R: Running Local LLMs with 'llama.cpp' Backend

# Running Local LLMs with 'llama.cpp' Backend

## Documentation for package ‘localLLM’ version 1.2.1

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## Help Pages

localLLM-package | R Interface to llama.cpp with Runtime Library Loading
ag_news_sample | AG News classification sample
annotation_sink_csv | Create a CSV sink for streaming annotation chunks
apply_chat_template | Apply Chat Template to Format Conversations
apply_gemma_chat_template | Apply Gemma-Compatible Chat Template
backend_free | Free localLLM backend
backend_init | Initialize localLLM backend
compute_confusion_matrices | Compute confusion matrices from multi-model annotations
context_create | Create Inference Context for Text Generation
detokenize | Convert Token IDs Back to Text
document_end | Finish automatic run documentation
document_start | Start automatic run documentation
download_model | Download a model manually
explore | Compare multiple LLMs over a shared set of prompts
generate | Generate Text Using Language Model Context
generate_parallel | Generate Text in Parallel for Multiple Prompts
get_lib_path | Get Backend Library Path
get_model_cache_dir | Get the model cache directory
hardware_profile | Inspect detected hardware resources
install_localLLM | Install localLLM Backend Library
intercoder_reliability | Intercoder reliability for LLM annotations
lib_is_installed | Check if Backend Library is Installed
list_cached_models | List cached models on disk
list_ollama_models | List GGUF models managed by Ollama
localLLM | R Interface to llama.cpp with Runtime Library Loading
model_load | Load Language Model with Automatic Download Support
quick_llama | Quick LLaMA Inference
quick_llama_reset | Reset quick_llama state
set_hf_token | Configure Hugging Face access token
smart_chat_template | Smart Chat Template Application
tokenize | Convert Text to Token IDs
tokenize_test | Test tokenize function (debugging)
validate | Validate model predictions against gold labels and peer agreement