diff --git a/llama.cpp b/llama.cpp
index 6bd7d7d15..ef02ba740 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -6336,7 +6336,7 @@ struct llm_build_context {
                     cur_gate = ggml_silu(ctx0, cur_gate);
                     cb(cur_gate, "ffn_moe_silu", il);
 
-                    cur_expert = ggml_mul(ctx0, cur_up, cur_gate); // [n_tokens, n_embd]
+                    cur_expert = ggml_mul(ctx0, cur_up, cur_gate);
                     cb(cur_expert, "ffn_moe_gate_par", il);
 
                     cur_expert = ggml_mul_mat_id(ctx0, model.layers[il].ffn_down_exps, selected_experts, i, cur_expert); // [n_tokens, n_embd]
@@ -6871,7 +6871,7 @@ struct llm_build_context {
                 cur_gate = ggml_gelu(ctx0, cur_gate);
                 cb(cur_gate, "ffn_moe_gelu", il);
 
-                cur_expert = ggml_mul(ctx0, cur_up, cur_gate); // [n_tokens, n_embd]
+                cur_expert = ggml_mul(ctx0, cur_up, cur_gate);
                 cb(cur_expert, "ffn_moe_gate_par", il);
 
                 cur_expert = ggml_mul_mat_id(ctx0, model.layers[il].ffn_down_exps, selected_experts, i, cur_expert); // [n_tokens, n_embd]
@@ -12945,9 +12945,6 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
             // sprinkled in the model. Hence, simply dividing i_ffn_down by n_expert does not work
             // for getting the current layer as I initially thought, and we need to resort to parsing the
             // tensor name.
-
-            // hack
-            //n_layer /= n_expert;
             if (sscanf(name, "blk.%d.", &i_layer) != 1) {
                 throw std::runtime_error(format("Failed to determine layer for tensor %s", name));
             }
@@ -13371,10 +13368,19 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
             qs.has_output = true;
         }
     }
-    if (qs.n_attention_wv != qs.n_ffn_down || (uint32_t) qs.n_attention_wv != model.hparams.n_layer) {
-        LLAMA_LOG_WARN("%s ============ Strange model: n_attention_wv = %d, n_ffn_down = %d, hparams.n_layer = %d\n",
-                __func__, qs.n_attention_wv, qs.n_ffn_down, model.hparams.n_layer);
-    }
+    // REVIEW: i do not undertand why there is logic for counting the number of layers by counting the number of tensors
+    // instead of just using the n_layer metadata
+    // without this change, it would require different logic for merged experts and split experts models,
+    // as split expert models end with a ffn_* count n_expert times higher than the real number of layers,
+    // which then is corrected in layer_info by dividing the value by n_expert
+    // this code needs to be refactored
+    qs.n_ffn_down = qs.n_ffn_gate = qs.n_ffn_up = (int)model.hparams.n_layer;
+
+    //if (qs.n_ffn_down )
+    //if (qs.n_attention_wv != qs.n_ffn_down || (uint32_t) qs.n_attention_wv != model.hparams.n_layer) {
+    //    LLAMA_LOG_WARN("%s ============ Strange model: n_attention_wv = %d, n_ffn_down = %d, hparams.n_layer = %d\n",
+    //            __func__, qs.n_attention_wv, qs.n_ffn_down, model.hparams.n_layer);
+    //}
 
     size_t total_size_org = 0;
     size_t total_size_new = 0;