diff --git a/backends/qualcomm/CMakeLists.txt b/backends/qualcomm/CMakeLists.txt
index e93731e..4951e1d 100644
--- a/backends/qualcomm/CMakeLists.txt
+++ b/backends/qualcomm/CMakeLists.txt
@@ -308,8 +308,8 @@ if(${CMAKE_SYSTEM_PROCESSOR} MATCHES Hexagon)
   )
 endif()
 
-# QNN pybind
-if(${CMAKE_SYSTEM_PROCESSOR} MATCHES "x86_64")
+# QNN pybind — host Python bindings, not for Android cross-compile
+if(${CMAKE_SYSTEM_PROCESSOR} MATCHES "x86_64" AND NOT ANDROID)
   add_subdirectory(
     ${EXECUTORCH_SOURCE_DIR}/third-party/pybind11
     ${CMAKE_CURRENT_BINARY_DIR}/pybind11
diff --git a/extension/android/jni/jni_layer_llama.cpp b/extension/android/jni/jni_layer_llama.cpp
index 45f2414..7c4e1aa 100644
--- a/extension/android/jni/jni_layer_llama.cpp
+++ b/extension/android/jni/jni_layer_llama.cpp
@@ -171,14 +171,35 @@ class ExecuTorchLlmJni : public facebook::jni::HybridClass<ExecuTorchLlmJni> {
           model_path->toStdString().c_str(),
           data_files_vector,
           executorch::extension::Module::LoadMode::MmapUseMlockIgnoreErrors);
-      std::string decoder_model = "llama3"; // use llama3 for now
-      runner_ = std::make_unique<example::Runner<uint16_t>>( // QNN runner
-          std::move(module),
-          decoder_model.c_str(),
-          model_path->toStdString().c_str(),
-          tokenizer_path->toStdString().c_str(),
-          "",
-          "");
+      std::string decoder_model = "qwen3"; // Kazeia: our .pte was exported with --decoder_model qwen3-4b
+
+      // Mirror qnn_llama_runner.cpp main(): pick the Runner<T> template based
+      // on the model's get_kv_io_bit_width metadata. The 16-bit KV I/O models
+      // were introduced after the 8-bit ones, and using the wrong T treats
+      // KV-cache bytes as the wrong width → garbage logits → gibberish output.
+      example::KvBitWidth kv_bitwidth = example::KvBitWidth::kWidth8;
+      if (module->method_names()->count("get_kv_io_bit_width") > 0) {
+        kv_bitwidth = static_cast<example::KvBitWidth>(
+            module->get("get_kv_io_bit_width").get().toScalar().to<int64_t>());
+      }
+      auto make_runner = [&](auto sample) -> std::unique_ptr<llm::IRunner> {
+        using T = decltype(sample);
+        return std::make_unique<example::Runner<T>>(
+            std::move(module),
+            decoder_model.c_str(),
+            model_path->toStdString().c_str(),
+            tokenizer_path->toStdString().c_str(),
+            /* performance_output_path */ "",
+            /* dump_logits_path */ "",
+            /* temperature */ 0.0f, // greedy
+            /* eval_mode */ 0, // EvalMode::kKVCached
+            /* shared_buffer */ true);
+      };
+      if (kv_bitwidth == example::KvBitWidth::kWidth16) {
+        runner_ = make_runner(uint16_t{0});
+      } else {
+        runner_ = make_runner(uint8_t{0});
+      }
       model_type_category_ = MODEL_TYPE_CATEGORY_LLM;
 #endif
 #if defined(EXECUTORCH_BUILD_MEDIATEK)