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dragonfarm: Fine-Tune Small Language Models with LoRA from R

Fine-tune small (100M to 3B parameter) causal language models with LoRA (Low-Rank Adaptation) from R. Datasets are mapped to chat-format prompts and responses, training runs in a background 'Python' process built on Hugging Face 'transformers' and 'peft', and a 'shiny' app offers drag-and-drop dataset upload and column mapping. 'Python' dependencies are declared through 'reticulate' and resolved automatically on first use.

Version: 0.3.3
Depends: R (≥ 4.1)
Imports: bslib (≥ 0.6.0), cli, glue, jsonlite, plotly, processx, ps, reticulate (≥ 1.41.0), rlang, shiny (≥ 1.8.0), sortable, stats, tools, utils, withr, zip
Suggests: arrow, ellmer, httr2, knitr, pkgload, rmarkdown, testthat (≥ 3.0.0)
Published: 2026-10-07
DOI: 10.32614/CRAN.package.dragonfarm (may not be active yet)
Author: Tejas Patel [aut, cre]
Maintainer: Tejas Patel <algocrat at gmail.com>
BugReports: https://github.com/tejas4patel/dragon-farm/issues
License: MIT + file LICENSE
URL: https://github.com/tejas4patel/dragon-farm
NeedsCompilation: no
SystemRequirements: 'Python' (>= 3.10). The 'uv' tool is installed automatically by 'reticulate' to build the 'Python' environment.
Materials: README, NEWS
CRAN checks: dragonfarm results [issues need fixing before 2026-10-29]

Documentation:

Reference manual: dragonfarm.html , dragonfarm.pdf
Vignettes: The post-training loop: fine-tune, judge, prefer, repeat (source, R code)
Quickstart: fine-tune a small model from R (source, R code)
Reinforcement learning with rewards you can check (source, R code)
The dragon-farm app (source, R code)

Downloads:

Package source: dragonfarm_0.3.3.tar.gz
Windows binaries: r-devel: not available, r-release: not available, r-oldrel: dragonfarm_0.3.3.zip
macOS binaries: r-release (arm64): dragonfarm_0.3.3.tgz, r-oldrel (arm64): dragonfarm_0.3.3.tgz, r-release (x86_64): dragonfarm_0.3.3.tgz, r-oldrel (x86_64): dragonfarm_0.3.3.tgz

Linking:

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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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