For architecture details see ARCHITECTURE.md.

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Start in 5 minutes

SKaiNET is a Kotlin Multiplatform AI framework. New here? Choose the path that matches what you want to try first.

Goal	Start here	Time
Run tensor operations	Quickstart (below)	2–5 min
Build and train a neural net	Hello Neural Net (below)	5 min
Run a local GGUF model	SKaiNET Transformers starter	5 min after model setup

Working in Java? SKaiNET ships first-class Java support — see the Java getting-started guide.

Use the version shown in this README as the source of truth for first-run snippets. If another page shows a different version, please open an issue or PR.

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Quickstart

Add the core dependencies (Gradle Kotlin DSL):

dependencies {
    // Recommended: import the umbrella BOM and drop versions on the engine modules.
    implementation(platform("sk.ainet:skainet-bom:0.26.0"))

    implementation("sk.ainet.core:skainet-lang-core")
    implementation("sk.ainet.core:skainet-backend-cpu")
}

The BOM was first correctly published to Maven Central in 0.22.2 — earlier versions shipped at the wrong coordinates and could not be imported. Pin versions directly if you need an older release.

Hello Neural Net

val model = nn {
    input(28 * 28)
    dense(out = 128)
    relu()
    dense(out = 10)
}

Core Tensor Ops

val a = tensor(shape(2, 2)) { float(1f, 2f, 3f, 4f) }
val b = tensor(shape(2, 2)) { float(5f, 6f, 7f, 8f) }

val c = a matMul b
val d = c.relu()

GGUF Model Loading

// Recommended: streaming reader — memory-efficient, supports quantized types
val source = JvmRandomAccessSource.open("model.gguf")
StreamingGGUFReader.open(source).use { reader ->
    println("Tensors: ${reader.tensorCount}")
    
    // Load specific tensor on demand (no whole-file loading)
    val bytes = reader.loadTensor("token_embd.weight")
    
    // Or get a TensorStorage descriptor with encoding/placement metadata
    val storage = reader.loadTensorStorage("token_embd.weight")
}

More examples: SKaiNET-examples | SKaiNET-notebook

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Ecosystem

SKaiNET is a modular ecosystem. While this repository contains the core engine, specialized high-level libraries are maintained in standalone repositories:

Project	Description
SKaiNET-transformers	Pre-built transformer architectures and layers
SKaiNET-examples	Sample projects and integration demos

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Explore

Goal	Start here
Examples and sample projects	SKaiNET-examples
Interactive notebooks	SKaiNET-notebook

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Official Benchmarks

SKaiNET ships an official Phoronix-Test-Suite-compatible benchmark program for the compute engine. See the methodology and replay docs, the release manifest, and the CI workflow. Smoke runs fire on every PR via ubuntu-latest; full publishable runs fire on a self-hosted Linux x86 runner on release.

Quick local replay:

./gradlew :skainet-backends:benchmarks:jvm-cpu-publish:shadowJar
./scripts/run_engine_smoke.sh

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Architecture goal

SKaiNET is built around one path: a model is defined once in the Kotlin DSL, then either compiled to native code or executed eagerly — without rewriting it.

1. Define the model with the DSL (nn { } / dag { }).
2. Capture it as a tape (traced execution) or a DAG (explicit graph).
3. Run it one of two ways:
   • Compile — lower the graph to MLIR / StableHLO (HloGenerator) and compile to native code (IREE-compatible) for native / edge targets.
   • Eager — execute directly on an available backend. On the JVM this is the primary, go-to path.

flowchart LR
    DSL["Model — Kotlin DSL"] --> Graph["Tape / DAG"]
    Graph --> HLO["MLIR / StableHLO"]
    Graph --> Eager["Eager backend (JVM, …)"]
    HLO --> Native["Native code"]

Loading

The same DSL model feeds both paths — eager execution for development and JVM deployment, the StableHLO path for native and edge targets.

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Features

Kotlin Multiplatform

• Targets: JVM, macOS (Native), JS, WASM (Browser + WasmWasi)
• Single codebase shared across all platforms via Kotlin Multiplatform

Optimized Execution

• ComputeGraphExecutor: Optimized engine with fusion passes and trace-to-DAG bridging.
• SDPA & Gather: High-performance Scaled Dot-Product Attention and indexing operations.
• TurboQuant: Runtime KV-cache compression (~8x at 4-bit) for long-context LLM inference. Presets: safe-lowbit, balanced, experimental-max. See TurboQuantUsage for integration guide.

