diff --git a/common/arg.cpp b/common/arg.cpp
index a091c888d..024270342 100644
--- a/common/arg.cpp
+++ b/common/arg.cpp
@@ -120,32 +120,6 @@ std::string common_arg::to_string() {
 // utils
 //
 
-#ifdef __GNUC__
-#ifdef __MINGW32__
-#define LLAMA_COMMON_ATTRIBUTE_FORMAT(...) __attribute__((format(gnu_printf, __VA_ARGS__)))
-#else
-#define LLAMA_COMMON_ATTRIBUTE_FORMAT(...) __attribute__((format(printf, __VA_ARGS__)))
-#endif
-#else
-#define LLAMA_COMMON_ATTRIBUTE_FORMAT(...)
-#endif
-
-LLAMA_COMMON_ATTRIBUTE_FORMAT(1, 2)
-static std::string format(const char * fmt, ...) {
-    va_list ap;
-    va_list ap2;
-    va_start(ap, fmt);
-    va_copy(ap2, ap);
-    int size = vsnprintf(NULL, 0, fmt, ap);
-    GGML_ASSERT(size >= 0 && size < INT_MAX); // NOLINT
-    std::vector<char> buf(size + 1);
-    int size2 = vsnprintf(buf.data(), size + 1, fmt, ap2);
-    GGML_ASSERT(size2 == size);
-    va_end(ap2);
-    va_end(ap);
-    return std::string(buf.data(), size);
-}
-
 static void common_params_handle_model_default(common_params & params) {
     if (!params.hf_repo.empty()) {
         // short-hand to avoid specifying --hf-file -> default it to --model
@@ -200,7 +174,7 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
                     continue;
                 }
             } catch (std::exception & e) {
-                throw std::invalid_argument(format(
+                throw std::invalid_argument(string_format(
                     "error while handling environment variable \"%s\": %s\n\n", opt.env, e.what()));
             }
         }
@@ -221,7 +195,7 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
             std::replace(arg.begin(), arg.end(), '_', '-');
         }
         if (arg_to_options.find(arg) == arg_to_options.end()) {
-            throw std::invalid_argument(format("error: invalid argument: %s", arg.c_str()));
+            throw std::invalid_argument(string_format("error: invalid argument: %s", arg.c_str()));
         }
         auto opt = *arg_to_options[arg];
         if (opt.has_value_from_env()) {
@@ -253,7 +227,7 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
                 continue;
             }
         } catch (std::exception & e) {
-            throw std::invalid_argument(format(
+            throw std::invalid_argument(string_format(
                 "error while handling argument \"%s\": %s\n\n"
                 "usage:\n%s\n\nto show complete usage, run with -h",
                 arg.c_str(), e.what(), arg_to_options[arg]->to_string().c_str()));
@@ -392,28 +366,28 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ));
     add_opt(common_arg(
         {"--verbose-prompt"},
-        format("print a verbose prompt before generation (default: %s)", params.verbose_prompt ? "true" : "false"),
+        string_format("print a verbose prompt before generation (default: %s)", params.verbose_prompt ? "true" : "false"),
         [](common_params & params) {
             params.verbose_prompt = true;
         }
     ));
     add_opt(common_arg(
         {"--no-display-prompt"},
-        format("don't print prompt at generation (default: %s)", !params.display_prompt ? "true" : "false"),
+        string_format("don't print prompt at generation (default: %s)", !params.display_prompt ? "true" : "false"),
         [](common_params & params) {
             params.display_prompt = false;
         }
     ).set_examples({LLAMA_EXAMPLE_MAIN}));
     add_opt(common_arg(
         {"-co", "--color"},
-        format("colorise output to distinguish prompt and user input from generations (default: %s)", params.use_color ? "true" : "false"),
+        string_format("colorise output to distinguish prompt and user input from generations (default: %s)", params.use_color ? "true" : "false"),
         [](common_params & params) {
             params.use_color = true;
         }
     ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_INFILL, LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP}));
     add_opt(common_arg(
         {"-t", "--threads"}, "N",
-        format("number of threads to use during generation (default: %d)", params.cpuparams.n_threads),
+        string_format("number of threads to use during generation (default: %d)", params.cpuparams.n_threads),
         [](common_params & params, int value) {
             params.cpuparams.n_threads = value;
             if (params.cpuparams.n_threads <= 0) {
@@ -473,14 +447,14 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ));
     add_opt(common_arg(
         {"--cpu-strict"}, "<0|1>",
-        format("use strict CPU placement (default: %u)\n", (unsigned) params.cpuparams.strict_cpu),
+        string_format("use strict CPU placement (default: %u)\n", (unsigned) params.cpuparams.strict_cpu),
         [](common_params & params, const std::string & value) {
             params.cpuparams.strict_cpu = std::stoul(value);
         }
     ));
     add_opt(common_arg(
         {"--prio"}, "N",
-        format("set process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: %d)\n", params.cpuparams.priority),
+        string_format("set process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: %d)\n", params.cpuparams.priority),
         [](common_params & params, int prio) {
             if (prio < 0 || prio > 3) {
                 throw std::invalid_argument("invalid value");
@@ -490,7 +464,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ));
     add_opt(common_arg(
         {"--poll"}, "<0...100>",
-        format("use polling level to wait for work (0 - no polling, default: %u)\n", (unsigned) params.cpuparams.poll),
+        string_format("use polling level to wait for work (0 - no polling, default: %u)\n", (unsigned) params.cpuparams.poll),
         [](common_params & params, const std::string & value) {
             params.cpuparams.poll = std::stoul(value);
         }
@@ -524,7 +498,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ));
     add_opt(common_arg(
         {"--prio-batch"}, "N",
-        format("set process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: %d)\n", params.cpuparams_batch.priority),
+        string_format("set process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: %d)\n", params.cpuparams_batch.priority),
         [](common_params & params, int prio) {
             if (prio < 0 || prio > 3) {
                 throw std::invalid_argument("invalid value");
@@ -568,7 +542,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
     add_opt(common_arg(
         {"--prio-draft"}, "N",
-        format("set draft process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: %d)\n", params.draft_cpuparams.priority),
+        string_format("set draft process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: %d)\n", params.draft_cpuparams.priority),
         [](common_params & params, int prio) {
             if (prio < 0 || prio > 3) {
                 throw std::invalid_argument("invalid value");
@@ -612,7 +586,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
     add_opt(common_arg(
         {"--prio-batch-draft"}, "N",
-        format("set draft process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: %d)\n", params.draft_cpuparams_batch.priority),
+        string_format("set draft process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: %d)\n", params.draft_cpuparams_batch.priority),
         [](common_params & params, int prio) {
             if (prio < 0 || prio > 3) {
                 throw std::invalid_argument("invalid value");
@@ -629,14 +603,14 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
     add_opt(common_arg(
         {"--draft"}, "N",
-        format("number of tokens to draft for speculative decoding (default: %d)", params.n_draft),
+        string_format("number of tokens to draft for speculative decoding (default: %d)", params.n_draft),
         [](common_params & params, int value) {
             params.n_draft = value;
         }
     ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP}));
     add_opt(common_arg(
         {"-ps", "--p-split"}, "N",
-        format("speculative decoding split probability (default: %.1f)", (double)params.p_split),
+        string_format("speculative decoding split probability (default: %.1f)", (double)params.p_split),
         [](common_params & params, const std::string & value) {
             params.p_split = std::stof(value);
         }
@@ -657,56 +631,56 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_examples({LLAMA_EXAMPLE_LOOKUP}));
     add_opt(common_arg(
         {"-c", "--ctx-size"}, "N",
-        format("size of the prompt context (default: %d, 0 = loaded from model)", params.n_ctx),
+        string_format("size of the prompt context (default: %d, 0 = loaded from model)", params.n_ctx),
         [](common_params & params, int value) {
             params.n_ctx = value;
         }
     ).set_env("LLAMA_ARG_CTX_SIZE"));
     add_opt(common_arg(
         {"-n", "--predict", "--n-predict"}, "N",
-        format("number of tokens to predict (default: %d, -1 = infinity, -2 = until context filled)", params.n_predict),
+        string_format("number of tokens to predict (default: %d, -1 = infinity, -2 = until context filled)", params.n_predict),
         [](common_params & params, int value) {
             params.n_predict = value;
         }
     ).set_env("LLAMA_ARG_N_PREDICT"));
     add_opt(common_arg(
         {"-b", "--batch-size"}, "N",
-        format("logical maximum batch size (default: %d)", params.n_batch),
+        string_format("logical maximum batch size (default: %d)", params.n_batch),
         [](common_params & params, int value) {
             params.n_batch = value;
         }
     ).set_env("LLAMA_ARG_BATCH"));
     add_opt(common_arg(
         {"-ub", "--ubatch-size"}, "N",
-        format("physical maximum batch size (default: %d)", params.n_ubatch),
+        string_format("physical maximum batch size (default: %d)", params.n_ubatch),
         [](common_params & params, int value) {
             params.n_ubatch = value;
         }
     ).set_env("LLAMA_ARG_UBATCH"));
     add_opt(common_arg(
         {"--keep"}, "N",
-        format("number of tokens to keep from the initial prompt (default: %d, -1 = all)", params.n_keep),
+        string_format("number of tokens to keep from the initial prompt (default: %d, -1 = all)", params.n_keep),
         [](common_params & params, int value) {
             params.n_keep = value;
         }
     ));
     add_opt(common_arg(
         {"--no-context-shift"},
-        format("disables context shift on inifinite text generation (default: %s)", params.ctx_shift ? "disabled" : "enabled"),
+        string_format("disables context shift on inifinite text generation (default: %s)", params.ctx_shift ? "disabled" : "enabled"),
         [](common_params & params) {
             params.ctx_shift = false;
         }
     ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_NO_CONTEXT_SHIFT"));
     add_opt(common_arg(
         {"--chunks"}, "N",
-        format("max number of chunks to process (default: %d, -1 = all)", params.n_chunks),
+        string_format("max number of chunks to process (default: %d, -1 = all)", params.n_chunks),
         [](common_params & params, int value) {
             params.n_chunks = value;
         }
     ).set_examples({LLAMA_EXAMPLE_IMATRIX, LLAMA_EXAMPLE_PERPLEXITY, LLAMA_EXAMPLE_RETRIEVAL}));
     add_opt(common_arg(
         {"-fa", "--flash-attn"},
-        format("enable Flash Attention (default: %s)", params.flash_attn ? "enabled" : "disabled"),
+        string_format("enable Flash Attention (default: %s)", params.flash_attn ? "enabled" : "disabled"),
         [](common_params & params) {
             params.flash_attn = true;
         }
@@ -722,7 +696,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ));
     add_opt(common_arg(
         {"--no-perf"},
-        format("disable internal libllama performance timings (default: %s)", params.no_perf ? "true" : "false"),
+        string_format("disable internal libllama performance timings (default: %s)", params.no_perf ? "true" : "false"),
         [](common_params & params) {
             params.no_perf = true;
             params.sparams.no_perf = true;
@@ -734,7 +708,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params, const std::string & value) {
             std::ifstream file(value);
             if (!file) {
-                throw std::runtime_error(format("error: failed to open file '%s'\n", value.c_str()));
+                throw std::runtime_error(string_format("error: failed to open file '%s'\n", value.c_str()));
             }
             // store the external file name in params
             params.prompt_file = value;
@@ -750,7 +724,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params, const std::string & value) {
             std::ifstream file(value);
             if (!file) {
-                throw std::runtime_error(format("error: failed to open file '%s'\n", value.c_str()));
+                throw std::runtime_error(string_format("error: failed to open file '%s'\n", value.c_str()));
             }
             params.in_files.push_back(value);
         }
@@ -761,7 +735,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params, const std::string & value) {
             std::ifstream file(value, std::ios::binary);
             if (!file) {
-                throw std::runtime_error(format("error: failed to open file '%s'\n", value.c_str()));
+                throw std::runtime_error(string_format("error: failed to open file '%s'\n", value.c_str()));
             }
             // store the external file name in params
             params.prompt_file = value;
@@ -773,7 +747,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ));
     add_opt(common_arg(
         {"-e", "--escape"},
-        format("process escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\) (default: %s)", params.escape ? "true" : "false"),
+        string_format("process escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\) (default: %s)", params.escape ? "true" : "false"),
         [](common_params & params) {
             params.escape = true;
         }
@@ -787,7 +761,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ));
     add_opt(common_arg(
         {"-ptc", "--print-token-count"}, "N",
-        format("print token count every N tokens (default: %d)", params.n_print),
+        string_format("print token count every N tokens (default: %d)", params.n_print),
         [](common_params & params, int value) {
             params.n_print = value;
         }
@@ -822,14 +796,14 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_examples({LLAMA_EXAMPLE_MAIN}));
     add_opt(common_arg(
         {"-sp", "--special"},
-        format("special tokens output enabled (default: %s)", params.special ? "true" : "false"),
+        string_format("special tokens output enabled (default: %s)", params.special ? "true" : "false"),
         [](common_params & params) {
             params.special = true;
         }
     ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER}));
     add_opt(common_arg(
         {"-cnv", "--conversation"},
-        format(
+        string_format(
             "run in conversation mode:\n"
             "- does not print special tokens and suffix/prefix\n"
             "- interactive mode is also enabled\n"
@@ -842,14 +816,14 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_examples({LLAMA_EXAMPLE_MAIN}));
     add_opt(common_arg(
         {"-i", "--interactive"},
-        format("run in interactive mode (default: %s)", params.interactive ? "true" : "false"),
+        string_format("run in interactive mode (default: %s)", params.interactive ? "true" : "false"),
         [](common_params & params) {
             params.interactive = true;
         }
     ).set_examples({LLAMA_EXAMPLE_MAIN}));
     add_opt(common_arg(
         {"-if", "--interactive-first"},
-        format("run in interactive mode and wait for input right away (default: %s)", params.interactive_first ? "true" : "false"),
+        string_format("run in interactive mode and wait for input right away (default: %s)", params.interactive_first ? "true" : "false"),
         [](common_params & params) {
             params.interactive_first = true;
         }
@@ -894,7 +868,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_examples({LLAMA_EXAMPLE_MAIN}));
     add_opt(common_arg(
         {"--spm-infill"},
-        format(
+        string_format(
             "use Suffix/Prefix/Middle pattern for infill (instead of Prefix/Suffix/Middle) as some models prefer this. (default: %s)",
             params.spm_infill ? "enabled" : "disabled"
         ),
@@ -904,7 +878,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_INFILL}));
     add_opt(common_arg(
         {"--samplers"}, "SAMPLERS",
-        format("samplers that will be used for generation in the order, separated by \';\'\n(default: %s)", sampler_type_names.c_str()),
+        string_format("samplers that will be used for generation in the order, separated by \';\'\n(default: %s)", sampler_type_names.c_str()),
         [](common_params & params, const std::string & value) {
             const auto sampler_names = string_split(value, ';');
             params.sparams.samplers = common_sampler_types_from_names(sampler_names, true);
@@ -912,14 +886,14 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_sparam());
     add_opt(common_arg(
         {"-s", "--seed"}, "SEED",
-        format("RNG seed (default: %d, use random seed for %d)", params.sparams.seed, LLAMA_DEFAULT_SEED),
+        string_format("RNG seed (default: %d, use random seed for %d)", params.sparams.seed, LLAMA_DEFAULT_SEED),
         [](common_params & params, const std::string & value) {
             params.sparams.seed = std::stoul(value);
         }
     ).set_sparam());
     add_opt(common_arg(
         {"--sampling-seq"}, "SEQUENCE",
-        format("simplified sequence for samplers that will be used (default: %s)", sampler_type_chars.c_str()),
+        string_format("simplified sequence for samplers that will be used (default: %s)", sampler_type_chars.c_str()),
         [](common_params & params, const std::string & value) {
             params.sparams.samplers = common_sampler_types_from_chars(value);
         }
@@ -933,14 +907,14 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_sparam());
     add_opt(common_arg(
         {"--penalize-nl"},
-        format("penalize newline tokens (default: %s)", params.sparams.penalize_nl ? "true" : "false"),
+        string_format("penalize newline tokens (default: %s)", params.sparams.penalize_nl ? "true" : "false"),
         [](common_params & params) {
             params.sparams.penalize_nl = true;
         }
     ).set_sparam());
     add_opt(common_arg(
         {"--temp"}, "N",
-        format("temperature (default: %.1f)", (double)params.sparams.temp),
+        string_format("temperature (default: %.1f)", (double)params.sparams.temp),
         [](common_params & params, const std::string & value) {
             params.sparams.temp = std::stof(value);
             params.sparams.temp = std::max(params.sparams.temp, 0.0f);
@@ -948,42 +922,56 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_sparam());
     add_opt(common_arg(
         {"--top-k"}, "N",
-        format("top-k sampling (default: %d, 0 = disabled)", params.sparams.top_k),
+        string_format("top-k sampling (default: %d, 0 = disabled)", params.sparams.top_k),
         [](common_params & params, int value) {
             params.sparams.top_k = value;
         }
     ).set_sparam());
     add_opt(common_arg(
         {"--top-p"}, "N",
-        format("top-p sampling (default: %.1f, 1.0 = disabled)", (double)params.sparams.top_p),
+        string_format("top-p sampling (default: %.1f, 1.0 = disabled)", (double)params.sparams.top_p),
         [](common_params & params, const std::string & value) {
             params.sparams.top_p = std::stof(value);
         }
     ).set_sparam());
     add_opt(common_arg(
         {"--min-p"}, "N",
-        format("min-p sampling (default: %.1f, 0.0 = disabled)", (double)params.sparams.min_p),
+        string_format("min-p sampling (default: %.1f, 0.0 = disabled)", (double)params.sparams.min_p),
         [](common_params & params, const std::string & value) {
             params.sparams.min_p = std::stof(value);
         }
     ).set_sparam());
     add_opt(common_arg(
         {"--tfs"}, "N",
-        format("tail free sampling, parameter z (default: %.1f, 1.0 = disabled)", (double)params.sparams.tfs_z),
+        string_format("tail free sampling, parameter z (default: %.1f, 1.0 = disabled)", (double)params.sparams.tfs_z),
         [](common_params & params, const std::string & value) {
             params.sparams.tfs_z = std::stof(value);
         }
     ).set_sparam());
+    add_opt(common_arg(
+        {"--xtc-probability"}, "N",
+        string_format("xtc probability (default: %.1f, 0.0 = disabled)", (double)params.sparams.xtc_probability),
+        [](common_params & params, const std::string & value) {
+            params.sparams.xtc_probability = std::stof(value);
+        }
+    ).set_sparam());
+    add_opt(common_arg(
+        {"--xtc-threshold"}, "N",
+        string_format("xtc threshold (default: %.1f, 1.0 = disabled)", (double)params.sparams.xtc_threshold),
+        [](common_params & params, const std::string & value) {
+            params.sparams.xtc_threshold = std::stof(value);
+        }
+    ).set_sparam());
     add_opt(common_arg(
         {"--typical"}, "N",
-        format("locally typical sampling, parameter p (default: %.1f, 1.0 = disabled)", (double)params.sparams.typ_p),
+        string_format("locally typical sampling, parameter p (default: %.1f, 1.0 = disabled)", (double)params.sparams.typ_p),
         [](common_params & params, const std::string & value) {
             params.sparams.typ_p = std::stof(value);
         }
     ).set_sparam());
     add_opt(common_arg(
         {"--repeat-last-n"}, "N",
-        format("last n tokens to consider for penalize (default: %d, 0 = disabled, -1 = ctx_size)", params.sparams.penalty_last_n),
+        string_format("last n tokens to consider for penalize (default: %d, 0 = disabled, -1 = ctx_size)", params.sparams.penalty_last_n),
         [](common_params & params, int value) {
             params.sparams.penalty_last_n = value;
             params.sparams.n_prev = std::max(params.sparams.n_prev, params.sparams.penalty_last_n);
@@ -991,42 +979,42 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_sparam());
     add_opt(common_arg(
         {"--repeat-penalty"}, "N",
-        format("penalize repeat sequence of tokens (default: %.1f, 1.0 = disabled)", (double)params.sparams.penalty_repeat),
+        string_format("penalize repeat sequence of tokens (default: %.1f, 1.0 = disabled)", (double)params.sparams.penalty_repeat),
         [](common_params & params, const std::string & value) {
             params.sparams.penalty_repeat = std::stof(value);
         }
     ).set_sparam());
     add_opt(common_arg(
         {"--presence-penalty"}, "N",
-        format("repeat alpha presence penalty (default: %.1f, 0.0 = disabled)", (double)params.sparams.penalty_present),
+        string_format("repeat alpha presence penalty (default: %.1f, 0.0 = disabled)", (double)params.sparams.penalty_present),
         [](common_params & params, const std::string & value) {
             params.sparams.penalty_present = std::stof(value);
         }
     ).set_sparam());
     add_opt(common_arg(
         {"--frequency-penalty"}, "N",
-        format("repeat alpha frequency penalty (default: %.1f, 0.0 = disabled)", (double)params.sparams.penalty_freq),
+        string_format("repeat alpha frequency penalty (default: %.1f, 0.0 = disabled)", (double)params.sparams.penalty_freq),
         [](common_params & params, const std::string & value) {
             params.sparams.penalty_freq = std::stof(value);
         }
     ).set_sparam());
     add_opt(common_arg(
         {"--dynatemp-range"}, "N",
-        format("dynamic temperature range (default: %.1f, 0.0 = disabled)", (double)params.sparams.dynatemp_range),
+        string_format("dynamic temperature range (default: %.1f, 0.0 = disabled)", (double)params.sparams.dynatemp_range),
         [](common_params & params, const std::string & value) {
             params.sparams.dynatemp_range = std::stof(value);
         }
     ).set_sparam());
     add_opt(common_arg(
         {"--dynatemp-exp"}, "N",
-        format("dynamic temperature exponent (default: %.1f)", (double)params.sparams.dynatemp_exponent),
+        string_format("dynamic temperature exponent (default: %.1f)", (double)params.sparams.dynatemp_exponent),
         [](common_params & params, const std::string & value) {
             params.sparams.dynatemp_exponent = std::stof(value);
         }
     ).set_sparam());
     add_opt(common_arg(
         {"--mirostat"}, "N",
-        format("use Mirostat sampling.\nTop K, Nucleus, Tail Free and Locally Typical samplers are ignored if used.\n"
+        string_format("use Mirostat sampling.\nTop K, Nucleus, Tail Free and Locally Typical samplers are ignored if used.\n"
         "(default: %d, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)", params.sparams.mirostat),
         [](common_params & params, int value) {
             params.sparams.mirostat = value;
@@ -1034,14 +1022,14 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_sparam());
     add_opt(common_arg(
         {"--mirostat-lr"}, "N",
-        format("Mirostat learning rate, parameter eta (default: %.1f)", (double)params.sparams.mirostat_eta),
+        string_format("Mirostat learning rate, parameter eta (default: %.1f)", (double)params.sparams.mirostat_eta),
         [](common_params & params, const std::string & value) {
             params.sparams.mirostat_eta = std::stof(value);
         }
     ).set_sparam());
     add_opt(common_arg(
         {"--mirostat-ent"}, "N",
-        format("Mirostat target entropy, parameter tau (default: %.1f)", (double)params.sparams.mirostat_tau),
+        string_format("Mirostat target entropy, parameter tau (default: %.1f)", (double)params.sparams.mirostat_tau),
         [](common_params & params, const std::string & value) {
             params.sparams.mirostat_tau = std::stof(value);
         }
@@ -1070,7 +1058,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_sparam());
     add_opt(common_arg(
         {"--grammar"}, "GRAMMAR",
-        format("BNF-like grammar to constrain generations (see samples in grammars/ dir) (default: '%s')", params.sparams.grammar.c_str()),
+        string_format("BNF-like grammar to constrain generations (see samples in grammars/ dir) (default: '%s')", params.sparams.grammar.c_str()),
         [](common_params & params, const std::string & value) {
             params.sparams.grammar = value;
         }
@@ -1081,7 +1069,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params, const std::string & value) {
             std::ifstream file(value);
             if (!file) {
-                throw std::runtime_error(format("error: failed to open file '%s'\n", value.c_str()));
+                throw std::runtime_error(string_format("error: failed to open file '%s'\n", value.c_str()));
             }
             std::copy(
                 std::istreambuf_iterator<char>(file),
@@ -1151,53 +1139,53 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_env("LLAMA_ARG_ROPE_FREQ_SCALE"));
