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license: MIT
title: 'dragonfarm: Fine-Tune Language Models with LoRA on Your Documents and Data'
version: 0.4.0
doi: 10.32614/CRAN.package.dragonfarm
abstract: Fine-tune causal language models with LoRA (Low-Rank Adaptation) from R,
  and answer questions from your own documents. Documents (PDF, Word, HTML, Markdown,
  spreadsheets, web pages) are read into chunks, retrieved for each question by combined
  keyword and embedding search, and answered with cited sources; a model can then
  write training questions from them for review. Tables of prompts and responses work
  too. Training runs in a background 'Python' process built on Hugging Face 'transformers'
  and 'peft', with 4-bit loading (QLoRA) so a 7-billion-parameter model trains on
  an 8 GB GPU, and a 'shiny' app covers the whole workflow. 'Python' dependencies
  are declared through 'reticulate' and resolved automatically on first use.
authors:
- family-names: Patel
  given-names: Tejas
  email: algocrat@gmail.com
repository: https://tejas4patel.r-universe.dev
repository-code: https://github.com/tejas4patel/dragonfarm
commit: 3224a25fe9a157521db775dc8f6e4532c5854382
url: https://dragonfarm.dev
date-released: '2026-10-18'
contact:
- family-names: Patel
  given-names: Tejas
  email: algocrat@gmail.com
keywords:
- fine-tuning
- LoRA
- QLoRA
- large-language-models
- llm
- retrieval-augmented-generation
- rag
- documents
- transformers
- peft
- shiny
- local-llm
- huggingface