Release 0.56.2

CHANGELOG.md

@@ -9,6 +9,19 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 
+## [0.56.2] — 2026-09-22
+
+A transformers-only release against **SKaiNET engine 0.56.0** (unchanged).
+
+### Added
+
+- **`IreeMoonshineStream`** (`llm-runtime:iree-android`, `libskainet_moonshine_stream.so`, arm64-v8a +
+  armeabi-v7a, Vulkan + local-task): the streaming Moonshine v2 speech-to-text runtime over the five
+  graphs of `MoonshineV2ExportCli` — PCM in, cumulative partial transcripts out, exact final on
+  `finish()`. The counterpart of `IreeKvSession` for ASR: the last piece a Moonshine cartridge needed
+  from a released artifact. `native/build-moonshine-stream.sh` builds it with the same image and
+  cache as the other two libraries.
+
 ### Added
 
 - **`IreeMoonshineStream`** (`llm-runtime:iree-android`, `libskainet_moonshine_stream.so`, arm64-v8a +

README.md

@@ -109,19 +109,21 @@ Honest status — see the project-status note at the top of this README.
 
 ## Current release
 
-The current release is **0.56.1** (against **SKaiNET 0.56.0** — a transformers-only release, same
+The current release is **0.56.2** (against **SKaiNET 0.56.0** — a transformers-only release, same
 pattern as 0.54.1: no new engine version needed).
 
-**Moonshine v2 streaming, every checkpoint of the family, exported from the published module.**
-`MoonshineV2ExportCli` turns a Hugging Face `moonshine_streaming` snapshot into the five StableHLO
-graphs of the streaming contract (frontend, encoder, adapter, masked prefill, dynamic-cache step)
-plus the host-side embedding and vocabulary tables — no Python, nothing to configure per language
-or size, and it fails if a checkpoint tensor goes unused. The model gains per-layer attention bands
-(the German checkpoints need them) and split encoder/decoder widths (the small ones). See
-*Export Moonshine v2 Streaming to StableHLO* in the docs.
+**`IreeMoonshineStream`: the streaming Moonshine v2 speech-to-text runtime.** `libskainet_moonshine_stream.so`
+(`llm-runtime:iree-android`, arm64-v8a + armeabi-v7a, Vulkan or CPU) drives the five graphs of
+`MoonshineV2ExportCli` — frontend, encoder, adapter, masked prefill, dynamic with-past step — plus the
+shared decoder parameter archive as a real streaming loop on the device: PCM in, cumulative partial
+transcripts out, one exact full re-decode on `finish()`. The Kotlin binding is shaped like `IreeKvSession`:
+files by absolute path, no `Context`. With the exporter (0.56.1) this is the last piece a Moonshine
+cartridge needed from a released artifact rather than from C source of its own.
 
-It builds on **0.56.0**, which put the two version lines back in lock-step with **SKaiNET 0.56.0**
-(the engine skipped 0.55.0 to realign them).
+It builds on **0.56.1**, which released `MoonshineV2ExportCli` (a Hugging Face snapshot in, the five
+StableHLO graphs and two host-side tables out, no Python, nothing to configure per language or size), and
+**0.56.0**, which put the two version lines back in lock-step with **SKaiNET 0.56.0** (the engine skipped
+0.55.0 to realign them).
 
