1. The one rule that matters | 2. Data parallelism: replicas | 3. When the model does not fit: layer vs row | 4. The hybrid: TP × DP | 5. Measuring it yourself | 6. Serving | 7. Summary

1. Check availability | 2. Device enumeration | 3. Autograd device selection | 4. Mixed precision (f16 / bf16) | 5. Vulkan memory info | 6. Multi-GPU | 7. Sequential / functional API on GPU | 8. ONNX inference on GPU | 9. Build configuration

The one call: RunGGML() | A worked example | Normalize and scale on the GPU | PCA (embed) | UMAP (umap) | Neighbour graphs (neighbors) | Provenance | The layers underneath | Bioconductor: SingleCellExperiment | A real dataset, end to end | Speed-up | Agreement | What is and isn't accelerated

1. Which mode do you need? | Are you sure you want this layer? | 2. The transport, and why it constrains everything | 3. Tensor parallelism | 4. Pipeline parallelism | 5. Hybrid: DP over TP or PP | 6. Clean shutdown (and the --enable-hard-exit flag) | 7. Where to look next | 8. Summary

1. The chat object: chat_llamar() | Mode A — spawn a server for a model | Mode B — connect to a running server | System prompt | 2. The server: llama_serve_openai() | Connecting OpenCode | 3. The command-line example | 4. ragnar: retrieval-augmented chat | 5. Tool calling: llama_chat_build() / llama_chat_parse() | 6. Claude Code on a local model: llama_serve_anthropic() | 7. Concurrency | See also

Next steps

Installation | 1. Sequential API | 1.1 Prepare data — iris (3-class classification) | 1.2 Build and compile | 1.3 Train | 1.4 Evaluate and predict | 1.5 Save and reload | 2. Functional API | 2.1 Single-input DAG — residual-style network on iris | 2.2 Multi-input model — mtcars regression | 3. Callbacks | 3.1 Early stopping | 3.2 Learning-rate schedulers | 4. Autograd Engine (ag_*) | 4.1 Prepare data (iris, col-major) | 4.2 Build a model with ag_sequential | 4.3 Training loop | 4.4 Inference and accuracy | 4.5 LR scheduler + gradient clipping | 4.6 Manual model from raw ag_param | 5. GPU / device selection | API summary

1. Core primitives | ag_tensor and ag_param | Forward pass and gradient tape | 2. Built-in operations | 3. ag_sequential — module API | 4. Optimizers | Training loop | 5. LR schedulers | 6. Gradient clipping | 7. Dataloader | 8. Eval mode and inference | 9. Raw ag_param — full manual control | 10. Mixed precision | 11. Data-parallel training

1. Classification | 2. Regression | 3. Engine parameters | 4. Resampling with rsample | 5. Recipes for preprocessing | 6. Hyperparameter tuning | 7. Comparison with other engines | 8. Extracting the fitted engine and fit time | 9. Limitations of the engine object | Summary

Quick start | Classification | Regression | Learner parameters | GPU acceleration | Custom model architecture | Resampling and benchmarking | Hyperparameter tuning | Callbacks | Observation weights | Marshal and parallel execution | Summary

1. Quantization formats | 2. Quantize and dequantize | 3. K-quants (better quality) | 4. IQ quants — importance matrix | 5. Comparing formats | 6. Reference (row-level) functions

1. Concept | 2. Minimal example | 3. Multi-GPU | 4. Gradient clipping | 5. ag_dataloader — batched training loop | 6. Full example

1. Load and inspect a model | 2. Run inference | 3. GPU inference | 4. Dynamic input shapes | 5. FP16 inference | 6. Supported operators | 7. Examples | 8. Debugging tips

Part A — Using the ggmlR R API from your package | A1. Depend on ggmlR | A2. Everything the examples use is exported | A3. tidymodels / mlr3 engines | Part B — Linking ggml from your package's C/C++ code | 1. What ggmlR exports | 2. DESCRIPTION | 3. src/Makevars | 4. configure — detect ggmlR at build time | 5. C code — minimal example | 6. R wrapper | 7. Thread count | 8. Vulkan in downstream packages | 9. Real-world references

1. Getting a model | 2. Loading a model and creating a context | 3. Generating text | 4. Chat models and templates | 5. Tokenization | 6. Embeddings | ragnar-compatible provider | 7. Serving and chatting | See also

Choosing a variant | Performance snapshot | A minimal example of each | Where to go next

Quick start | How it works | Algorithms (type =) | Scope (scope =) | Prefix matching (routes =) | Per-IP limiting | Operational notes

How it works | dr_stream() — the base API | dr_stream_sse() — Server-Sent Events | Threading: keep each pump short | Cancellation contract | Errors inside next_chunk() | Middleware does not wrap individual chunks | Native (C / C++) streaming | Caveats

The handler ABI | A complete example | Threading rule (critical) | Memory ownership cheat-sheet | Where this fits

Building an app | The req object | Building responses | Middleware | Static files | Starting and stopping the server | When to reach for the other variants

The one-line swap | What the shim supports | What the shim rejects | A minimal end-to-end example | When to migrate to native

1. A first graph | 2. Force-directed layout | 3. Highlighting a path | 4. Camera control | 5. Recording a video | 6. Headless mode | Where to next