diff --git a/examples/llama.android/app/src/main/java/com/example/llama/revamp/engine/InferenceEngine.kt b/examples/llama.android/app/src/main/java/com/example/llama/revamp/engine/InferenceEngine.kt
new file mode 100644
index 0000000000..a7e5a9bcf9
--- /dev/null
+++ b/examples/llama.android/app/src/main/java/com/example/llama/revamp/engine/InferenceEngine.kt
@@ -0,0 +1,159 @@
+package com.example.llama.revamp.engine
+
+import kotlinx.coroutines.delay
+import kotlinx.coroutines.flow.Flow
+import kotlinx.coroutines.flow.MutableStateFlow
+import kotlinx.coroutines.flow.StateFlow
+import kotlinx.coroutines.flow.flow
+
+/**
+ * LLM inference engine that handles model loading and text generation.
+ */
+class InferenceEngine {
+    companion object {
+        const val DEFAULT_PREDICT_LENGTH = 1024
+    }
+
+    sealed class State {
+        object Uninitialized : State()
+        object LibraryLoaded : State()
+
+        object LoadingModel : State()
+        object ModelLoaded : State()
+
+        object ProcessingSystemPrompt : State()
+        object AwaitingUserPrompt : State()
+
+        object ProcessingUserPrompt : State()
+        object Generating : State()
+
+        object Benchmarking : State()
+
+        data class Error(
+            val errorMessage: String = ""
+        ) : State()
+    }
+
+    private val _state = MutableStateFlow<State>(State.Uninitialized)
+    val state: StateFlow<State> = _state
+
+    // Keep track of current benchmark results
+    private var _benchmarkResults: String? = null
+    val benchmarkResults: StateFlow<String?> = MutableStateFlow(_benchmarkResults)
+
+    init {
+        // Simulate library loading
+        _state.value = State.LibraryLoaded
+    }
+
+    /**
+     * Loads a model from the given path with an optional system prompt.
+     */
+    suspend fun loadModel(pathToModel: String, systemPrompt: String? = null) {
+        try {
+            _state.value = State.LoadingModel
+
+            // Simulate model loading
+            delay(1000)
+
+            _state.value = State.ModelLoaded
+
+            if (systemPrompt != null) {
+                _state.value = State.ProcessingSystemPrompt
+
+                // Simulate processing system prompt
+                delay(500)
+            }
+
+            _state.value = State.AwaitingUserPrompt
+        } catch (e: Exception) {
+            _state.value = State.Error(e.message ?: "Unknown error during model loading")
+        }
+    }
+
+    /**
+     * Sends a user prompt to the loaded model and returns a Flow of generated tokens.
+     */
+    fun sendUserPrompt(message: String, predictLength: Int = DEFAULT_PREDICT_LENGTH): Flow<String> {
+        _state.value = State.ProcessingUserPrompt
+
+        // This would be replaced with actual token generation logic
+        return flow {
+            delay(500) // Simulate processing time
+
+            _state.value = State.Generating
+
+            // Simulate token generation
+            val response =
+                "This is a simulated response from the LLM model. The actual implementation would generate tokens one by one based on the input: $message"
+            val words = response.split(" ")
+
+            for (word in words) {
+                emit(word + " ")
+                delay(50) // Simulate token generation delay
+            }
+
+            _state.value = State.AwaitingUserPrompt
+        }
+    }
+
+    /**
+     * Runs a benchmark with the specified parameters.
+     */
+    suspend fun bench(pp: Int, tg: Int, pl: Int, nr: Int = 1): String {
+        _state.value = State.Benchmarking
+
+        try {
+            // Simulate benchmark running
+            delay(2000)
+
+            // Generate fake benchmark results
+            val modelDesc = "LlamaModel"
+            val model_size = "7"
+            val model_n_params = "7"
+            val backend = "CPU"
+
+            // Random values for benchmarks
+            val pp_avg = (15.0 + Math.random() * 10.0).toFloat()
+            val pp_std = (0.5 + Math.random() * 2.0).toFloat()
+            val tg_avg = (20.0 + Math.random() * 15.0).toFloat()
+            val tg_std = (0.7 + Math.random() * 3.0).toFloat()
+
+            val result = StringBuilder()
+            result.append("| model | size | params | backend | test | t/s |\n")
+            result.append("| --- | --- | --- | --- | --- | --- |\n")
+            result.append("| $modelDesc | ${model_size}GiB | ${model_n_params}B | ")
+            result.append("$backend | pp $pp | $pp_avg ± $pp_std |\n")
+            result.append("| $modelDesc | ${model_size}GiB | ${model_n_params}B | ")
+            result.append("$backend | tg $tg | $tg_avg ± $tg_std |\n")
+
+            _benchmarkResults = result.toString()
+            (benchmarkResults as MutableStateFlow).value = _benchmarkResults
+
+            _state.value = State.AwaitingUserPrompt
+
+            return _benchmarkResults ?: ""
+        } catch (e: Exception) {
+            _state.value = State.Error(e.message ?: "Unknown error during benchmarking")
+            return "Error: ${e.message}"
+        }
+    }
+
+    /**
+     * Unloads the currently loaded model.
+     */
+    suspend fun unloadModel() {
+        // Simulate model unloading time
+        delay(300)
+        _state.value = State.LibraryLoaded
+        _benchmarkResults = null
+        (benchmarkResults as MutableStateFlow).value = null
+    }
+
+    /**
+     * Cleans up resources when the engine is no longer needed.
+     */
+    fun destroy() {
+        // In a real implementation, this would release native resources
+    }
+}