adding re-ranking

.github/workflows/ci.yml

@@ -6,6 +6,8 @@ on:
 env:
   MODEL_URL: https://huggingface.co/TheBloke/CodeLlama-7B-GGUF/resolve/main/codellama-7b.Q2_K.gguf
   MODEL_NAME: codellama-7b.Q2_K.gguf
+  RERANKING_MODEL_URL: https://huggingface.co/gpustack/jina-reranker-v1-tiny-en-GGUF/resolve/main/jina-reranker-v1-tiny-en-Q4_0.gguf
+  RERANKING_MODEL_NAME: jina-reranker-v1-tiny-en-Q4_0.gguf
 jobs:
 
   build-and-test-linux:
@@ -21,8 +23,12 @@ jobs:
         run: |
           mvn compile
           .github/build.sh -DLLAMA_VERBOSE=ON
-      - name: Download model
+      - name: Download text generation model
         run: curl -L ${MODEL_URL} --create-dirs -o models/${MODEL_NAME}
+      - name: Download reranking model 
+        run: curl -L ${RERANKING_MODEL_URL} --create-dirs -o models/${RERANKING_MODEL_NAME}
+      - name: List files in models directory
+        run: ls -l models/
       - name: Run tests
         run: mvn test
       - if: failure()
@@ -53,8 +59,12 @@ jobs:
         run: |
           mvn compile
           .github/build.sh ${{ matrix.target.cmake }}
-      - name: Download model
+      - name: Download text generaton model model
         run: curl -L ${MODEL_URL} --create-dirs -o models/${MODEL_NAME}
+      - name: Download reranking model 
+        run: curl -L ${RERANKING_MODEL_URL} --create-dirs -o models/${RERANKING_MODEL_NAME}
+      - name: List files in models directory
+        run: ls -l models/
       - name: Run tests
         run: mvn test
       - if: failure()
@@ -79,6 +89,10 @@ jobs:
           .github\build.bat -DLLAMA_VERBOSE=ON
       - name: Download model
         run: curl -L $env:MODEL_URL --create-dirs -o models/$env:MODEL_NAME
+      - name: Download reranking model 
+        run: curl -L $env:RERANKING_MODEL_URL --create-dirs -o models/$env:RERANKING_MODEL_NAME
+      - name: List files in models directory
+        run: ls -l models/
       - name: Run tests
         run: mvn test
       - if: failure()

.github/workflows/release.yaml

@@ -11,6 +11,8 @@ on:
 env:
   MODEL_URL: "https://huggingface.co/TheBloke/CodeLlama-7B-GGUF/resolve/main/codellama-7b.Q2_K.gguf"
   MODEL_NAME: "codellama-7b.Q2_K.gguf"
+  RERANKING_MODEL_URL: "https://huggingface.co/gpustack/jina-reranker-v1-tiny-en-GGUF/resolve/main/jina-reranker-v1-tiny-en-Q4_0.gguf"
+  RERANKING_MODEL_NAME: "jina-reranker-v1-tiny-en-Q4_0.gguf"
 jobs:
 
 # todo: doesn't work with the newest llama.cpp version
@@ -144,8 +146,10 @@ jobs:
         with:
           name: Linux-x86_64-libraries
           path: ${{ github.workspace }}/src/main/resources/de/kherud/llama/
-      - name: Download model
+      - name: Download text generation model
         run: curl -L ${MODEL_URL} --create-dirs -o models/${MODEL_NAME}
+      - name: Download reranking model 
+        run: curl -L ${RERANKING_MODEL_URL} --create-dirs -o models/${RERANKING_MODEL_NAME}
       - uses: actions/setup-java@v4
         with:
           distribution: 'zulu'

CMakeLists.txt

@@ -25,7 +25,7 @@ set(LLAMA_BUILD_COMMON ON)
 FetchContent_Declare(
 	llama.cpp
 	GIT_REPOSITORY https://github.com/ggerganov/llama.cpp.git
-	GIT_TAG        b4831
+	GIT_TAG        b4916
 )
 FetchContent_MakeAvailable(llama.cpp)
 