Neural Network DSL

• Sequential: nn { input(); dense(); relu(); dense() }
• DAG / Graph: arbitrary wiring with dag { } for ResNet, YOLO-style architectures
• Layers: Dense, Conv1d/2d/3d, MaxPool, AvgPool, BatchNorm, Dropout, LeakyReLU, ELU
• KAN (Kolmogorov–Arnold Networks) layer (experimental)
• Autograd engine with reverse-mode gradients, SGD and Adam/AdamW optimizers

Data and I/O

• Built-in loaders: MNIST, Fashion-MNIST, CIFAR-10
• Formats: GGUF, ONNX, SafeTensors, JSON, Image (JPEG, PNG)
• Type-safe transform DSL: resize, crop, normalize, toTensor

Edge AI: Arduino / C99 Export

• Export trained models to standalone, optimized C99 with static memory allocation
• Ready-to-use Arduino library output

Compiler: MLIR / StableHLO

• Lower Kotlin DSL to MLIR StableHLO dialect
• Optimization passes: constant folding, operation fusion, dead code elimination
• Valid IREE-compilable output with streaming API and public HloGenerator

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What's New in 0.26.0

• Q4_0 is now a first-class quantized format. The older GGML 4-bit format joins Q8_0 / Q4_K across the full provider stack: a heap Q4_0TensorData any loader can produce, a Q4_0MatmulKernel SPI with scalar / Panama-Vector / native-FFM implementations auto-selected by KernelRegistry, and a Q4_0Quantizer to pack dense FP32 weights into canonical ggml Q4_0 without going through GGUF. (PRs #648–#651)
• tanh is now a first-class activation primitive. Promoted from a NotImplementedError stub to a fully wired @Diff @ActivationDsl op — TensorOps interface, Tensor.tanh() extension, CPU backend, recording decorator, and autograd backward (1 - output^2) — so downstream consumers no longer re-derive the 2*sigmoid(2x)-1 polyfill. Pinned end-to-end by a micrograd tanh-MLP training test on the moons dataset. (Issue #630, PR #631)
• CPU tensor convert op. Dtype conversion now has a real CPU backend implementation. (PR #636)
• Plus test, build, and CI hygiene: portable KMP @Ignore for common tests, restored BatchNorm coverage, Gradle build-warning cleanup, and narrower feature-PR CI triggers. (PRs #633, #634, #638, #640, #645)

Recent releases

• 0.25.0 — BF16 and Q8_0 matmul kernels end-to-end across the provider stack, autograd completeness for pow/log and the conv/pool/upsample/split family, the hybrid adaptive dtype-constraint DSL, the @DarcValidated operator-doc flag, and the SentencePiece special-token splitter. (PRs #595, #605–#628)
• 0.23.0 — Real-model GGUFs no longer OOM at network construction (lazy TensorDataFactory.placeholder(...)); Kotlin/Native can finally load GGUFs over 2 GiB via the new POSIX-pread-backed PosixPreadRandomAccessSource. (Issues #587, #589; PRs #588, #591)
• 0.22.2 — sk.ainet:skainet-bom now resolves from Maven Central (earlier versions shipped at the wrong coordinates). (Issue #584)
• 0.22.1 — StreamingShardedSafeTensorsReader.loadTensorStorageMapped for zero-copy reads of multi-shard tensors above the 2 GB JVM ByteArray limit. (PR #582)
• 0.22.0 — Native (FFM) CPU kernel provider: 4–6× faster Q4_K matmul, 1.5–1.8× FP32 SGEMM vs Panama Vector; auto-selected via KernelRegistry.bestAvailable(). (PR #571)

See CHANGELOG.md for the full release history.

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Roadmap

• Q1 2026: Comprehensive documentation ✅
• Q2 2026: TurboQuant KV-cache compression ✅ (shipped in 0.18.0); Qwen/LLaMA tokenizers ✅ (shipped in 0.20.0)
• Q3 2026: Agentic AI enhancements ✅ (tool calling shipped in 0.13.0; ongoing)
• Q4 2026: Federated learning support for multi-device training

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Contributing & Community

We love contributions! Whether it's a new operator, documentation, or a bug fix:

1. Read our Contribution Guide.
2. Check the Good First Issues.
3. Open a discussion or issue on GitHub.

Browse the full codebase documentation on DeepWiki.

Contributors (0.14.0)

• Dhia Chemingui (@dhiaspaner) — Android KMP plugin migration (#385, #386)

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License

MIT — see LICENCE.