diff --git a/llama.cpp b/llama.cpp
index fe24f5096..9b17bf347 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -13344,8 +13344,8 @@ static void llama_copy_state_data_internal(struct llama_context * ctx, llama_dat
         const auto & hparams = ctx->model.hparams;
 
         const uint32_t n_layer      = hparams.n_layer;
-        const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa();
-        const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa();
+        const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa() + hparams.n_embd_k_s();
+        const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa() + hparams.n_embd_v_s();
 
         const size_t   kv_buf_size = kv_self.total_size();
         const uint32_t kv_head     = llama_kv_cache_cell_max(kv_self);
@@ -13366,6 +13366,17 @@ static void llama_copy_state_data_internal(struct llama_context * ctx, llama_dat
                 ggml_backend_tensor_get(kv_self.k_l[il], tmp_buf.data(), 0, tmp_buf.size());
                 data_ctx->write(tmp_buf.data(), tmp_buf.size());
 
+                if (kv_self.recurrent) {
+                    // v is contiguous for recurrent models
+                    // TODO: use other tensors for state models than k and v
+                    const size_t v_size = ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa*kv_head);
+
+                    tmp_buf.resize(v_size);
+                    ggml_backend_tensor_get(kv_self.v_l[il], tmp_buf.data(), 0, tmp_buf.size());
+                    data_ctx->write(tmp_buf.data(), tmp_buf.size());
+                    continue;
+                }
+
                 // v is not contiguous, copy row by row
                 const size_t v_row_size   = ggml_row_size(kv_self.v_l[il]->type, kv_head);
                 const size_t v_row_stride = ggml_row_size(kv_self.v_l[il]->type, kv_size);
@@ -13456,8 +13467,8 @@ size_t llama_set_state_data(struct llama_context * ctx, const uint8_t * src) {
         const auto & hparams = ctx->model.hparams;
 
         const uint32_t n_layer      = hparams.n_layer;
-        const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa();
-        const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa();
+        const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa() + hparams.n_embd_k_s();
+        const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa() + hparams.n_embd_v_s();
 
         size_t   kv_buf_size;
         uint32_t kv_head;
@@ -13478,6 +13489,16 @@ size_t llama_set_state_data(struct llama_context * ctx, const uint8_t * src) {
                 ggml_backend_tensor_set(kv_self.k_l[il], inp, 0, k_size);
                 inp += k_size;
 
+                if (kv_self.recurrent) {
+                    // v is contiguous for recurrent models
+                    // TODO: use other tensors for state models than k and v
+                    const size_t v_size = ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa*kv_head);
+
+                    ggml_backend_tensor_set(kv_self.v_l[il], inp, 0, v_size);
+                    inp += v_size;
+                    continue;
+                }
+
                 // v is not contiguous, copy row by row
                 const size_t v_row_size   = ggml_row_size(kv_self.v_l[il]->type, kv_head);
                 const size_t v_row_stride = ggml_row_size(kv_self.v_l[il]->type, kv_size);