specifically optimized for Python
 ## Usage
 
 ### Quick Start
+
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
 import torch
@@ -40,21 +41,25 @@ model = AutoModelForCausalLM.from_pretrained(
     torch_dtype=torch.float16,
     device_map="auto"
 )
+```
+
+### Example: Complete Python code
 
-# Example: Complete Python code
+```python
 prompt = "def calculate_average(numbers):\n    "
 inputs = tokenizer(prompt, return_tensors="pt")
 outputs = model.generate(**inputs, max_length=100, temperature=0.7)
 completion = tokenizer.decode(outputs[0], skip_special_tokens=True)
 print(completion)
+```
 
-# Limitations
+### Limitations
 
 - Optimized specifically for Python; performance on other languages may vary
 - Best suited for short to medium-length completions
 - May require significant computational resources due to model size (70B parameters)
 
-# Ethical Considerations
+### Ethical Considerations
 
 - Should not be used as the sole tool for production code without human review
 - May reflect biases present in the training data
@@ -67,7 +72,7 @@ This model is subject to the Meta Llama 2 Community License Agreement. By using
 # Citation
 
 If you use this model in your research or applications, please cite:
-```
+```bibtex
 @misc{python-tab-completion-codellama-70b,
   author = {Emissary AI},
   title = {Python Tab Completion CodeLlama 70B},