get-started.R
In localLLM: Running Local LLMs with 'llama.cpp' Backend

## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  eval = FALSE
)

## -----------------------------------------------------------------------------
# install.packages("localLLM")
# packageVersion("localLLM")

## -----------------------------------------------------------------------------
# options(download.file.method = "libcurl")
# install.packages("localLLM", type = "source")
# packageVersion("localLLM")

## -----------------------------------------------------------------------------
# # install.packages("remotes")
# 
# # A specific release (recommended), e.g. v1.4.1
# remotes::install_github("EddieYang211/localLLM@v1.4.1", subdir = "localLLM")
# 
# # The latest development version
# remotes::install_github("EddieYang211/localLLM", subdir = "localLLM")

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# remotes::install_version("localLLM", version = "1.3.0")  # from the CRAN archive

## -----------------------------------------------------------------------------
# library(localLLM)
# install_localLLM()
# 
# # Force CPU build even when a GPU is detected
# install_localLLM(force_cpu = TRUE)
# 
# # Reinstall after adding a GPU driver (re-runs detection)
# install_localLLM(force_reinstall = TRUE)

## -----------------------------------------------------------------------------
# library(localLLM)
# 
# response <- quick_llama("What is the capital of France?")
# cat(response)

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# response <- quick_llama(
#   'Classify the sentiment of the following tweet into one of two
#    categories: Positive or Negative.
# 
#    Tweet: "This paper is amazing! I really like it."'
# )
# 
# cat(response)

## -----------------------------------------------------------------------------
# # Process multiple prompts at once
# prompts <- c(
# 
#   "What is 2 + 2?",
#   "Name one planet in our solar system.",
#   "What color is the sky?"
# )
# 
# responses <- quick_llama(prompts)
# print(responses)

## -----------------------------------------------------------------------------
# # From Hugging Face URL
# response <- quick_llama(
#   "Explain quantum physics simply",
#   model_path = "https://huggingface.co/unsloth/gemma-3-4b-it-qat-GGUF/resolve/main/gemma-3-4b-it-qat-Q5_K_M.gguf"
# )
# 
# # From local file
# response <- quick_llama(
#   "Explain quantum physics simply",
#   model_path = "/path/to/your/model.gguf"
# )
# 
# # From cache (name fragment)
# response <- quick_llama(
#   "Explain quantum physics simply",
#   model_path = "Llama-3.2"
# )

## -----------------------------------------------------------------------------
# # List all cached models
# cached <- list_cached_models()
# print(cached)

## -----------------------------------------------------------------------------
# # Delete a cached model by name
# file.remove(cached$path[cached$name == "Llama-3.2-3B-Instruct-Q5_K_M.gguf"])

## -----------------------------------------------------------------------------
# response <- quick_llama(
#   prompt = "Write a haiku about programming",
#   temperature = 0.8,      # Higher = more creative (default: 0)
#   max_tokens = 100,       # Maximum response length
#   seed = 42,              # For reproducibility
#   n_gpu_layers = 999      # Use GPU if available
# )