NEWS.md

In llamaR: Interface for Large Language Models via 'llama.cpp'

llamaR 0.2.4

Streaming generation

• llama_gen_begin() / llama_gen_next() / llama_gen_end() — token-by-token generation matching llama_generate() output, with valid-UTF-8 chunks.

OpenAI-compatible server

• llama_serve_openai() — serve a local GGUF model over an OpenAI-compatible HTTP API (/v1/models, /v1/chat/completions, streaming and blocking) via the optional drogonR package.

ellmer integration

• chat_llamar() — returns an ellmer::Chat backed by a local model, connecting to a running server (base_url=) or spawning one (model_path=); chat_llamar_stop() stops a spawned server.

Bug fixes

• Long prompts no longer abort: prefill is now split into llama_n_batch()-sized chunks (was GGML_ASSERT(n_tokens_all <= cparams.n_batch)).

llamaR 0.2.3

Context getters

• llama_n_ctx_seq() — per-sequence context window size.
• llama_n_batch() — logical batch size (max tokens per llama_decode call).
• llama_n_ubatch() — physical micro-batch size.
• llama_n_seq_max() — maximum number of concurrent sequences.
• llama_n_threads() / llama_n_threads_batch() — read back thread counts set via llama_set_threads().
• llama_pooling_type() — pooling type of the context as a string ("none", "mean", "cls", "last", "rank").

Bug fixes

• Fixed macOS compilation error: removed fflush macro from r_llama_compat.h that broke std::fflush in <fstream> (Apple clang / libc++).

Logits

• llama_get_logits_ith() — logit vector for a specific token position in the last decoded batch. Supports negative indexing (-1 = last token).

llamaR 0.2.2

ragnar integration

• embed_llamar() — high-level embedding provider compatible with ragnar_store_create(embed = ...). Supports partial application (lazy model loading), direct call returning a matrix, and data.frame input. L2 normalization on by default.

Batch embeddings

• llama_embed_batch() — embed multiple texts in one call. Uses true pooled batch decode (llama_get_embeddings_seq) for embedding models, with automatic fallback to sequential last-token decode for generative models.
• llama_get_embeddings_ith() — get embedding vector for the i-th token (supports negative indexing).
• llama_get_embeddings_seq() — get pooled embedding for a sequence ID.

Context embedding mode

• llama_new_context() gains embedding parameter. When TRUE, sets cparams.embeddings = true and disables causal attention at creation time. llama_embed_batch() uses this flag to choose the optimal code path.

Backend & device selection

• llama_load_model() gains devices parameter for explicit backend selection. Accepts device names from llama_backend_devices(), type keywords ("cpu", "gpu"), or numeric indices. Multiple devices enable multi-GPU split.
• llama_backend_devices() — list all available compute devices (CPU, GPU, iGPU, accelerator) as a data.frame.

Hardware & system

• llama_numa_init() — NUMA optimization with strategies: disabled, distribute, isolate, numactl, mirror.
• llama_time_us() — current time in microseconds.

Tests

• 40+ new test blocks covering all new functions.
• Total: 143 passing, 4 expected skips.

llamaR 0.2.1

New functions

• llama_token_to_piece() — convert a single token ID to its text piece.
• llama_encode() — run the encoder pass for encoder-decoder models (e.g. T5, BART).
• llama_batch_init() / llama_batch_free() — low-level batch allocation and release with automatic GC finalizer.

Bug fixes

• Fixed compilation failure on macOS with Apple clang 17 / Xcode 16.4: removed extern "C" block wrapping #include <R.h> in r_llama_compat.h (C++ templates cannot appear inside extern "C" linkage).
• Fixed macro conflict between Rinternals.h #define length(x) and std::codecvt::length() in r_llama_interface.cpp: C++ standard headers are now included before R headers, followed by #undef length.

Tests

• Added 9 new test blocks covering llama_token_to_piece, llama_batch_init, llama_batch_free, and llama_encode, including GPU context variants.
• Total: 103 passing, 4 expected skips.

llamaR 0.2.0

Hugging Face integration

New functions

• llama_hf_list() — list GGUF files in a Hugging Face repository.
• llama_hf_download() — download a GGUF model with local caching. Supports exact filename, glob pattern, or Ollama-style tag selection.
• llama_load_model_hf() — download and load a model in one step.
• llama_hf_cache_dir() — get the cache directory path.
• llama_hf_cache_info() — inspect cached models.
• llama_hf_cache_clear() — clear the model cache.

Dependencies

• Added jsonlite and utils to Imports.

llamaR 0.1.3

GPU and build system improvements

Vulkan GPU support on Windows

• Added Vulkan linking support to configure.win and Makevars.win.in.
• Windows builds now link with Vulkan when ggmlR is built with GPU support.

CRAN compliance

• Added exit() / _Exit() overrides to r_llama_compat.h to prevent process termination (redirects to Rf_error()).

Dependencies

• Requires ggmlR >= 0.5.4.
• Bumped minimum R version to 4.1.0 (matches ggmlR).

DESCRIPTION

• Updated description to mention Vulkan GPU support via ggmlR.

llamaR 0.1.2

CRAN compliance fixes

Documentation

• Expanded all acronyms in DESCRIPTION (LLMs, GPU).
• Added detailed \value tags to all exported functions describing return class, structure, and meaning.
• Replaced \dontrun{} with \donttest{} in all examples.

DESCRIPTION

• Added Georgi Gerganov as copyright holder (cph) for bundled 'llama.cpp' code.

Packaging

• Included NEWS.md in the package tarball (removed from .Rbuildignore).
• Created cran-comments.md.
• Cleaned up duplicate entries in .Rbuildignore.

llamaR 0.1.1

R interface — first working release

Full LLM inference cycle is now available from R:

• llama_load_model() / llama_free_model() — load and free GGUF models
• llama_new_context() / llama_free_context() — context management
• llama_tokenize() / llama_detokenize() — tokenization and detokenization
• llama_generate() — text generation with temperature, top_k, top_p, greedy support
• llama_embeddings() — embedding extraction
• llama_model_info() — model metadata

Memory management

Model and context are wrapped as ExternalPtr with automatic GC finalizers. The context holds a reference to the model ExternalPtr, preventing premature collection.

Generation internals

llama_generate() runs the full pipeline in a single C++ call: prompt tokenization → encode → autoregressive decode loop with a sampler chain → detokenization of generated tokens.

Tests

19 assertions across 7 test blocks, all passing.

llamaR 0.1.0

Initial Release

• Basic package structure with llama.cpp integration
• Links against libggml.a from ggmlR package
• Includes all llama.cpp model implementations (~100 architectures)
• Vulkan GPU support (optional)

Dependencies

• Requires ggmlR >= 0.5.1 for static library export

Known Limitations

• ggml_build_forward_select replaced with simplified branch selection

llamaR documentation built on May 28, 2026, 1:06 a.m.