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ggmlR 0.5.1

New Features

• Export static library libggml.a for linking by dependent packages (llamaR)
• Added gguf.cpp for GGUF file format support
• Headers exported via inst/include/ for LinkingTo

CRAN Submission Fixes

• Expanded acronyms in DESCRIPTION: ‘AdamW’ (Adam with Weight decay), ‘SGD’ (Stochastic Gradient Descent), ‘MSE’ (Mean Squared Error), GPU (Graphics Processing Unit)
• Added all contributors and copyright holders to Authors@R:
  • Georgi Gerganov (GGML library author)
  • Jeffrey Quesnelle and Bowen Peng (ops.cpp contributors)
  • Mozilla Foundation (llamafile/sgemm.cpp)
• Replaced \dontrun{} with \donttest{} in all examples
• Added \value documentation to all exported functions and constants

Documentation

• Added vignette: Vulkan GPU Backend (vignette("vulkan-backend"))
• Added vignette: Multi-GPU Inference (vignette("multi-gpu"))
• Added vignette: Working with Quantized Models (vignette("quantization"))

Internal

• r_ggml_io.o moved to GGML_OBJECTS for proper symbol export
• Static library excluded from source tarball via .Rbuildignore

ggmlR 0.5.0

Major Features

• Added full optimization/training API (39 new functions)
  • ggml_opt_init(), ggml_opt_free(), ggml_opt_reset() — optimizer lifecycle
  • ggml_opt_fit() — high-level training loop
  • ggml_opt_epoch() — single epoch with R callback support
  • ggml_opt_eval(), ggml_opt_alloc() — model evaluation
  • ggml_opt_prepare_alloc() — non-static graph support
  • ggml_opt_grad_acc() — gradient accumulator access
• Dataset management
  • ggml_opt_dataset_init(), ggml_opt_dataset_free()
  • ggml_opt_dataset_data(), ggml_opt_dataset_labels()
  • ggml_opt_dataset_shuffle(), ggml_opt_dataset_get_batch()
• Training results
  • ggml_opt_result_init(), ggml_opt_result_free(), ggml_opt_result_reset()
  • ggml_opt_result_ndata(), ggml_opt_result_loss(), ggml_opt_result_accuracy()
  • ggml_opt_result_pred() — get predictions as integer vector
• Loss functions: MSE, cross-entropy, mean, sum
• Optimizers: AdamW, SGD

R Callback Support

• ggml_opt_epoch() now supports custom R callback functions
• Callbacks receive: train flag, batch index, max batches, start time, result pointer
• Built-in progress bar callback available via callback_train = TRUE

Extended Backend API (~50 new functions)

• Device management

  • ggml_backend_dev_count(), ggml_backend_dev_get(), ggml_backend_dev_by_name()
  • ggml_backend_dev_by_type() — find devices by type (CPU, GPU, etc.)
  • ggml_backend_dev_supports_op(), ggml_backend_dev_supports_buft()
  • ggml_backend_dev_memory(), ggml_backend_dev_description(), ggml_backend_dev_get_props()
  • ggml_backend_dev_init() — initialize backend from device

• Device type constants: ggml_backend_device_type_cpu/gpu/igpu/accel()

• Buffer usage constants: ggml_backend_buffer_usage_any/weights/compute()

• Backend registry

  • ggml_backend_reg_count(), ggml_backend_reg_get(), ggml_backend_reg_by_name()
  • ggml_backend_load(), ggml_backend_unload(), ggml_backend_load_all()

• Events for synchronization

  • ggml_backend_event_new(), ggml_backend_event_free()
  • ggml_backend_event_record(), ggml_backend_event_synchronize(), ggml_backend_event_wait()

• Async operations

  • ggml_backend_tensor_set_async(), ggml_backend_tensor_get_async()
  • ggml_backend_tensor_copy_async()

• Graph planning

  • ggml_backend_graph_plan_create(), ggml_backend_graph_plan_free()
  • ggml_backend_graph_plan_compute()

• Buffer management

  • ggml_backend_buffer_clear(), ggml_backend_buffer_set_usage()
  • ggml_backend_buffer_get_usage(), ggml_backend_buffer_reset(), ggml_backend_buffer_is_host()

• Direct backend initialization

  • ggml_backend_init_by_name(), ggml_backend_init_by_type(), ggml_backend_init_best()
  • ggml_backend_synchronize(), ggml_backend_get_device()

Testing

• Added 67 tests for optimization functions
• Added 60 tests for extended backend functions
• R CMD check: 0 errors, 0 warnings

ggmlR 0.4.1

• Fixed spelling notes for CRAN submission
• Updated documentation

ggmlR 0.4.0

• Added multi-GPU backend scheduler API (14 new functions)
• Added Vulkan GPU backend support (10 new functions)
• Fixed integer overflow for large tensors (>2 GB)
• Improved OpenMP handling for mixed C/C++ code

ggmlR 0.2.0

• Initial CRAN submission
• R bindings for ‘GGML’ tensor library
• Core tensor operations: creation, arithmetic, reshaping
• Neural network operations: attention, convolutions, normalization
• Activation functions: GELU, SiLU, ReLU, and variants
• Quantization support (Q4_0, Q4_1, Q8_0)
• OpenMP parallelization for CPU backend
• Computation graph API for building and executing models

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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