openevolve/configs/minimax_config.yaml at 78e5e6c7647c12e1968eda74d69e78b22200fa32 · algorithmicsuperintelligence/openevolve · GitHub

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# OpenEvolve Configuration for MiniMax
# MiniMax provides OpenAI-compatible API with powerful models like MiniMax-M2.7
# Get your API key from: https://platform.minimaxi.com/
#
# Set your API key:
#   export MINIMAX_API_KEY="your-minimax-api-key"

# General settings
max_iterations: 100
checkpoint_interval: 10
log_level: "INFO"
random_seed: 42

# LLM configuration for MiniMax
llm:
  api_base: "https://api.minimax.io/v1"
  api_key: "${MINIMAX_API_KEY}"

  # MiniMax models for evolution
  models:
    - name: "MiniMax-M2.7"
      weight: 0.6
    - name: "MiniMax-M2.7-highspeed"
      weight: 0.4

  # MiniMax models for LLM feedback
  evaluator_models:
    - name: "MiniMax-M2.7-highspeed"
      weight: 1.0

  # Generation parameters
  # Note: MiniMax requires temperature to be in (0.0, 1.0] — zero is not accepted
  temperature: 0.7
  top_p: 0.95
  max_tokens: 4096

  # Request parameters
  timeout: 120
  retries: 3
  retry_delay: 5

# Evolution settings
diff_based_evolution: true
max_code_length: 10000

# Prompt configuration
prompt:
  system_message: "You are an expert coder helping to improve programs through evolution."
  evaluator_system_message: "You are an expert code reviewer."
  num_top_programs: 3
  num_diverse_programs: 2
  use_template_stochasticity: true
  include_artifacts: true

# Database configuration
database:
  population_size: 1000
  num_islands: 5
  migration_interval: 50
  migration_rate: 0.1
  feature_dimensions:
    - "complexity"
    - "diversity"
  feature_bins: 10

# Evaluator configuration
evaluator:
  timeout: 300
  cascade_evaluation: true
  cascade_thresholds:
    - 0.5
    - 0.75
    - 0.9
  parallel_evaluations: 4