Tranformers based LLM application layer on top of the SKaiNET engine. Provides model-specific inference, agentic chat with tool calling, and a unified CLI for transformer-based models, all in Kotlin Multiplatform.

[!WARNING] Project status — early / experimental. This repository is an initial version. Nothing here is stable, and there is no support or status guarantee for any feature, model, or API. Model coverage, tool calling, and the runtime APIs are all work in progress and may not work for a given model or model version — for example, tool calling can fail to trigger or parse even on a model that generates plain text correctly. The capabilities described below are goals, not promises. Treat everything as a preview and expect things to break.

SKaiNET Transformers is Kotlin Multiplatform. The fastest way to verify it on your machine is the unified skainet-cli:

1. Get a local GGUF model file (e.g. a small quantized TinyLlama or Qwen).
2. Run the CLI, pointing it at the model.
3. Confirm the prompt returns a generated answer.

./gradlew :llm-apps:skainet-cli:run \
  --args="-m /absolute/path/to/model.gguf 'The capital of France is'"

Expected result: the CLI auto-detects the model architecture, loads the model, and streams a generated answer. See the getting-started tutorial for model setup notes.

Working in Java? SKaiNET Transformers ships first-class Java support — see the kllama-java-sample starter and the Java getting-started guide.

Use the version shown in this README as the source of truth for first-run snippets.

The list below describes the project's intended scope. Maturity varies widely per item and many paths are unverified — see the project-status note above.

• Multi-model support (in progress). Architecture code exists for Llama / Mistral, Qwen 2 / 3, Gemma 2 / 3 / 3n, Apertus (Swiss AI) and BERT. Llama is the most exercised path; the other families are at varying, often early, stages and are not all verified end-to-end.
• Native CPU performance. Auto-discovers SKaiNET's priority-100 FFM (Foreign Function & Memory) native kernel provider when present (4–6× faster Q4_K matmul, 1.5–1.8× faster FP32 SGEMM vs the priority-50 Panama Vector path; Linux x86_64 / macOS ARM64 / Windows x86_64 in the published JAR — no manual setup).
• Tool calling (experimental). Family-specific chat templates and tool-call parsers (Llama 3, Qwen, Gemma, Apertus, ChatML/Hermes) and a Java surface (KLlamaJava, JavaTools.definition, JavaAgentLoop) exist, but tool calling is not reliable yet — it may fail to trigger or parse even when plain generation works.
• GGUF + SafeTensors loading. Streaming reader for any model size; NATIVE_OPTIMIZED quant policy keeps weights in their packed SIMD-friendly form.
• Kotlin Multiplatform. JVM, Android, Kotlin/Native (Linux x64/ARM64, macOS ARM64, iOS arm64/sim arm64), JS, Wasm targets where applicable.

SKaiNET Transformers follows the SKaiNET engine's core path: a transformer model is defined once in the Kotlin DSL, captured as a tape or DAG, and then either compiled to native code or executed eagerly — without rewriting it.

1. Define the model with the decoder DSL (llamaNetwork(), apertusNetwork(), …).
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 and compile to native code.
   • Eager — execute directly on a backend. On the JVM this is the primary, go-to path.

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

Today every model family runs through the eager JVM path. The StableHLO / native path is shared with the engine and not yet wired for full transformer models.

Honest status — see the project-status note at the top of this README.

• Make the eager JVM path reliable per family — including tool calling — before extending scope.
• Verify each generative architecture end-to-end with smoke tests.
• Wire the StableHLO / native compilation path for full transformer models. As of 0.28.1 a full gemma3 graph exports to StableHLO and iree-compiles to a vmfb (GemmaMlirDumpTest); next is running the compiled module and extending the same path to the other families.

The current release is 0.28.1 — version-aligned with SKaiNET 0.28.1. Skips 0.26.x / 0.27.x: SKaiNET-transformers tracked the engine internally across that window without a tagged release. The headline is that the engine's Kotlin DSL → StableHLO → IREE export path is now complete — a full gemma3 graph traces and lowers to StableHLO that iree-compiles to a vmfb (GemmaMlirDumpTest / GemmaTraceTest are green against 0.28.1). SKaiNET 0.28.0/0.28.1 fixed the remaining export bugs: result-type inference for reshape/matmul/concatenate (#673) and conv1d/gather/pooling/flatten shapes plus the reduce_window emission form (#675).

