Update README.md

README.md

@@ -29,17 +29,20 @@ The model is finetuned Using a custom version of UltraChat on TPU-v4 POD using [
 LinguaMatic utilizes the llama2 prompting method to generate responses. This method, named after the friendly and intelligent llama, enhances the model's ability to engage in meaningful conversations. The `prompt_model` function provided below demonstrates how the llama2 prompting method is implemented:
 
 ```python
-def prompt_model(message: str, chat_history,
-                 system_prompt: str) -> str:
+def llama_prompt(
+    message: str,
+    chat_history,
+    system: str
+) -> str:
     do_strip = False
-    texts = [f'<s>[INST] <<SYS>>\n{system_prompt}\n<</SYS>>\n\n']
+    texts = [f"<s>[INST] <<SYS>>\n{system}\n<</SYS>>\n\n"] if system is not None else "<s>[INST] "
     for user_input, response in chat_history:
         user_input = user_input.strip() if do_strip else user_input
         do_strip = True
-        texts.append(f'{user_input} [/INST] {response.strip()} </s><s>[INST] ')
+        texts.append(f"{user_input} [/INST] {response.strip()} </s><s>[INST] ")
     message = message.strip() if do_strip else message
-    texts.append(f'{message} [/INST]')
-    return ''.join(texts)
+    texts.append(f"{message} [/INST]")
+    return "".join(texts)
 ```
 
 The `prompt_model` function takes a `message` as input, along with the `chat_history` and `system_prompt`. It generates a formatted text that includes the system prompt, user inputs, and the current message. This approach allows LinguaMatic to maintain context and provide more coherent and context-aware responses.