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ggmlR 0.6.1

• dp_train(make_model, data, loss_fn, forward_fn, target_fn, n_gpu, n_iter, lr, max_norm, verbose) — data-parallel training across multiple replicas. Weights are broadcast from replica 0 before the first step; gradients are averaged across replicas each iteration; weights are re-broadcast after each optimizer update. Returns list(params, loss_history, model).
• ag_mul and ag_sub now support CPU broadcast: [d×s] * [1×s] and [d×s] * [d×1] shapes work correctly with proper gradient reduction.
• ag_softmax_cross_entropy_loss accepts integer target vectors (0-based class indices) and converts them to one-hot automatically.
• ggml_sum_rows f16 on Vulkan: F16→F16 dispatch now supported natively (no CPU fallback).

ggmlR 0.6.0

Dynamic autograd engine (PyTorch-style training)

• ag_tensor() / ag_param() — environment-backed tensors with reference semantics; in-place optimizer updates visible to all references.
• with_grad_tape({ ... }) — enables the global gradient tape for the enclosed forward pass.
• backward(loss) — reverse-mode automatic differentiation; returns a gradient environment keyed by tensor id.
• Differentiable ops: ag_matmul, ag_add (with bias broadcast), ag_sub, ag_mul, ag_scale.
• Activations: ag_relu, ag_sigmoid, ag_tanh, ag_softmax.
• Reduction / math ops: ag_sum, ag_mean, ag_log, ag_exp, ag_pow, ag_clamp.
• Shape ops: ag_reshape, ag_transpose.
• Loss functions: ag_mse_loss, ag_cross_entropy_loss, ag_softmax_cross_entropy_loss (numerically-stable fused).
• optimizer_sgd() — SGD with optional momentum.
• optimizer_adam() — Adam with bias-corrected moment estimates.
• ag_linear() — Glorot-initialised dense layer (closure-based, returns $forward, $params()).
• ag_gradcheck() — central finite-difference gradient checker (like torch.autograd.gradcheck).

Layer objects (environment-based, train/eval modes)

• ag_sequential(...) — ordered layer container; collects all parameters for the optimizer.
• ag_dropout(rate) — inverted dropout; identity in eval mode.
• ag_batch_norm(num_features) — batch normalisation with running statistics and learnable γ/β.
• ag_embedding(vocab_size, dim) — token lookup with scatter-add backward.
• ag_train(model) / ag_eval(model) — switch all sub-layers between train and eval mode.

Training utilities

• ag_dataloader(x, y, batch_size, shuffle, col_major) — mini-batch iterator with shuffle and $epoch() helper.
• lr_scheduler_step(optimizer, step_size, gamma) — step-decay learning rate.
• lr_scheduler_cosine(optimizer, T_max, lr_min, restart) — cosine-annealing (with optional SGDR warm restarts).
• clip_grad_norm(params, grads, max_norm) — clips all gradients by global L2 norm in-place.

ggmlR 0.5.9

• ggml_layer_lstm() — LSTM recurrent layer (unrolled BPTT).
• ggml_layer_gru() — GRU recurrent layer (unrolled BPTT).
• ggml_layer_global_max_pooling_2d() — reduces [H,W,C] to [C] via max pooling.
• ggml_layer_global_average_pooling_2d() — reduces [H,W,C] to [C] via average pooling.
• ggml_save_model() — saves full model (architecture + weights) to RDS file.
• ggml_load_model() — restores a model saved with ggml_save_model().
• ggml_dense(), ggml_conv_2d(), ggml_conv_1d(), ggml_batch_norm(), ggml_embedding(), ggml_lstm(), ggml_gru() — layer object constructors returning a reusable ggml_layer object.
• ggml_apply(tensor, layer) — applies a ggml_layer object to a tensor node; shared weights by object identity.