The recommended way to consume is via the BOM. It pins every published skainet-transformers-* artifact and re-exports the upstream sk.ainet:skainet-bom, so the engine-side sk.ainet.core:skainet-* artifacts get the matching version too — you only need to declare the BOM version in one place.

dependencies {
    implementation(platform("sk.ainet.transformers:skainet-transformers-bom:0.28.1"))

    // Versions resolved from the BOM:
    implementation("sk.ainet.transformers:skainet-transformers-core")
    implementation("sk.ainet.transformers:skainet-transformers-runtime-kllama") // or runtime-kgemma, inference-qwen, inference-apertus
    implementation("sk.ainet.transformers:skainet-transformers-agent")          // chat templates + tool calling
}

To opt in to the native FFM CPU provider (recommended for JVM consumers):

dependencies {
    implementation("sk.ainet.core:skainet-backend-cpu")        // priority-50 Panama Vector
    implementation("sk.ainet.core:skainet-backend-native-cpu") // priority-100 FFM (auto-discovered)
}

KernelRegistry picks the highest-priority available provider; on hosts where the native lib doesn't load (sandboxed JDKs, unsupported arches), it cleanly falls back to Panama with no functional regression.

• JDK 21 or higher
• Gradle 8.10+

# Plain generation
./gradlew :llm-apps:skainet-cli:shadowJar
java -jar llm-apps/skainet-cli/build/libs/skainet-all.jar \
  -m /path/to/model.gguf "The capital of France is"

# Tool-calling demo (calculator + file-listing tools auto-registered)
java -jar skainet-all.jar -m model.gguf --demo --template=llama3 "What is 17 * 23?"

# Interactive agent
java -jar skainet-all.jar -m model.gguf --agent --template=apertus

--template accepts llama3, chatml, qwen, gemma, apertus (auto-detected from GGUF metadata if omitted).

try (KLlamaSession session = KLlamaJava.loadGGUF(modelPath, /* systemPrompt */ null)) {
    JavaTool calc = new JavaTool() {
        @Override public ToolDefinition getDefinition() {
            return JavaTools.definition(
                "calculator", "Evaluate an arithmetic expression.",
                "{\"type\":\"object\",\"properties\":{\"expression\":{\"type\":\"string\"}},\"required\":[\"expression\"]}"
            );
        }
        @Override public String execute(Map<String, ?> args) { /* ... */ }
    };
    JavaAgentLoop agent = JavaAgentLoop.builder()
        .session(session).tool(calc).template("llama3").build();
    String response = agent.chat("What is 17 * 23?");
}

See llm-test/llm-test-java/src/test/java/.../KLlamaJavaToolCallingTest.java for a runnable reference.

• Engine pin skainet 0.27.0 → 0.28.1. Picks up the completed Kotlin DSL → StableHLO → IREE export path. Every shape-changing op now declares its inferred output type (reshape/matmul/concatenate, #673; conv1d/gather/pooling/flatten, #675), and reduce_window is emitted in IREE's generic region form — so a full gemma3 graph traced via GemmaMlirDumpTest lowers to StableHLO that iree-compiles to a vmfb. No transformers-side API changes; existing callers compile unchanged.
• Verified end-to-end: :llm-inference:gemma:jvmTest green against the published 0.28.1 (GemmaMlirDumpTest, GemmaTraceTest pass).

