Reformat code samples on llm university text-generation section

fern/pages/llm-university/intro-text-generation/building-a-chatbot.mdx

@@ -20,7 +20,8 @@ To set up, we first import the Cohere module and create a client.
 
 ```python PYTHON
 import cohere
-co = cohere.Client("COHERE_API_KEY") # Your Cohere API key
+
+co = cohere.Client("COHERE_API_KEY")  # Your Cohere API key
 ```
 
 At its most basic, we only need to pass to the Chat endpoint the user message using the `message` parameter – the only required parameter for the endpoint.
@@ -53,9 +54,11 @@ In the quickstart example, we didn’t have to define a preamble because a defau
 Here’s an example. We added a preamble telling the chatbot to assume the persona of an expert public speaking coach. As a result, we get a response that adopts that persona.
 
 ```python PYTHON
-response = co.chat(message="Hello",
-                   model="command-r-plus",
-                   preamble="You are an expert public speaking coach. Don't use any greetings.")
+response = co.chat(
+    message="Hello",
+    model="command-r-plus",
+    preamble="You are an expert public speaking coach. Don't use any greetings.",
+)
 print(response.text)
 ```
 
@@ -78,13 +81,15 @@ In streaming mode, the endpoint will generate a series of objects. To get the ac
 If you have not already, make your own copy of the Google Colaboratory notebook and run the code in this section to see the same example with streamed responses activated.
 
 ```python PYTHON
-stream = co.chat_stream(message="Hello. I'd like to learn about techniques for effective audience engagement",
-                        model="command-r-plus",
-                        preamble="You are an expert public speaking coach")
+stream = co.chat_stream(
+    message="Hello. I'd like to learn about techniques for effective audience engagement",
+    model="command-r-plus",
+    preamble="You are an expert public speaking coach",
+)
 
 for event in stream:
     if event.event_type == "text-generation":
-        print(event.text, end='')
+        print(event.text, end="")
 ```
 
 ```
@@ -149,21 +154,23 @@ while True:
     message = input("User: ")
 
     # Typing "quit" ends the conversation
-    if message.lower() == 'quit':
+    if message.lower() == "quit":
         print("Ending chat.")
         break
 
     # Chatbot response
-    stream = co.chat_stream(message=message,
-                            model="command-r-plus",
-                            preamble=preamble,
-                            conversation_id=conversation_id)
+    stream = co.chat_stream(
+        message=message,
+        model="command-r-plus",
+        preamble=preamble,
+        conversation_id=conversation_id,
+    )
 
-    print("Chatbot: ", end='')
+    print("Chatbot: ", end="")
 
     for event in stream:
         if event.event_type == "text-generation":
-            print(event.text, end='')
+            print(event.text, end="")
         if event.event_type == "stream-end":
             chat_history = event.response.chat_history
 
@@ -237,12 +244,12 @@ The chat history is a list of multiple turns of messages from the user and the c
 from cohere import ChatMessage
 
 chat_history = [
-  ChatMessage(role="USER", message="What is 2 + 2"),
-  ChatMessage(role="CHATBOT", message="The answer is 4"),
-  ChatMessage(role="USER", message="Add 5 to that number"),
-  ChatMessage(role="CHATBOT", message="Sure. The answer is 9"),
-  ...
-  ]
+    ChatMessage(role="USER", message="What is 2 + 2"),
+    ChatMessage(role="CHATBOT", message="The answer is 4"),
+    ChatMessage(role="USER", message="Add 5 to that number"),
+    ChatMessage(role="CHATBOT", message="Sure. The answer is 9"),
+    ...,
+]
 ```
 
 The following modifies the previous implementation by using `chat_history` instead of `conversation_id` for managing the conversation history.
@@ -262,29 +269,33 @@ while True:
     message = input("User: ")
 
     # Typing "quit" ends the conversation
-    if message.lower() == 'quit':
+    if message.lower() == "quit":
         print("Ending chat.")
         break
 
     # Chatbot response
-    stream = co.chat_stream(message=message,
-                            model="command-r-plus",
-                            preamble=preamble,
-                            chat_history=chat_history)
+    stream = co.chat_stream(
+        message=message,
+        model="command-r-plus",
+        preamble=preamble,
+        chat_history=chat_history,
+    )
 
     chatbot_response = ""
-    print("Chatbot: ", end='')
+    print("Chatbot: ", end="")
 
     for event in stream:
         if event.event_type == "text-generation":
-            print(event.text, end='')
+            print(event.text, end="")
             chatbot_response += event.text
     print("\n")
 
     # Add to chat history
     chat_history.extend(
-        [ChatMessage(role="USER", message=message),
-         ChatMessage(role="CHATBOT", message=chatbot_response)]
+        [
+            ChatMessage(role="USER", message=message),
+            ChatMessage(role="CHATBOT", message=chatbot_response),
+        ]
     )
 ```
 

fern/pages/llm-university/intro-text-generation/creating-custom-models.mdx

@@ -134,9 +134,10 @@ Using a custom model is as simple as substituting the baseline model with the mo
 
 ```python PYTHON
 response = co.generate(
-  model='26db2994-cf88-4243-898d-31258411c120-ft', # REPLACE WITH YOUR MODEL ID
-  prompt="""Turn the following message to a virtual assistant into the correct action:
-	Send a message to Alison to ask if she can pick me up tonight to go to the concert together""")
+    model="26db2994-cf88-4243-898d-31258411c120-ft",  # REPLACE WITH YOUR MODEL ID
+    prompt="""Turn the following message to a virtual assistant into the correct action:
+	Send a message to Alison to ask if she can pick me up tonight to go to the concert together""",
+)
 ```
 
 But of course, we need to know if this model is performing better than the baseline in the first place. For this, there are a couple of ways we can evaluate our model.
@@ -178,17 +179,19 @@ We run the following code for each of the baseline (`command`) and the finetuned
 
