In this example, you chat with a whimsical character named Alpaca that sometimes spits at you. It should used with Alpaca-style models that are tuned to behave like an assistant chatbot with a specific format.
Alpaca-style models put the user's text and the chatbot's response after their name headers, so we do a little trick to condense them into single-line messages. This is why "Alpaca" is a character in this example rather than being direct responses from the instruction-tuned model. The prompt was adapted from the SuperCOT-LoRA model card and looks like:
Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
### Instruction:
Suggest how to continue the input transcript of a conversation between User and Alpaca.
{{... description of Alpaca character ...}}
### Input:
User: Hello!
{{... more lines of dialogue as time goes on ...}}
### Response:
Based on the description of Alpaca and input chat history, the following would be a creative and realistic next line of dialogue.
Alpaca:
Acknowledging the instruction in the response seems to help guide the transformer's attention mechanism (e.g., Alapaca reliably spits if it gets annoyed), but the flow of conversation sometimes suffers.