inst/doc/tutorial-ollama-integration.R

In localLLM: Running Local LLMs with 'llama.cpp' Backend

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

## -----------------------------------------------------------------------------
# library(localLLM)
# 
# models <- list_ollama_models()
# print(models[, c("name", "size_gb", "modified")])

## -----------------------------------------------------------------------------
# model <- model_load("ollama:llama3.2")

## -----------------------------------------------------------------------------
# model <- model_load("ollama:deepseek-r1:8b")

## -----------------------------------------------------------------------------
# # Use at least 8 characters of the SHA256 hash
# model <- model_load("ollama:6340dc32")

## -----------------------------------------------------------------------------
# # Lists all models and prompts for selection
# model <- model_load("ollama")

## -----------------------------------------------------------------------------
# # Use Ollama model with quick_llama
# response <- quick_llama(
#   "Explain quantum computing in simple terms",
#   model_path = "ollama:llama3.2"
# )
# cat(response)

## -----------------------------------------------------------------------------
# # See what's available
# available <- list_ollama_models()
# 
# if (nrow(available) > 0) {
#   cat("Found", nrow(available), "Ollama models:\n")
#   print(available[, c("name", "size_gb")])
# } else {
#   cat("No Ollama models found. Install some with: ollama pull llama3.2\n")
# }

## -----------------------------------------------------------------------------
# # Load by exact name
# model <- model_load("ollama:llama3.2")
# 
# # Create context and generate
# ctx <- context_create(model, n_ctx = 4096)
# 
# messages <- list(
#   list(role = "user", content = "What is machine learning?")
# )
# prompt <- apply_chat_template(model, messages)
# response <- generate(ctx, prompt, max_tokens = 200)
# cat(response)

## -----------------------------------------------------------------------------
# # Compare Ollama models
# models <- list(
#   list(
#     id = "llama3.2",
#     model_path = "ollama:llama3.2",
#     n_gpu_layers = 999
#   ),
#   list(
#     id = "deepseek",
#     model_path = "ollama:deepseek-r1:8b",
#     n_gpu_layers = 999
#   )
# )
# 
# # Run comparison
# results <- explore(
#   models = models,
#   prompts = my_prompts,
#   engine = "parallel"
# )

## -----------------------------------------------------------------------------
# model <- model_load("ollama:nonexistent")

## -----------------------------------------------------------------------------
# models <- list_ollama_models()

## -----------------------------------------------------------------------------
# model <- model_load("ollama:llama")

## -----------------------------------------------------------------------------
# library(localLLM)
# 
# # 1. Check what's available
# available <- list_ollama_models()
# print(available)
# 
# # 2. Load a model
# model <- model_load("ollama:llama3.2", n_gpu_layers = 999)
# 
# # 3. Create context
# ctx <- context_create(model, n_ctx = 4096)
# 
# # 4. Generate text
# messages <- list(
#   list(role = "system", content = "You are a helpful assistant."),
#   list(role = "user", content = "Write a haiku about coding.")
# )
# 
# prompt <- apply_chat_template(model, messages)
# response <- generate(ctx, prompt, max_tokens = 50, temperature = 0.7)
# 
# cat(response)