 **Grouped-query attention without head expansion, and the compiled leg of SKEEP-005.** The engine's
 `scaledDotProductAttention` is grouped-query native, so `MultiHeadAttention` and
@@ -254,7 +256,7 @@ The recommended way to consume is via the BOM. It pins every published `skainet-
 
 ```kotlin
 dependencies {
-    implementation(platform("sk.ainet.transformers:skainet-transformers-bom:0.56.1"))
+    implementation(platform("sk.ainet.transformers:skainet-transformers-bom:0.56.2"))
 
     // Versions resolved from the BOM:
     implementation("sk.ainet.transformers:skainet-transformers-core")

docs/modules/ROOT/pages/how-to/export-moonshine-v2-streaming.adoc

@@ -32,7 +32,7 @@ From your own build, resolve the published module and run its main class:
 ----
 val exportTool by configurations.creating
 dependencies {
-    exportTool(platform("sk.ainet.transformers:skainet-transformers-bom:0.56.1"))
+    exportTool(platform("sk.ainet.transformers:skainet-transformers-bom:0.56.2"))
     exportTool("sk.ainet.transformers:skainet-transformers-inference-moonshine")
 }
 tasks.register<JavaExec>("exportMoonshine") {

docs/modules/ROOT/pages/reference/moonshine-encoder.adoc

@@ -24,7 +24,7 @@ a DSL decoder is future work.
 [source,kotlin]
 ----
 dependencies {
-    implementation(platform("sk.ainet.transformers:skainet-transformers-bom:0.56.1"))
+    implementation(platform("sk.ainet.transformers:skainet-transformers-bom:0.56.2"))
     implementation("sk.ainet.transformers:skainet-transformers-inference-moonshine")
 }
 ----

docs/modules/ROOT/pages/tutorials/android-getting-started.adoc

@@ -55,7 +55,7 @@ dependencies {
     // self-registers it on ART at process start — nothing to call.
     runtimeOnly("sk.ainet.core:skainet-backend-jni-cpu")
 
-    implementation(platform("sk.ainet.transformers:skainet-transformers-bom:0.56.1"))
+    implementation(platform("sk.ainet.transformers:skainet-transformers-bom:0.56.2"))
     implementation("sk.ainet.transformers:skainet-transformers-core")
     implementation("sk.ainet.transformers:skainet-transformers-runtime-kllama")
     implementation("sk.ainet.transformers:skainet-transformers-inference-llama")

docs/modules/ROOT/pages/tutorials/getting-started-java.adoc

@@ -25,7 +25,7 @@ In your `build.gradle.kts`:
 [source,kotlin]
 ----
 dependencies {
-    implementation(platform("sk.ainet.transformers:skainet-transformers-bom:0.56.1"))
+    implementation(platform("sk.ainet.transformers:skainet-transformers-bom:0.56.2"))
 
     implementation("sk.ainet.transformers:skainet-transformers-runtime-kllama")
     implementation("sk.ainet.transformers:skainet-transformers-agent")
@@ -41,7 +41,7 @@ Or in Maven (Maven needs the `-jvm` classifier suffix on platform artifacts):
     <dependency>
       <groupId>sk.ainet.transformers</groupId>
       <artifactId>skainet-transformers-bom</artifactId>
-      <version>0.56.1</version>
+      <version>0.56.2</version>
       <type>pom</type>
       <scope>import</scope>
     </dependency>

docs/modules/ROOT/pages/tutorials/getting-started-leaf.adoc

@@ -34,7 +34,7 @@ encoder output to the advertised dimensionality. The runtime applies it automati
 [source,kotlin]
 ----
 dependencies {
-    implementation(platform("sk.ainet.transformers:skainet-transformers-bom:0.56.1"))
+    implementation(platform("sk.ainet.transformers:skainet-transformers-bom:0.56.2"))
     implementation("sk.ainet.transformers:skainet-transformers-providers")
 }
 ----

docs/modules/ROOT/pages/tutorials/llama3-tool-calling.adoc

@@ -52,7 +52,7 @@ The pieces you need live in three modules:
 [source,kotlin]
 ----
 dependencies {
-    implementation(platform("sk.ainet.transformers:skainet-transformers-bom:0.56.1"))
+    implementation(platform("sk.ainet.transformers:skainet-transformers-bom:0.56.2"))
 
     implementation("sk.ainet.transformers:skainet-transformers-runtime-kllama")
     implementation("sk.ainet.transformers:skainet-transformers-agent")

gradle.properties

@@ -1,5 +1,5 @@
 GROUP=sk.ainet.transformers
-VERSION_NAME=0.56.1
+VERSION_NAME=0.56.2
 
 POM_DESCRIPTION=SKaiNET-transformers
 

llm-inference/gemma/README.md

@@ -4,7 +4,7 @@ Reusable **Gemma** model (incl. the FunctionGemma tool-calling fine-tune) author
 a portable graph producer with **no runtime/board/Torq code**. Pair it with the runtime module below to
 decode on-device.
 
-- **Coordinate:** `sk.ainet.transformers:skainet-transformers-inference-gemma:0.56.1`
+- **Coordinate:** `sk.ainet.transformers:skainet-transformers-inference-gemma:0.56.2`
 - **Targets:** `android`, `iosArm64`, `iosSimulatorArm64`, `macosArm64`, `linuxX64`, `linuxArm64` (broadly
   portable — mobile through server).
 - **Entry point:** `gemmaNetwork()` / `GemmaNetworkLoader` (loads a GGUF, builds the DSL graph, incl. the
@@ -24,8 +24,8 @@ FunctionGemma has a one-liner facade in `…:skainet-transformers-runtime-kgemma
 
 ```kotlin
 dependencies {
-    implementation("sk.ainet.transformers:skainet-transformers-inference-gemma:0.56.1")
-    implementation("sk.ainet.transformers:skainet-transformers-runtime-gemma-iree:0.56.1") // on-device decode
+    implementation("sk.ainet.transformers:skainet-transformers-inference-gemma:0.56.2")
+    implementation("sk.ainet.transformers:skainet-transformers-runtime-gemma-iree:0.56.2") // on-device decode
 }
 ```