     add_opt(common_arg(
         {"--yarn-orig-ctx"}, "N",
-        format("YaRN: original context size of model (default: %d = model training context size)", params.yarn_orig_ctx),
+        string_format("YaRN: original context size of model (default: %d = model training context size)", params.yarn_orig_ctx),
         [](common_params & params, int value) {
             params.yarn_orig_ctx = value;
         }
     ).set_env("LLAMA_ARG_YARN_ORIG_CTX"));
     add_opt(common_arg(
         {"--yarn-ext-factor"}, "N",
-        format("YaRN: extrapolation mix factor (default: %.1f, 0.0 = full interpolation)", (double)params.yarn_ext_factor),
+        string_format("YaRN: extrapolation mix factor (default: %.1f, 0.0 = full interpolation)", (double)params.yarn_ext_factor),
         [](common_params & params, const std::string & value) {
             params.yarn_ext_factor = std::stof(value);
         }
     ).set_env("LLAMA_ARG_YARN_EXT_FACTOR"));
     add_opt(common_arg(
         {"--yarn-attn-factor"}, "N",
-        format("YaRN: scale sqrt(t) or attention magnitude (default: %.1f)", (double)params.yarn_attn_factor),
+        string_format("YaRN: scale sqrt(t) or attention magnitude (default: %.1f)", (double)params.yarn_attn_factor),
         [](common_params & params, const std::string & value) {
             params.yarn_attn_factor = std::stof(value);
         }
     ).set_env("LLAMA_ARG_YARN_ATTN_FACTOR"));
     add_opt(common_arg(
         {"--yarn-beta-slow"}, "N",
-        format("YaRN: high correction dim or alpha (default: %.1f)", (double)params.yarn_beta_slow),
+        string_format("YaRN: high correction dim or alpha (default: %.1f)", (double)params.yarn_beta_slow),
         [](common_params & params, const std::string & value) {
             params.yarn_beta_slow = std::stof(value);
         }
     ).set_env("LLAMA_ARG_YARN_BETA_SLOW"));
     add_opt(common_arg(
         {"--yarn-beta-fast"}, "N",
-        format("YaRN: low correction dim or beta (default: %.1f)", (double)params.yarn_beta_fast),
+        string_format("YaRN: low correction dim or beta (default: %.1f)", (double)params.yarn_beta_fast),
         [](common_params & params, const std::string & value) {
             params.yarn_beta_fast = std::stof(value);
         }
     ).set_env("LLAMA_ARG_YARN_BETA_FAST"));
     add_opt(common_arg(
         {"-gan", "--grp-attn-n"}, "N",
-        format("group-attention factor (default: %d)", params.grp_attn_n),
+        string_format("group-attention factor (default: %d)", params.grp_attn_n),
         [](common_params & params, int value) {
             params.grp_attn_n = value;
         }
-    ).set_env("LLAMA_ARG_GRP_ATTN_N"));
+    ).set_env("LLAMA_ARG_GRP_ATTN_N").set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_PASSKEY}));
     add_opt(common_arg(
         {"-gaw", "--grp-attn-w"}, "N",
-        format("group-attention width (default: %.1f)", (double)params.grp_attn_w),
+        string_format("group-attention width (default: %d)", params.grp_attn_w),
         [](common_params & params, int value) {
             params.grp_attn_w = value;
         }
-    ).set_env("LLAMA_ARG_GRP_ATTN_W"));
+    ).set_env("LLAMA_ARG_GRP_ATTN_W").set_examples({LLAMA_EXAMPLE_MAIN}));
     add_opt(common_arg(
         {"-dkvc", "--dump-kv-cache"},
         "verbose print of the KV cache",
@@ -1214,7 +1202,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_env("LLAMA_ARG_NO_KV_OFFLOAD"));
     add_opt(common_arg(
         {"-ctk", "--cache-type-k"}, "TYPE",
-        format("KV cache data type for K (default: %s)", params.cache_type_k.c_str()),
+        string_format("KV cache data type for K (default: %s)", params.cache_type_k.c_str()),
         [](common_params & params, const std::string & value) {
             // TODO: get the type right here
             params.cache_type_k = value;
@@ -1222,7 +1210,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_env("LLAMA_ARG_CACHE_TYPE_K"));
     add_opt(common_arg(
         {"-ctv", "--cache-type-v"}, "TYPE",
-        format("KV cache data type for V (default: %s)", params.cache_type_v.c_str()),
+        string_format("KV cache data type for V (default: %s)", params.cache_type_v.c_str()),
         [](common_params & params, const std::string & value) {
             // TODO: get the type right here
             params.cache_type_v = value;
@@ -1230,7 +1218,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_env("LLAMA_ARG_CACHE_TYPE_V"));
     add_opt(common_arg(
         {"--perplexity", "--all-logits"},
-        format("return logits for all tokens in the batch (default: %s)", params.logits_all ? "true" : "false"),
+        string_format("return logits for all tokens in the batch (default: %s)", params.logits_all ? "true" : "false"),
         [](common_params & params) {
             params.logits_all = true;
         }
@@ -1244,7 +1232,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_examples({LLAMA_EXAMPLE_PERPLEXITY}));
     add_opt(common_arg(
         {"--hellaswag-tasks"}, "N",
-        format("number of tasks to use when computing the HellaSwag score (default: %zu)", params.hellaswag_tasks),
+        string_format("number of tasks to use when computing the HellaSwag score (default: %zu)", params.hellaswag_tasks),
         [](common_params & params, int value) {
             params.hellaswag_tasks = value;
         }
@@ -1258,7 +1246,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_examples({LLAMA_EXAMPLE_PERPLEXITY}));
     add_opt(common_arg(
         {"--winogrande-tasks"}, "N",
-        format("number of tasks to use when computing the Winogrande score (default: %zu)", params.winogrande_tasks),
+        string_format("number of tasks to use when computing the Winogrande score (default: %zu)", params.winogrande_tasks),
         [](common_params & params, int value) {
             params.winogrande_tasks = value;
         }
@@ -1272,7 +1260,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_examples({LLAMA_EXAMPLE_PERPLEXITY}));
     add_opt(common_arg(
         {"--multiple-choice-tasks"}, "N",
-        format("number of tasks to use when computing the multiple choice score (default: %zu)", params.multiple_choice_tasks),
+        string_format("number of tasks to use when computing the multiple choice score (default: %zu)", params.multiple_choice_tasks),
         [](common_params & params, int value) {
             params.multiple_choice_tasks = value;
         }
@@ -1293,42 +1281,42 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_examples({LLAMA_EXAMPLE_PERPLEXITY}));
     add_opt(common_arg(
         {"--ppl-stride"}, "N",
-        format("stride for perplexity calculation (default: %d)", params.ppl_stride),
+        string_format("stride for perplexity calculation (default: %d)", params.ppl_stride),
         [](common_params & params, int value) {
             params.ppl_stride = value;
         }
     ).set_examples({LLAMA_EXAMPLE_PERPLEXITY}));
     add_opt(common_arg(
         {"--ppl-output-type"}, "<0|1>",
-        format("output type for perplexity calculation (default: %d)", params.ppl_output_type),
+        string_format("output type for perplexity calculation (default: %d)", params.ppl_output_type),
         [](common_params & params, int value) {
             params.ppl_output_type = value;
         }
     ).set_examples({LLAMA_EXAMPLE_PERPLEXITY}));
     add_opt(common_arg(
         {"-dt", "--defrag-thold"}, "N",
-        format("KV cache defragmentation threshold (default: %.1f, < 0 - disabled)", (double)params.defrag_thold),
+        string_format("KV cache defragmentation threshold (default: %.1f, < 0 - disabled)", (double)params.defrag_thold),
         [](common_params & params, const std::string & value) {
             params.defrag_thold = std::stof(value);
         }
     ).set_env("LLAMA_ARG_DEFRAG_THOLD"));
     add_opt(common_arg(
         {"-np", "--parallel"}, "N",
-        format("number of parallel sequences to decode (default: %d)", params.n_parallel),
+        string_format("number of parallel sequences to decode (default: %d)", params.n_parallel),
         [](common_params & params, int value) {
             params.n_parallel = value;
         }
     ).set_env("LLAMA_ARG_N_PARALLEL"));
     add_opt(common_arg(
         {"-ns", "--sequences"}, "N",
-        format("number of sequences to decode (default: %d)", params.n_sequences),
+        string_format("number of sequences to decode (default: %d)", params.n_sequences),
         [](common_params & params, int value) {
             params.n_sequences = value;
         }
     ).set_examples({LLAMA_EXAMPLE_PARALLEL}));
     add_opt(common_arg(
         {"-cb", "--cont-batching"},
-        format("enable continuous batching (a.k.a dynamic batching) (default: %s)", params.cont_batching ? "enabled" : "disabled"),
+        string_format("enable continuous batching (a.k.a dynamic batching) (default: %s)", params.cont_batching ? "enabled" : "disabled"),
         [](common_params & params) {
             params.cont_batching = true;
         }
@@ -1452,7 +1440,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             std::vector<std::string> split_arg{ it, {} };
             if (split_arg.size() >= llama_max_devices()) {
                 throw std::invalid_argument(
-                    format("got %d input configs, but system only has %d devices", (int)split_arg.size(), (int)llama_max_devices())
+                    string_format("got %d input configs, but system only has %d devices", (int)split_arg.size(), (int)llama_max_devices())
                 );
             }
             for (size_t i = 0; i < llama_max_devices(); ++i) {
@@ -1469,7 +1457,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_env("LLAMA_ARG_TENSOR_SPLIT"));
     add_opt(common_arg(
         {"-mg", "--main-gpu"}, "INDEX",
-        format("the GPU to use for the model (with split-mode = none), or for intermediate results and KV (with split-mode = row) (default: %d)", params.main_gpu),
+        string_format("the GPU to use for the model (with split-mode = none), or for intermediate results and KV (with split-mode = row) (default: %d)", params.main_gpu),
         [](common_params & params, int value) {
             params.main_gpu = value;
             if (!llama_supports_gpu_offload()) {
@@ -1479,7 +1467,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_env("LLAMA_ARG_MAIN_GPU"));
     add_opt(common_arg(
         {"--check-tensors"},
-        format("check model tensor data for invalid values (default: %s)", params.check_tensors ? "true" : "false"),
+        string_format("check model tensor data for invalid values (default: %s)", params.check_tensors ? "true" : "false"),
         [](common_params & params) {
             params.check_tensors = true;
         }
@@ -1490,7 +1478,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         "types: int, float, bool, str. example: --override-kv tokenizer.ggml.add_bos_token=bool:false",
         [](common_params & params, const std::string & value) {
             if (!string_parse_kv_override(value.c_str(), params.kv_overrides)) {
-                throw std::runtime_error(format("error: Invalid type for KV override: %s\n", value.c_str()));
+                throw std::runtime_error(string_format("error: Invalid type for KV override: %s\n", value.c_str()));
             }
         }
     ));
@@ -1544,7 +1532,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         {"-m", "--model"}, "FNAME",
         ex == LLAMA_EXAMPLE_EXPORT_LORA
             ? std::string("model path from which to load base model")
-            : format(
+            : string_format(
                 "model path (default: `models/$filename` with filename from `--hf-file` "
                 "or `--model-url` if set, otherwise %s)", DEFAULT_MODEL_PATH
             ),
@@ -1593,42 +1581,42 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params, const std::string & value) {
             std::ifstream file(value, std::ios::binary);
             if (!file) {
-                throw std::runtime_error(format("error: failed to open file '%s'\n", value.c_str()));
+                throw std::runtime_error(string_format("error: failed to open file '%s'\n", value.c_str()));
             }
             params.context_files.push_back(value);
         }
     ).set_examples({LLAMA_EXAMPLE_RETRIEVAL}));
     add_opt(common_arg(
         {"--chunk-size"}, "N",
-        format("minimum length of embedded text chunks (default: %d)", params.chunk_size),
+        string_format("minimum length of embedded text chunks (default: %d)", params.chunk_size),
         [](common_params & params, int value) {
             params.chunk_size = value;
         }
     ).set_examples({LLAMA_EXAMPLE_RETRIEVAL}));
     add_opt(common_arg(
         {"--chunk-separator"}, "STRING",
-        format("separator between chunks (default: '%s')", params.chunk_separator.c_str()),
+        string_format("separator between chunks (default: '%s')", params.chunk_separator.c_str()),
         [](common_params & params, const std::string & value) {
             params.chunk_separator = value;
         }
     ).set_examples({LLAMA_EXAMPLE_RETRIEVAL}));
     add_opt(common_arg(
         {"--junk"}, "N",
-        format("number of times to repeat the junk text (default: %d)", params.n_junk),
+        string_format("number of times to repeat the junk text (default: %d)", params.n_junk),
         [](common_params & params, int value) {
             params.n_junk = value;
         }
     ).set_examples({LLAMA_EXAMPLE_PASSKEY}));
     add_opt(common_arg(
         {"--pos"}, "N",
-        format("position of the passkey in the junk text (default: %d)", params.i_pos),
+        string_format("position of the passkey in the junk text (default: %d)", params.i_pos),
         [](common_params & params, int value) {
             params.i_pos = value;
         }
     ).set_examples({LLAMA_EXAMPLE_PASSKEY}));
     add_opt(common_arg(
         {"-o", "--output", "--output-file"}, "FNAME",
-        format("output file (default: '%s')",
+        string_format("output file (default: '%s')",
             ex == LLAMA_EXAMPLE_EXPORT_LORA
                 ? params.lora_outfile.c_str()
                 : ex == LLAMA_EXAMPLE_CVECTOR_GENERATOR
@@ -1642,42 +1630,42 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_examples({LLAMA_EXAMPLE_IMATRIX, LLAMA_EXAMPLE_CVECTOR_GENERATOR, LLAMA_EXAMPLE_EXPORT_LORA}));
     add_opt(common_arg(
         {"-ofreq", "--output-frequency"}, "N",
-        format("output the imatrix every N iterations (default: %d)", params.n_out_freq),
+        string_format("output the imatrix every N iterations (default: %d)", params.n_out_freq),
         [](common_params & params, int value) {
             params.n_out_freq = value;
         }
     ).set_examples({LLAMA_EXAMPLE_IMATRIX}));
     add_opt(common_arg(
         {"--save-frequency"}, "N",
-        format("save an imatrix copy every N iterations (default: %d)", params.n_save_freq),
+        string_format("save an imatrix copy every N iterations (default: %d)", params.n_save_freq),
         [](common_params & params, int value) {
             params.n_save_freq = value;
         }
     ).set_examples({LLAMA_EXAMPLE_IMATRIX}));
     add_opt(common_arg(
         {"--process-output"},
-        format("collect data for the output tensor (default: %s)", params.process_output ? "true" : "false"),
+        string_format("collect data for the output tensor (default: %s)", params.process_output ? "true" : "false"),
         [](common_params & params) {
             params.process_output = true;
         }
     ).set_examples({LLAMA_EXAMPLE_IMATRIX}));
     add_opt(common_arg(
         {"--no-ppl"},
-        format("do not compute perplexity (default: %s)", params.compute_ppl ? "true" : "false"),
+        string_format("do not compute perplexity (default: %s)", params.compute_ppl ? "true" : "false"),
         [](common_params & params) {
             params.compute_ppl = false;
         }
     ).set_examples({LLAMA_EXAMPLE_IMATRIX}));
     add_opt(common_arg(
         {"--chunk", "--from-chunk"}, "N",
-        format("start processing the input from chunk N (default: %d)", params.i_chunk),
+        string_format("start processing the input from chunk N (default: %d)", params.i_chunk),
         [](common_params & params, int value) {
             params.i_chunk = value;
         }
     ).set_examples({LLAMA_EXAMPLE_IMATRIX}));
     add_opt(common_arg(
         {"-pps"},
-        format("is the prompt shared across parallel sequences (default: %s)", params.is_pp_shared ? "true" : "false"),
+        string_format("is the prompt shared across parallel sequences (default: %s)", params.is_pp_shared ? "true" : "false"),
         [](common_params & params) {
             params.is_pp_shared = true;
         }
@@ -1708,7 +1696,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_examples({LLAMA_EXAMPLE_BENCH}));
     add_opt(common_arg(
         {"--embd-normalize"}, "N",
-        format("normalisation for embendings (default: %d) (-1=none, 0=max absolute int16, 1=taxicab, 2=euclidean, >2=p-norm)", params.embd_normalize),
+        string_format("normalisation for embendings (default: %d) (-1=none, 0=max absolute int16, 1=taxicab, 2=euclidean, >2=p-norm)", params.embd_normalize),
         [](common_params & params, int value) {
             params.embd_normalize = value;
         }
@@ -1729,35 +1717,35 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_examples({LLAMA_EXAMPLE_EMBEDDING}));
     add_opt(common_arg(
         {"--host"}, "HOST",
-        format("ip address to listen (default: %s)", params.hostname.c_str()),
+        string_format("ip address to listen (default: %s)", params.hostname.c_str()),
         [](common_params & params, const std::string & value) {
             params.hostname = value;
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_HOST"));
     add_opt(common_arg(
         {"--port"}, "PORT",
-        format("port to listen (default: %d)", params.port),
+        string_format("port to listen (default: %d)", params.port),
         [](common_params & params, int value) {
             params.port = value;
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_PORT"));
     add_opt(common_arg(
         {"--path"}, "PATH",
-        format("path to serve static files from (default: %s)", params.public_path.c_str()),
+        string_format("path to serve static files from (default: %s)", params.public_path.c_str()),
         [](common_params & params, const std::string & value) {
             params.public_path = value;
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_STATIC_PATH"));
     add_opt(common_arg(
         {"--embedding", "--embeddings"},
-        format("restrict to only support embedding use case; use only with dedicated embedding models (default: %s)", params.embedding ? "enabled" : "disabled"),
+        string_format("restrict to only support embedding use case; use only with dedicated embedding models (default: %s)", params.embedding ? "enabled" : "disabled"),
         [](common_params & params) {
             params.embedding = true;
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_EMBEDDINGS"));
     add_opt(common_arg(
         {"--reranking", "--rerank"},
-        format("enable reranking endpoint on server (default: %s)", params.reranking ? "enabled" : "disabled"),
+        string_format("enable reranking endpoint on server (default: %s)", params.reranking ? "enabled" : "disabled"),
         [](common_params & params) {
             params.reranking = true;
         }
@@ -1775,7 +1763,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params, const std::string & value) {
             std::ifstream key_file(value);
             if (!key_file) {
-                throw std::runtime_error(format("error: failed to open file '%s'\n", value.c_str()));
+                throw std::runtime_error(string_format("error: failed to open file '%s'\n", value.c_str()));
             }
             std::string key;
             while (std::getline(key_file, key)) {
@@ -1802,7 +1790,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_SSL_CERT_FILE"));
     add_opt(common_arg(
         {"-to", "--timeout"}, "N",
-        format("server read/write timeout in seconds (default: %d)", params.timeout_read),
+        string_format("server read/write timeout in seconds (default: %d)", params.timeout_read),
         [](common_params & params, int value) {
             params.timeout_read  = value;
             params.timeout_write = value;
@@ -1810,45 +1798,35 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_TIMEOUT"));
     add_opt(common_arg(
         {"--threads-http"}, "N",
-        format("number of threads used to process HTTP requests (default: %d)", params.n_threads_http),
+        string_format("number of threads used to process HTTP requests (default: %d)", params.n_threads_http),
         [](common_params & params, int value) {
             params.n_threads_http = value;
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_THREADS_HTTP"));
     add_opt(common_arg(
-        {"-spf", "--system-prompt-file"}, "FNAME",
-        "set a file to load a system prompt (initial prompt of all slots), this is useful for chat applications",
-        [](common_params & params, const std::string & value) {
-            std::ifstream file(value);
-            if (!file) {
-                throw std::runtime_error(format("error: failed to open file '%s'\n", value.c_str()));
-            }
-            std::string system_prompt;
-            std::copy(
-                        std::istreambuf_iterator<char>(file),
-                        std::istreambuf_iterator<char>(),
-                        std::back_inserter(system_prompt)
-                        );
-            params.system_prompt = system_prompt;
+        {"--cache-reuse"}, "N",
+        string_format("min chunk size to attempt reusing from the cache via KV shifting (default: %d)", params.n_cache_reuse),
+        [](common_params & params, int value) {
+            params.n_cache_reuse = value;
         }
-    ).set_examples({LLAMA_EXAMPLE_SERVER}));
+    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_CACHE_REUSE"));
     add_opt(common_arg(
         {"--metrics"},
-        format("enable prometheus compatible metrics endpoint (default: %s)", params.endpoint_metrics ? "enabled" : "disabled"),
+        string_format("enable prometheus compatible metrics endpoint (default: %s)", params.endpoint_metrics ? "enabled" : "disabled"),
         [](common_params & params) {
             params.endpoint_metrics = true;
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_ENDPOINT_METRICS"));
     add_opt(common_arg(
         {"--slots"},
-        format("enable slots monitoring endpoint (default: %s)", params.endpoint_slots ? "enabled" : "disabled"),
+        string_format("enable slots monitoring endpoint (default: %s)", params.endpoint_slots ? "enabled" : "disabled"),
         [](common_params & params) {
             params.endpoint_slots = true;
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_ENDPOINT_SLOTS"));
     add_opt(common_arg(
         {"--props"},
-        format("enable changing global properties via POST /props (default: %s)", params.endpoint_props ? "enabled" : "disabled"),
+        string_format("enable changing global properties via POST /props (default: %s)", params.endpoint_props ? "enabled" : "disabled"),
         [](common_params & params) {
             params.endpoint_props = true;
         }
@@ -1878,7 +1856,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         "only commonly used templates are accepted:\nhttps://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template",
         [](common_params & params, const std::string & value) {
             if (!common_chat_verify_template(value)) {
-                throw std::runtime_error(format(
+                throw std::runtime_error(string_format(
                     "error: the supplied chat template is not supported: %s\n"
                     "note: llama.cpp does not use jinja parser, we only support commonly used templates\n",
                     value.c_str()
@@ -1889,14 +1867,14 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_CHAT_TEMPLATE"));
     add_opt(common_arg(
         {"-sps", "--slot-prompt-similarity"}, "SIMILARITY",
-        format("how much the prompt of a request must match the prompt of a slot in order to use that slot (default: %.2f, 0.0 = disabled)\n", params.slot_prompt_similarity),
+        string_format("how much the prompt of a request must match the prompt of a slot in order to use that slot (default: %.2f, 0.0 = disabled)\n", params.slot_prompt_similarity),
         [](common_params & params, const std::string & value) {
             params.slot_prompt_similarity = std::stof(value);
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER}));
     add_opt(common_arg(
         {"--lora-init-without-apply"},
-        format("load LoRA adapters without applying them (apply later via POST /lora-adapters) (default: %s)", params.lora_init_without_apply ? "enabled" : "disabled"),
+        string_format("load LoRA adapters without applying them (apply later via POST /lora-adapters) (default: %s)", params.lora_init_without_apply ? "enabled" : "disabled"),
         [](common_params & params) {
             params.lora_init_without_apply = true;
         }
@@ -1921,28 +1899,28 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ));
     add_opt(common_arg(
         {"--positive-file"}, "FNAME",
-        format("positive prompts file, one prompt per line (default: '%s')", params.cvector_positive_file.c_str()),
+        string_format("positive prompts file, one prompt per line (default: '%s')", params.cvector_positive_file.c_str()),
         [](common_params & params, const std::string & value) {
             params.cvector_positive_file = value;
         }
     ).set_examples({LLAMA_EXAMPLE_CVECTOR_GENERATOR}));
     add_opt(common_arg(
         {"--negative-file"}, "FNAME",
-        format("negative prompts file, one prompt per line (default: '%s')", params.cvector_negative_file.c_str()),
+        string_format("negative prompts file, one prompt per line (default: '%s')", params.cvector_negative_file.c_str()),
         [](common_params & params, const std::string & value) {
             params.cvector_negative_file = value;
         }
     ).set_examples({LLAMA_EXAMPLE_CVECTOR_GENERATOR}));
     add_opt(common_arg(
         {"--pca-batch"}, "N",
-        format("batch size used for PCA. Larger batch runs faster, but uses more memory (default: %d)", params.n_pca_batch),
+        string_format("batch size used for PCA. Larger batch runs faster, but uses more memory (default: %d)", params.n_pca_batch),
         [](common_params & params, int value) {
             params.n_pca_batch = value;
         }
     ).set_examples({LLAMA_EXAMPLE_CVECTOR_GENERATOR}));
     add_opt(common_arg(
         {"--pca-iter"}, "N",
-        format("number of iterations used for PCA (default: %d)", params.n_pca_iterations),
+        string_format("number of iterations used for PCA (default: %d)", params.n_pca_iterations),
         [](common_params & params, int value) {
             params.n_pca_iterations = value;
         }
diff --git a/common/common.cpp b/common/common.cpp
index a33edf1dd..1a9dcd368 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -14,6 +14,7 @@
 
