SKaiNET-transformers

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.

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. Llama / Mistral, Qwen 2 / 2.5 / 3, Gemma 3 / 4, Apertus (Swiss AI) and BitNet b1.58 are each verified token-for-token against a reference implementation (mainline llama.cpp; bitnet.cpp + the HF BF16 reference for BitNet) by model-gated golden-token parity tests on the DSL path the CLIs ship. BERT (vs sentence-transformers) and T5/GTR (real-weights round-trip) are verified on the embedding side. Voxtral is the remaining unverified family — see the status table below.
• 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). On Android, the runtime facades ship the engine's JNI NEON backend the same way — native kernels out of the box, ~6.4× measured on SmolLM2-135M Q8_0 (see the supported-targets matrix).
• 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.
• Parallel attention heads (unreleased, SKaiNET SKEEP-005). MultiHeadAttention spreads heads over all cores through the engine's Schedule on the execution context — bit-identical to the sequential path — and reads K/V in place from the positional cache (withKVCacheKind(POSITIONAL)), instead of copying the prefix per token. No DSL change; see the Parallel Attention — Getting Started tutorial.
• 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 — see the supported targets matrix for exactly which module publishes which target.

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"]

The eager JVM path is the primary way every model family runs today. The StableHLO / native path is shared with the engine and wired for the first families: FunctionGemma exports a compiled edge build (0.35.0), and the BERT encoder traces to an optimized ComputeGraph and lowers to StableHLO (0.36.0); full generative-model coverage is still in progress.

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 0.55.0 (against SKaiNET 0.54.0 — a transformers-only release, same pattern as 0.54.1: no new engine version needed).

A new asr-domain module, and BackendProvider learns capabilities/options. Generic ASR task types (Transcription, DecodingOptions, FeatureFrames, ...) move up from the downstream ASR cartridge ecosystem, where they lived only because that's where the original whisper-cli extraction happened to put them — both the Whisper and Moonshine cartridge families depend on these, so keeping them in one family's repo made the other structurally dependent on it for generic plumbing. BackendProvider gains a capabilities: BackendCapabilities property and a defaulted createContext(options: BackendOptions = BackendOptions()) (was parameterless) — source-compatible with every existing implementer. Downstream's own execution-backend registry seam is not duplicated as a second module here; it's unified into this existing BackendProvider/BackendRegistry instead, since both already did the same job. (#432)

It builds on 0.54.1, which made attention heads run in parallel and stopped positional K/V caches from copying the whole prefix per layer per token.

Attention heads run in parallel. MultiHeadAttention is the first consumer of the engine's SKEEP-005 Schedule: heads (or GQA groups) map onto cores via AttentionSchedulePolicy (Sequential / PerHead / PerKVGroup / Auto), bit-identical to the sequential path. KVCache.updateInPlace returns copy-free KVBufferViews for positional caches instead of copying the whole prefix per layer per token, and DecoderKVCacheKind.POSITIONAL brings that to Llama and Qwen. Verified on Llama-3.2-1B, Qwen2.5-0.5B and Qwen3-1.7B against mainline llama.cpp.

It builds on 0.54.0, which restored version lock-step with the engine, plus two bugs fixed on real hardware and the FunctionGemma/IREE-Android chunked-KV work closed out.

A stateful Android KV session for FunctionGemma. IreeKvSession / IreeKvDecoder (llm-runtime/iree-android) prefill the tool catalog once per process, snapshot the KV state, and per-turn prefill only the new chunk — device-resident K/V, zero-copy tail views for the sliding layers, embedding rows read straight from the archive. Measured on a MagentaTV One (Mali via Vulkan): p50 5.87 s / max 6.25 s per utterance, down from minutes on the stateless redecode contract. Position-selected graphs (gemma_at/gemma_prefill_at) and a chunk prefill-with-past graph (gemma_prefill_with_past, #415, #417) get there without every step re-running the LM head over the whole sequence.

Two bugs found on real hardware, fixed. runtime-kgemma's published POM depended on an unpublished :llm-runtime:kgemma3n coordinate and failed to resolve for any external consumer — fixed by unpublishing Gemma 3n itself rather than papering over the dependency (it's maturity-gate 0/5 and postponed per its own tracking issue). The official FunctionGemma tool-call parser crashed with PatternSyntaxException on every Android device (ICU rejects an unescaped } that the JVM's regex engine tolerates) — a one-character fix.