README.md

@@ -94,8 +94,8 @@ public class Example {
 
     public static void main(String... args) throws IOException {
         ModelParameters modelParams = new ModelParameters()
-                .setModelFilePath("/path/to/model.gguf")
-                .setNGpuLayers(43);
+                .setModel("models/mistral-7b-instruct-v0.2.Q2_K.gguf")
+                .setGpuLayers(43);
 
         String system = "This is a conversation between User and Llama, a friendly chatbot.\n" +
                 "Llama is helpful, kind, honest, good at writing, and never fails to answer any " +
@@ -114,8 +114,8 @@ public class Example {
                 InferenceParameters inferParams = new InferenceParameters(prompt)
                         .setTemperature(0.7f)
                         .setPenalizeNl(true)
-                        .setMirostat(InferenceParameters.MiroStat.V2)
-                        .setAntiPrompt("\n");
+                        .setMiroStat(MiroStat.V2)
+                        .setStopStrings("User:");
                 for (LlamaOutput output : model.generate(inferParams)) {
                     System.out.print(output);
                     prompt += output;
@@ -135,7 +135,7 @@ model to your prompt in order to extend the context. If there is repeated conten
 cache this, to improve performance.
 
 ```java
-ModelParameters modelParams = new ModelParameters().setModelFilePath("/path/to/model.gguf");
+ModelParameters modelParams = new ModelParameters().setModel("/path/to/model.gguf");
 InferenceParameters inferParams = new InferenceParameters("Tell me a joke.");
 try (LlamaModel model = new LlamaModel(modelParams)) {
     // Stream a response and access more information about each output.
@@ -167,9 +167,8 @@ for every inference task. All non-specified options have sensible defaults.
 
 ```java
 ModelParameters modelParams = new ModelParameters()
-        .setModelFilePath("/path/to/model.gguf")
-        .setLoraAdapter("/path/to/lora/adapter")
-        .setLoraBase("/path/to/lora/base");
+        .setModel("/path/to/model.gguf")
+        .addLoraAdapter("/path/to/lora/adapter");
 String grammar = """
 		root  ::= (expr "=" term "\\n")+
 		expr  ::= term ([-+*/] term)*

pom.xml

@@ -1,4 +1,5 @@
-<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+<project xmlns="http://maven.apache.org/POM/4.0.0"
+	xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
 	xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
 	<modelVersion>4.0.0</modelVersion>
 
@@ -8,7 +9,8 @@
 	<packaging>jar</packaging>
 
 	<name>${project.groupId}:${project.artifactId}</name>
-	<description>Java Bindings for llama.cpp - A Port of Facebook's LLaMA model in C/C++.</description>
+	<description>Java Bindings for llama.cpp - A Port of Facebook's LLaMA model
+		in C/C++.</description>
 	<url>https://github.com/kherud/java-llama.cpp</url>
 
 	<licenses>
@@ -39,7 +41,8 @@
 		</snapshotRepository>
 		<repository>
 			<id>ossrh</id>
-			<url>https://s01.oss.sonatype.org/service/local/staging/deploy/maven2/</url>
+			<url>
+				https://s01.oss.sonatype.org/service/local/staging/deploy/maven2/</url>
 		</repository>
 	</distributionManagement>
 