 #include <algorithm>
 #include <cinttypes>
+#include <climits>
 #include <cmath>
 #include <codecvt>
 #include <cstdarg>
@@ -25,10 +26,10 @@
 #include <regex>
 #include <sstream>
 #include <string>
+#include <thread>
 #include <unordered_map>
 #include <unordered_set>
 #include <vector>
-#include <thread>
 
 #if defined(__APPLE__) && defined(__MACH__)
 #include <sys/types.h>
@@ -402,6 +403,21 @@ std::string common_params_get_system_info(const common_params & params) {
 // String utils
 //
 
+std::string string_format(const char * fmt, ...) {
+    va_list ap;
+    va_list ap2;
+    va_start(ap, fmt);
+    va_copy(ap2, ap);
+    int size = vsnprintf(NULL, 0, fmt, ap);
+    GGML_ASSERT(size >= 0 && size < INT_MAX); // NOLINT
+    std::vector<char> buf(size + 1);
+    int size2 = vsnprintf(buf.data(), size + 1, fmt, ap2);
+    GGML_ASSERT(size2 == size);
+    va_end(ap2);
+    va_end(ap);
+    return std::string(buf.data(), size);
+}
+
 std::vector<std::string> string_split(std::string input, char separator) {
     std::vector<std::string> parts;
     size_t separator_pos = input.find(separator);
@@ -2090,6 +2106,8 @@ void yaml_dump_non_result_info(FILE * stream, const common_params & params, cons
     fprintf(stream, "top_k: %d # default: 40\n", sparams.top_k);
     fprintf(stream, "top_p: %f # default: 0.95\n", sparams.top_p);
     fprintf(stream, "min_p: %f # default: 0.0\n", sparams.min_p);
+    fprintf(stream, "xtc_probability: %f # default: 0.0\n", sparams.xtc_probability);
+    fprintf(stream, "xtc_threshold: %f # default: 0.1\n", sparams.xtc_threshold);
     fprintf(stream, "typ_p: %f # default: 1.0\n", sparams.typ_p);
     fprintf(stream, "verbose_prompt: %s # default: false\n", params.verbose_prompt ? "true" : "false");
     fprintf(stream, "display_prompt: %s # default: true\n", params.display_prompt ? "true" : "false");
diff --git a/common/common.h b/common/common.h
index 2a26f4031..f21e349c5 100644
--- a/common/common.h
+++ b/common/common.h
@@ -86,6 +86,8 @@ enum common_sampler_type {
     COMMON_SAMPLER_TYPE_TFS_Z       = 4,
     COMMON_SAMPLER_TYPE_TYPICAL_P   = 5,
     COMMON_SAMPLER_TYPE_TEMPERATURE = 6,
+    COMMON_SAMPLER_TYPE_XTC         = 7,
+    COMMON_SAMPLER_TYPE_INFILL      = 8,
 };
 
 // dimensionality reduction methods, used by cvector-generator
@@ -104,6 +106,8 @@ struct common_sampler_params {
     int32_t top_k             = 40;    // <= 0 to use vocab size
     float   top_p             = 0.95f; // 1.0 = disabled
     float   min_p             = 0.05f; // 0.0 = disabled
+    float   xtc_probability   = 0.00f; // 0.0 = disabled
+    float   xtc_threshold     = 0.10f; // > 0.5 disables XTC
     float   tfs_z             = 1.00f; // 1.0 = disabled
     float   typ_p             = 1.00f; // typical_p, 1.0 = disabled
     float   temp              = 0.80f; // <= 0.0 to sample greedily, 0.0 to not output probabilities
@@ -120,13 +124,15 @@ struct common_sampler_params {
     bool    ignore_eos        = false;
     bool    no_perf           = false; // disable performance metrics
 