 ```python PYTHON
 # Create a function to call the endpoint
-def generate_text(prompt,temperature,num_gens):
-  response = co.generate(
-    model='command', # Repeat with the custom model
-    prompt=prompt,
-    temperature=temperature,
-    num_generations = num_gens,
-    stop_sequences=["\n\n"])
-  return response
+def generate_text(prompt, temperature, num_gens):
+    response = co.generate(
+        model="command",  # Repeat with the custom model
+        prompt=prompt,
+        temperature=temperature,
+        num_generations=num_gens,
+        stop_sequences=["\n\n"],
+    )
+    return response
+
 
 # Define the prompt
-prompt="""Turn the following message to a virtual assistant into the correct action:
+prompt = """Turn the following message to a virtual assistant into the correct action:
 Send a message to Alison to ask if she can pick me up tonight to go to the concert together"""
 
 # Define the range of temperature values and num_generations
@@ -198,14 +201,14 @@ num_gens = 3
 # Iterate generation over the range of temperature values
 print(f"Temperature range: {temperatures}")
 for temperature in temperatures:
-  response = generate_text(prompt,temperature,num_gens)
-  print("-"*10)
-  print(f'Temperature: {temperature}')
-  print("-"*10)
-  for i in range(3):
-    text = response.generations[i].text
-    print(f'Generation #{i+1}')
-    print(f'Text: {text}\n')
+    response = generate_text(prompt, temperature, num_gens)
+    print("-" * 10)
+    print(f"Temperature: {temperature}")
+    print("-" * 10)
+    for i in range(3):
+        text = response.generations[i].text
+        print(f"Generation #{i+1}")
+        print(f"Text: {text}\n")
 ```
 
 Here are the responses.

fern/pages/llm-university/intro-text-generation/fine-tuning-for-chat.mdx

@@ -108,21 +108,20 @@ user_message = "Make the text coherent: Pimelodella kronei is a species of three
 preamble = "You are a writing assistant that helps the user write coherent text."
 
 # Get default model response
-response_pretrained=co.chat(
-    	message=user_message,
-    	preamble=preamble,
-    	)
+response_pretrained = co.chat(
+    message=user_message,
+    preamble=preamble,
+)
 
 # Get fine-tuned model response
 response_finetuned = co.chat(
-    	message=user_message,
-    	model='acb944bb-fb49-4c29-a15b-e6a245a7bdf9-ft',
-    	preamble=preamble,
-    	)
+    message=user_message,
+    model="acb944bb-fb49-4c29-a15b-e6a245a7bdf9-ft",
+    preamble=preamble,
+)
 
-print(f"Default response: {response_pretrained.text}","\n-----")
+print(f"Default response: {response_pretrained.text}", "\n-----")
 print(f"Fine-tuned response: {response_finetuned.text}")
-
 ```
 
 For this example, the output appears as follows:
@@ -159,14 +158,14 @@ while True:
 
 	# Typing "quit" ends the conversation
 	if message.lower() == 'quit':
-    	print("Ending chat.")
-    	break
+        print("Ending chat.")
+        break
 
 	# Chatbot response
 	stream = co.chat_stream(message=message,
-      model='acb944bb-fb49-4c29-a15b-e6a245a7bdf9-ft',
-      preamble=preamble,
-      conversation_id=conversation_id)
+        model='acb944bb-fb49-4c29-a15b-e6a245a7bdf9-ft',
+        preamble=preamble,
+        conversation_id=conversation_id)
 
 	print("Chatbot: ", end='')
 

fern/pages/llm-university/intro-text-generation/parameters-for-controlling-outputs.mdx

@@ -18,7 +18,8 @@ To set up, we first import the Cohere module and create a client.
 
 ```python PYTHON
 import cohere
-co = cohere.Client("COHERE_API_KEY") # Your Cohere API key
+
+co = cohere.Client("COHERE_API_KEY")  # Your Cohere API key
 ```
 
 ## Model Type
@@ -35,8 +36,7 @@ With the [Chat endpoint](/reference/chat) , you can choose from several variatio
 Use the `model` parameter to select a variation that suits your requirements. In the code cell, we select `command-r`.
 
 ```python PYTHON
-response = co.chat(message="Hello",
-                   model="command-r-plus")
+response = co.chat(message="Hello", model="command-r-plus")
 print(response.text)
 ```
 
@@ -80,9 +80,9 @@ message = """Suggest a more exciting title for a blog post titled: Intro to Retr
 Respond in a single line."""
 
 for _ in range(5):
-    response = co.chat(message=message,
-                       temperature=0,
-                       model="command-r-plus")
+    response = co.chat(
+        message=message, temperature=0, model="command-r-plus"
+    )
     print(response.text)
 ```
 
@@ -103,9 +103,9 @@ message = """Suggest a more exciting title for a blog post titled: Intro to Retr
 Respond in a single line."""
 
 for _ in range(5):
-    response = co.chat(message=message,
-                       temperature=1,
-                       model="command-r-plus")
+    response = co.chat(
+        message=message, temperature=1, model="command-r-plus"
+    )
     print(response.text)
 ```