It builds on 0.53.0, which restored version lock-step with the engine (SKaiNET 0.53.0's billion-parameter export fixes and sharded SafeTensors loader), collapsed the per-family hand-rolled SafeTensors materialization onto the engine's loaders, shipped skainet-decode as the repository's first Android application, and re-verified tool calling against the new engine.

It builds on 0.40.2, which rounded out the compiled on-device path (standalone DSL → StableHLO → IREE export modules for FunctionGemma and SmolLM2, the generic Android JNI runtime llm-runtime/iree-android), landed the tool-calling epic substrate behind #35 (chat-template auto-detection, registerable parser strategies, resolution diagnostics), hoisted the packed-quant packing into BlockQuantPacking with Q5_0/Q5_1 kept packed, and brought SmolLM2 into the tool-calling families.

It builds on 0.38.0, which completed Moonshine v2 streaming ASR entirely in the SKaiNET NN DSL (frontend, sliding-window encoder, adapter, KV-cache decoder — no vendor neural binaries, with true-dynamic KV-cache graphs on engine 0.38.0's Dim) and added narrow-float KEEP_NATIVE weights — FP16/BF16 stay packed on the SafeTensors and GGUF paths instead of widening to FP32 (BF16 1.8–1.9×, FP16 1.5–1.7× over FP32), wired across llama / qwen / gemma / apertus / voxtral — plus Gemma row-dequant of the packed token_embd at load.

It builds on 0.36.1, which added BGE embedding models on the BERT DSL path (CLS pooling + retrieval prefixes) and beam search for the T5 decoder and the vec2text inversion loop — both additive, drop-in for existing consumers.

It builds on 0.36.0, in which BERT became completely defined in the SKaiNET NN DSL and the deprecated hand-coded eager BERT stack was removed (BREAKING):

• bertNetwork() is a numerically complete tokens → hidden-states encoder: the new BertEmbeddings module adds the absolute-position and token-type embeddings the DSL definition previously omitted, and each encoder layer is wired as two post-norm blocks so every residual lands on the right value.
• BertEncoderRuntime runs the same definition eagerly (DIRECT, default) or as a traced, LLM-pipeline-optimized ComputeGraph (OPTIMIZED, bit-exact vs eager), adds masked mean pooling, the optional sentence-transformers 2_Dense projection, and L2 normalization — and exportTape(...) lowers the encoder to StableHLO.
• One-call consumption: BertEmbeddingModel.fromHuggingFace("MongoDB/mdbr-leaf-mt") / fromSafeTensors(dir) behind the neutral EmbeddingModel SPI, with built-in Hub download (HF_TOKEN-aware, cached, offline-safe after the first run).
• Downstream effect: indexing the leaf-cli reference corpus dropped 676.9 s → 44.5 s (~15×) with identical embeddings. Migration notes for the removed BertRuntime stack are in the CHANGELOG and the BERT-as-DSL explanation.

0.36.0 also added the T5 encoder-decoder runtime (llm-inference/t5) with GtrEmbedder, and the vec2text embedding-inversion pipeline (llm-inference/vec2text) that iteratively reconstructs text from a GTR embedding — verified end-to-end against real sentence-transformers/gtr-t5-base weights. That is the pipeline 0.36.1's beam search extends.

Both build on 0.35.0, which added FunctionGemma self-compiled from the SKaiNET NN DSL: a one-dependency function-calling sLLM (skainet-transformers-runtime-kgemma) with an eager one-line API (FunctionGemma.fromGguf(gguf).call("turn the light on") → ToolCall(set_lights, {state="on"}), runs anywhere on CPU, no iree) and a no-Python compiled edge export (FunctionGemma.exportCompiled / compile-gemma.sh) verified token-for-token against llama.cpp on the SL2610 board, using the engine's new argMax op to fold the logits → token-ids argmax tail into the DSL trace; and on 0.34.1 — a patch that layer-qualifies the Moonshine encoder's attention/LayerNorm parameter names so by-name weight loading can tell the layers apart (no public API change) — and on 0.34.0, which adds the first Moonshine speech-to-text encoder authored entirely in the SKaiNET NN DSL (skainet-transformers-inference-moonshine, bf16-native) — it emits portable StableHLO and transcribes correctly on both CPU and the Synaptics Torq NPU. Supporting this, transformer-core RoPE now computes its rotation and cos/sin tables in f32 and uses the full-head (ONNX) interleaved form for bit-exact accuracy on NPU targets, and gains partial-rotary support (partialRotaryFactor / freqDenomRotaryDim); VoidDense gains an optional addBias for faithful FFNs. On top of the 0.33.0 engine adoption, the 0.32.1 streaming-detokenization fix and the 0.32.0 real-GGUF Llama eager + StableHLO/IREE export work:

