task: llama_math_algebra
dataset_path: EleutherAI/hendrycks_math
process_docs: !function utils.process_docs
dataset_name: algebra
output_type: generate_until
training_split: train
test_split: test
doc_to_text: "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\nYou are a helpful AI Assistant that provides well-reasoned and detailed responses.\nYou first think about the reasoning process as an internal monologue and then provide the user with the answer.\nRespond in the following format:\n<think>\n...\n</think>\n<answer>\n...\n</answer><|eot_id|><|start_header_id|>user<|end_header_id|>\n\nSolve the following math problem efficiently and clearly:\n\n- For simple problems (2 steps or fewer):\nProvide a concise solution with minimal explanation.\n\n- For complex problems (3 steps or more):\nUse this step-by-step format:\n\n## Step 1: [Concise description]\n[Brief explanation and calculations]\n\n## Step 2: [Concise description]\n[Brief explanation and calculations]\n\n...\n\nRegardless of the approach, always conclude with:\n\nTherefore, the final answer is: $\\\\boxed{answer}$. I hope it is correct.\n\nWhere [answer] is just the final number or expression that solves the problem.\n\nProblem: {{ problem }}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
process_results: !function utils.process_results
doc_to_target: "{{answer if few_shot is undefined else solution}}"
generation_kwargs:
  until:
    - "Problem:"
    - "</answer>"
  max_gen_toks: 4096
  do_sample: false
  temperature: 0
metric_list:
  - metric: exact_match
    aggregation: mean
    higher_is_better: true
num_fewshot: 0
metadata:
  version: 1.0
dataset_kwargs:
  trust_remote_code: true
