[Doc] Add README.md for LXM Service API and example

Tizen.platform/lxm_service/README.md

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+---
+title: On device Machine LXM(LLM, LVM, etc.) Service API example
+...
+
+## On device Machine LXM(LLM, LVM, etc.) Service API example
+
+This example demonstrates how to implement a Large Language Model (LLM) service using `ml-lxm-service`(unreleased).
+
+## Overview
+The sample application provides an interactive CLI(Command Line Interface) to:
+- Create/destroy LXM sessions
+- Set instructions
+- Create/destroy prompts
+- Append text/instructions to prompts
+- Generate token-streamed responses
+
+
+
+## Prerequisites
+- `ml-api-service` and `flare` development packages installed on your target device
+
+## Building the Example
+### Build
+```
+meson setup build --prefix=${NNST_ROOT} --libdir=lib --bindir=bin --includedir=include
+ninja -C build install
+```
+### For rpm
+```
+./gen-ml-lxm-service-rpm.sh
+```
+### Run
+```
+cp config.conf tokenlizer.json sflare_if_4bit_3b.bin ${PROJECT}/
+./build/ml-lxm-service-example
+```
+
+
+## API Reference
+
+#### LXM service availability status.
+```cpp
+typedef enum
+{
+  ML_LXM_AVAILABILITY_AVAILABLE = 0,
+  ML_LXM_AVAILABILITY_DEVICE_NOT_ELIGIBLE,
+  ML_LXM_AVAILABILITY_SERVICE_DISABLED,
+  ML_LXM_AVAILABILITY_MODEL_NOT_READY,
+  ML_LXM_AVAILABILITY_UNKNOWN
+} ml_lxm_availability_e;
+```
+#### Availability Check
+```cpp
+/**
+ * @brief Checks LXM service availability.
+ * @param[out] status Current availability status.
+ * @return ML_ERROR_NONE on success, error code otherwise.
+ */
+int ml_lxm_check_availability (ml_lxm_availability_e * status);
+```
+
+### Data Type
+```cpp
+/**
+ * @brief Token streaming callback type.
+ * @param token Generated token string.
+ * @param user_data User-defined context.
+ */
+typedef void (*ml_lxm_token_cb)(ml_service_event_e event, ml_information_h event_data, void *user_data);
+
+/**
+ * @brief Generation options for LXM responses.
+ */
+typedef struct {
+  double temperature;
+  size_t max_tokens;
+} ml_lxm_generation_options_s;
+```
+#### Session Management
+```cpp
+/**
+ * @brief Creates an LXM session.
+ * @param[out] session Session handle.
+ * @param[in] config_path Path to configuration file.
+ * @param[in] instructions Initial instructions (optional).
+ * @return ML_ERROR_NONE on success.
+ */
+int ml_lxm_session_create(ml_lxm_session_h *session, const char *config_path, const char *instructions);
+
+/**
+ * @brief Destroys an LXM session.
+ * @param[in] session Session handle.
+ * @return ML_ERROR_NONE on success.
+ */
+int ml_lxm_session_destroy(ml_lxm_session_h session);
+
+/**
+ * @brief Sets runtime instructions for a session.
+ * @param[in] session Session handle.
+ * @param[in] instructions New instructions.
+ * @return ML_ERROR_NONE on success.
+ */
+int ml_lxm_session_set_instructions(ml_lxm_session_h session, const char *instructions);
+```
+
+#### Prompt Handling
+```cpp
+/**
+ * @brief Creates a prompt object.
+ * @param[out] prompt Prompt handle.
+ * @return ML_ERROR_NONE on success.
+ */
+int ml_lxm_prompt_create(ml_lxm_prompt_h *prompt);
+
+/**
+ * @brief Destroys a prompt object.
+ * @param[in] prompt Prompt handle.
+ * @return ML_ERROR_NONE on success.
+ */
+int ml_lxm_prompt_destroy(ml_lxm_prompt_h prompt);
+
+/**
+ * @brief Appends text to a prompt.
+ * @param[in] prompt Prompt handle.
+ * @param[in] text Text to append.
+ * @return ML_ERROR_NONE on success.
+ */
+int ml_lxm_prompt_append_text(ml_lxm_prompt_h prompt, const char *text);
+
+/**
+ * @brief Appends an instruction to a prompt.
+ * @param[in] prompt Prompt handle.
+ * @param[in] instruction Instruction to append.
+ * @return ML_ERROR_NONE on success.
+ */
+int ml_lxm_prompt_append_instruction(ml_lxm_prompt_h prompt, const char *instruction);
+```
+
+#### Response Generation
+```cpp
+/**
+ * @brief Generates an token-streamed response.
+ * @param[in] session Session handle.
+ * @param[in] prompt Prompt handle.
