diff --git a/CMakeLists.txt b/CMakeLists.txt
index 4f6cd687..b8c6896b 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -189,3 +189,4 @@ if (LLAMA_BUILD_EXAMPLES)
     add_subdirectory(examples)
     add_subdirectory(pocs)
 endif()
+add_subdirectory(../ext_server ext_server) # ollama
diff --git a/src/llama.cpp b/src/llama.cpp
index 2b9ace28..b0151571 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -18609,6 +18609,20 @@ static int llama_apply_lora_from_file_internal(
             return 1;
         }
 
+        // show tensor data
+        auto show_tensor = [](std::string name, ggml_tensor *t) {
+            LLAMA_LOG_INFO("%s\n", name.c_str());
+
+            for(int i=0; i<3; i++) {
+                for(int j=0; j<3; j++) {
+                    float v = ggml_get_f32_nd(t, i, j, 0, 0);
+                    LLAMA_LOG_INFO("%.8f ", v);
+                }
+                LLAMA_LOG_INFO(" ...\n");
+            }
+            LLAMA_LOG_INFO(" ...\n");
+        };
+
         // load tensor data
         auto load_tensor = [&read_buf, &fin](const tensor_meta & tensor_meta, ggml_tensor * tensor) {
             read_buf.resize(ggml_nbytes(tensor));
@@ -18619,6 +18633,9 @@ static int llama_apply_lora_from_file_internal(
         load_tensor(metaA, loraA);
         load_tensor(metaB, loraB);
 
+        show_tensor(base_name + ".loraA", loraA);
+        show_tensor(base_name + ".loraB", loraB);
+
         // load base model tensor data
         if (ml) {
             ml->load_data_for(base_t);
@@ -18633,8 +18650,10 @@ static int llama_apply_lora_from_file_internal(
         }
 
         if (base_t->ne[0] != loraA->ne[1] || base_t->ne[1] != loraB->ne[1]) {
-            LLAMA_LOG_ERROR("%s: incompatible tensor dimensions (%" PRId64 " and %" PRId64 ");"
-                            " are you sure that this adapter is for this model?\n", __func__, base_t->ne[0], loraA->ne[1]);
+            LLAMA_LOG_ERROR("%s: incompatible tensors: base [%lld, %lld] loraA [%lld, %lld] loraB [%lld, %lld]\n", __func__,
+                            base_t->ne[0], base_t->ne[1],
+                            loraA->ne[0], loraA->ne[1],
+                            loraB->ne[0], loraB->ne[1]);
             ggml_free(lora_ctx);
             ggml_backend_buffer_free(lora_buf);
             ggml_backend_free(backend_cpu);
@@ -18643,14 +18662,18 @@ static int llama_apply_lora_from_file_internal(
 
         auto build_lora_graph = [&]() {
             // w = w + BA*s
-            ggml_tensor * BA = ggml_mul_mat(lora_ctx, loraA, loraB);
+            ggml_tensor * BA = ggml_mul_mat(lora_ctx, loraB, loraA);
             ggml_set_name(BA, "BA");
 
             if (scaling != 1.0f) {
-                BA = ggml_scale(lora_ctx, BA, scaling);
+                //BA = ggml_scale(lora_ctx, BA, scaling);
+                BA = ggml_scale(lora_ctx, BA, 20.0);
                 ggml_set_name(BA, "BA_scaled");
             }
 
+            // transpose matrix before we add
+            BA = ggml_cont(lora_ctx, ggml_transpose(lora_ctx, BA));
+
             ggml_tensor * r;
             r = ggml_add_inplace(lora_ctx, base_t, BA);
             ggml_set_name(r, "r_add");