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- 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
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