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Setup

dragon_check()
Check the Python environment and hardware
dragon_python_requirements()
Python requirements used by dragonfarm
dragon_presets()
Recommended small models

Data

dragon_dataset()
Create a dataset for fine-tuning
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_conversations()
Multi-turn conversations as training data
dragon_synthesize()
Write fine-tuning data with a teacher model
dragon_synthesize_pairs()
Build preference pairs from a model's own samples
dragon_prompts()
Prompts from a run's data files
dragon_preview()
Preview mapped rows as chat turns
dragon_split()
Hold out rows for evaluation
dragon_example_data()
Path to the bundled example dataset

Settings

dragon_lora()
LoRA settings
dragon_train_args()
Training settings
dragon_hardware()
Hardware settings

Training stages

dragon_train()
Fine-tune a model with LoRA
dragon_prefer()
Preference optimization with DPO or ORPO
dragon_reinforce()
Reinforcement learning with verifiable rewards (GRPO)
dragon_reward()
Verifiable rewards for reinforcement learning
dragon_resume()
Resume a run from its latest checkpoint
dragon_wait()
Wait for a run to finish
dragon_cancel()
Cancel a run

Runs

dragon_run()
Reopen an existing run
dragon_runs()
List runs
dragon_archived_runs()
Archived runs
dragon_archive_run() dragon_unarchive_run() dragon_delete_run()
Archive, restore, or delete a run
dragon_runs_dir()
Directory where runs are stored
dragon_status() dragon_progress() dragon_logs()
Inspect a run
dragon_code()
R code that reproduces a run

Evaluation

dragon_evaluate()
Evaluate a finished run
dragon_metrics() dragon_metric_regex()
Task metrics for generated replies
dragon_judge()
Judge a run's replies with a language model
dragon_llm_anthropic() dragon_judge_anthropic() dragon_llm_ellmer() dragon_judge_ellmer()
Language models as functions: the Claude API and ellmer
dragon_compare()
Compare runs side by side

Inference and chat

dragon_backend_local() dragon_backend_server() dragon_backend_ollama() dragon_backend()
Inference backends
dragon_serve_ollama()
Serve a run's model with Ollama
dragon_worker_stop()
Stop the local inference worker
dragon_chat() dragon_chat_load()
Talk to a model, with memory of the conversation
dragon_feedback()
Training data from chat feedback

After training

dragon_generate()
Generate replies from a fine-tuned model
dragon_merge()
Merge the adapter into the base model
dragon_export_gguf()
Export a merged model to GGUF
dragon_publish()
Push a run's model to the Hugging Face Hub

Pipelines

Cloud GPUs

dragon_bundle()
Package a run for a cloud GPU
dragon_remote()
Open a cloud GPU provider for a bundled run
dragon_import()
Import results trained on another machine
dragon_remote_providers()
Cloud GPU providers

App

dragon_app()
Launch the dragon-farm app