dragon_app              Launch the dragon-farm app
dragon_archive_run      Archive, restore, or delete a run
dragon_archived_runs    Archived runs
dragon_backend          Inference backends
dragon_bundle           Package a run for a cloud GPU
dragon_cancel           Cancel a run
dragon_chat             Talk to a model, with memory of the
                        conversation
dragon_check            Check the Python environment and hardware
dragon_code             R code that reproduces a run
dragon_compare          Compare runs side by side
dragon_conversations    Multi-turn conversations as training data
dragon_dataset          Create a dataset for fine-tuning
dragon_evaluate         Evaluate a finished run
dragon_example_data     Path to the bundled example dataset
dragon_export_gguf      Export a merged model to GGUF
dragon_feedback         Training data from chat feedback
dragon_generate         Generate replies from a fine-tuned model
dragon_hardware         Hardware settings
dragon_import           Import results trained on another machine
dragon_judge            Judge a run's replies with a language model
dragon_llm_anthropic    Language models as functions: the Claude API
                        and ellmer
dragon_lora             LoRA settings
dragon_map              Map dataset columns to prompt, response, and
                        system text
dragon_map_pairs        Map columns for preference optimization
dragon_map_prompts      Map columns for reinforcement learning
dragon_merge            Merge the adapter into the base model
dragon_metrics          Task metrics for generated replies
dragon_pipeline         Run several post-training stages as one
                        pipeline
dragon_pipeline_cancel
                        Cancel a pipeline
dragon_pipeline_status
                        Progress of a pipeline
dragon_prefer           Preference optimization with DPO or ORPO
dragon_presets          Recommended small models
dragon_preview          Preview mapped rows as chat turns
dragon_prompts          Prompts from a run's data files
dragon_publish          Push a run's model to the Hugging Face Hub
dragon_python_requirements
                        Python requirements used by dragonfarm
dragon_reinforce        Reinforcement learning with verifiable rewards
                        (GRPO)
dragon_remote           Open a cloud GPU provider for a bundled run
dragon_remote_providers
                        Cloud GPU providers
dragon_resume           Resume a run from its latest checkpoint
dragon_reward           Verifiable rewards for reinforcement learning
dragon_run              Reopen an existing run
dragon_runs             List runs
dragon_runs_dir         Directory where runs are stored
dragon_serve_ollama     Serve a run's model with Ollama
dragon_split            Hold out rows for evaluation
dragon_status           Inspect a run
dragon_step             Steps of a post-training pipeline
dragon_synthesize       Write fine-tuning data with a teacher model
dragon_synthesize_pairs
                        Build preference pairs from a model's own
                        samples
dragon_train            Fine-tune a model with LoRA
dragon_train_args       Training settings
dragon_wait             Wait for a run to finish
dragon_worker_stop      Stop the local inference worker