• The eager NATIVE_OPTIMIZED path now works for Llama (Q4_K/Q6_K): weights stay packed and LlamaNetworkLoader.fromGguf(NATIVE_OPTIMIZED) + OptimizedLLMRuntime decodes coherently, matching llama.cpp — fixing the packed token-embedding gather: unsupported input rank 1.
• Fused decode-attention (seqQ == 1) skips the repeatKVHeads concat + SDPA plumbing for a faster decode loop (~1.5×), bit-identical output.
• Interleaved RoPE is now traceable, so Llama/Mistral/GGUF graphs export to StableHLO (and iree-compile to a vmfb) instead of baking a disconnected constant.

The earlier transformer-core extraction (0.31.1) and the Gemma NATIVE_OPTIMIZED footprint work (0.31.0) still apply.

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:0.55.0"))

    // 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.

Which Maven artifact publishes which Kotlin target (derived from each module's build.gradle.kts; "iOS" = iosArm64 + iosSimulatorArm64, "Linux" = linuxX64 + linuxArm64):

¹ transformer-core additionally publishes androidNativeArm32/androidNativeArm64. ² moonshine publishes iosArm64 and androidNativeArm64 but no simulator or Android (AGP) variant.

On Android, the runtime facades kllama and kgemma ship the engine's NEON kernel backend (sk.ainet.core:skainet-backend-jni-cpu, engine ≥ 0.39.1) as a transitive runtime dependency, so apps get native ARM kernels out of the box instead of the scalar Kotlin fallback. Apps that drive llm-inference/llama + transformer-core directly (the same wiring the iOS path uses) should add that AAR themselves; excluding the artifact opts back into pure-Kotlin execution.

• 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, smollm (auto-detected if omitted — from GGUF metadata, or from tokenizer_config.json / chat_template.json / config.json next to safetensors checkpoints).

Tool calling is modeled as a capability (#35/#36): each model family ships a ToolCallingSupport provider bundling its chat template and tool-call parser, and ToolCallingSupportResolver picks one per model. Resolution order:

1. Explicit — --template=NAME (or a templateName passed to ChatSession) always wins.
2. Auto-detection — best-effort, from model metadata (GGUF general.architecture / tokenizer.chat_template, or HF sidecar configs). Metadata is a hint, not proof of tool-calling capability.
3. Generic fallback — if nothing matches, a ChatML-based generic provider is used and reported as mode=GENERIC so the demo/agent output makes fallback selection visible.

NATIVE means a dedicated, family-tested template + parser; GENERIC is best-effort and may fail to trigger or parse on models that were not trained for Hermes-style <tool_call> blocks. New families register at runtime via ToolCallingSupportResolver.register(provider) (and, for custom output formats, ToolCallParser.registerStrategy(strategy)) — no llm-agent changes needed; the FunctionGemma provider in :llm-runtime:gemma-iree is the reference example.

Sentence embeddings with MongoDB's compact LEAF retrieval models need a single factory call — the model downloads from the Hugging Face Hub and is cached on first use:

import sk.ainet.llm.providers.BertEmbeddingModel

BertEmbeddingModel.fromHuggingFace("MongoDB/mdbr-leaf-ir").use { model ->
    val vector = model.embed("The quick brown fox")   // L2-normalized FloatArray
}

See the Getting Started with LEAF tutorial and the BERT-as-DSL explanation.

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.

• Streaming detokenization keeps word spaces. A generation loop decoding one token at a time (tokenizer.decode(tokenId)) no longer runs words together. SentencePieceSpecialTokens and UpstreamTokenizerAdapter route decode(Int) through engine 0.32.4's Tokenizer.decodeToken, which preserves each SentencePiece piece's leading space (llama.cpp token_to_piece semantics). Engine pin 0.32.2 → 0.32.4.