ggmlR 0.5.7

• ggml_layer_dropout() — dropout with deterministic or stochastic (per-epoch Bernoulli mask) mode.
• ggml_layer_embedding() — token embedding lookup for integer inputs.
• ggml_input() gains dtype argument ("float32" or "int32").
• Multi-output support in ggml_model() and ggml_predict().

ggmlR 0.5.6

• ggml_input() — declare a symbolic input tensor node (Functional API).
• ggml_model() — assemble a ggml_functional_model from input/output nodes.
• ggml_layer_add() — element-wise addition of tensor nodes (residual connections).
• ggml_layer_concatenate() — concatenate tensor nodes along an axis.
• All ggml_layer_*() functions now accept a ggml_tensor_node as first argument (Functional API mode).
• ggml_compile(), ggml_fit(), ggml_evaluate(), ggml_predict() are now S3 generics with methods for ggml_functional_model.

ggmlR 0.5.5

• ggml_fit_opt() — low-level optimizer loop with callbacks and learning-rate control.
• ggml_callback_early_stopping() — stops training when a metric stagnates.
• ggml_schedule_step_decay() — step learning-rate decay.
• ggml_schedule_cosine_decay() — cosine learning-rate annealing.
• ggml_schedule_reduce_on_plateau() — reduces LR when metric stops improving.
• ggml_opt_init_for_fit(), ggml_opt_set_lr(), ggml_opt_get_lr() — learning-rate control without recreating the optimizer context.

ggmlR 0.5.4

• Vulkan GPU backend support on Windows via configure.win.
• Vulkan auto-detected at build time on Linux and Windows.

ggmlR 0.5.3

• ggml_layer_conv_1d() — 1D convolution layer.
• ggml_layer_batch_norm() — batch normalization layer.
• ggml_predict_classes() — argmax wrapper returning 1-based class indices.
• summary.ggml_sequential_model() — detailed model summary with parameter counts.
• ggml_fit() now returns model$history (class ggml_history) with print and plot methods.
• Sequential API: ggml_model_sequential(), ggml_layer_dense(), ggml_layer_conv_2d(), ggml_layer_max_pooling_2d(), ggml_layer_flatten(), ggml_compile(), ggml_fit(), ggml_evaluate(), ggml_predict(), ggml_save_weights(), ggml_load_weights().
• Vulkan GPU backend covering 90%+ of ML operations.

ggmlR 0.5.2

• ggml_timestep_embedding() — sinusoidal timestep embeddings.
• N-D tensor access: ggml_set_f32_nd(), ggml_get_f32_nd(), ggml_set_i32_nd(), ggml_get_i32_nd().
• Tensor utilities: ggml_tensor_nb(), ggml_tensor_num(), ggml_tensor_copy(), ggml_tensor_set_f32_scalar(), ggml_get_first_tensor(), ggml_get_next_tensor().

ggmlR 0.5.1

• Static library libggml.a exported for linking by dependent packages.
• gguf.cpp added for GGUF file format support.
• Headers exported via inst/include/ for LinkingTo.

ggmlR 0.5.0

• Full optimization/training API: ggml_opt_init(), ggml_opt_free(), ggml_opt_fit(), ggml_opt_epoch(), ggml_opt_eval().
• Dataset management: ggml_opt_dataset_init(), ggml_opt_dataset_data(), ggml_opt_dataset_labels(), ggml_opt_dataset_shuffle().
• Training results: ggml_opt_result_init(), ggml_opt_result_loss(), ggml_opt_result_accuracy(), ggml_opt_result_pred().
• Extended backend API: device management, registry, async operations, graph planning, buffer management (~50 new functions).
• Loss functions: MSE, cross-entropy. Optimizers: AdamW, SGD.

ggmlR 0.4.0

• Multi-GPU backend scheduler API.
• Vulkan GPU backend support.

ggmlR 0.2.0

• Initial release: R bindings for GGML tensor library.
• Core tensor operations, neural network ops, activation functions, quantization (Q4_0, Q4_1, Q8_0), OpenMP parallelization, computation graph API.

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.