Added code examples that correspond to each prompt format

Output from Single-turn demo:

GPT4 Correct User: Hello, how are you? GPT4 Correct Assistant: I'm doing great, thank you for asking! How can I assist you today?

Output from multi-turn demo:

GPT4 Correct User: Hello GPT4 Correct Assistant: GPT4 Correct User: How are you today? GPT4 Correct Assistant: I'm doing great, thank you for asking! How about you?

Output from coding demo:

Coding conversation response: Code User: Implement quicksort using C++ Code Assistant: Here's an example of how you can implement quicksort in C++:

```cpp
#include <iostream>
using namespace std;

void quickSort(int arr[], int left, int right) {
int i = left, j = right;
int tmp;
int pivot = arr[(left + right) / 2];

/* partition */
while (i <= j) {
while (arr[i] < pivot)
i++;
while (arr[j] > pivot)
j--;
if (i <= j) {
tmp = arr[i];
arr[i] = arr[j];
arr[j] = tmp;
i++;
j--;
}
};

/* recursion */
if (left < j)
quickSort(arr, left, j);
if (i < right)
quickSort(arr, i, right);
}
```

README.md

@@ -78,7 +78,46 @@ assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 42
 tokens = tokenizer("Code User: Implement quicksort using C++<|end_of_turn|>Code Assistant:").input_ids
 assert tokens == [1, 7596, 1247, 28747, 26256, 2936, 7653, 1413, 334, 1680, 32000, 7596, 21631, 28747]
 ```
+## Code Examples
 
+```python
+import transformers
+
+tokenizer = transformers.AutoTokenizer.from_pretrained("berkeley-nest/Starling-LM-7B-alpha")
+model = transformers.AutoModelForCausalLM.from_pretrained("berkeley-nest/Starling-LM-7B-alpha")
+
+def generate_response(prompt):
+    input_ids = tokenizer(prompt, return_tensors="pt").input_ids
+    outputs = model.generate(
+        input_ids,
+        max_length=256,
+        pad_token_id=tokenizer.pad_token_id,
+        eos_token_id=tokenizer.eos_token_id,
+    )
+    response_ids = outputs[0]
+    response_text = tokenizer.decode(response_ids, skip_special_tokens=True)
+    return response_text
+
+# Single-turn conversation
+prompt = "Hello, how are you?"
+single_turn_prompt = f"GPT4 Correct User: {prompt}<|end_of_turn|>GPT4 Correct Assistant:"
+response_text = generate_response(single_turn_prompt)
+print("Response:", response_text)
+
+## Multi-turn conversation
+prompt = "Hello"
+follow_up_question =  "How are you today?"
+response = ""
+multi_turn_prompt = f"GPT4 Correct User: {prompt}<|end_of_turn|>GPT4 Correct Assistant: {response}<|end_of_turn|>GPT4 Correct User: {follow_up_question}<|end_of_turn|>GPT4 Correct Assistant:"
+response_text = generate_response(multi_turn_prompt)
+print("Multi-turn conversation response:", response_text)
+
+### Coding conversation
+prompt = "Implement quicksort using C++"
+coding_prompt = f"Code User: {prompt}<|end_of_turn|>Code Assistant:"
+response = generate_response(coding_prompt)
+print("Coding conversation response:", response)
+```
 
 ## License
 The dataset, model and online demo is a research preview intended for non-commercial use only, subject to the data distillation [License](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) of LLaMA, [Terms of Use](https://openai.com/policies/terms-of-use) of the data generated by OpenAI, and [Privacy Practices](https://chrome.google.com/webstore/detail/sharegpt-share-your-chatg/daiacboceoaocpibfodeljbdfacokfjb) of ShareGPT. Please contact us if you find any potential violation.