# Endless Terminals Environment -- Default Configuration # # Trains agents on terminal tasks from the Endless Terminals HuggingFace dataset. # Each task includes a pre-built Apptainer container and pytest-based verification. # # Dataset: https://huggingface.co/datasets/obiwan96/endless-terminals-train # # Prerequisites: # 1. Download dataset: huggingface-cli download obiwan96/endless-terminals-train \ # --repo-type dataset --local-dir ~/endless-terminals-data \ # --local-dir-use-symlinks False # 2. Install Apptainer/Singularity (for test execution) # 3. Set TASKS_BASE_DIR environment variable or configure tasks_base_dir below # # Usage: # python environments/endless_terminals/endless_terminals_env.py process \ # --config environments/endless_terminals/default.yaml env: # Toolsets enabled_toolsets: ["terminal", "file"] # Agent configuration max_agent_turns: 32 max_token_length: 4096 agent_temperature: 1.0 # Terminal backend terminal_backend: "local" # Change to "modal" or "docker" for cloud isolation # Dataset settings use_dataset: true dataset_name: "obiwan96/endless-terminals-train" dataset_split: "train" dataset_cache_dir: "~/.cache/huggingface/datasets" tasks_base_dir: "" # Set to directory containing task_* folders (e.g., ~/endless-terminals-data) # Test execution test_timeout_s: 60 # Training configuration group_size: 4 total_steps: 1000 steps_per_eval: 100 # Tool call parsing (for Phase 2 VLLM training) tool_call_parser: "hermes" tokenizer_name: "NousResearch/Hermes-3-Llama-3.1-8B" # Logging use_wandb: true wandb_name: "endless-terminals" # System prompt system_prompt: > You are a skilled Linux system administrator and programmer. You have access to a terminal and file tools to complete system administration and programming tasks. Use the tools effectively to solve the given task, and verify your solution works correctly before finishing. openai: base_url: "http://localhost:8000/v1" model_name: "NousResearch/Hermes-3-Llama-3.1-8B" server_type: "openai" api_key: "" health_check: false