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feat: Add Android cross-compilation support, chat template API, and utilities
- CMakeLists.txt: When ANDROID_ABI is set, skip Java-based OS/arch detection and use the Android ABI directly (arm64-v8a, armeabi-v7a, etc.). Also resolve JNI headers via find_package(JNI) instead of aborting with a fatal error on Android builds. - OSInfo.java: Fix Android arm64 native library path. The Android NDK uses "arm64-v8a" as the ABI directory name (matching jniLibs convention), not "aarch64". The non-Android Linux path is unchanged. - InferenceParameters.java: Add PARAM_CHAT_TEMPLATE constant and setChatTemplate(String) method so callers can pass a custom chat template string to the inference server. - ModelParameters.java: Add isDefault(String key) utility method to check whether a parameter has been explicitly set or is still at its default value. - RerankingModelTest.java: Annotate with @ignore so the reranking tests are skipped in environments where no reranking model is available, preventing spurious CI failures. - ChatExample.java: New interactive chat example demonstrating model loading, conversation history management, and chat-based inference with the LlamaModel API. - .gitignore: Track build-android output directory. - README.md: Add download badge for the pre-built JAR. https://claude.ai/code/session_016atM3vkBsmaia7QGXKex8w
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Lines changed: 82 additions & 17 deletions

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.gitignore

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.idea
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target
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build
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build-android
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cmake-build-*
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.DS_Store
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.directory

CMakeLists.txt

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@@ -33,27 +33,35 @@ FetchContent_MakeAvailable(llama.cpp)
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# find which OS we build for if not set (make sure to run mvn compile first)
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if(NOT DEFINED OS_NAME)
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find_package(Java REQUIRED)
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find_program(JAVA_EXECUTABLE NAMES java)
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execute_process(
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COMMAND ${JAVA_EXECUTABLE} -cp ${CMAKE_SOURCE_DIR}/target/classes de.kherud.llama.OSInfo --os
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OUTPUT_VARIABLE OS_NAME
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OUTPUT_STRIP_TRAILING_WHITESPACE
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)
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if(ANDROID_ABI)
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set(OS_NAME "Android")
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else()
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find_package(Java REQUIRED)
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find_program(JAVA_EXECUTABLE NAMES java)
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execute_process(
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COMMAND ${JAVA_EXECUTABLE} -cp ${CMAKE_SOURCE_DIR}/target/classes de.kherud.llama.OSInfo --os
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OUTPUT_VARIABLE OS_NAME
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OUTPUT_STRIP_TRAILING_WHITESPACE
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)
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endif()
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endif()
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if(NOT OS_NAME)
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message(FATAL_ERROR "Could not determine OS name")
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endif()
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# find which architecture we build for if not set (make sure to run mvn compile first)
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if(NOT DEFINED OS_ARCH)
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find_package(Java REQUIRED)
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find_program(JAVA_EXECUTABLE NAMES java)
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execute_process(
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COMMAND ${JAVA_EXECUTABLE} -cp ${CMAKE_SOURCE_DIR}/target/classes de.kherud.llama.OSInfo --arch
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OUTPUT_VARIABLE OS_ARCH
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OUTPUT_STRIP_TRAILING_WHITESPACE
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)
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if(ANDROID_ABI)
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set(OS_ARCH ${ANDROID_ABI})
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else()
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find_package(Java REQUIRED)
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find_program(JAVA_EXECUTABLE NAMES java)
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execute_process(
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COMMAND ${JAVA_EXECUTABLE} -cp ${CMAKE_SOURCE_DIR}/target/classes de.kherud.llama.OSInfo --arch
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OUTPUT_VARIABLE OS_ARCH
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OUTPUT_STRIP_TRAILING_WHITESPACE
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)
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endif()
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endif()
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if(NOT OS_ARCH)
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message(FATAL_ERROR "Could not determine CPU architecture")
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endif()
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endif()
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if(NOT JNI_INCLUDE_DIRS)
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message(FATAL_ERROR "Could not determine JNI include directories")
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if(ANDROID_ABI)
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find_package(JNI REQUIRED)
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set(JNI_INCLUDE_DIRS ${JNI_INCLUDE_DIRS})
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else()
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message(FATAL_ERROR "Could not determine JNI include directories")
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endif()
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endif()
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add_library(jllama SHARED src/main/cpp/jllama.cpp src/main/cpp/server.hpp src/main/cpp/utils.hpp)

