COT_prompt_template: >
  Question: {question}\nContext: {context}\n
        Answer this question using the information given in the context above. Here is things to pay attention to:
        - First provide step-by-step reasoning on how to answer the question.
        - In the reasoning, if you need to copy paste some sentences from the context, include them in ##begin_quote## and ##end_quote##. This would mean that things outside of ##begin_quote## and ##end_quote## are not directly copy paste from the context.
        - End your response with final answer in the form <ANSWER>: $answer, the answer should be succinct.
        You MUST begin your final answer with the tag "<ANSWER>:

# question_prompt_template: >
#   You are a synthetic question-answer pair generator. Given a chunk of context about
#   some topic(s), generate {num_questions} example questions a user could ask and would be answered
#   \using information from the chunk. For example, if the given context was a Wikipedia
#   paragraph about the United States, an example question could be 'How many states are
#   in the United States?
#   The questions should be able to be answered in a few words or less. Include only the
#   questions in your response.
question_prompt_template: >
  You are a language model skilled in creating quiz questions.
  You will be provided with a document,
  read it and please generate question and answer pairs that are most likely be asked by a user of Llama language models,
  which includes LLama, Llama2, Meta Llama3, Code Llama, Meta Llama Guard 1,	Meta Llama Guard 2,
  Output only the questions related to Llama:
  please make sure you follow those rules:
  1. Generate {num_questions} question answer pairs, you can generate less answer if there is nothing related to model, training, fine-tuning and evaluation details of Llama language models, .
  2. The questions can be answered based *solely* on the given passage.
  3. Avoid asking questions with similar meaning.
  4. Never use any abbreviation.
  5. Include only the questions in your response.

data_dir: "./data"

num_questions: 2