+
     std::vector<enum common_sampler_type> samplers = {
         COMMON_SAMPLER_TYPE_TOP_K,
         COMMON_SAMPLER_TYPE_TFS_Z,
         COMMON_SAMPLER_TYPE_TYPICAL_P,
         COMMON_SAMPLER_TYPE_TOP_P,
         COMMON_SAMPLER_TYPE_MIN_P,
-        COMMON_SAMPLER_TYPE_TEMPERATURE
+        COMMON_SAMPLER_TYPE_XTC,
+        COMMON_SAMPLER_TYPE_TEMPERATURE,
     };
 
     std::string grammar; // optional BNF-like grammar to constrain sampling
@@ -273,12 +279,12 @@ struct common_params {
     int32_t port           = 8080;         // server listens on this network port
     int32_t timeout_read   = 600;          // http read timeout in seconds
     int32_t timeout_write  = timeout_read; // http write timeout in seconds
-    int     n_threads_http = -1;           // number of threads to process HTTP requests (TODO: support threadpool)
+    int32_t n_threads_http = -1;           // number of threads to process HTTP requests (TODO: support threadpool)
+    int32_t n_cache_reuse  = 0;            // min chunk size to reuse from the cache via KV shifting
 
     std::string hostname      = "127.0.0.1";
     std::string public_path   = "";                                                                         // NOLINT
     std::string chat_template = "";                                                                         // NOLINT
-    std::string system_prompt = "";                                                                         // NOLINT
     bool enable_chat_template = true;
 
     std::vector<std::string> api_keys;
@@ -348,15 +354,28 @@ void common_init();
 
 std::string common_params_get_system_info(const common_params & params);
 
-bool parse_cpu_range(const std::string& range, bool(&boolmask)[GGML_MAX_N_THREADS]);
-bool parse_cpu_mask(const std::string& mask, bool(&boolmask)[GGML_MAX_N_THREADS]);
-void postprocess_cpu_params(cpu_params& cpuparams, const cpu_params* role_model = nullptr);
+bool parse_cpu_range(const std::string & range, bool(&boolmask)[GGML_MAX_N_THREADS]);
+bool parse_cpu_mask(const std::string & mask, bool(&boolmask)[GGML_MAX_N_THREADS]);
+void postprocess_cpu_params(cpu_params & cpuparams, const cpu_params * role_model = nullptr);
 bool set_process_priority(enum ggml_sched_priority prio);
 
 //
 // String utils
 //
 
+#ifdef __GNUC__
+#ifdef __MINGW32__
+#define LLAMA_COMMON_ATTRIBUTE_FORMAT(...) __attribute__((format(gnu_printf, __VA_ARGS__)))
+#else
+#define LLAMA_COMMON_ATTRIBUTE_FORMAT(...) __attribute__((format(printf, __VA_ARGS__)))
+#endif
+#else
+#define LLAMA_COMMON_ATTRIBUTE_FORMAT(...)
+#endif
+
+LLAMA_COMMON_ATTRIBUTE_FORMAT(1, 2)
+std::string string_format(const char * fmt, ...);
+
 std::vector<std::string> string_split(std::string input, char separator);
 
 std::string string_strip(const std::string & str);
diff --git a/common/json-schema-to-grammar.cpp b/common/json-schema-to-grammar.cpp
index 881eb49e3..dadc18c8b 100644
--- a/common/json-schema-to-grammar.cpp
+++ b/common/json-schema-to-grammar.cpp
@@ -611,7 +611,7 @@ private:
             }
             return join_seq();
         };
-        return _add_rule(name, "\"\\\"\" " + to_rule(transform()) + " \"\\\"\" space");
+        return _add_rule(name, "\"\\\"\" (" + to_rule(transform()) + ") \"\\\"\" space");
     }
 
     /*
diff --git a/common/sampling.cpp b/common/sampling.cpp
index cd49ade69..56cd0df6b 100644
--- a/common/sampling.cpp
+++ b/common/sampling.cpp
@@ -130,10 +130,10 @@ std::string common_sampler_params::print() const {
 
     snprintf(result, sizeof(result),
             "\trepeat_last_n = %d, repeat_penalty = %.3f, frequency_penalty = %.3f, presence_penalty = %.3f\n"
-            "\ttop_k = %d, tfs_z = %.3f, top_p = %.3f, min_p = %.3f, typical_p = %.3f, temp = %.3f\n"
+            "\ttop_k = %d, tfs_z = %.3f, top_p = %.3f, min_p = %.3f, xtc_probability = %.3f, xtc_threshold = %.3f, typical_p = %.3f, temp = %.3f\n"
             "\tmirostat = %d, mirostat_lr = %.3f, mirostat_ent = %.3f",
             penalty_last_n, penalty_repeat, penalty_freq, penalty_present,
-            top_k, tfs_z, top_p, min_p, typ_p, temp,
+            top_k, tfs_z, top_p, min_p, xtc_probability, xtc_threshold, typ_p, temp,
             mirostat, mirostat_eta, mirostat_tau);
 
     return std::string(result);
@@ -184,6 +184,9 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
                     case COMMON_SAMPLER_TYPE_MIN_P:
                         llama_sampler_chain_add(result->chain, llama_sampler_init_min_p    (params.min_p, params.min_keep));
                         break;
+                    case COMMON_SAMPLER_TYPE_XTC:
+                        llama_sampler_chain_add(result->chain, llama_sampler_init_xtc      (params.xtc_probability, params.xtc_threshold, params.min_keep, params.seed));
+                        break;
                     case COMMON_SAMPLER_TYPE_TFS_Z:
                         llama_sampler_chain_add(result->chain, llama_sampler_init_tail_free(params.tfs_z, params.min_keep));
                         break;
@@ -193,6 +196,9 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
                     case COMMON_SAMPLER_TYPE_TEMPERATURE:
                         llama_sampler_chain_add(result->chain, llama_sampler_init_temp_ext (params.temp, params.dynatemp_range, params.dynatemp_exponent));
                         break;
+                    case COMMON_SAMPLER_TYPE_INFILL:
+                        llama_sampler_chain_add(result->chain, llama_sampler_init_infill   (model));
+                        break;
                     default:
                         GGML_ASSERT(false && "unknown sampler type");
                 }
@@ -372,6 +378,8 @@ char common_sampler_type_to_chr(enum common_sampler_type cnstr) {
         case COMMON_SAMPLER_TYPE_TOP_P:       return 'p';
         case COMMON_SAMPLER_TYPE_MIN_P:       return 'm';
         case COMMON_SAMPLER_TYPE_TEMPERATURE: return 't';
+        case COMMON_SAMPLER_TYPE_XTC:         return 'x';
+        case COMMON_SAMPLER_TYPE_INFILL:      return 'i';
         default : return '?';
     }
 }
@@ -384,6 +392,8 @@ std::string common_sampler_type_to_str(enum common_sampler_type cnstr) {
         case COMMON_SAMPLER_TYPE_TOP_P:       return "top_p";
         case COMMON_SAMPLER_TYPE_MIN_P:       return "min_p";
         case COMMON_SAMPLER_TYPE_TEMPERATURE: return "temperature";
+        case COMMON_SAMPLER_TYPE_XTC:         return "xtc";
+        case COMMON_SAMPLER_TYPE_INFILL:      return "infill";
         default : return "";
     }
 }
@@ -396,6 +406,8 @@ std::vector<common_sampler_type> common_sampler_types_from_names(const std::vect
         { "min_p",       COMMON_SAMPLER_TYPE_MIN_P },
         { "tfs_z",       COMMON_SAMPLER_TYPE_TFS_Z },
         { "temperature", COMMON_SAMPLER_TYPE_TEMPERATURE },
+        { "xtc",         COMMON_SAMPLER_TYPE_XTC },
+        { "infill",      COMMON_SAMPLER_TYPE_INFILL },
     };
 
     // since samplers names are written multiple ways
@@ -441,7 +453,9 @@ std::vector<common_sampler_type> common_sampler_types_from_chars(const std::stri
         { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TYPICAL_P),   COMMON_SAMPLER_TYPE_TYPICAL_P },
         { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TOP_P),       COMMON_SAMPLER_TYPE_TOP_P },
         { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_MIN_P),       COMMON_SAMPLER_TYPE_MIN_P },
-        { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TEMPERATURE), COMMON_SAMPLER_TYPE_TEMPERATURE }
+        { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TEMPERATURE), COMMON_SAMPLER_TYPE_TEMPERATURE },
+        { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_XTC),         COMMON_SAMPLER_TYPE_XTC },
+        { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_INFILL),      COMMON_SAMPLER_TYPE_INFILL },
     };
 
     std::vector<common_sampler_type> samplers;
diff --git a/examples/json_schema_to_grammar.py b/examples/json_schema_to_grammar.py
index a8779bf3b..fc9f0097f 100755
--- a/examples/json_schema_to_grammar.py
+++ b/examples/json_schema_to_grammar.py
@@ -540,7 +540,7 @@ class SchemaConverter:
         return self._add_rule(
             name,
             to_rule(transform()) if self._raw_pattern \
-                else "\"\\\"\" " + to_rule(transform()) + " \"\\\"\" space")
+                else "\"\\\"\" (" + to_rule(transform()) + ") \"\\\"\" space")
 
 
     def _resolve_ref(self, ref):
diff --git a/examples/llava/llava.cpp b/examples/llava/llava.cpp
index 8558c6bdc..2c96973c8 100644
--- a/examples/llava/llava.cpp
+++ b/examples/llava/llava.cpp
@@ -432,7 +432,7 @@ struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * c
     bool image_embed_result = llava_image_embed_make_with_clip_img(ctx_clip, n_threads, img, &image_embed, &n_image_pos);
     if (!image_embed_result) {
         clip_image_u8_free(img);
-        LOG_ERR("%s: coulnd't embed the image\n", __func__);
+        LOG_ERR("%s: couldn't embed the image\n", __func__);
         return NULL;
     }
 
diff --git a/examples/main/main.cpp b/examples/main/main.cpp
index 1bfbcdc99..a4fee0065 100644
--- a/examples/main/main.cpp
+++ b/examples/main/main.cpp
@@ -570,30 +570,30 @@ int main(int argc, char ** argv) {
                     if (!params.ctx_shift){
                         LOG_DBG("\n\n%s: context full and context shift is disabled => stopping\n", __func__);
                         break;
-                    } else {
-                        if (params.n_predict == -2) {
-                            LOG_DBG("\n\n%s: context full and n_predict == -%d => stopping\n", __func__, params.n_predict);
-                            break;
-                        }
-
-                        const int n_left    = n_past - params.n_keep;
-                        const int n_discard = n_left/2;
-
-                        LOG_DBG("context full, swapping: n_past = %d, n_left = %d, n_ctx = %d, n_keep = %d, n_discard = %d\n",
-                                n_past, n_left, n_ctx, params.n_keep, n_discard);
-
-                        llama_kv_cache_seq_rm (ctx, 0, params.n_keep            , params.n_keep + n_discard);
-                        llama_kv_cache_seq_add(ctx, 0, params.n_keep + n_discard, n_past, -n_discard);
-
-                        n_past -= n_discard;
-
-                        LOG_DBG("after swap: n_past = %d\n", n_past);
-
-                        LOG_DBG("embd: %s\n", string_from(ctx, embd).c_str());
-
-                        LOG_DBG("clear session path\n");
-                        path_session.clear();
                     }
+
+                    if (params.n_predict == -2) {
+                        LOG_DBG("\n\n%s: context full and n_predict == -%d => stopping\n", __func__, params.n_predict);
+                        break;
+                    }
+
+                    const int n_left    = n_past - params.n_keep;
+                    const int n_discard = n_left/2;
+
+                    LOG_DBG("context full, swapping: n_past = %d, n_left = %d, n_ctx = %d, n_keep = %d, n_discard = %d\n",
+                            n_past, n_left, n_ctx, params.n_keep, n_discard);
+
+                    llama_kv_cache_seq_rm (ctx, 0, params.n_keep            , params.n_keep + n_discard);
+                    llama_kv_cache_seq_add(ctx, 0, params.n_keep + n_discard, n_past, -n_discard);
+
+                    n_past -= n_discard;
+
+                    LOG_DBG("after swap: n_past = %d\n", n_past);
+
+                    LOG_DBG("embd: %s\n", string_from(ctx, embd).c_str());
+
+                    LOG_DBG("clear session path\n");
+                    path_session.clear();
                 }
             } else {
                 // context extension via Self-Extend
diff --git a/examples/server/public/index-new.html b/examples/server/public/index-new.html
index c87dd8f1e..ad4183cd9 100644
--- a/examples/server/public/index-new.html
+++ b/examples/server/public/index-new.html
@@ -43,6 +43,8 @@
       top_k: 0, // <= 0 to use vocab size
       top_p: 1.0, // 1.0 = disabled
       min_p: 0.05, // 0 = disabled; recommended for non-english: ~ 0.4
+      xtc_probability: 0.0, // 0 = disabled;
+      xtc_threshold: 0.1, // > 0.5 disables XTC;
       tfs_z: 1.0, // 1.0 = disabled
       typical_p: 1.0, // 1.0 = disabled
       presence_penalty: 0.0, // 0.0 = disabled
@@ -836,6 +838,8 @@ return html`
           ${FloatField({ label: "TFS-Z", title: "Activates tail-free sampling, a method used to limit the prediction of tokens that are too frequent. The parameter z controls the strength of this limitation. A value of 1.0 means that this function is deactivated.", max: 1.0, min: 0.0, name: "tfs_z", step: 0.01, value: params.value.tfs_z })}
           ${FloatField({ label: "Frequency Penalty", title: "A penalty that is applied based on the frequency with which certain tokens occur in the training data set. A higher value results in rare tokens being favoured.", max: 1.0, min: 0.0, name: "frequency_penalty", step: 0.01, value: params.value.frequency_penalty })}
           ${FloatField({ label: "Typical-P", title: "Activates local typical sampling, a method used to limit the prediction of tokens that are atypical in the current context. The parameter p controls the strength of this limitation. A value of 1.0 means that this function is deactivated.", max: 1.0, min: 0.0, name: "typical_p", step: 0.01, value: params.value.typical_p })}
+          ${FloatField({ label: "XTC probability", title: "Sets the chance for token removal (checked once on sampler start)", max: 1.0, min: 0.0, name: "xtc_probability", step: 0.01, value: params.value.xtc_probability })}
+          ${FloatField({ label: "XTC threshold", title: "Sets a minimum probability threshold for tokens to be removed", max: 0.5, min: 0.0, name: "xtc_threshold", step: 0.01, value: params.value.xtc_threshold })}
           ${IntField({ label: "Min Keep", title: "If greater than 0, samplers are forced to return N possible tokens at minimum. Default is 0", max: 10, min: 0, name: "min_keep", value: params.value.min_keep })}
         </fieldset>
 
@@ -1132,6 +1136,8 @@ document.addEventListener('DOMContentLoaded', (event) => {
   const snapSettings = {
     temperature: { snapValue: 1.0, snapRangeMultiplier: 6 },
     min_p: { snapValue: 0.05, snapRangeMultiplier: 2 },
+    xtc_probability: { snapValue: 0.0, snapRangeMultiplier: 4 },
+    xtc_threshold: { snapValue: 0.5, snapRangeMultiplier: 4 },
     top_p: { snapValue: 1.0, snapRangeMultiplier: 4 },
     tfs_z: { snapValue: 1.0, snapRangeMultiplier: 4 },
     typical_p: { snapValue: 1.0, snapRangeMultiplier: 4 },
diff --git a/examples/server/public/index.html b/examples/server/public/index.html
index 07fec6a38..88065705f 100644
--- a/examples/server/public/index.html
+++ b/examples/server/public/index.html
@@ -307,6 +307,8 @@
       top_k: 40, // <= 0 to use vocab size
       top_p: 0.95, // 1.0 = disabled
       min_p: 0.05, // 0 = disabled
+      xtc_probability: 0.0, // 0 = disabled;
+      xtc_threshold: 0.1, // > 0.5 disables XTC;
       tfs_z: 1.0, // 1.0 = disabled
       typical_p: 1.0, // 1.0 = disabled
       presence_penalty: 0.0, // 0.0 = disabled
@@ -1013,6 +1015,8 @@
               ${FloatField({ label: "Typical P", max: 1.0, min: 0.0, name: "typical_p", step: 0.01, value: params.value.typical_p })}
               ${FloatField({ label: "Presence penalty", max: 1.0, min: 0.0, name: "presence_penalty", step: 0.01, value: params.value.presence_penalty })}
               ${FloatField({ label: "Frequency penalty", max: 1.0, min: 0.0, name: "frequency_penalty", step: 0.01, value: params.value.frequency_penalty })}
+              ${FloatField({ label: "XTC probability", max: 1.0, min: 0.0, name: "xtc_probability", step: 0.01, value: params.value.xtc_probability })}
+              ${FloatField({ label: "XTC threshold", max: 0.5, min: 0.0, name: "xtc_threshold", step: 0.01, value: params.value.xtc_threshold })}
             </fieldset>
             <hr />
             <fieldset class="three">
diff --git a/examples/server/public/index.js b/examples/server/public/index.js
index fe615ca25..32ec6e9e1 100644
--- a/examples/server/public/index.js
+++ b/examples/server/public/index.js
@@ -1 +1 @@
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diff --git a/examples/server/public/json-schema-to-grammar.mjs b/examples/server/public/json-schema-to-grammar.mjs
index 7267f3f9c..e67bb15c1 100644
--- a/examples/server/public/json-schema-to-grammar.mjs
+++ b/examples/server/public/json-schema-to-grammar.mjs
@@ -529,7 +529,7 @@ export class SchemaConverter {
       return joinSeq();
     };
 
