diff --git a/llama.cpp b/llama.cpp
index 46c82b4ad..0effc6db3 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -1709,6 +1709,14 @@ struct llama_kv_cell {
     bool has_seq_id(const llama_seq_id & id) const {
         return seq_id.find(id) != seq_id.end();
     }
+
+    bool is_empty() const {
+        return seq_id.empty();
+    }
+
+    bool is_same_seq(const llama_kv_cell & other) const {
+        return seq_id == other.seq_id;
+    }
 };
 
 // ring-buffer of cached KV data
@@ -2101,7 +2109,7 @@ static bool llama_kv_cache_find_slot(
 // find how many cells are currently in use
 static int32_t llama_kv_cache_cell_max(const struct llama_kv_cache & cache) {
     for (uint32_t i = cache.size - 1; i > 0; --i) {
-        if (cache.cells[i].pos >= 0 && !cache.cells[i].seq_id.empty()) {
+        if (cache.cells[i].pos >= 0 && !cache.cells[i].is_empty()) {
             return i + 1;
         }
     }
@@ -2137,7 +2145,7 @@ static void llama_kv_cache_seq_rm(
             } else {
                 continue;
             }
-            if (cache.cells[i].seq_id.empty()) {
+            if (cache.cells[i].is_empty()) {
                 // keep count of the number of used cells
                 if (cache.cells[i].pos >= 0) cache.used--;
 
@@ -2206,10 +2214,14 @@ static void llama_kv_cache_seq_add(
             cache.cells[i].delta += delta;
 
             if (cache.cells[i].pos < 0) {
-                if (!cache.cells[i].seq_id.empty()) cache.used--;
+                if (!cache.cells[i].is_empty()) {
+                    cache.used--;
+                }
                 cache.cells[i].pos = -1;
                 cache.cells[i].seq_id.clear();
-                if (new_head == cache.size) new_head = i;
+                if (new_head == cache.size) {
+                    new_head = i;
+                }
             }
         }
     }
@@ -11618,8 +11630,7 @@ struct llama_context * llama_new_context_with_model(
         }
         ctx->backends.push_back(ctx->backend_cpu);
 
-        if (!llama_kv_cache_init(ctx->kv_self, ctx->model, type_k, type_v,
-                cparams.n_ctx, cparams.offload_kqv)) {
+        if (!llama_kv_cache_init(ctx->kv_self, ctx->model, type_k, type_v, cparams.n_ctx, cparams.offload_kqv)) {
             LLAMA_LOG_ERROR("%s: llama_kv_cache_init() failed for self-attention cache\n", __func__);
             llama_free(ctx);
             return nullptr;
@@ -12203,10 +12214,10 @@ static void llama_copy_state_data_internal(struct llama_context * ctx, llama_dat
         const auto & hparams = ctx->model.hparams;
         const auto & cparams = ctx->cparams;
 
-        const auto   n_layer      = hparams.n_layer;
-        const auto   n_embd_k_gqa = hparams.n_embd_k_gqa();
-        const auto   n_embd_v_gqa = hparams.n_embd_v_gqa();
-        const auto   n_ctx        = cparams.n_ctx;
+        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_ctx        = cparams.n_ctx;
 
         const size_t   kv_buf_size = kv_self.total_size();
         const uint32_t kv_head     = kv_self.head;
@@ -12221,14 +12232,16 @@ static void llama_copy_state_data_internal(struct llama_context * ctx, llama_dat
         if (kv_buf_size) {
             std::vector<uint8_t> tmp_buf;
             for (int il = 0; il < (int) n_layer; ++il) {
-                size_t k_size = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa*kv_head);
+                const size_t k_size = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa*kv_head);
+
                 tmp_buf.resize(k_size);
                 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());
 
                 // v is not contiguous, copy row by row
-                size_t v_row_size = ggml_row_size(kv_self.v_l[il]->type, kv_head);
-                size_t v_row_stride = ggml_row_size(kv_self.v_l[il]->type, n_ctx);
+                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, n_ctx);
+
                 tmp_buf.resize(v_row_size);
                 for (int ir = 0; ir < (int) n_embd_v_gqa; ++ir) {
                     ggml_backend_tensor_get(kv_self.v_l[il], tmp_buf.data(), ir*v_row_stride, tmp_buf.size());
@@ -12315,10 +12328,10 @@ size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src) {
         const auto & hparams = ctx->model.hparams;
         const auto & cparams = ctx->cparams;
 
-        const int    n_layer      = hparams.n_layer;
-        const int    n_embd_k_gqa = hparams.n_embd_k_gqa();
-        const int    n_embd_v_gqa = hparams.n_embd_v_gqa();
-        const int    n_ctx        = cparams.n_ctx;
+        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_ctx        = cparams.n_ctx;
 
         size_t   kv_buf_size;
         uint32_t kv_head;
@@ -12334,13 +12347,15 @@ size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src) {
             GGML_ASSERT(kv_self.total_size() == kv_buf_size);
 
             for (int il = 0; il < (int) n_layer; ++il) {
-                size_t k_size = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa*kv_head);
+                const size_t k_size = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa*kv_head);
+
                 ggml_backend_tensor_set(kv_self.k_l[il], inp, 0, k_size);
                 inp += k_size;
 
                 // v is not contiguous, copy row by row
-                size_t v_row_size = ggml_row_size(kv_self.v_l[il]->type, kv_head);
-                size_t v_row_stride = ggml_row_size(kv_self.v_l[il]->type, n_ctx);
+                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, n_ctx);
+
                 for (int ir = 0; ir < (int) n_embd_v_gqa; ++ir) {
                     ggml_backend_tensor_set(kv_self.v_l[il], inp, ir*v_row_stride, v_row_size);
                     inp += v_row_size;