@@ -71,17 +74,21 @@
 				<artifactId>maven-compiler-plugin</artifactId>
 				<version>3.13.0</version>
 				<executions>
-					<!-- We have to perform a separate build pass for cuda classifier -->
+					<!-- We have to perform a separate build pass for cuda
+					classifier -->
 					<execution>
 						<id>gpu</id>
 						<phase>compile</phase>
-						<goals><goal>compile</goal></goals>
+						<goals>
+							<goal>compile</goal>
+						</goals>
 						<configuration>
 							<compilerArgs>
 								<arg>-h</arg>
 								<arg>src/main/cpp</arg>
 							</compilerArgs>
-							<outputDirectory>${project.build.outputDirectory}_cuda</outputDirectory>
+							<outputDirectory>
+								${project.build.outputDirectory}_cuda</outputDirectory>
 						</configuration>
 					</execution>
 				</executions>
@@ -98,10 +105,12 @@
 							<goal>copy-resources</goal>
 						</goals>
 						<configuration>
-							<outputDirectory>${project.build.outputDirectory}_cuda</outputDirectory>
+							<outputDirectory>
+								${project.build.outputDirectory}_cuda</outputDirectory>
 							<resources>
 								<resource>
-									<directory>${basedir}/src/main/resources_linux_cuda/</directory>
+									<directory>
+										${basedir}/src/main/resources_linux_cuda/</directory>
 									<includes>
 										<include>**/*.*</include>
 									</includes>
@@ -176,7 +185,8 @@
 						<artifactId>maven-jar-plugin</artifactId>
 						<version>3.4.2</version>
 						<executions>
-							<!-- Pick class files AND libs from custom output directory -->
+							<!-- Pick class files AND libs from custom output
+							directory -->
 							<execution>
 								<id>cuda</id>
 								<phase>package</phase>
@@ -185,7 +195,8 @@
 								</goals>
 								<configuration>
 									<classifier>cuda12-linux-x86-64</classifier>
-									<classesDirectory>${project.build.outputDirectory}_cuda</classesDirectory>
+									<classesDirectory>
+										${project.build.outputDirectory}_cuda</classesDirectory>
 								</configuration>
 							</execution>
 						</executions>

src/main/cpp/jllama.cpp

@@ -634,7 +634,6 @@ JNIEXPORT jfloatArray JNICALL Java_de_kherud_llama_LlamaModel_embed(JNIEnv *env,
     json error = nullptr;
 
     server_task_result_ptr result = ctx_server->queue_results.recv(id_task);
-    ctx_server->queue_results.remove_waiting_task_id(id_task);
 
     json response_str = result->to_json();
     if (result->is_error()) {
@@ -644,6 +643,10 @@ JNIEXPORT jfloatArray JNICALL Java_de_kherud_llama_LlamaModel_embed(JNIEnv *env,
         return nullptr;
     }
 
+    if (result->is_stop()) {
+        ctx_server->queue_results.remove_waiting_task_id(id_task);
+    }
+
     const auto out_res = result->to_json();
 
     // Extract "embedding" as a vector of vectors (2D array)
@@ -679,6 +682,102 @@ JNIEXPORT jfloatArray JNICALL Java_de_kherud_llama_LlamaModel_embed(JNIEnv *env,
     return j_embedding;
 }
 