• Eager NATIVE_OPTIMIZED for real-GGUF Llama. LlamaNetworkLoader.fromGguf(NATIVE_OPTIMIZED) now keeps Q4_K/Q6_K weights packed and runs them through OptimizedLLMRuntime, mirroring the Gemma path (new LlamaQuantLayout + LlamaPackedWeights.convertLlamaWeightsPacked). Output is coherent and matches llama.cpp; fixes the packed token-embedding gather: unsupported input rank 1. This is the low-footprint path real-GGUF Llama inference on constrained ARM was missing. (ccbd87e)
• Fused decode-attention fast path. For the decode step (seqQ == 1), MultiHeadAttention runs scores → softmax → GQA-weighted-V straight from the cached K/V, bypassing the repeatKVHeads concat and the unsqueeze → SDPA → squeeze → permute chain. ~1.5× decode throughput on the JVM eager path; bit-for-bit-equivalent output. Prefill keeps the general SDPA path. (3791f88)
• Traceable interleaved RoPE (graph export). RoPE in INTERLEAVED mode (Llama / Mistral / most GGUF) used a raw-array path (copyToFloatArray / fromFloatArray) that, under graph tracing, recorded the rotated Q/K as a disconnected constant — severing them from the projection weights and crashing iree-compile downstream. It now records the rotation as tensor ops when tracing (gated on the tracing wrapper; eager keeps the fast raw-array path byte-identical). Unblocks TinyLlama → StableHLO → IREE. (019b049)
• Engine pin skainet 0.31.0 → 0.32.2.

• transformer-core module — NN primitives reusable on all targets incl. androidNative. The attention / KV-cache / embedding / norm / RoPE / FFN / linear-projection primitives were trapped in llm-core (whose io/compile/backend deps lack androidNative); they only need skainet-lang-core (which has it), so they're extracted into transformer-core and llm-core re-exports them. Existing consumers are unaffected; ARM-native downstreams (on-device whisper, future models) reuse them instead of reimplementing. Ships against engine 0.31.0 (additive, no engine change). (#183)

• Tied Q8_0 lm_head stays packed (eager NATIVE_OPTIMIZED). FunctionGemma's token_embd is Q8_0 and tied, so convertGemmaWeightsPacked was dequantizing both token_embd and output to FP32 (2×~0.67 GB) — OOM on the 1.9 GB SL2610. output/lm_head now packs as Q8_0 (runs on the NEON Q8_0 kernel); token_embd stays FP32 (it's gathered) but is wrapped no-copy. Footprint ~1.34 GB → ~0.76 GB; byte-identical decode (GemmaQ5KPackedParityTest), stable ~1.06 GB load on the SL2610.
• GemmaNetworkLoader.load(maxInferenceLen = …) — cap the context so the KV cache + RoPE tables stay tiny on constrained devices (default min(contextLength, 4096)).
• Engine pin skainet 0.30.0 → 0.31.0 — picks up ops.transpose's lazy-rewrap fix for all packed matmul dtypes (Q8_0/Q4_0), required so the packed lm_head transposes through linearProject instead of ClassCastException.

• Q5_K stays packed in the eager Gemma runtime. GemmaMemSegConverter used to dequantize Q5_K weights to FP32 on load; SKaiNET 0.30.0 provides a first-class Q5_K packed matmul (Q5_KBlockTensorData + Q5KMatmulKernel), so the converter now relayouts the GGUF bytes to block-major and keeps them packed (176 B/block). FunctionGemma-270M (Q5_K_M) decodes byte-identically to the FP32 baseline (GemmaQ5KPackedParityTest).
• Gemma NATIVE_OPTIMIZED path is Kotlin/Native–ready. The reusable layout + packing helpers (GemmaQuantLayout.kt, GemmaPackedWeights.kt) moved to commonMain, and GemmaNetworkLoader.load() now runs convertGemmaWeightsPacked under NATIVE_OPTIMIZED — so the board binary keeps K-quant weights packed with no java.lang.foreign MemSeg dependency. Verified on JVM and linuxX64.
• Engine pin skainet 0.28.1 → 0.30.0 — released Q5_K packed matmul, NEON native kernels, and Kotlin/Native cinterop. The mavenLocal()-first dev shim is reverted; the release resolves the engine from Maven Central.
• Fixes. Kernel-less quant types under NATIVE_OPTIMIZED now dequant to FP32 [out, in] instead of crashing on a rank-1 transpose; DecoderGgufMemSegConverter dequantizes Q4_1 and every other non-packed quant type instead of passing raw bytes through to a matmul crash (#654).

• 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).

Superseded (unreleased, engine 0.38.0). The narrow-float limits described below are gone: the GGUF chain now honors DTypePolicy and keeps F16/BF16 packed, Require(FP16) is accepted alongside Require(BF16), and the two formats are resolved independently. Conversely, Gemma and Apertus now reject Require(BF16) — their own weight chains never honored it. See the [Unreleased] section of CHANGELOG.md.

• 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.