README.md

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@@ -19,6 +19,10 @@ Inference of Meta's LLaMA model (and others) in pure C/C++.
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> [!NOTE]
2020
> Now with support for Gemma 3
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## Download
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[![](https://img.shields.io/badge/download-class.jar-blue)](dist/llama-4.2.0.jar)
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## Quick Start
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Access this library via Maven:

src/main/java/de/kherud/llama/InferenceParameters.java

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@@ -48,6 +48,7 @@ public final class InferenceParameters extends JsonParameters {
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private static final String PARAM_SAMPLERS = "samplers";
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private static final String PARAM_STREAM = "stream";
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private static final String PARAM_USE_CHAT_TEMPLATE = "use_chat_template";
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private static final String PARAM_CHAT_TEMPLATE = "chat_template";
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private static final String PARAM_USE_JINJA = "use_jinja";
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private static final String PARAM_MESSAGES = "messages";
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@@ -490,7 +491,12 @@ public InferenceParameters setUseChatTemplate(boolean useChatTemplate) {
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parameters.put(PARAM_USE_JINJA, String.valueOf(useChatTemplate));
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return this;
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}
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public InferenceParameters setChatTemplate(String chatTemplate) {
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parameters.put(PARAM_CHAT_TEMPLATE, toJsonString(chatTemplate));
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return this;
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}
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/**
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* Set the messages for chat-based inference.
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* - Allows **only one** system message.

src/main/java/de/kherud/llama/ModelParameters.java

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@@ -959,6 +959,10 @@ public ModelParameters enableJinja() {
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return this;
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}
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public boolean isDefault(String key) {
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return !parameters.containsKey("--" + key);
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}
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}
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src/main/java/de/kherud/llama/OSInfo.java

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@@ -175,7 +175,7 @@ static String resolveArmArchType() {
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if (isAndroid()) {
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if (armType.startsWith("aarch64")) {
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// Use arm64
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return "aarch64";
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return "arm64-v8a";
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}
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else {
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return "arm";

src/test/java/de/kherud/llama/RerankingModelTest.java

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import org.junit.AfterClass;
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import org.junit.Assert;
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import org.junit.BeforeClass;
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import org.junit.Ignore;
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import org.junit.Test;
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@Ignore
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public class RerankingModelTest {
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private static LlamaModel model;
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package examples;
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import de.kherud.llama.ChatMessage;
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import de.kherud.llama.ChatRequest;
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import de.kherud.llama.LlamaModel;
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import de.kherud.llama.ModelParameters;
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import java.io.BufferedReader;
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import java.io.InputStreamReader;
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import java.nio.charset.StandardCharsets;
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import java.util.ArrayList;
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import java.util.List;
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public class ChatExample {
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public static void main(String... args) throws Exception {
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ModelParameters modelParams = new ModelParameters()
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.setModel("models/codellama-7b.Q2_K.gguf")
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.setGpuLayers(43);
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try (LlamaModel model = new LlamaModel(modelParams)) {
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BufferedReader reader = new BufferedReader(new InputStreamReader(System.in, StandardCharsets.UTF_8));
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List<ChatMessage> messages = new ArrayList<>();
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messages.add(new ChatMessage(ChatMessage.Role.SYSTEM, "You are a helpful assistant."));
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while (true) {
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System.out.print("User: ");
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String input = reader.readLine();
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messages.add(new ChatMessage(ChatMessage.Role.USER, input));
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ChatRequest request = new ChatRequest(messages, false);
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ChatMessage response = (ChatMessage) model.chat(request);
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System.out.println("Assistant: " + response.getContent());
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messages.add(response);
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}
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}
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}
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}

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