diff --git a/src/llama-kv-cache-recurrent.cpp b/src/llama-kv-cache-recurrent.cpp
index 8f6f120f6..917d2a60c 100644
--- a/src/llama-kv-cache-recurrent.cpp
+++ b/src/llama-kv-cache-recurrent.cpp
@@ -16,12 +16,13 @@
 //
 
 llama_kv_cache_recurrent::llama_kv_cache_recurrent(
-        const llama_model & model,
-                ggml_type   type_k,
-                ggml_type   type_v,
-                     bool   offload,
-                 uint32_t   kv_size,
-                 uint32_t   n_seq_max) : hparams(model.hparams), n_seq_max(n_seq_max) {
+        const llama_model &  model,
+          layer_filter_cb && filter,
+                ggml_type    type_k,
+                ggml_type    type_v,
+                     bool    offload,
+                 uint32_t    kv_size,
+                 uint32_t    n_seq_max) : hparams(model.hparams), n_seq_max(n_seq_max) {
     const int32_t n_layer = hparams.n_layer;
 
     LLAMA_LOG_INFO("%s: kv_size = %u, n_seq_max = %u, type_k = '%s', type_v = '%s', n_layer = %d\n",
@@ -63,6 +64,11 @@ llama_kv_cache_recurrent::llama_kv_cache_recurrent(
     v_l.reserve(n_layer);
 
     for (int i = 0; i < n_layer; i++) {
+        if (filter && !filter(i)) {
+            LLAMA_LOG_DEBUG("%s: layer %3d: skipped\n", __func__, i);
+            continue;
+        }
+
         const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(i) + hparams.n_embd_k_s();
         const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(i) + hparams.n_embd_v_s();
 
@@ -88,8 +94,8 @@ llama_kv_cache_recurrent::llama_kv_cache_recurrent(
         ggml_tensor * v = ggml_new_tensor_1d(ctx, type_v, n_embd_v_gqa*kv_size);
         ggml_format_name(k, "cache_k_l%d", i);
         ggml_format_name(v, "cache_v_l%d", i);
-        k_l.push_back(k);
-        v_l.push_back(v);
+        k_l[i] = k;
+        v_l[i] = v;
     }
 
     // allocate tensors and initialize the buffers to avoid NaNs in the padding
diff --git a/src/llama-kv-cache-recurrent.h b/src/llama-kv-cache-recurrent.h
index f9b01a651..b89590b7b 100644
--- a/src/llama-kv-cache-recurrent.h
+++ b/src/llama-kv-cache-recurrent.h
@@ -15,13 +15,18 @@
 //       see the implementation of llama_kv_cache_unified_state_i for an example how to do it
 class llama_kv_cache_recurrent : public llama_memory_i {
 public:
+
+    // this callback is used to filter out layers that should not be included in the cache
+    using layer_filter_cb = std::function<bool(int32_t il)>;
+
     llama_kv_cache_recurrent(
-            const llama_model & model,
-                    ggml_type   type_k,
-                    ggml_type   type_v,
-                         bool   offload,
-                     uint32_t   kv_size,
-                     uint32_t   n_seq_max);
+            const llama_model &  model,
+              layer_filter_cb && filter,
+                    ggml_type    type_k,
+                    ggml_type    type_v,
+                         bool    offload,
+                     uint32_t    kv_size,
+                     uint32_t    n_seq_max);
 
     ~llama_kv_cache_recurrent() = default;
 
diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index a49a2c09f..c82cdc627 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -13759,6 +13759,7 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
             {
                 res = new llama_kv_cache_recurrent(
                         *this,
+                        nullptr,
                         GGML_TYPE_F32,
                         GGML_TYPE_F32,
                         cparams.offload_kqv,