-    return this._addRule(name, "\"\\\"\" " + toRule(transform()) + " \"\\\"\" space")
+    return this._addRule(name, "\"\\\"\" (" + toRule(transform()) + ") \"\\\"\" space")
   }
 
   _notStrings(strings) {
diff --git a/examples/server/server.cpp b/examples/server/server.cpp
index 508c1c065..5e0a236d3 100644
--- a/examples/server/server.cpp
+++ b/examples/server/server.cpp
@@ -129,14 +129,14 @@ struct slot_params {
     bool stream       = true;
     bool cache_prompt = false; // remember the prompt to avoid reprocessing all prompt
 
-    int32_t  n_keep    =  0; // number of tokens to keep from initial prompt
-    int32_t  n_discard =  0; // number of tokens after n_keep that may be discarded when shifting context, 0 defaults to half
-    int32_t  n_predict = -1; // new tokens to predict
+    int32_t n_keep    =  0; // number of tokens to keep from initial prompt
+    int32_t n_discard =  0; // number of tokens after n_keep that may be discarded when shifting context, 0 defaults to half
+    int32_t n_predict = -1; // new tokens to predict
+
+    int64_t t_max_prompt_ms  = -1; // TODO: implement
+    int64_t t_max_predict_ms = -1; // if positive, limit the generation phase to this time limit
 
     std::vector<std::string> antiprompt;
-
-    json input_prefix;
-    json input_suffix;
 };
 
 struct server_slot {
@@ -166,8 +166,13 @@ struct server_slot {
 
     json prompt; // can be either a string, array of strings or array of token ids
 
+    json input_prefix;
+    json input_suffix;
+    json input_extra;
+
     // when a task is submitted, we first tokenize the prompt and store it here
     std::vector<llama_token> prompt_tokens;
+    std::vector<llama_token> extra_tokens;
 
     std::string generated_text;
     std::vector<llama_token> cache_tokens;
@@ -176,6 +181,7 @@ struct server_slot {
     server_task_cmpl_type cmpl_type = SERVER_TASK_CMPL_TYPE_NORMAL;
 
     bool has_next_token = true;
+    bool has_new_line   = false;
     bool truncated      = false;
     bool stopped_eos    = false;
     bool stopped_word   = false;
@@ -194,21 +200,15 @@ struct server_slot {
 
     llama_token sampled;
 
-    int32_t ga_i = 0;   // group-attention state
-    int32_t ga_n = 1;   // group-attention factor
-    int32_t ga_w = 512; // group-attention width
-
-    int32_t n_past_se = 0; // self-extend
-
     // stats
-    size_t n_sent_text = 0; // number of sent text character
+    size_t n_sent_text        = 0; // number of sent text character
     size_t n_sent_token_probs = 0;
 
     int64_t t_start_process_prompt;
     int64_t t_start_generation;
 
     double t_prompt_processing; // ms
-    double t_token_generation; // ms
+    double t_token_generation;  // ms
 
     std::function<void(int)> callback_on_release;
 
@@ -217,6 +217,7 @@ struct server_slot {
 
         n_prompt_tokens    = 0;
         generated_text     = "";
+        has_new_line       = false;
         truncated          = false;
         stopped_eos        = false;
         stopped_word       = false;
@@ -226,8 +227,6 @@ struct server_slot {
         n_sent_text        = 0;
         n_sent_token_probs = 0;
         cmpl_type          = SERVER_TASK_CMPL_TYPE_NORMAL;
-        ga_i               = 0;
-        n_past_se          = 0;
 
         generated_token_probs.clear();
     }
@@ -624,12 +623,6 @@ struct server_context {
 
     int32_t n_ctx; // total context for all clients / slots
 
-    // system prompt
-    bool system_need_update = false;
-
-    std::string              system_prompt;
-    std::vector<llama_token> system_tokens;
-
     // slots / clients
     std::vector<server_slot> slots;
     json default_generation_settings_for_props;
@@ -666,7 +659,7 @@ struct server_context {
     bool load_model(const common_params & params_) {
         params = params_;
 
-        // dedicate one sequence to the system prompt
+        // reserve one extra sequence (seq_id == 0) for extra features
         params.n_parallel += 1;
 
         common_init_result llama_init = common_init_from_params(params);
@@ -712,22 +705,6 @@ struct server_context {
 
             SLT_INF(slot, "new slot n_ctx_slot = %d\n", slot.n_ctx);
 
-            const int ga_n = params.grp_attn_n;
-            const int ga_w = params.grp_attn_w;
-
-            if (ga_n != 1) {
-                GGML_ASSERT(ga_n > 0                    && "ga_n must be positive");                       // NOLINT
-                GGML_ASSERT(ga_w % ga_n == 0            && "ga_w must be a multiple of ga_n");             // NOLINT
-                //GGML_ASSERT(n_ctx_train % ga_w == 0     && "n_ctx_train must be a multiple of ga_w");    // NOLINT
-                //GGML_ASSERT(n_ctx >= n_ctx_train * ga_n && "n_ctx must be at least n_ctx_train * ga_n"); // NOLINT
-
-                SLT_INF(slot, "slot self-extend: ga_n = %d, ga_w = %d\n", ga_n, ga_w);
-            }
-
-            slot.ga_i = 0;
-            slot.ga_n = ga_n;
-            slot.ga_w = ga_w;
-
             slot.sparams = params.sparams;
 
             slot.callback_on_release = [this](int) {
@@ -754,12 +731,7 @@ struct server_context {
         metrics.init();
     }
 
-    std::vector<llama_token> tokenize(const json & json_prompt, bool add_special) const {
-        // TODO: currently, we tokenize using special tokens by default
-        //       this is not always correct (see https://github.com/ggerganov/llama.cpp/pull/4160#issuecomment-1824826216)
-        //       but it's better compared to completely ignoring ChatML and other chat templates
-        const bool TMP_FORCE_SPECIAL = true;
-
+    std::vector<llama_token> tokenize(const json & json_prompt, bool add_special, bool parse_special) const {
         // If `add_bos` is true, we only add BOS, when json_prompt is a string,
         // or the first element of the json_prompt array is a string.
         std::vector<llama_token> prompt_tokens;
@@ -772,10 +744,10 @@ struct server_context {
 
                     std::vector<llama_token> p;
                     if (first) {
-                        p = common_tokenize(ctx, s, add_special, TMP_FORCE_SPECIAL);
+                        p = common_tokenize(ctx, s, add_special, parse_special);
                         first = false;
                     } else {
-                        p = common_tokenize(ctx, s, false, TMP_FORCE_SPECIAL);
+                        p = common_tokenize(ctx, s, false, parse_special);
                     }
 
                     prompt_tokens.insert(prompt_tokens.end(), p.begin(), p.end());
@@ -789,7 +761,7 @@ struct server_context {
             }
         } else {
             auto s = json_prompt.template get<std::string>();
-            prompt_tokens = common_tokenize(ctx, s, add_special, TMP_FORCE_SPECIAL);
+            prompt_tokens = common_tokenize(ctx, s, add_special, parse_special);
         }
 
         return prompt_tokens;
@@ -831,7 +803,7 @@ struct server_context {
                 int slot_prompt_len = slot_prompt.size();
 
                 // length of the Longest Common Prefix between the current slot's prompt and the input prompt
-                int lcp_len = common_part(slot_prompt, prompt);
+                int lcp_len = longest_common_prefix(slot_prompt, prompt);
 
                 // fraction of the common substring length compared to the current slot's prompt length
                 similarity = static_cast<float>(lcp_len) / slot_prompt_len;
@@ -892,6 +864,8 @@ struct server_context {
         slot.sparams.top_k             = json_value(data, "top_k",             default_sparams.top_k);
         slot.sparams.top_p             = json_value(data, "top_p",             default_sparams.top_p);
         slot.sparams.min_p             = json_value(data, "min_p",             default_sparams.min_p);
+        slot.sparams.xtc_probability   = json_value(data, "xtc_probability",   default_sparams.xtc_probability);
+        slot.sparams.xtc_threshold     = json_value(data, "xtc_threshold",     default_sparams.xtc_threshold);
         slot.sparams.tfs_z             = json_value(data, "tfs_z",             default_sparams.tfs_z);
         slot.sparams.typ_p             = json_value(data, "typical_p",         default_sparams.typ_p);
         slot.sparams.temp              = json_value(data, "temperature",       default_sparams.temp);
@@ -910,6 +884,8 @@ struct server_context {
         slot.sparams.seed              = json_value(data, "seed",              default_sparams.seed);
         slot.sparams.n_probs           = json_value(data, "n_probs",           default_sparams.n_probs);
         slot.sparams.min_keep          = json_value(data, "min_keep",          default_sparams.min_keep);
+      //slot.params.t_max_prompt_ms    = json_value(data, "t_max_prompt_ms",   default_params.t_max_prompt_ms); // TODO: implement
+        slot.params.t_max_predict_ms   = json_value(data, "t_max_predict_ms",  default_params.t_max_predict_ms);
 
         // process "json_schema" and "grammar"
         if (data.contains("json_schema") && !data.at("json_schema").is_null() && data.contains("grammar") && !data.at("grammar").is_null()) {
@@ -918,19 +894,14 @@ struct server_context {
         }
         if (data.contains("json_schema") && !data.contains("grammar")) {
             try {
-                auto schema                = json_value(data, "json_schema", json::object());
-                slot.sparams.grammar       = json_schema_to_grammar(schema);
+                auto schema          = json_value(data, "json_schema", json::object());
+                slot.sparams.grammar = json_schema_to_grammar(schema);
             } catch (const std::exception & e) {
                 send_error(task, std::string("\"json_schema\": ") + e.what(), ERROR_TYPE_INVALID_REQUEST);
                 return false;
             }
         } else {
-            slot.sparams.grammar       = json_value(data, "grammar",           default_sparams.grammar);
-        }
-
-        if (slot.params.cache_prompt && slot.ga_n != 1) {
-            slot.params.cache_prompt = false;
-            SLT_WRN(slot, "%s", "group-attention is not supported with prompt caching. disabling cache\n");
+            slot.sparams.grammar = json_value(data, "grammar", default_sparams.grammar);
         }
 
         if (slot.n_predict > 0 && slot.params.n_predict > slot.n_predict) {
@@ -940,11 +911,29 @@ struct server_context {
         }
 
         // infill
-        slot.params.input_prefix = json_value(data, "input_prefix", default_params.input_prefix);
-        slot.params.input_suffix = json_value(data, "input_suffix", default_params.input_suffix);
+        slot.input_prefix = json_value(data, "input_prefix", json());
+        slot.input_suffix = json_value(data, "input_suffix", json());
+        slot.input_extra  = json_value(data, "input_extra",  json());
+
+        SLT_DBG(slot, "extra_context chunks: %d\n", (int) slot.input_extra.size());
+        for (const auto & chunk : slot.input_extra) {
+            // { "text": string, "filename": string }
+            if (!chunk.contains("text") || !chunk["text"].is_string()) {
+                send_error(task, "extra_context chunk must contain a \"text\" field with a string value", ERROR_TYPE_INVALID_REQUEST);
+                return false;
+            }
+
+            // filename is optional
+            if (chunk.contains("filename") && !chunk["filename"].is_string()) {
+                send_error(task, "extra_context chunk's \"filename\" field must be a string", ERROR_TYPE_INVALID_REQUEST);
+                return false;
+            }
+
+            SLT_DBG(slot, "extra_context chunk in file '%s':\n%s\n", chunk.value("filename", "").c_str(), chunk.value("text", "").c_str());
+        }
 
         // get prompt
-        if (task.cmpl_type != SERVER_TASK_CMPL_TYPE_INFILL) {
+        {
             const auto & prompt = data.find("prompt");
             if (prompt == data.end()) {
                 send_error(task, "\"prompt\" must be provided", ERROR_TYPE_INVALID_REQUEST);
@@ -1067,51 +1056,6 @@ struct server_context {
         clean_kv_cache = false;
     }
 
-    void system_prompt_update() {
-        SRV_DBG("updating system prompt: '%s'\n", system_prompt.c_str());
-
-        kv_cache_clear();
-        system_tokens.clear();
-
-        if (!system_prompt.empty()) {
-            system_tokens = common_tokenize(ctx, system_prompt, true);
-
-            const int32_t n_batch = llama_n_batch(ctx);
-            const int32_t n_tokens_prompt = system_tokens.size();
-
-            for (int32_t i = 0; i < n_tokens_prompt; i += n_batch) {
-                const int32_t n_tokens = std::min(n_batch, n_tokens_prompt - i);
-
-                common_batch_clear(batch);
-
-                for (int32_t j = 0; j < n_tokens; ++j) {
-                    common_batch_add(batch, system_tokens[i + j], i + j, { 0 }, false);
-                }
-
-                if (llama_decode(ctx, batch) != 0) {
-                    SRV_ERR("%s", "llama_decode() failed\n");
-                    return;
-                }
-            }
-
-            // assign the system KV cache to all parallel sequences
-            for (int32_t i = 1; i <= params.n_parallel; ++i) {
-                llama_kv_cache_seq_cp(ctx, 0, i, -1, -1);
-            }
-        }
-
-        system_need_update = false;
-    }
-
-    bool system_prompt_set(const std::string & sys_prompt) {
-        SRV_DBG("system prompt set: '%s'\n", system_prompt.c_str());
-
-        system_prompt = sys_prompt;
-        // update system_tokens and KV cache as soon as all slots are idle
-        system_need_update = true;
-        return true;
-    }
-
     bool process_token(completion_token_output & result, server_slot & slot) {
         // remember which tokens were sampled - used for repetition penalties during sampling
         const std::string token_str = common_token_to_piece(ctx, result.tok, params.special);
@@ -1147,22 +1091,21 @@ struct server_context {
             size_t pos = std::min(slot.n_sent_text, slot.generated_text.size());
 
             const std::string str_test = slot.generated_text.substr(pos);
-            bool is_stop_full = false;
+            bool send_text = true;
 
             size_t stop_pos = slot.find_stopping_strings(str_test, token_str.size(), STOP_TYPE_FULL);
             if (stop_pos != std::string::npos) {
-                is_stop_full = true;
                 slot.generated_text.erase(
                     slot.generated_text.begin() + pos + stop_pos,
                     slot.generated_text.end());
                 pos = std::min(slot.n_sent_text, slot.generated_text.size());
-            } else {
-                is_stop_full = false;
+            } else if (slot.has_next_token) {
                 stop_pos = slot.find_stopping_strings(str_test, token_str.size(), STOP_TYPE_PARTIAL);
+                send_text = stop_pos == std::string::npos;
             }
 
             // check if there is any token to predict
-            if (stop_pos == std::string::npos || (!slot.has_next_token && !is_stop_full && stop_pos > 0)) {
+            if (send_text) {
                 // no send the stop word in the response
                 result.text_to_send = slot.generated_text.substr(pos, std::string::npos);
                 slot.n_sent_text += result.text_to_send.size();
@@ -1187,13 +1130,28 @@ struct server_context {
             SLT_DBG(slot, "stopped by limit, n_decoded = %d, n_predict = %d\n", slot.n_decoded, slot.params.n_predict);
         }
 
+        // if we have already seen a new line, we stop after a certain time limit
+        if (slot.has_new_line && slot.params.t_max_predict_ms > 0 &&
+            (ggml_time_us() - slot.t_start_generation > 1000.0f*slot.params.t_max_predict_ms)) {
+            slot.stopped_limit  = true;
+            slot.has_next_token = false;
+
+            SLT_DBG(slot, "stopped by time limit, n_decoded = %d, t_max_predict_ms = %d ms\n", slot.n_decoded, (int) slot.params.t_max_predict_ms);
+        }
+
+        // check if there is a new line in the generated text
+        if (result.text_to_send.find('\n') != std::string::npos) {
+            slot.has_new_line = true;
+        }
+
         // if context shift is disabled, we stop when it reaches the context limit
-        if (slot.n_decoded >= slot.n_ctx) {
+        if (slot.n_past >= slot.n_ctx) {
             slot.truncated      = true;
             slot.stopped_limit  = true;
             slot.has_next_token = false;
 
-            SLT_DBG(slot, "stopped due to running out of context capacity, n_decoded = %d, n_ctx = %d\n", slot.n_decoded, slot.n_ctx);
+            SLT_DBG(slot, "stopped due to running out of context capacity, n_past = %d, n_prompt_tokens = %d, n_decoded = %d, n_ctx = %d\n",
+                    slot.n_decoded, slot.n_prompt_tokens, slot.n_past, slot.n_ctx);
         }
 
         if (llama_token_is_eog(model, result.tok)) {
@@ -1205,18 +1163,18 @@ struct server_context {
 
         const auto n_ctx_train = llama_n_ctx_train(model);
 
-        if (slot.params.n_predict < 1 && slot.n_predict < 1 && slot.ga_n == 1 && slot.n_prompt_tokens + slot.n_decoded >= n_ctx_train) {
+        if (slot.params.n_predict < 1 && slot.n_predict < 1 && slot.n_prompt_tokens + slot.n_decoded >= n_ctx_train) {
             slot.truncated      = true;
             slot.stopped_limit  = true;
             slot.has_next_token = false; // stop prediction
 
             SLT_WRN(slot,
-                    "n_predict (%d) is not set and self-context extend is disabled. "
+                    "n_predict (%d) is set for infinite generation. "
                     "Limiting generated tokens to n_ctx_train (%d) to avoid EOS-less generation infinite loop\n",
                     slot.params.n_predict, n_ctx_train);
         }
 
