Applies a chat template to a list of messages

Parameters:

• template: The template string (nil to use model's default)
• messages: Array of chat messages
• add_assistant: Whether to end with an assistant message prefix

Returns:

• The formatted prompt string

Raises:

• Llama::Error if template application fails

Gets the list of built-in chat templates

Returns:

• Array of template names

Generates text from a prompt using a model

This is a convenience method that loads a model, creates a context, and generates text in a single call.

response = Llama.generate(
  "/path/to/model.gguf",
  "Once upon a time",
  max_tokens: 100,
  temperature: 0.7
)
puts response

Parameters:

• model_path: Path to the model file (.gguf format)
• prompt: The input prompt
• max_tokens: Maximum number of tokens to generate (must be positive)
• temperature: Sampling temperature (0.0 = greedy, 1.0 = more random)

Returns:

• The generated text

Raises:

• ArgumentError if parameters are invalid
• Llama::Model::Error if model loading fails
• Llama::Context::Error if text generation fails

Thread-safe, idempotent initialization of the llama.cpp backend. You do not need to call this manually in most cases.

Get the current log level

Returns:

• The current log level

Set the log level

Parameters:

• level : Int32 - log level (0=DEBUG, 1=INFO, 2=WARNING, 3=ERROR, 4=NONE)

Example: Llama.log_level = Llama::LOG_LEVEL_ERROR # Only show errors Llama.log_level = Llama::LOG_LEVEL_NONE # Disable all logging

Set a custom log callback

The block receives:

• level : Int32 - log level (0=DEBUG, 1=INFO, 2=WARNING, 3=ERROR)
• message : String - log message

Example: Llama.log_set do |level, message| if level >= Llama::LOG_LEVEL_ERROR STDERR.print message end end

Returns the maximum number of parallel sequences supported by backend This is a thin wrapper around LibLlama.llama_max_parallel_sequences.

Measures elapsed time in milliseconds for a block using llama.cpp's clock.

elapsed = Llama.measure_ms do
  # ... code to measure ...
end
puts "Elapsed: #{elapsed} ms"

Returns:

• Float64: elapsed milliseconds

Process escape sequences in a string

This method processes common escape sequences like \n, \t, etc. in a string, converting them to their actual character representations.

text = Llama.process_escapes("Hello\\nWorld")
puts text # Prints "Hello" and "World" on separate lines

Parameters:

• text: The input string containing escape sequences

Returns:

• A new string with escape sequences processed

Returns the llama.cpp system information

This method provides information about the llama.cpp build, including BLAS configuration, CPU features, and GPU support.

info = Llama.system_info
puts info

Returns:

• A string containing system information

Returns the current time in milliseconds since the Unix epoch (llama.cpp compatible).

t0 = Llama.time_ms
# ... some processing ...
t1 = Llama.time_ms
elapsed = t1 - t0
puts "Elapsed: #{elapsed} ms"

Returns:

• Int64: milliseconds since epoch

Returns the current time in microseconds since the Unix epoch (llama.cpp compatible).

This is a high-level wrapper for LibLlama.llama_time_us.

t0 = Llama.time_us
# ... some processing ...
t1 = Llama.time_us
elapsed_ms = (t1 - t0) / 1000.0
puts "Elapsed: #{elapsed_ms} ms"

Returns:

• Int64: microseconds since epoch

Tokenize text and return formatted output

This is a convenience method that tokenizes text and returns a formatted string representation of the tokens.

model = Llama::Model.new("/path/to/model.gguf")
result = Llama.tokenize_and_format(model.vocab, "Hello, world!", ids_only: true)
puts result # Prints "[1, 2, 3, ...]"

Parameters:

• vocab: The vocabulary to use for tokenization
• text: The text to tokenize
• add_bos: Whether to add BOS token (default: true)
• parse_special: Whether to parse special tokens (default: true)
• ids_only: Whether to return only token IDs (default: false)

Returns:

• A formatted string representation of the tokens

Thread-safe, idempotent finalization of the llama.cpp backend. Call this if you want to explicitly release all backend resources before program exit. All Model and Context instances must be released before calling this method.