Changelog

Source: NEWS.md

dragonfarm 0.1.1

• dragon_check() now explains a CPU-only result on a machine with an NVIDIA GPU: no driver, a driver too old for the installed torch, or a CPU-only torch build, each with the one-line fix.
• README gains a Requirements section covering the driver requirement, disk space, and that the CUDA Toolkit is not needed.
• New dragon_bundle(), dragon_remote(), and dragon_import() take a run to a cloud GPU (Google Colab, Kaggle, Lightning AI, or RunPod) and bring the trained adapter back, for machines without a GPU. dragon_remote_providers() lists the providers. The app gains the same path in its Train and Monitor panels, and dragon_check() points to it when it finds no GPU.

dragonfarm 0.1.0

First release.

• dragon_dataset(), dragon_map(), and dragon_split() turn a table into chat-format training data with glue templates for combining columns.
• dragon_train() runs LoRA fine-tuning of any Hugging Face causal language model in a background Python process. Runs live on disk and survive the R session; dragon_run() reopens them.
• dragon_status(), dragon_progress(), dragon_logs(), dragon_wait(), dragon_cancel(), and dragon_resume() manage a run.
• dragon_evaluate(), dragon_generate(), and dragon_merge() cover held-out evaluation, generation from the adapter or the base model, and merging into a standalone model. dragon_export_gguf() wraps the llama.cpp converter when one is available.
• dragon_app() launches a Shiny app with drag-and-drop dataset upload, drag-and-drop column mapping, a live loss curve, and a before-and-after comparison panel. dragon_code() returns the R script for any run.
• Python dependencies are declared with reticulate::py_require() and built automatically. On Windows with an NVIDIA GPU the CUDA build of torch is selected on first use.