-        SLT_DBG(slot, "n_decoded = %d, n_remaining = %d, next token: '%s'\n", slot.n_decoded, slot.n_remaining, token_str.c_str());
+        SLT_DBG(slot, "n_decoded = %d, n_remaining = %d, next token: %5d '%s'\n", slot.n_decoded, slot.n_remaining, result.tok, token_str.c_str());
 
         return slot.has_next_token; // continue
     }
@@ -1240,6 +1198,8 @@ struct server_context {
             {"top_k",                     slot.sparams.top_k},
             {"top_p",                     slot.sparams.top_p},
             {"min_p",                     slot.sparams.min_p},
+            {"xtc_probability",           slot.sparams.xtc_probability},
+            {"xtc_threshold",             slot.sparams.xtc_threshold},
             {"tfs_z",                     slot.sparams.tfs_z},
             {"typical_p",                 slot.sparams.typ_p},
             {"repeat_last_n",             slot.sparams.penalty_last_n},
@@ -1335,6 +1295,7 @@ struct server_context {
             {"tokens_evaluated",    slot.n_prompt_tokens},
             {"generation_settings", get_formated_generation(slot)},
             {"prompt",              slot.prompt},
+            {"has_new_line",        slot.has_new_line},
             {"truncated",           slot.truncated},
             {"stopped_eos",         slot.stopped_eos},
             {"stopped_word",        slot.stopped_word},
@@ -1484,9 +1445,8 @@ struct server_context {
         if (prompt.is_string() || json_is_array_of_numbers(prompt)) {
             data["index"] = 0;
             create_task(data, false, nullptr);
-        }
-        // otherwise, it's a multiple-prompt task, we break it into smaller tasks
-        else if (prompt.is_array()) {
+        } else if (prompt.is_array()) {
+            // otherwise, it's a multiple-prompt task, we break it into smaller tasks
             std::vector<json> prompts = prompt;
             if (cmpl_type == SERVER_TASK_CMPL_TYPE_RERANK) {
                 // prompts[0] is the question
@@ -1511,9 +1471,8 @@ struct server_context {
                     }
                 }
             }
-        }
-        // invalid case
-        else {
+        } else {
+            // invalid case
             throw std::runtime_error(error_msg);
         }
 
@@ -1663,6 +1622,7 @@ struct server_context {
                         slot_data["prompt"]     = slot.prompt;
                         slot_data["next_token"] = {
                             {"has_next_token", slot.has_next_token},
+                            {"has_new_line",   slot.has_new_line},
                             {"n_remain",       slot.n_remaining},
                             {"n_decoded",      slot.n_decoded},
                             {"stopped_eos",    slot.stopped_eos},
@@ -1786,6 +1746,9 @@ struct server_context {
                     }
                     slot->cache_tokens.resize(token_count);
 
+                    // TODO: maybe detokenize the slot->cache_tokens instead?
+                    slot->prompt = string_format("[restored %d tokens from file]", (int) token_count);
+
                     const int64_t t_end = ggml_time_us();
                     const double t_restore_ms = (t_end - t_start) / 1000.0;
 
@@ -1860,12 +1823,8 @@ struct server_context {
             }
 
             if (all_idle) {
-                if (system_need_update) {
-                    system_prompt_update();
-                }
-
                 SRV_INF("%s", "all slots are idle\n");
-                if (system_prompt.empty() && clean_kv_cache) {
+                if (clean_kv_cache) {
                     kv_cache_clear();
                 }
 
@@ -1886,38 +1845,36 @@ struct server_context {
         // apply context-shift if needed
         // TODO: simplify and improve
         for (server_slot & slot : slots) {
-            if (slot.ga_n == 1) {
-                if (slot.is_processing() && (int) system_tokens.size() + slot.n_past >= slot.n_ctx - 1) {
-                    if (!params.ctx_shift) {
-                        // this check is redundant (for good)
-                        // we should never get here, because generation should already stopped in process_token()
-                        slot.release();
-                        send_error(slot, "context shift is disabled", ERROR_TYPE_SERVER);
-                        continue;
-                    }
-
-                    // Shift context
-                    const int n_keep    = slot.params.n_keep + add_bos_token;
-                    const int n_left    = (int) system_tokens.size() + slot.n_past - n_keep;
-                    const int n_discard = slot.params.n_discard ? slot.params.n_discard : (n_left / 2);
-
-                    SLT_WRN(slot, "slot context shift, n_keep = %d, n_left = %d, n_discard = %d\n", n_keep, n_left, n_discard);
-
-                    llama_kv_cache_seq_rm (ctx, slot.id + 1, n_keep            , n_keep + n_discard);
-                    llama_kv_cache_seq_add(ctx, slot.id + 1, n_keep + n_discard, system_tokens.size() + slot.n_past, -n_discard);
-
-                    if (slot.params.cache_prompt) {
-                        for (size_t i = n_keep + n_discard; i < slot.cache_tokens.size(); i++) {
-                            slot.cache_tokens[i - n_discard] = slot.cache_tokens[i];
-                        }
-
-                        slot.cache_tokens.resize(slot.cache_tokens.size() - n_discard);
-                    }
-
-                    slot.n_past -= n_discard;
-
-                    slot.truncated = true;
+            if (slot.is_processing() && slot.n_past + 1 >= slot.n_ctx) {
+                if (!params.ctx_shift) {
+                    // this check is redundant (for good)
+                    // we should never get here, because generation should already stopped in process_token()
+                    slot.release();
+                    send_error(slot, "context shift is disabled", ERROR_TYPE_SERVER);
+                    continue;
                 }
+
+                // Shift context
+                const int n_keep    = slot.params.n_keep + add_bos_token;
+                const int n_left    = slot.n_past - n_keep;
+                const int n_discard = slot.params.n_discard ? slot.params.n_discard : (n_left / 2);
+
+                SLT_WRN(slot, "slot context shift, n_keep = %d, n_left = %d, n_discard = %d\n", n_keep, n_left, n_discard);
+
+                llama_kv_cache_seq_rm (ctx, slot.id + 1, n_keep            , n_keep + n_discard);
+                llama_kv_cache_seq_add(ctx, slot.id + 1, n_keep + n_discard, slot.n_past,        -n_discard);
+
+                if (slot.params.cache_prompt) {
+                    for (size_t i = n_keep + n_discard; i < slot.cache_tokens.size(); i++) {
+                        slot.cache_tokens[i - n_discard] = slot.cache_tokens[i];
+                    }
+
+                    slot.cache_tokens.resize(slot.cache_tokens.size() - n_discard);
+                }
+
+                slot.n_past -= n_discard;
+
+                slot.truncated = true;
             }
         }
 
@@ -1932,11 +1889,7 @@ struct server_context {
 
             slot.i_batch = batch.n_tokens;
 
-            const int32_t slot_npast = slot.n_past_se > 0 ? slot.n_past_se : slot.n_past;
-
-            // TODO: we always have to take into account the "system_tokens"
-            //       this is not great and needs to be improved somehow
-            common_batch_add(batch, slot.sampled, system_tokens.size() + slot_npast, { slot.id + 1 }, true);
+            common_batch_add(batch, slot.sampled, slot.n_past, { slot.id + 1 }, true);
 
             slot.n_past += 1;
 
@@ -1944,8 +1897,8 @@ struct server_context {
                 slot.cache_tokens.push_back(slot.sampled);
             }
 
-            SLT_DBG(slot, "slot decode token, n_ctx = %d, n_past = %d, n_system_tokens = %d, n_cache_tokens = %d, truncated = %d\n",
-                    slot.n_ctx, slot.n_past, (int) system_tokens.size(), (int) slot.cache_tokens.size(), slot.truncated);
+            SLT_DBG(slot, "slot decode token, n_ctx = %d, n_past = %d, n_cache_tokens = %d, truncated = %d\n",
+                    slot.n_ctx, slot.n_past, (int) slot.cache_tokens.size(), slot.truncated);
         }
 
         // process in chunks of params.n_batch
@@ -1972,63 +1925,126 @@ struct server_context {
                         slot.t_start_process_prompt = ggml_time_us();
                         slot.t_start_generation = 0;
 
-                        if (slot.cmpl_type == SERVER_TASK_CMPL_TYPE_INFILL) {
-                            const bool add_bos = llama_add_bos_token(model);
-                            bool suff_rm_leading_spc = true;
-                            if (params.input_suffix.find_first_of(' ') == 0 && params.input_suffix.size() > 1) {
-                                params.input_suffix.erase(0, 1);
-                                suff_rm_leading_spc = false;
-                            }
+                        switch (slot.cmpl_type) {
+                            case SERVER_TASK_CMPL_TYPE_NORMAL:
+                            case SERVER_TASK_CMPL_TYPE_EMBEDDING:
+                                {
+                                    prompt_tokens = tokenize(slot.prompt, llama_add_bos_token(model), true);
+                                } break;
+                            case SERVER_TASK_CMPL_TYPE_RERANK:
+                                {
+                                    // require slot.prompt to be array of 2 strings
+                                    if (!slot.prompt.is_array() || slot.prompt.size() != 2) {
+                                        SLT_ERR(slot, "%s", "invalid prompt for rerank task\n");
+                                        slot.release();
+                                        send_error(slot, "invalid prompt for rerank task", ERROR_TYPE_INVALID_REQUEST);
+                                        continue;
+                                    }
 
-                            auto prefix_tokens = tokenize(slot.params.input_prefix, false);
-                            auto suffix_tokens = tokenize(slot.params.input_suffix, false);
+                                    // prompt: [BOS]query[EOS][SEP]doc[EOS]
+                                    prompt_tokens.clear();
+                                    prompt_tokens.push_back(llama_token_bos(model));
+                                    {
+                                        const auto part = tokenize(slot.prompt[0], false, false);
+                                        prompt_tokens.insert(prompt_tokens.end(), part.begin(), part.end());
+                                    }
+                                    prompt_tokens.push_back(llama_token_eos(model));
+                                    prompt_tokens.push_back(llama_token_sep(model));
+                                    {
+                                        const auto part = tokenize(slot.prompt[1], false, false);
+                                        prompt_tokens.insert(prompt_tokens.end(), part.begin(), part.end());
+                                    }
+                                    prompt_tokens.push_back(llama_token_eos(model));
+                                } break;
+                            case SERVER_TASK_CMPL_TYPE_INFILL:
+                                {
+                                    // TODO: optimize this block by reducing memory allocations and movement
 
-                            const int space_token = 29871; // TODO: this should not be hardcoded
-                            if (suff_rm_leading_spc && !suffix_tokens.empty() && suffix_tokens[0] == space_token) {
-                                suffix_tokens.erase(suffix_tokens.begin());
-                            }
+                                    // use FIM repo-level pattern:
+                                    // ref: https://arxiv.org/pdf/2409.12186
+                                    //
+                                    // [FIM_REP]myproject
+                                    // [FIM_SEP]filename0
+                                    // extra chunk 0
+                                    // [FIM_SEP]filename1
+                                    // extra chunk 1
+                                    // ...
+                                    // [FIM_SEP]filename
+                                    // [FIM_PRE]prefix[FIM_SUF]suffix[FIM_MID]prompt
+                                    //
+                                    auto tokens_prefix = tokenize(slot.input_prefix, false, false);
+                                    auto tokens_suffix = tokenize(slot.input_suffix, false, false);
+                                    auto tokens_prompt = tokenize(slot.prompt,       false, false);
 
-                            prefix_tokens.insert(prefix_tokens.begin(), llama_token_prefix(model));
-                            suffix_tokens.insert(suffix_tokens.begin(), llama_token_suffix(model));
+                                    slot.extra_tokens.clear();
+                                    if (llama_token_fim_rep(model) != LLAMA_TOKEN_NULL) {
+                                        static const auto k_fim_repo = tokenize("myproject\n", false, false);
 
-                            auto embd_inp = params.spm_infill ? suffix_tokens : prefix_tokens;
-                            auto embd_end = params.spm_infill ? prefix_tokens : suffix_tokens;
-                            if (add_bos) {
-                                embd_inp.insert(embd_inp.begin(), llama_token_bos(model));
-                            }
-                            embd_inp.insert(embd_inp.end(), embd_end.begin(), embd_end.end());
+                                        slot.extra_tokens.push_back(llama_token_fim_rep(model));
+                                        slot.extra_tokens.insert(slot.extra_tokens.end(), k_fim_repo.begin(), k_fim_repo.end());
+                                    }
 
-                            const llama_token middle_token = llama_token_middle(model);
-                            if (middle_token >= 0) {
-                                embd_inp.push_back(middle_token);
-                            }
+                                    for (const auto & chunk : slot.input_extra) {
+                                        // { "text": string, "filename": string }
+                                        const std::string text     = chunk.value("text", "");
+                                        const std::string filename = chunk.value("filename", "tmp");
 
-                            prompt_tokens = embd_inp;
-                        } else if (slot.cmpl_type == SERVER_TASK_CMPL_TYPE_RERANK) {
-                            // require slot.prompt to be array of 2 strings
-                            if (!slot.prompt.is_array() || slot.prompt.size() != 2) {
-                                SLT_ERR(slot, "%s", "invalid prompt for rerank task\n");
-                                slot.release();
-                                send_error(slot, "invalid prompt for rerank task", ERROR_TYPE_INVALID_REQUEST);
-                                continue;
-                            }
+                                        if (llama_token_fim_sep(model) != LLAMA_TOKEN_NULL) {
+                                            const auto k_fim_file = tokenize(filename + "\n", false, false);
 
-                            // prompt: [BOS]query[EOS][SEP]doc[EOS]
-                            prompt_tokens.clear();
-                            prompt_tokens.push_back(llama_token_bos(model));
-                            {
-                                const auto part = tokenize(slot.prompt[0], false);
-                                prompt_tokens.insert(prompt_tokens.end(), part.begin(), part.end());
-                            }
-                            prompt_tokens.push_back(llama_token_eos(model));
-                            prompt_tokens.push_back(llama_token_sep(model));
-                            {
-                                const auto part = tokenize(slot.prompt[1], false);
-                                prompt_tokens.insert(prompt_tokens.end(), part.begin(), part.end());
-                            }
-                            prompt_tokens.push_back(llama_token_eos(model));
-                        } else {
-                            prompt_tokens = tokenize(slot.prompt, system_prompt.empty()); // add BOS if there isn't system prompt
+                                            slot.extra_tokens.insert(slot.extra_tokens.end(), llama_token_fim_sep(model));
+                                            slot.extra_tokens.insert(slot.extra_tokens.end(), k_fim_file.begin(), k_fim_file.end());
+                                        } else {
+                                            // chunk separator in binary form to avoid confusing the AI
+                                            static const char k_chunk_prefix_str[] = {0x0a, 0x0a, 0x2d, 0x2d, 0x2d, 0x20, 0x73, 0x6e, 0x69, 0x70, 0x70, 0x65, 0x74, 0x20, 0x2d, 0x2d, 0x2d, 0x0a, 0x0a, 0x00};
+                                            static const auto k_chunk_prefix_tokens = tokenize(k_chunk_prefix_str, false, false);
+
+                                            slot.extra_tokens.insert(slot.extra_tokens.end(), k_chunk_prefix_tokens.begin(), k_chunk_prefix_tokens.end());
+                                        }
+
+                                        const auto chunk_tokens = tokenize(text, false, false);
+                                        slot.extra_tokens.insert(slot.extra_tokens.end(), chunk_tokens.begin(), chunk_tokens.end());
+                                    }
+
+                                    if (llama_token_fim_sep(model) != LLAMA_TOKEN_NULL) {
+                                        // TODO: current filename
+                                        static const auto k_fim_file = tokenize("filename\n", false, false);
+
+                                        slot.extra_tokens.insert(slot.extra_tokens.end(), llama_token_fim_sep(model));
+                                        slot.extra_tokens.insert(slot.extra_tokens.end(), k_fim_file.begin(), k_fim_file.end());
+                                    }
+
+                                    // for now pick FIM context to fit in a batch (ratio prefix:suffix = 3:1, TODO: configurable?)
+                                    const int n_suffix_take = std::min<int>(tokens_suffix.size(),   (n_batch/4));
+                                    const int n_prefix_take = std::min<int>(tokens_prefix.size(), 3*(n_batch/4) - 3);
+
+                                    // fill the rest of the context with extra chunks
+                                    const int n_extra_take = std::min<int>(std::max<int>(0, slot.n_ctx - (n_batch) - 2*slot.n_predict), slot.extra_tokens.size());
+
+                                    tokens_prefix.erase(tokens_prefix.begin(), tokens_prefix.begin() + tokens_prefix.size() - n_prefix_take);
+                                    tokens_suffix.resize(n_suffix_take);
+
+                                    tokens_prefix.insert(tokens_prefix.begin(), llama_token_fim_pre(model));
+                                    tokens_prefix.insert(tokens_prefix.end(),   tokens_prompt.begin(), tokens_prompt.end());
+                                    tokens_suffix.insert(tokens_suffix.begin(), llama_token_fim_suf(model));
+
+                                    auto embd_inp = params.spm_infill ? tokens_suffix : tokens_prefix;
+                                    auto embd_end = params.spm_infill ? tokens_prefix : tokens_suffix;
+
+                                    if (llama_add_bos_token(model)) {
+                                        embd_inp.insert(embd_inp.begin(), llama_token_bos(model));
+                                    }
+
+                                    SLT_DBG(slot, "extra: n_ctx = %d, n_extra_take = %d, n_extra = %d\n", slot.n_ctx, n_extra_take, (int) slot.extra_tokens.size());
+
+                                    // put the extra context before the FIM prefix
+                                    embd_inp.insert(embd_inp.begin(), slot.extra_tokens.end() - n_extra_take, slot.extra_tokens.end());
+
+                                    embd_inp.insert(embd_inp.end(), embd_end.begin(), embd_end.end());
+                                    embd_inp.push_back(llama_token_fim_mid(model));
+
+                                    prompt_tokens = std::move(embd_inp);
+                                } break;
                         }
 