• DTypePolicy on every *NetworkLoader.fromGguf / .fromSafeTensors entry. A sealed DTypePolicy type (Any | Require | Prefer | OneOf, upstream of SKaiNET 0.25.0) is now accepted on every loader companion in LlamaNetworkLoader, QwenNetworkLoader, GemmaNetworkLoader, ApertusNetworkLoader, and VoxtralNetworkLoader. The policy is validated eagerly via sk.ainet.apps.llm.DTypePolicyValidation — Require(BF16) rejects on GGUF paths (no KEEP_NATIVE GGUF yet), accepts on SafeTensors paths. Default DTypePolicy.Any keeps the existing adaptive behaviour; every existing caller compiles unchanged.
• SafeTensors BF16 KEEP_NATIVE in DecoderSafeTensorsLoader. With Require(BF16) (or Prefer(BF16) / OneOf containing BF16) the loader stops dequanting BF16 SafeTensors weights and instead wraps the packed 2-bytes-per-element buffer in Bf16DenseTensorData. The matmul dispatch in DefaultCpuOpsJvm detects Bf16TensorData at runtime and routes to the SIMD BF16 kernel — a BF16 checkpoint now stays near its on-disk footprint in RAM instead of ~2× FP32 inflation.
• Catalog goes BOM-only. Every skainet-* alias in gradle/libs.versions.toml is now coordinate-only (no version.ref). Versions come from the sk.ainet:skainet-bom platform constraint re-exported by :llm-bom, and every consumer module pulls in implementation(project.dependencies.platform(project(":llm-bom"))) in each affected source set. Engine bumps are still a one-line edit at the top of the catalog, but every internal build now exercises the BOM end-to-end — a missing-from-BOM regression fails locally instead of leaking into a published artifact.
• Three reference smoke tests with @Tag("smoke-reference") — the smoke tier that pins the architectures we always want to run end- to-end: Qwen3ReferenceSmokeTest (Qwen3-1.7B Q8 GGUF; exercises the new 0.25.0 Q8_0MatmulKernel + Qwen's RoPEMode.SPLIT_HALF + QK-Norm), Gemma4ReferenceSmokeTest (Gemma-4 E2B SafeTensors; sliding-window attention + per-layer KV sharing), and BertLeafReferenceSmokeTest (MongoDB mdbr-leaf-ir SafeTensors via the Java KBertJava surface). Run with ./gradlew test -PsmokeReference -PincludeIntegration. Each test self-skips via JUnit Assumptions when the model file isn't reachable through the standard ~/.lmstudio/models/ / ~/.cache/huggingface/hub/ / env-var fallback chain.

0.23.5 — skainet-cli reliability on JDKs without the jdk.incubator.vector module: --enable-preview --add-modules jdk.incubator.vector flags reach the generated launchers (previously only gradle :run); detection of scalar-fallback CPU ops with auto weight dequant to FP32; backend label printed after the real ops probe so it can't disagree with the warning beside it.

0.23.4 — BOM is now correct and self-maintaining: :llm-inference:apertus and :llm-inference:voxtral were missing from the BOM's constraints and are now covered, so consumers pulling them through the BOM get proper version alignment; the constraint list is auto-discovered by a buildSrc/ convention plugin. The README and tutorial dependency snippets were also fixed to use the published artifact IDs (skainet-transformers-core etc.) via the BOM pattern.

0.23.3 — Prefill progress callback: generateUntilStop and AgentLoop expose (done, total) progress during the autoregressive prefill loop via a default-no-op AgentListener.onPrefillProgress method, so UIs on CPU-only runtimes can show that work is happening between round start and the first generated token.

0.23.2 — kllama-cli, kllama-native, kllama-wasm, and KLlamaJava swapped to the DSL path (OptimizedLLMRuntime + llamaNetwork()); GPU stubs deleted; SentencePiece + GGUF tokenizers unified through upstream sk.ainet.io.tokenizer; markdown-fenced Llama 3 JSON tool calls now parse correctly; Qwen3 NEOX RoPE pairing fix; QK-norm RMSNorm-eps wiring fix.

0.23.1 — Apertus end-to-end (routing through OptimizedLLMRuntime + apertusNetwork(), chat template + tool calling, real-GGUF Q4_K loading); Gemma 4 chat-model JVM facade with mmap-arena cleanup; multi-id EOS / stop-token support in the chat layer; SentencePiece auto-detect in fromTokenizerJson; LEAF + Llama 3 single-JVM smoke test; ServiceLoader shadow-jar fix-up so the priority-100 native-cpu provider is picked up post-merge.

See CHANGELOG.md for the full set of changes.

This project uses SKaiNET as its underlying execution engine — tensor ops, neural-network DSL, kernel SPI, GGUF / SafeTensors I/O.

MIT — see LICENCE.