Data preparation and analysis for chat model fine-tuning
With some lightweight analysis we can identify potential issues in the dataset, like missing messages, and provide statistical insights into message and token counts.

Missing System/User Messages: Counts the number of conversations missing a "system" or "user" message. Such messages are critical for defining the assistant's behavior and initiating the conversation.
Number of Messages Per Example: Summarizes the distribution of the number of messages in each conversation, providing insight into dialogue complexity.
Total Tokens Per Example: Calculates and summarizes the distribution of the total number of tokens in each conversation. Important for understanding fine-tuning costs.
Tokens in Assistant's Messages: Calculates the number of tokens in the assistant's messages per conversation and summarizes this distribution. Useful for understanding the assistant's verbosity.
Token Limit Warnings: Checks if any examples exceed the maximum token limit (4096 tokens), as such examples will be truncated during fine-tuning, potentially resulting in data loss.