                         slot.n_past = 0;
@@ -2036,6 +2052,19 @@ struct server_context {
 
                         SLT_INF(slot, "prompt tokenized, n_ctx_slot = %d, n_keep = %d, n_prompt_tokens = %d\n", slot.n_ctx, slot.params.n_keep, slot.n_prompt_tokens);
 
+                        // print prompt tokens (for debugging)
+                        if (1) {
+                            // first 16 tokens (avoid flooding logs)
+                            for (int i = 0; i < std::min<int>(16, prompt_tokens.size()); i++) {
+                                SLT_DBG(slot, "prompt token %3d: %6d '%s'\n", i, prompt_tokens[i], common_token_to_piece(ctx, prompt_tokens[i]).c_str());
+                            }
+                        } else {
+                            // all
+                            for (int i = 0; i < (int) prompt_tokens.size(); i++) {
+                                SLT_DBG(slot, "prompt token %3d: %6d '%s'\n", i, prompt_tokens[i], common_token_to_piece(ctx, prompt_tokens[i]).c_str());
+                            }
+                        }
+
                         // empty prompt passed -> release the slot and send empty response
                         if (prompt_tokens.empty()) {
                             SLT_WRN(slot, "%s", "empty prompt - releasing slot\n");
@@ -2056,7 +2085,9 @@ struct server_context {
                         } else {
                             if (!params.ctx_shift) {
                                 // if context shift is disabled, we make sure prompt size is smaller than KV size
-                                if ((int) system_tokens.size() + slot.n_prompt_tokens >= slot.n_ctx) {
+                                // TODO: there should be a separate parameter that control prompt truncation
+                                //       context shift should be applied only during the generation phase
+                                if (slot.n_prompt_tokens >= slot.n_ctx) {
                                     slot.release();
                                     send_error(slot, "the request exceeds the available context size. try increasing the context size or enable context shift", ERROR_TYPE_INVALID_REQUEST);
                                     continue;
@@ -2067,8 +2098,8 @@ struct server_context {
                             }
                             slot.params.n_keep = std::min(slot.n_ctx - 4, slot.params.n_keep);
 
-                            // if input prompt is too big, truncate it (if group attention self-extend is disabled)
-                            if (slot.ga_n == 1 && slot.n_prompt_tokens >= slot.n_ctx) {
+                            // if input prompt is too big, truncate it
+                            if (slot.n_prompt_tokens >= slot.n_ctx) {
                                 const int n_left = slot.n_ctx - slot.params.n_keep;
 
                                 const int n_block_size = n_left / 2;
@@ -2095,19 +2126,61 @@ struct server_context {
 
                             common_sampler_reset(slot.smpl);
 
-                            if (!slot.params.cache_prompt) {
-                                slot.n_past_se = 0;
-                                slot.ga_i      = 0;
-                            } else {
-                                GGML_ASSERT(slot.ga_n == 1);
-
+                            if (slot.params.cache_prompt) {
                                 // reuse any previously computed tokens that are common with the new prompt
-                                slot.n_past = common_part(slot.cache_tokens, prompt_tokens);
+                                slot.n_past = longest_common_prefix(slot.cache_tokens, prompt_tokens);
 
                                 // push the prompt into the sampling context (do not apply grammar)
                                 for (int i = 0; i < slot.n_past; ++i) {
                                     common_sampler_accept(slot.smpl, slot.cache_tokens[i], false);
                                 }
+
+                                // reuse chunks from the cached prompt by shifting their KV cache in the new position
+                                if (params.n_cache_reuse > 0) {
+                                    size_t head_c = slot.n_past; // cache
+                                    size_t head_p = slot.n_past; // current prompt
+
+                                    SLT_DBG(slot, "trying to reuse chunks with size > %d, slot.n_past = %d\n", params.n_cache_reuse, slot.n_past);
+
+                                    while (head_c < slot.cache_tokens.size() &&
+                                           head_p < prompt_tokens.size()) {
+
+                                        size_t n_match = 0;
+                                        while (head_c + n_match < slot.cache_tokens.size() &&
+                                               head_p + n_match < prompt_tokens.size()     &&
+                                               slot.cache_tokens[head_c + n_match] == prompt_tokens[head_p + n_match]) {
+
+                                            n_match++;
+                                        }
+
+                                        if (n_match >= (size_t) params.n_cache_reuse) {
+                                            SLT_INF(slot, "reusing chunk with size %zu, shifting KV cache [%zu, %zu) -> [%zu, %zu)\n", n_match, head_c, head_c + n_match, head_p, head_p + n_match);
+                                            //for (size_t i = head_p; i < head_p + n_match; i++) {
+                                            //    SLT_DBG(slot, "cache token %3zu: %6d '%s'\n", i, prompt_tokens[i], common_token_to_piece(ctx, prompt_tokens[i]).c_str());
+                                            //}
+
+                                            const int64_t kv_shift = (int64_t) head_p - (int64_t) head_c;
+
+                                            llama_kv_cache_seq_rm (ctx, slot.id + 1, head_p, head_c);
+                                            llama_kv_cache_seq_add(ctx, slot.id + 1, head_c, -1,     kv_shift);
+
+                                            for (size_t i = 0; i < n_match; i++) {
+                                                slot.cache_tokens[head_p + i] = slot.cache_tokens[head_c + i];
+
+                                                common_sampler_accept(slot.smpl, slot.cache_tokens[head_p + i], false);
+
+                                                slot.n_past++;
+                                            }
+
+                                            head_c += n_match;
+                                            head_p += n_match;
+                                        } else {
+                                            head_c += 1;
+                                        }
+                                    }
+
+                                    SLT_DBG(slot, "after context reuse, new slot.n_past = %d\n", slot.n_past);
+                                }
                             }
                         }
 
@@ -2116,9 +2189,6 @@ struct server_context {
                             SLT_WRN(slot, "need to evaluate at least 1 token to generate logits, n_past = %d, n_prompt_tokens = %d\n", slot.n_past, slot.n_prompt_tokens);
 
                             slot.n_past--;
-                            if (slot.ga_i > 0) {
-                                slot.n_past_se--;
-                            }
                         }
 
                         slot.n_prompt_tokens_processed = 0;
@@ -2144,55 +2214,31 @@ struct server_context {
                     }
 
                     // keep only the common part
-                    int p0 = (int) system_tokens.size() + slot.n_past;
-                    if (!llama_kv_cache_seq_rm(ctx, slot.id + 1, p0, -1)) {
+                    if (!llama_kv_cache_seq_rm(ctx, slot.id + 1, slot.n_past, -1)) {
                         // could not partially delete (likely using a non-Transformer model)
                         llama_kv_cache_seq_rm(ctx, slot.id + 1, -1, -1);
 
-                        p0 = (int) system_tokens.size();
-                        if (p0 != 0) {
-                            // copy over the system prompt when there is one
-                            llama_kv_cache_seq_cp(ctx, 0, slot.id + 1, -1, -1);
-                        }
-
-                        // there is no common part left (except for the system prompt)
+                        // there is no common part left
                         slot.n_past = 0;
-                        slot.n_past_se = 0;
-                        slot.ga_i = 0;
-                        // TODO: is the system prompt ever in the sampling context?
+
                         common_sampler_reset(slot.smpl);
                     }
 
+                    SLT_INF(slot, "kv cache rm [%d, end)\n", slot.n_past);
+
                     // remove the non-common part from the cache
                     slot.cache_tokens.resize(slot.n_past);
 
-                    SLT_INF(slot, "kv cache rm [%d, end)\n", p0);
-
-                    int32_t slot_npast = slot.n_past_se > 0 ? slot.n_past_se : slot.n_past;
-
-                    int32_t ga_i = slot.ga_i;
-                    int32_t ga_n = slot.ga_n;
-                    int32_t ga_w = slot.ga_w;
-
                     // add prompt tokens for processing in the current batch
-                    // TODO: the self-extend stuff here is a mess - simplify and/or abstract it somehow
-                    for (; slot.n_past < slot.n_prompt_tokens && batch.n_tokens < n_batch; ++slot.n_past) {
-                        if (slot.ga_n != 1) {
-                            while (slot_npast >= ga_i + ga_w) {
-                                const int bd = (ga_w/ga_n)*(ga_n - 1);
-                                slot_npast -= bd;
-                                ga_i += ga_w/ga_n;
-                            }
-                        }
-
-                        common_batch_add(batch, prompt_tokens[slot.n_past], system_tokens.size() + slot_npast, { slot.id + 1 }, false);
+                    while (slot.n_past < slot.n_prompt_tokens && batch.n_tokens < n_batch) {
+                        common_batch_add(batch, prompt_tokens[slot.n_past], slot.n_past, { slot.id + 1 }, false);
 
                         if (slot.params.cache_prompt) {
                             slot.cache_tokens.push_back(prompt_tokens[slot.n_past]);
                         }
 
                         slot.n_prompt_tokens_processed++;
-                        slot_npast++;
+                        slot.n_past++;
                     }
 
                     SLT_INF(slot, "prompt processing progress, n_past = %d, n_tokens = %d, progress = %f\n", slot.n_past, batch.n_tokens, (float) slot.n_prompt_tokens_processed / slot.n_prompt_tokens);
@@ -2233,34 +2279,6 @@ struct server_context {
         for (int32_t i = 0; i < batch.n_tokens; i += n_batch) {
             const int32_t n_tokens = std::min(n_batch, batch.n_tokens - i);
 
-            for (auto & slot : slots) {
-                if (slot.ga_n != 1) {
-                    // context extension via Self-Extend
-                    // TODO: simplify and/or abstract this
-                    while (slot.n_past_se >= slot.ga_i + slot.ga_w) {
-                        const int ib = (slot.ga_n * slot.ga_i) / slot.ga_w;
-                        const int bd = (slot.ga_w / slot.ga_n) * (slot.ga_n - 1);
-                        const int dd = (slot.ga_w / slot.ga_n) - ib * bd - slot.ga_w;
-
-                        SLT_DBG(slot, "shift: [%6d, %6d] + %6d -> [%6d, %6d]\n", slot.ga_i, slot.n_past_se, ib * bd, slot.ga_i + ib * bd, slot.n_past_se + ib * bd);
-                        SLT_DBG(slot, "div:   [%6d, %6d] / %6d -> [%6d, %6d]\n", slot.ga_i + ib * bd, slot.ga_i + ib * bd + slot.ga_w, slot.ga_n, (slot.ga_i + ib * bd) / slot.ga_n, (slot.ga_i + ib * bd + slot.ga_w) / slot.ga_n);
-                        SLT_DBG(slot, "shift: [%6d, %6d] + %6d -> [%6d, %6d]\n", slot.ga_i + ib * bd + slot.ga_w, slot.n_past_se + ib * bd, dd, slot.ga_i + ib * bd + slot.ga_w + dd, slot.n_past_se + ib * bd + dd);
-
-                        llama_kv_cache_seq_add(ctx, slot.id + 1, slot.ga_i, slot.n_past_se, ib * bd);
-                        llama_kv_cache_seq_div(ctx, slot.id + 1, slot.ga_i + ib * bd, slot.ga_i + ib * bd + slot.ga_w, slot.ga_n);
-                        llama_kv_cache_seq_add(ctx, slot.id + 1, slot.ga_i + ib * bd + slot.ga_w, slot.n_past_se + ib * bd, dd);
-
-                        slot.n_past_se -= bd;
-
-                        slot.ga_i += slot.ga_w / slot.ga_n;
-
-                        SLT_DBG(slot, "\nn_past_old = %d, n_past = %d, ga_i = %d\n\n", slot.n_past_se + bd, slot.n_past_se, slot.ga_i);
-                    }
-
-                    slot.n_past_se += n_tokens;
-                }
-            }
-
             llama_batch batch_view = {
                 n_tokens,
                 batch.token    + i,
@@ -2415,10 +2433,6 @@ int main(int argc, char ** argv) {
     // struct that contains llama context and inference
     server_context ctx_server;
 
-    if (!params.system_prompt.empty()) {
-        ctx_server.system_prompt_set(params.system_prompt);
-    }
-
     if (params.model_alias == "unknown") {
         params.model_alias = params.model;
     }
@@ -2846,7 +2860,6 @@ int main(int argc, char ** argv) {
 
     const auto handle_props = [&ctx_server, &res_ok](const httplib::Request &, httplib::Response & res) {
         json data = {
-            { "system_prompt",               ctx_server.system_prompt },
             { "default_generation_settings", ctx_server.default_generation_settings_for_props },
             { "total_slots",                 ctx_server.params.n_parallel },
             { "chat_template",               llama_get_chat_template(ctx_server.model) },
@@ -2862,10 +2875,8 @@ int main(int argc, char ** argv) {
         }
 
         json data = json::parse(req.body);
-        if (data.contains("system_prompt")) {
-            std::string system_prompt = data.at("system_prompt");
-            ctx_server.system_prompt_set(system_prompt);
-        }
+
+        // update any props here
 
         res_ok(res, {{ "success", true }});
     };
@@ -2925,7 +2936,23 @@ int main(int argc, char ** argv) {
         return handle_completions_generic(SERVER_TASK_CMPL_TYPE_NORMAL, data, res);
     };
 
-    const auto handle_infill = [&handle_completions_generic](const httplib::Request & req, httplib::Response & res) {
+    const auto handle_infill = [&ctx_server, &res_error, &handle_completions_generic](const httplib::Request & req, httplib::Response & res) {
+        std::string err;
+        if (llama_token_fim_pre(ctx_server.model) == LLAMA_TOKEN_NULL) {
+            err += "prefix token is missing. ";
+        }
+        if (llama_token_fim_suf(ctx_server.model) == LLAMA_TOKEN_NULL) {
+            err += "suffix token is missing. ";
+        }
+        if (llama_token_fim_mid(ctx_server.model) == LLAMA_TOKEN_NULL) {
+            err += "middle token is missing. ";
+        }
+
+        if (!err.empty()) {
+            res_error(res, format_error_response(string_format("Infill is not supported by this model: %s", err.c_str()), ERROR_TYPE_NOT_SUPPORTED));
+            return;
+        }
+
         json data = json::parse(req.body);
         return handle_completions_generic(SERVER_TASK_CMPL_TYPE_INFILL, data, res);
     };
@@ -3011,7 +3038,8 @@ int main(int argc, char ** argv) {
         if (body.count("content") != 0) {
             const bool add_special = json_value(body, "add_special", false);
             const bool with_pieces = json_value(body, "with_pieces", false);
-            std::vector<llama_token> tokens = ctx_server.tokenize(body.at("content"), add_special);
+
+            std::vector<llama_token> tokens = ctx_server.tokenize(body.at("content"), add_special, true);
 
             if (with_pieces) {
                 for (const auto& token : tokens) {
@@ -3362,6 +3390,7 @@ int main(int argc, char ** argv) {
 
     ctx_server.queue_tasks.on_new_task(std::bind(
                 &server_context::process_single_task, &ctx_server, std::placeholders::_1));
+
     ctx_server.queue_tasks.on_update_slots(std::bind(
                 &server_context::update_slots, &ctx_server));
 
diff --git a/examples/server/tests/features/ctx_shift.feature b/examples/server/tests/features/ctx_shift.feature
index ba3afcf06..ae6c6b01b 100644
--- a/examples/server/tests/features/ctx_shift.feature
+++ b/examples/server/tests/features/ctx_shift.feature
@@ -13,6 +13,10 @@ Feature: llama.cpp server
     And   32 as batch size
     And   2 slots
 
+    # the prompt is 301 tokens
+    # the slot context is 256/2 = 128 tokens
+    # the prompt is truncated to keep the last 109 tokens
+    # 64 tokens are generated thanks to shifting the context when it gets full
   Scenario: Inference with context shift
     And   64 server max tokens to predict
     Then  the server is starting
diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp
index ad99e9574..69519ef95 100644
--- a/examples/server/utils.hpp
+++ b/examples/server/utils.hpp
@@ -195,14 +195,14 @@ static std::string gen_chatcmplid() {
 // other common utils
 //
 
-static size_t common_part(const std::vector<llama_token> & a, const std::vector<llama_token> & b) {
+static size_t longest_common_prefix(const std::vector<llama_token> & a, const std::vector<llama_token> & b) {
     size_t i;
     for (i = 0; i < a.size() && i < b.size() && a[i] == b[i]; i++) {}
 
     return i;
 }
 
-static size_t common_part(const std::string & a, const std::string & b) {
+static size_t longest_common_prefix(const std::string & a, const std::string & b) {
     size_t i;
     for (i = 0; i < a.size() && i < b.size() && a[i] == b[i]; i++) {}
 
@@ -360,9 +360,9 @@ static json oaicompat_completion_params_parse(
 
     // Handle "logprobs" field
     // TODO: The response format of this option is not yet OAI-compatible, but seems like no one really using it; We may need to fix it in the future
-    if (body.contains("logprobs")) {
+    if (json_value(body, "logprobs", false)) {
         llama_params["n_probs"] = json_value(body, "top_logprobs", 20);
-    } else if (body.contains("top_logprobs")) {
+    } else if (body.contains("top_logprobs") && !body.at("top_logprobs").is_null()) {
         throw std::runtime_error("top_logprobs requires logprobs to be set to true");
     }
 
diff --git a/ggml/include/ggml-vulkan.h b/ggml/include/ggml-vulkan.h
index e074042ef..c03bbfe5e 100644
--- a/ggml/include/ggml-vulkan.h
+++ b/ggml/include/ggml-vulkan.h
@@ -24,6 +24,8 @@ GGML_API ggml_backend_buffer_type_t ggml_backend_vk_buffer_type(size_t dev_num);
 // pinned host buffer for use with the CPU backend for faster copies between CPU and GPU
 GGML_API ggml_backend_buffer_type_t ggml_backend_vk_host_buffer_type(void);
 
+GGML_API ggml_backend_reg_t ggml_backend_vk_reg(void);
+
 #ifdef  __cplusplus
 }
 #endif
diff --git a/ggml/src/ggml-alloc.c b/ggml/src/ggml-alloc.c
index 70187b9b6..041de9e3e 100644
--- a/ggml/src/ggml-alloc.c
+++ b/ggml/src/ggml-alloc.c
@@ -14,7 +14,7 @@
 
 //#define GGML_ALLOCATOR_DEBUG
 
-//#define AT_PRINTF(...) fprintf(stderr, __VA_ARGS__)
+//#define AT_PRINTF(...) GGML_LOG_DEBUG(__VA_ARGS__)
 #define AT_PRINTF(...)
 
