config/evaluate_api_llm.yaml

# export OPENAI_BASE_URL="https://api.ohmygpt.com/v1"
# export OPENAI_API_KEY="sk-o4sMxBkN5BB100C4D4a3T3BlBkFJF7791CA39EA14ca98041"
# python -m ragen.eval_api hydra.searchpath='[file://./verl/verl/trainer/config]'
defaults:
  - base # this is a symbolic link to the verl/verl/trainer/config/ppo_trainer.yaml file

model_config:
  model_name: ark-deepseek-v3-250324 # should be registered in model_info
  max_concurrency: 16

model_info:
  Qwen2.5-7B-Instruct:
    provider_name: together
    model_name: Qwen/Qwen2.5-7B-Instruct-Turbo
    generation_kwargs:
      temperature: 0
      max_tokens: 512
  Qwen2.5-72B-Instruct:
    provider_name: together
    model_name: Qwen/Qwen2.5-72B-Instruct-Turbo
    generation_kwargs:
      temperature: 0
      max_tokens: 512
  claude-3.7:
    provider_name: anthropic
    model_name: claude-3-7-sonnet-20250219
    generation_kwargs:
      temperature: 0
      max_tokens: 512 # max_completion_tokens if o1-mini
  gpt-4o-mini:
    provider_name: openai
    model_name: gpt-4o-mini
    generation_kwargs:
      temperature: 0
      max_tokens: 512 # max_completion_tokens if o1-mini
  deepseek-r1:
    provider_name: deepseek
    model_name: deepseek-reasoner
    generation_kwargs:
      temperature: 0
      max_completion_tokens: 512
  deepseek-v3:
    provider_name: deepseek
    model_name: deepseek-chat
    generation_kwargs:
      temperature: 0
      max_completion_tokens: 512
  ark-deepseek-v3-250324:
    provider_name: openai
    model_name: ark-deepseek-v3-250324
    generation_kwargs:
      temperature: 0
      max_tokens: 8192
  gemini-2.5-pro:
    provider_name: openai
    model_name: gemini-2.5-pro
    generation_kwargs:
      temperature: 0
      max_tokens: 8192
      # max_retries: 5

agent_proxy:
  max_turn: 5
es_manager:
  val:
    env_groups: 32
    group_size: 1 # should be set to 1 because val temperature is set to 0 and same prompt leads to same output
    env_configs:
      tags: ["rubikscube"]
      n_groups: [32] # If not set, all env names divide nums equally. Under the same group, the env config and env seed (prompt) are equal in each generation


rollout:
  max_model_len: 7200