+JNIEXPORT jobject JNICALL Java_de_kherud_llama_LlamaModel_rerank(JNIEnv *env, jobject obj, jstring jprompt,
+                                                                 jobjectArray documents) {
+    jlong server_handle = env->GetLongField(obj, f_model_pointer);
+    auto *ctx_server = reinterpret_cast<server_context *>(server_handle); // NOLINT(*-no-int-to-ptr)
+
+    if (!ctx_server->params_base.reranking || ctx_server->params_base.embedding) {
+        env->ThrowNew(c_llama_error,
+                      "This server does not support reranking. Start it with `--reranking` and without `--embedding`");
+        return nullptr;
+    }
+
+    const std::string prompt = parse_jstring(env, jprompt);
+
+    const auto tokenized_query = tokenize_mixed(ctx_server->vocab, prompt, true, true);
+
+    json responses = json::array();
+
+    std::vector<server_task> tasks;
+    const jsize amount_documents = env->GetArrayLength(documents);
+    auto *document_array = parse_string_array(env, documents, amount_documents);
+    auto document_vector = std::vector<std::string>(document_array, document_array + amount_documents);
+    free_string_array(document_array, amount_documents);
+
+    std::vector<llama_tokens> tokenized_docs = tokenize_input_prompts(ctx_server->vocab, document_vector, true, true);
+
+    tasks.reserve(tokenized_docs.size());
+    for (int i = 0; i < tokenized_docs.size(); i++) {
+        auto task = server_task(SERVER_TASK_TYPE_RERANK);
+        task.id = ctx_server->queue_tasks.get_new_id();
+        task.index = i;
+        task.prompt_tokens = format_rerank(ctx_server->vocab, tokenized_query, tokenized_docs[i]);
+        tasks.push_back(task);
+    }
+    ctx_server->queue_results.add_waiting_tasks(tasks);
+    ctx_server->queue_tasks.post(tasks);
+
+    // get the result
+    std::unordered_set<int> task_ids = server_task::get_list_id(tasks);
+    std::vector<server_task_result_ptr> results(task_ids.size());
+
+    // Create a new HashMap instance
+    jobject o_probabilities = env->NewObject(c_hash_map, cc_hash_map);
+    if (o_probabilities == nullptr) {
+        env->ThrowNew(c_llama_error, "Failed to create HashMap object.");
+        return nullptr;
+    }
+
+    for (int i = 0; i < (int)task_ids.size(); i++) {
+        server_task_result_ptr result = ctx_server->queue_results.recv(task_ids);
+        if (result->is_error()) {
+            auto response = result->to_json()["message"].get<std::string>();
+            for (const int id_task : task_ids) {
+                ctx_server->queue_results.remove_waiting_task_id(id_task);
+            }
+            env->ThrowNew(c_llama_error, response.c_str());
+            return nullptr;
+        }
+
+        const auto out_res = result->to_json();
+
+        if (result->is_stop()) {
+            for (const int id_task : task_ids) {
+                ctx_server->queue_results.remove_waiting_task_id(id_task);
+            }
+        }
+
+        int index = out_res["index"].get<int>();
+        float score = out_res["score"].get<float>();
+        std::string tok_str = document_vector[index];
+        jstring jtok_str = env->NewStringUTF(tok_str.c_str());
+
+        jobject jprob = env->NewObject(c_float, cc_float, score);
+        env->CallObjectMethod(o_probabilities, m_map_put, jtok_str, jprob);
+        env->DeleteLocalRef(jtok_str);
+        env->DeleteLocalRef(jprob);
+    }
+    jbyteArray jbytes = parse_jbytes(env, prompt);
+    return env->NewObject(c_output, cc_output, jbytes, o_probabilities, true);
+}
+
+JNIEXPORT jstring JNICALL Java_de_kherud_llama_LlamaModel_applyTemplate(JNIEnv *env, jobject obj, jstring jparams) {
+    jlong server_handle = env->GetLongField(obj, f_model_pointer);
+    auto *ctx_server = reinterpret_cast<server_context *>(server_handle); // NOLINT(*-no-int-to-ptr)
+
+    std::string c_params = parse_jstring(env, jparams);
+    json data = json::parse(c_params);
+
+    json templateData =
+        oaicompat_completion_params_parse(data, ctx_server->params_base.use_jinja,
+                                          ctx_server->params_base.reasoning_format, ctx_server->chat_templates.get());
+    std::string tok_str = templateData.at("prompt");
+    jstring jtok_str = env->NewStringUTF(tok_str.c_str());
+
+    return jtok_str;
+}
+
 JNIEXPORT jintArray JNICALL Java_de_kherud_llama_LlamaModel_encode(JNIEnv *env, jobject obj, jstring jprompt) {
     jlong server_handle = env->GetLongField(obj, f_model_pointer);
     auto *ctx_server = reinterpret_cast<server_context *>(server_handle); // NOLINT(*-no-int-to-ptr)