{ "_id": "6ad298342c5c76d3dd0c91ef", "Package": "dragonfarm", "Title": "Fine-Tune Language Models with LoRA on Your Documents and Data", "Version": "0.4.0", "Authors@R": "person(\"Tejas\", \"Patel\", role = c(\"aut\", \"cre\"), email = \"algocrat@gmail.com\")", "Description": "Fine-tune causal language models with LoRA (Low-Rank\nAdaptation) from R, and answer questions from your own\ndocuments. Documents (PDF, Word, HTML, Markdown, spreadsheets,\nweb pages) are read into chunks, retrieved for each question by\ncombined keyword and embedding search, and answered with cited\nsources; a model can then write training questions from them\nfor review. Tables of prompts and responses work too. Training\nruns in a background 'Python' process built on Hugging Face\n'transformers' and 'peft', with 4-bit loading (QLoRA) so a\n7-billion-parameter model trains on an 8 GB GPU, and a 'shiny'\napp covers the whole workflow. 'Python' dependencies are\ndeclared through 'reticulate' and resolved automatically on\nfirst use.", "License": "MIT + file LICENSE", "Encoding": "UTF-8", "Roxygen": "list(markdown = TRUE)", "VignetteBuilder": "knitr", "Config/testthat/edition": "3", "Language": "en-GB", "SystemRequirements": "'Python' (>= 3.10). The 'uv' tool is installed\nautomatically by 'reticulate' to build the 'Python'\nenvironment.", "URL": "https://dragonfarm.dev, https://github.com/tejas4patel/dragonfarm", "BugReports": "https://github.com/tejas4patel/dragonfarm/issues", "Config/roxygen2/version": "8.1.0", "Config/pak/sysreqs": "cmake make libicu-dev libpng-dev libuv1-dev\nlibssl-dev python3 zlib1g-dev", "Repository": "https://tejas4patel.r-universe.dev", "Date/Publication": "2026-10-16 20:14:51 UTC", "RemoteUrl": "https://github.com/tejas4patel/dragonfarm", "RemoteRef": "HEAD", "RemoteSha": "65c85ecd361c4bb762b4cc3d399be9d2294abab3", "NeedsCompilation": "no", "Packaged": { "Date": "2026-10-16 21:29:41 UTC", "User": "root" }, "Author": "Tejas Patel [aut, cre]", "Maintainer": "Tejas Patel ", "_user": "tejas4patel", "_type": "src", "_file": "dragonfarm_0.4.0.tar.gz", "_fileid": "https://r2.ropensci.org/5ccff7b76ab2cf0082a2334513dab9c762a15ec8d02d22a4974240036f973d92", "_filesize": 650191, "_sha256": "5ccff7b76ab2cf0082a2334513dab9c762a15ec8d02d22a4974240036f973d92", "_expires": "2027-01-24T21:33:34.000Z", "_created": "2026-10-16T21:29:41.000Z", "_published": "2026-10-16T21:33:39.980Z", "_buildurl": "https://github.com/r-universe/tejas4patel/actions/runs/38969420291", "_status": "success", "_host": "GitHub-Actions", "_jobs": [ { "job": 117205116744, "time": 187, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "12075244849" }, { "job": 117205116739, "time": 187, "config": "linux-release-x86_64", "r": "4.6.1", "check": "OK", "artifact": "12076735429" }, { "job": 117205116721, "time": 134, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "OK", "artifact": "12076337218" }, { "job": 117205116803, "time": 130, "config": "macos-release-arm64", "r": "4.6.1", "check": "OK", "artifact": "12075964548" }, { "job": 117203944405, "time": 186, "config": "source", "r": "4.6.1", "check": "OK", "artifact": "12076163751" }, { "job": 117205116854, "time": 134, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "12075369664" }, { "job": 117205116993, "time": 153, "config": "windows-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "12077000042" }, { "job": 117205116842, "time": 150, "config": "windows-oldrel-x86_64", "r": "4.5.3", "check": "OK", "artifact": "12075914954" }, { "job": 117205116810, "time": 150, "config": "windows-release-x86_64", "r": "4.6.1", "check": "OK", "artifact": "12076665826" } ], "_upstream": "https://github.com/tejas4patel/dragonfarm", "_commit": { "id": "65c85ecd361c4bb762b4cc3d399be9d2294abab3", "author": "Algocrat ", "committer": "Algocrat ", "message": "Lead with fine-tuning, with RAG built in as the foundation\n\nThe home page, README, launch post, social card, and README banner now\nopen with \"fine-tune your own model from R, starting from your\ndocuments\": it writes the training data, trains on your laptop, and\nkeeps answers grounded with built-in RAG. A new home section gives the\nthree reasons. The launch post's Costco example moves under \"RAG built\nin, from the start\".\n\nCo-Authored-By: Claude Opus 5.5 \n", "time": 1792181691 }, "_maintainer": { "name": "Tejas Patel", "email": "algocrat@gmail.com", "login": "algocrat", "linkedin": "in/algocrat", "description": "Data Modernization Accelerator at CDC (DMAC): Tejas is a data strategist specializing in deep learning, data analytics.", "uuid": 31281845 }, "_distro": "resolute", "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 4.1", "role": "Depends" }, { "package": "bslib", "version": ">= 0.6.0", "role": "Imports" }, { "package": "cli", "role": "Imports" }, { "package": "commonmark", "role": "Imports" }, { "package": "glue", "role": "Imports" }, { "package": "jsonlite", "role": "Imports" }, { "package": "plotly", "role": "Imports" }, { "package": "processx", "role": "Imports" }, { "package": "ps", "role": "Imports" }, { "package": "reticulate", "version": ">= 1.41.0", "role": "Imports" }, { "package": 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