diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index d2abee864..66632a4ed 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -395,7 +395,7 @@ struct llama_client_slot
     }
 
     bool isProcessing() {
-        return (state == IDLE || state == SLEEPING) && command == LOAD_PROMPT || state == PROCESSING;
+        return ((state == IDLE || state == SLEEPING) && command == LOAD_PROMPT) || state == PROCESSING;
     }
 
     completion_token_output next() {
@@ -1041,26 +1041,22 @@ struct llama_server_context
                         //if input prompt is too big, truncate like normal
                         if (slot.num_prompt_tokens >= (size_t)n_ctx)
                         {
-                            const int n_left = (n_ctx - params.n_keep) / 2;
+                            const int n_left = n_ctx - params.n_keep;
                             std::vector<llama_token> new_tokens(prompt_tokens.begin(), prompt_tokens.begin() + params.n_keep);
-                            const int erased_blocks = (slot.num_prompt_tokens - params.n_keep - n_left - 1) / n_left;
-                            new_tokens.insert(new_tokens.end(), prompt_tokens.begin() + params.n_keep + erased_blocks * n_left, prompt_tokens.end());
-                            std::copy(prompt_tokens.end() - n_ctx, prompt_tokens.end(), slot.last_n_tokens.begin());
-
+                            // Use half the left-over space in the context for the prompt
+                            new_tokens.insert(new_tokens.end(), prompt_tokens.end() - n_left / 2, prompt_tokens.end());
                             LOG_VERBOSE("input truncated", {
                                                             {"n_ctx", n_ctx},
                                                             {"n_keep", params.n_keep},
                                                             {"n_left", n_left},
                                                             {"new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend())},
                                                         });
-
                             slot.truncated = true;
                             prompt_tokens = new_tokens;
-                        } else {
-                            const size_t ps = slot.num_prompt_tokens;
-                            std::fill(slot.last_n_tokens.begin(), slot.last_n_tokens.end()  - ps, 0);
-                            std::copy(prompt_tokens.begin(), prompt_tokens.end(), slot.last_n_tokens.end() - ps);
                         }
+                        const size_t ps = slot.num_prompt_tokens;
+                        std::fill(slot.last_n_tokens.begin(), slot.last_n_tokens.end()  - ps, 0);
+                        std::copy(prompt_tokens.begin(), prompt_tokens.end(), slot.last_n_tokens.end() - ps);
                     }
 
                     llama_kv_cache_seq_rm(ctx, slot.id, num_tokens_system + slot.n_past, -1);
@@ -1925,7 +1921,7 @@ static void beam_search_callback(void *callback_data, llama_beams_state beams_st
         llama.slot->generated_token_probs.resize(llama.slot->generated_token_probs.size() + n);
         assert(0u < beams_state.n_beams);
         const llama_token * tokens = beams_state.beam_views[0].tokens;
-        const auto map = [](llama_token tok) { return completion_token_output{{},tok}; };
+        const auto map = [](llama_token tok) { return completion_token_output{{},tok,""}; };
         std::transform(tokens, tokens + n, llama.slot->generated_token_probs.end() - n, map);
         printf("%zu", n);
     }