+ * @param[in] options Generation parameters.
+ * @param[in] token_callback Callback for each generated token.
+ * @param[in] user_data User context passed to callback.
+ * @return ML_ERROR_NONE on success.
+ */
+int ml_lxm_session_respond(
+  ml_lxm_session_h session,
+  ml_lxm_prompt_h prompt,
+  const ml_lxm_generation_options_s *options,
+  ml_lxm_token_cb token_cb,
+  void *user_data
+);
+```
+#### Error Codes
+- ML_ERROR_NONE: Operation successful
+- ML_ERROR_INVALID_PARAMETER: Invalid parameters detected
+- ML_ERROR_OUT_OF_MEMORY: Memory allocation failure
+- ML_ERROR_IO_ERROR: File/DB operation failure
+
+
+## Sample Code Explanation
+
+### Configuration file
+```
+{
+    "single" :
+    {
+        "framework" : "flare",
+        "model" : ["sflare_if_4bit_3b.bin"],
+        "adapter" : ["history_lora.bin"],
+        "custom" : "tokenizer_path:tokenizer.json,backend:CPU,output_size:1024,model_type:3B,data_type:W4A32",
+        "invoke_dynamic" : "true",
+    }
+}
+```
+
+### Key Components
+```cpp
+#include <ml-api-service.h>
+#include "ml-lxm-service.h"
+
+// Global handles
+ml_lxm_session_h g_session = NULL;
+ml_lxm_prompt_h g_prompt = NULL;
+
+```
+### Main Workflow
+1. Session Creation
+```cpp
+ret = ml_lxm_session_create(&g_session, config_path, "Default instructions");
+```
+2. Prompt Handling
+```cpp
+ml_lxm_prompt_create(&g_prompt);
+ml_lxm_prompt_append_text(g_prompt, "Explain quantum computing");
+```
+3. Response Generation
+```cpp
+ml_lxm_generation_options_s options = {
+  .temperature = 1.2,
+  .max_tokens = 128
+};
+
+ml_lxm_session_respond(
+  g_session,
+  g_prompt,
+  &options,
+  token_handler,
+  NULL
+);
+```
+4. Token Callback
+```cpp
+static void token_handler(
+  ml_service_event_e event,
+  ml_information_h event_data,
+  void *user_data
+) {
+  /* Process tokens here */
+}
+```
+5. Cleanup
+```cpp
+ml_lxm_prompt_destroy(g_prompt);
+ml_lxm_session_destroy(g_session);
+```
+### Full Example Snippet
+```cpp
+#include <ml-api-service.h>
+#include "ml-lxm-service.h"
+
+static void token_handler(ml_service_event_e event,
+                          ml_information_h event_data,
+                          void *user_data);
+int main() {
+  ml_lxm_session_h session;
+  ml_lxm_prompt_h prompt;
+
+  // 1. Create session
+  ml_lxm_session_create(&session, "/path/to/config", NULL);
+
+  // 2. Create prompt
+  ml_lxm_prompt_create(&prompt);
+  ml_lxm_prompt_append_text(prompt, "Hello AI");
+
+  // 3. Generate response
+  ml_lxm_generation_options_s options = {1.0, 50};
+  ml_lxm_session_respond(session, prompt, &options, token_handler, NULL);
+
+  // 4. Cleanup
+  ml_lxm_prompt_destroy(prompt);
+  ml_lxm_session_destroy(session);
+
+  return 0;
+}
+
+static void token_handler(ml_service_event_e event,
+                          ml_information_h event_data,
+                          void *user_data) {
+  ml_tensors_data_h data = NULL;
+  void *_raw = NULL;
+  size_t _size = 0;
+  int ret;
+
+  switch (event) {
+  case ML_SERVICE_EVENT_NEW_DATA:
+    if (event_data != NULL) {
+
+      ret = ml_information_get(event_data, "data", &data);
+      if (ret != ML_ERROR_NONE) {
+        g_print("Failed to get data from event_data\n");
+        return;
+      }
+
+      ret = ml_tensors_data_get_tensor_data(data, 0U, &_raw, &_size);
+      if (ret != ML_ERROR_NONE) {
+        g_print("Failed to get tensor data\n");
+        return;
+      }
+
+      std::cout.write(static_cast<const char *>(_raw), _size);
+      std::cout.flush();
+    }
+  default:
+    break;
+  }
+}
+```

Tizen.platform/lxm_service/config.conf

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+{
+    "single" :
+    {
+        "framework" : "flare",
+        "model" : ["sflare_if_4bit_3b.bin"],
+        "adapter" : ["history_lora.bin"],
+        "custom" : "tokenizer_path:tokenizer.json,backend:CPU,output_size:1024,model_type:3B,data_type:W4A32",
+        "invoke_dynamic" : "true",
+        "invoke_async" : "false"
+    }
+}