R: Interface for Large Language Models via 'llama.cpp'

# Interface for Large Language Models via 'llama.cpp'

## Documentation for package ‘llamaR’ version 0.2.4

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

## Help Pages

chat_llamar | Chat with a local model through an ellmer::Chat object
chat_llamar_stop | Stop the server spawned by chat_llamar()
embed_llamar | Embedding provider for ragnar / standalone use
llama_backend_devices | List available backend devices
llama_batch_free | Free a llama batch allocated with 'llama_batch_init()'
llama_batch_init | Initialise a llama batch
llama_chat_apply_template | Apply chat template to messages
llama_chat_builtin_templates | List built-in chat templates
llama_chat_template | Get model's built-in chat template
llama_detokenize | Detokenize token IDs back to text
llama_embeddings | Extract embeddings for a text
llama_embed_batch | Batch embeddings for multiple texts
llama_encode | Encode tokens using the encoder (encoder-decoder models only)
llama_free_context | Free an inference context
llama_free_model | Free a loaded model
llama_generate | Generate text from a prompt
llama_generate_batch | Generate completions for multiple prompts in parallel
llama_gen_begin | Begin a streaming (token-by-token) generation
llama_gen_end | Finish a streaming generation
llama_gen_next | Pull the next chunk of a streaming generation
llama_get_embeddings | Get all output token embeddings as a matrix
llama_get_embeddings_ith | Get embeddings for the i-th token in the batch
llama_get_embeddings_seq | Get pooled embeddings for a sequence
llama_get_logits | Get logits from the last decode step
llama_get_logits_ith | Get logits for a specific token position
llama_get_model | Get the model associated with a context
llama_get_verbosity | Get current verbosity level
llama_hf_cache_clear | Clear the model cache
llama_hf_cache_dir | Get the cache directory for downloaded models
llama_hf_cache_info | Show information about the model cache
llama_hf_download | Download a GGUF model from Hugging Face
llama_hf_list | List GGUF files in a Hugging Face repository
llama_load_model | Load a GGUF model file
llama_load_model_hf | Load a model directly from Hugging Face
llama_lora_apply | Apply a LoRA adapter to context
llama_lora_clear | Remove all LoRA adapters from context
llama_lora_load | Load a LoRA adapter
llama_lora_remove | Remove a LoRA adapter from context
llama_max_devices | Get maximum number of devices
llama_memory_breakdown_print | Print memory breakdown by device
llama_memory_can_shift | Check if the KV cache supports shifting
llama_memory_clear | Clear the KV cache
llama_memory_seq_add | Shift token positions in a sequence
llama_memory_seq_cp | Copy a sequence in the KV cache
llama_memory_seq_div | Integer-divide token positions in a sequence
llama_memory_seq_keep | Keep only one sequence in the KV cache
llama_memory_seq_pos_range | Get position range for a sequence
llama_memory_seq_rm | Remove tokens from a sequence in the KV cache
llama_model_info | Get model metadata
llama_model_meta | Get all model metadata as a named character vector
llama_model_meta_val | Get a single model metadata value by key
llama_new_context | Create an inference context
llama_numa_init | Initialize NUMA optimization
llama_n_batch | Get logical batch size
llama_n_ctx | Get context window size
llama_n_ctx_seq | Get per-sequence context window size
llama_n_seq_max | Get maximum number of sequences
llama_n_threads | Get number of threads for single-token generation
llama_n_threads_batch | Get number of threads for batch processing
llama_n_ubatch | Get physical micro-batch size
llama_perf | Get performance statistics
llama_perf_print | Print performance statistics to the console
llama_perf_reset | Reset performance counters
llama_pooling_type | Get pooling type
llama_serve_openai | Serve an OpenAI-compatible HTTP API for a local model
llama_set_abort_callback | Set or clear the abort callback
llama_set_causal_attn | Set causal attention mode
llama_set_threads | Set the number of threads for a context
llama_set_verbosity | Set logging verbosity level
llama_set_warmup | Set warmup mode
llama_state_get_size | Get the size of the serialized context state in bytes
llama_state_load | Load context state from file
llama_state_save | Save context state to file
llama_supports_gpu | Check whether GPU offloading is available
llama_supports_mlock | Check whether memory locking is supported
llama_supports_mmap | Check whether memory-mapped file I/O is supported
llama_supports_rpc | Check whether RPC backend is available
llama_synchronize | Synchronize asynchronous computation
llama_system_info | Get system information string
llama_time_us | Get current time in microseconds
llama_tokenize | Tokenize text into token IDs
llama_token_to_piece | Convert a single token ID to its text piece
llama_vocab_get_score | Get the score of a token
llama_vocab_get_text | Get the text representation of a token
llama_vocab_info | Get vocabulary special token IDs
llama_vocab_is_control | Check if a token is a control token
llama_vocab_is_eog | Check if a token is an end-of-generation token
llama_vocab_type | Get vocabulary type