Changelog

v0.4.2

Error handling — Chat.apply_template/3, Tokenizer.encode/3, and Tokenizer.decode/2 now return {:error, reason} instead of crashing when NIFs raise.
Telemetry documentation — Server moduledoc documents all telemetry events, measurements, and metadata.
Typespecs — Added @spec to Server.start_link/1.

Full model loading params — main_gpu, split_mode, tensor_split for multi-GPU placement; use_mlock and use_direct_io for memory control; vocab_only for cheap model introspection without loading weights.
Server GPU forwarding — Server.start_link/1 now forwards main_gpu, split_mode, tensor_split, use_mlock, and use_direct_io to Model.load/2.

Jinja chat templates — switched from llama_chat_apply_template() C API to the full Jinja-based common_chat_templates_apply() engine from llama.cpp's common library.
enable_thinking option — pass enable_thinking: false to Chat.apply_template/3, chat/3, stream_chat/3, chat_completion/3, and stream_chat_completion/3 to disable CoT reasoning for models like Qwen3/3.5.
chat_template_kwargs option — pass arbitrary key-value pairs to the Jinja template engine.
Penalty parameters — penalty_repeat, penalty_freq, and penalty_present options for repetition/frequency/presence penalties in sampling.
OpenAI-compatible response format — chat_completion/3 and stream_chat_completion/3 return ChatCompletion and ChatCompletionChunk structs.
Qwen3.5 benchmark results in README — Qwen3.5-27B and Qwen3.5-35B-A3B on Apple M4 Max.

Chat.apply_template/3 now uses the Jinja engine and takes the model ref directly (no longer accepts :template option for raw template strings).
Linked libcommon.a from llama.cpp build (previously excluded).
LlamaModel RAII wrapper now caches common_chat_templates at model load time.

Continuous batching server (LlamaCppEx.Server) — GenServer with slot pool for concurrent multi-sequence inference. One forward pass per tick with decode tokens and prefill chunks mixed in a single batch.
Embeddings (LlamaCppEx.Embedding) — embed/3 and embed_batch/3 with L2 normalization and configurable pooling type.
Grammar-constrained generation — GBNF grammar support via grammar and grammar_root options in Sampler.create/2 and generate/3.
Batched inference primitives — prefill/3, decode_batch/3, decode_token/4, batch_eval/2, sampler_sample_at/3 NIFs for building custom inference loops.
Streaming via Server — LlamaCppEx.Server.stream/3 for token-by-token streaming through the batched server.
Telemetry events — [:llama_cpp_ex, :server, :tick] and [:llama_cpp_ex, :server, :request, :done] for observability.
Benchmark suite (bench/) — Benchee-based benchmarks for single-sequence and server generation, plus a custom continuous batching harness measuring throughput scaling.

Sampler.create/1 now requires the model as the first argument: Sampler.create(model, opts).
Context.create/2 accepts new options: :embeddings, :pooling_type, :n_seq_max.

Initial release.