# llamaR 0.2.6

* Context and per-sequence state snapshots (`llama_state_get_data`, `llama_state_seq_get_data`/`_set_data`, `llama_state_seq_save_file`/`_load_file`) — checkpoint a conversation, or cache a shared prefix instead of recomputing it.
* New introspection and steering bindings: extra `llama_model_info()` fields, `llama_vocab_get_attr()` and friends, `llama_lora_meta()`, `llama_apply_control_vector()`, `llama_perf_sampler()`.

---

# llamaR 0.2.5

* Engine updated to upstream llama.cpp (expanded architectures, incl. Qwen3.5).
* Multimodal (vision/OCR) support via the mtmd subsystem (`llama_mtmd_load`, `llama_image_load`, `llama_image_eval`, `llama_gen_begin_at`).
* Tool calling via chat templates with structured output parsing (`llama_chat_build`, `llama_chat_parse`).
* Anthropic Messages API server (`llama_serve_anthropic`) for local models — runs Claude Code against a local model.
* `llama_serve_anthropic()` optional vision: `vision_model_path` + `mmproj_path` enable a caption-then-reason pipeline (a vision model describes images, the text model answers).
* `enable_thinking` argument on `llama_chat_build()` / `llama_serve_anthropic()` to toggle hybrid thinking models' reasoning.

---

# llamaR 0.2.4

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

---

# 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"`).


## 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
