# OpenThoughts-TBLite Evaluation -- Local vLLM Backend
#
# Runs against a local vLLM server with Docker sandboxes.
#
# Start the vLLM server from the atropos directory:
#   python -m example_trainer.vllm_api_server \
#       --model Qwen/Qwen3-4B-Instruct-2507 \
#       --port 9001 \
#       --gpu-memory-utilization 0.8 \
#       --max-model-len=32000
#
# Then run:
#   python environments/benchmarks/tblite/tblite_env.py evaluate \
#       --config environments/benchmarks/tblite/local_vllm.yaml

env:
  enabled_toolsets: ["terminal", "file"]
  max_agent_turns: 60
  max_token_length: 16000
  agent_temperature: 0.6
  terminal_backend: "docker"
  terminal_timeout: 300
  tool_pool_size: 16
  dataset_name: "NousResearch/openthoughts-tblite"
  test_timeout: 600
  task_timeout: 1200
  eval_concurrency: 8
  tool_call_parser: "hermes"
  system_prompt: "You are an expert terminal agent. You MUST use the provided tools to complete tasks. Use the terminal tool to run shell commands, read_file to read files, write_file to write files, search_files to search, and patch to edit files. Do NOT write out solutions as text - execute them using the tools. Always start by exploring the environment with terminal commands."
  tokenizer_name: "Qwen/Qwen3-4B-Instruct-2507"
  use_wandb: false
  wandb_name: "tblite-qwen3-4b-instruct"
  ensure_scores_are_not_same: false
  data_dir_to_save_evals: "environments/benchmarks/evals/tblite-qwen3-4b-local"

openai:
  base_url: "http://localhost:9001"
  model_name: "Qwen/Qwen3-4B-Instruct-2507"
  server_type: "vllm"
  health_check: false