 
@@ -89,7 +89,7 @@ void ggml_tallocr_alloc(struct ggml_tallocr * talloc, struct ggml_tensor * tenso
     size = GGML_PAD(size, talloc->alignment);
 
     if (talloc->offset + size > ggml_backend_buffer_get_size(talloc->buffer)) {
-        fprintf(stderr, "%s: not enough space in the buffer to allocate %s (needed %zu, available %zu)\n",
+        GGML_LOG_ERROR("%s: not enough space in the buffer to allocate %s (needed %zu, available %zu)\n",
                 __func__, tensor->name, size, ggml_backend_buffer_get_size(talloc->buffer) - talloc->offset);
         GGML_ABORT("not enough space in the buffer");
     }
@@ -172,7 +172,7 @@ static size_t ggml_dyn_tallocr_alloc(struct ggml_dyn_tallocr * alloc, size_t siz
             best_fit_block = alloc->n_free_blocks - 1;
         } else {
             // this should never happen
-            fprintf(stderr, "%s: not enough space in the buffer to allocate %zu bytes, largest block available %zu bytes\n",
+            GGML_LOG_ERROR("%s: not enough space in the buffer to allocate %zu bytes, largest block available %zu bytes\n",
                     __func__, size, max_avail);
             GGML_ABORT("not enough space in the buffer");
         }
@@ -209,16 +209,16 @@ static size_t ggml_dyn_tallocr_alloc(struct ggml_dyn_tallocr * alloc, size_t siz
                 }
             }
         }
-        fprintf(stderr, "max_size = %.2f MB: tensors: ", cur_max / 1024.0 / 1024.0);
+        GGML_LOG_DEBUG("max_size = %.2f MB: tensors: ", cur_max / 1024.0 / 1024.0);
         for (int i = 0; i < 1024; i++) {
             if (alloc->allocated_tensors[i].tensor) {
-                fprintf(stderr, "%s [%zx-%zx] (%.2f MB) ", alloc->allocated_tensors[i].tensor->name,
+                GGML_LOG_DEBUG("%s [%zx-%zx] (%.2f MB) ", alloc->allocated_tensors[i].tensor->name,
                     alloc->allocated_tensors[i].offset,
                     alloc->allocated_tensors[i].offset + ggml_nbytes(alloc->allocated_tensors[i].tensor),
                     ggml_nbytes(alloc->allocated_tensors[i].tensor) / 1024.0 / 1024.0);
             }
         }
-        fprintf(stderr, "\n");
+        GGML_LOG_DEBUG("\n");
     }
 #endif
 
@@ -348,7 +348,6 @@ struct tensor_alloc {
 };
 
 struct leaf_alloc {
-    int buffer_id;
     struct tensor_alloc leaf;
 };
 
@@ -740,7 +739,6 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c
     for (int i = 0; i < graph->n_leafs; i++) {
         struct ggml_tensor * leaf = graph->leafs[i];
         struct hash_node * hn = ggml_gallocr_hash_get(galloc, leaf);
-        galloc->leaf_allocs[i].buffer_id = hn->buffer_id;
         if (leaf->view_src || leaf->data) {
             galloc->leaf_allocs[i].leaf.buffer_id = -1;
             galloc->leaf_allocs[i].leaf.offset = SIZE_MAX;
@@ -768,13 +766,13 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c
         // even if there are no tensors allocated in this buffer, we still need to allocate it to initialize views
         if (new_size > cur_size || galloc->buffers[i] == NULL) {
 #ifndef NDEBUG
-            fprintf(stderr, "%s: reallocating %s buffer from size %.02f MiB to %.02f MiB\n", __func__, ggml_backend_buft_name(galloc->bufts[i]), cur_size / 1024.0 / 1024.0, new_size / 1024.0 / 1024.0);
+            GGML_LOG_DEBUG("%s: reallocating %s buffer from size %.02f MiB to %.02f MiB\n", __func__, ggml_backend_buft_name(galloc->bufts[i]), cur_size / 1024.0 / 1024.0, new_size / 1024.0 / 1024.0);
 #endif
 
             ggml_backend_buffer_free(galloc->buffers[i]);
             galloc->buffers[i] = ggml_backend_buft_alloc_buffer(galloc->bufts[i], new_size);
             if (galloc->buffers[i] == NULL) {
-                fprintf(stderr, "%s: failed to allocate %s buffer of size %zu\n", __func__, ggml_backend_buft_name(galloc->bufts[i]), new_size);
+                GGML_LOG_ERROR("%s: failed to allocate %s buffer of size %zu\n", __func__, ggml_backend_buft_name(galloc->bufts[i]), new_size);
                 return false;
             }
             ggml_backend_buffer_set_usage(galloc->buffers[i], GGML_BACKEND_BUFFER_USAGE_COMPUTE);
@@ -825,14 +823,14 @@ static bool ggml_gallocr_node_needs_realloc(ggml_gallocr_t galloc, struct ggml_t
 static bool ggml_gallocr_needs_realloc(ggml_gallocr_t galloc, struct ggml_cgraph * graph) {
     if (galloc->n_nodes != graph->n_nodes) {
 #ifndef NDEBUG
-        fprintf(stderr, "%s: graph has different number of nodes\n", __func__);
+        GGML_LOG_DEBUG("%s: graph has different number of nodes\n", __func__);
 #endif
         return true;
     }
 
     if (galloc->n_leafs != graph->n_leafs) {
 #ifndef NDEBUG
-        fprintf(stderr, "%s: graph has different number of leafs\n", __func__);
+        GGML_LOG_DEBUG("%s: graph has different number of leafs\n", __func__);
 #endif
         return true;
     }
@@ -843,7 +841,7 @@ static bool ggml_gallocr_needs_realloc(ggml_gallocr_t galloc, struct ggml_cgraph
 
         if (!ggml_gallocr_node_needs_realloc(galloc, node, &node_alloc->dst)) {
 #ifndef NDEBUG
-            fprintf(stderr, "%s: node %s is not valid\n", __func__, node->name);
+            GGML_LOG_DEBUG("%s: node %s is not valid\n", __func__, node->name);
 #endif
             return true;
         }
@@ -855,7 +853,7 @@ static bool ggml_gallocr_needs_realloc(ggml_gallocr_t galloc, struct ggml_cgraph
             }
             if (!ggml_gallocr_node_needs_realloc(galloc, src, &node_alloc->src[j])) {
 #ifndef NDEBUG
-                fprintf(stderr, "%s: src %d (%s) of node %s is not valid\n", __func__, j, src->name, node->name);
+                GGML_LOG_DEBUG("%s: src %d (%s) of node %s is not valid\n", __func__, j, src->name, node->name);
 #endif
                 return true;
             }
@@ -869,14 +867,14 @@ bool ggml_gallocr_alloc_graph(ggml_gallocr_t galloc, struct ggml_cgraph * graph)
     if (ggml_gallocr_needs_realloc(galloc, graph)) {
         if (galloc->n_buffers == 1) {
 #ifndef NDEBUG
-            fprintf(stderr, "%s: reallocating buffers automatically\n", __func__);
+            GGML_LOG_DEBUG("%s: reallocating buffers automatically\n", __func__);
 #endif
             if (!ggml_gallocr_reserve(galloc, graph)) {
                 return false;
             }
         } else {
 #ifndef NDEBUG
-            fprintf(stderr, "%s: cannot reallocate multi buffer graph automatically, call reserve\n", __func__);
+            GGML_LOG_DEBUG("%s: cannot reallocate multi buffer graph automatically, call reserve\n", __func__);
 #endif
             return false;
         }
@@ -940,7 +938,7 @@ static bool alloc_tensor_range(struct ggml_context * ctx,
     ggml_backend_buffer_t buffer = ggml_backend_buft_alloc_buffer(buft, size);
     if (buffer == NULL) {
 #ifndef NDEBUG
-        fprintf(stderr, "%s: failed to allocate %s buffer of size %zu\n", __func__, ggml_backend_buft_name(buft), size);
+        GGML_LOG_DEBUG("%s: failed to allocate %s buffer of size %zu\n", __func__, ggml_backend_buft_name(buft), size);
 #endif
         for (size_t i = 0; i < *n_buffers; i++) {
             ggml_backend_buffer_free((*buffers)[i]);
@@ -990,7 +988,7 @@ ggml_backend_buffer_t ggml_backend_alloc_ctx_tensors_from_buft(struct ggml_conte
         }
 
         if (this_size > max_size) {
-            fprintf(stderr, "%s: tensor %s is too large to fit in a %s buffer (tensor size: %zu, max buffer size: %zu)\n",
+            GGML_LOG_ERROR("%s: tensor %s is too large to fit in a %s buffer (tensor size: %zu, max buffer size: %zu)\n",
                     __func__, t->name,
                     ggml_backend_buft_name(buft),
                     this_size, max_size);
@@ -1022,7 +1020,7 @@ ggml_backend_buffer_t ggml_backend_alloc_ctx_tensors_from_buft(struct ggml_conte
 
     if (n_buffers == 0) {
 #ifndef NDEBUG
-        fprintf(stderr, "%s: all tensors in the context are already allocated\n", __func__);
+        GGML_LOG_DEBUG("%s: all tensors in the context are already allocated\n", __func__);
 #endif
         return NULL;
     }
diff --git a/ggml/src/ggml-backend.cpp b/ggml/src/ggml-backend.cpp
index 9eb00a340..d13e71d84 100644
--- a/ggml/src/ggml-backend.cpp
+++ b/ggml/src/ggml-backend.cpp
@@ -379,7 +379,7 @@ void ggml_backend_tensor_copy(struct ggml_tensor * src, struct ggml_tensor * dst
         ggml_backend_tensor_get(src, dst->data, 0, ggml_nbytes(src));
     } else if (!ggml_backend_buffer_copy_tensor(src, dst)) {
 #ifndef NDEBUG
-        fprintf(stderr, "%s: warning: slow copy from %s to %s\n", __func__, ggml_backend_buffer_name(src->buffer), ggml_backend_buffer_name(dst->buffer));
+        GGML_LOG_DEBUG("%s: warning: slow copy from %s to %s\n", __func__, ggml_backend_buffer_name(src->buffer), ggml_backend_buffer_name(dst->buffer));
 #endif
         size_t nbytes = ggml_nbytes(src);
         void * data = malloc(nbytes);
@@ -538,6 +538,10 @@ void * ggml_backend_reg_get_proc_address(ggml_backend_reg_t reg, const char * na
 #include "ggml-metal.h"
 #endif
 
+#ifdef GGML_USE_VULKAN
+#include "ggml-vulkan.h"
+#endif
+
 #ifdef GGML_USE_BLAS
 #include "ggml-blas.h"
 #endif
@@ -557,6 +561,9 @@ struct ggml_backend_registry {
 #ifdef GGML_USE_METAL
         register_backend(ggml_backend_metal_reg());
 #endif
+#ifdef GGML_USE_VULKAN
+        register_backend(ggml_backend_vk_reg());
+#endif
 #ifdef GGML_USE_BLAS
         register_backend(ggml_backend_blas_reg());
 #endif
@@ -564,14 +571,14 @@ struct ggml_backend_registry {
         register_backend(ggml_backend_rpc_reg());
 #endif
 
-        // TODO: sycl, vulkan, kompute, cann
+        // TODO: sycl, kompute, cann
 
         register_backend(ggml_backend_cpu_reg());
     }
 
     void register_backend(ggml_backend_reg_t reg) {
 #ifndef NDEBUG
-        fprintf(stderr, "%s: registered backend %s (%zu devices)\n",
+        GGML_LOG_DEBUG("%s: registered backend %s (%zu devices)\n",
             __func__, ggml_backend_reg_name(reg), ggml_backend_reg_dev_count(reg));
 #endif
         backends.push_back(reg);
@@ -582,7 +589,7 @@ struct ggml_backend_registry {
 
     void register_device(ggml_backend_dev_t device) {
 #ifndef NDEBUG
-        fprintf(stderr, "%s: registered device %s (%s)\n", __func__, ggml_backend_dev_name(device), ggml_backend_dev_description(device));
+        GGML_LOG_DEBUG("%s: registered device %s (%s)\n", __func__, ggml_backend_dev_name(device), ggml_backend_dev_description(device));
 #endif
         devices.push_back(device);
     }
@@ -682,8 +689,6 @@ ggml_backend_t ggml_backend_init_best(void) {
 
 // backend CPU
 
-static const size_t TENSOR_ALIGNMENT = 32; // required for mmap as gguf only guarantees 32-byte alignment
-
 static const char * ggml_backend_cpu_buffer_get_name(ggml_backend_buffer_t buffer) {
     return "CPU";
 
@@ -702,7 +707,7 @@ static void * ggml_backend_cpu_buffer_get_base(ggml_backend_buffer_t buffer) {
 }
 
 static void ggml_backend_cpu_buffer_free_buffer(ggml_backend_buffer_t buffer) {
-    free(buffer->context);
+    ggml_aligned_free(buffer->context, buffer->size);
 }
 
 static void ggml_backend_cpu_buffer_memset_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, uint8_t value, size_t offset, size_t size) {
@@ -770,14 +775,19 @@ static const char * ggml_backend_cpu_buffer_type_get_name(ggml_backend_buffer_ty
 }
 
 static ggml_backend_buffer_t ggml_backend_cpu_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
-    size += TENSOR_ALIGNMENT;   // malloc may return an address that is not aligned
-    void * data = malloc(size); // TODO: use GGML_ALIGNED_MALLOC (move to ggml-impl.h)
+    auto alloc_size = size;
+    if (alloc_size == 0) {
+        alloc_size = 1;
+    }
+
+    void * data = ggml_aligned_malloc(alloc_size);
+
     if (data == NULL) {
-        fprintf(stderr, "%s: failed to allocate buffer of size %zu\n", __func__, size);
+        GGML_LOG_ERROR("%s: failed to allocate buffer of size %zu\n", __func__, alloc_size);
         return NULL;
     }
 
-    return ggml_backend_buffer_init(buft, ggml_backend_cpu_buffer_i, data, size);
+    return ggml_backend_buffer_init(buft, ggml_backend_cpu_buffer_i, data, alloc_size);
 }
 
 static size_t ggml_backend_cpu_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
@@ -836,7 +846,7 @@ static ggml_backend_buffer_t ggml_backend_cpu_hbm_buffer_type_alloc_buffer(ggml_
     void * ptr;
     int result = hbw_posix_memalign(&ptr, ggml_backend_cpu_buffer_type_get_alignment(buft), size);
     if (result != 0) {
-        fprintf(stderr, "failed to allocate HBM buffer of size %zu\n", size);
+        GGML_LOG_ERROR("failed to allocate HBM buffer of size %zu\n", size);
         return NULL;
     }
 
@@ -1459,7 +1469,7 @@ static int ggml_backend_sched_backend_from_buffer(ggml_backend_sched_t sched, co
     }
 
 #ifndef NDEBUG
-    fprintf(stderr, "%s: warning: no backend supports op %s with a weight with buffer type %s used in tensor %s, the weight will need to be copied\n",
+    GGML_LOG_DEBUG("%s: warning: no backend supports op %s with a weight with buffer type %s used in tensor %s, the weight will need to be copied\n",
         __func__, ggml_op_desc(tensor), ggml_backend_buffer_name(buffer), tensor->name);
 #endif
 
@@ -1554,13 +1564,13 @@ static void ggml_backend_sched_print_assignments(ggml_backend_sched_t sched, str
     for (int i = 0; i < graph->n_nodes; i++) {
         if (cur_split < sched->n_splits && i == sched->splits[cur_split].i_start) {
             ggml_backend_t split_backend = sched->backends[sched->splits[cur_split].backend_id];
-            fprintf(stderr, "\n## SPLIT #%d: %s # %d inputs: ", cur_split, ggml_backend_name(split_backend),
+            GGML_LOG_DEBUG("\n## SPLIT #%d: %s # %d inputs: ", cur_split, ggml_backend_name(split_backend),
                 sched->splits[cur_split].n_inputs);
             for (int j = 0; j < sched->splits[cur_split].n_inputs; j++) {
-                fprintf(stderr, "[%s (%5.5s)] ", sched->splits[cur_split].inputs[j]->name,
+                GGML_LOG_DEBUG("[%s (%5.5s)] ", sched->splits[cur_split].inputs[j]->name,
                     fmt_size(ggml_nbytes(sched->splits[cur_split].inputs[j])));
             }
-            fprintf(stderr, "\n");
+            GGML_LOG_DEBUG("\n");
             cur_split++;
         }
         struct ggml_tensor * node = graph->nodes[i];
@@ -1568,7 +1578,7 @@ static void ggml_backend_sched_print_assignments(ggml_backend_sched_t sched, str
             continue;
         }
         ggml_backend_t tensor_backend = ggml_backend_sched_get_tensor_backend(sched, node);
-        fprintf(stderr, "node #%3d (%10.10s): %20.20s (%5.5s) [%5.5s %8.8s]:", i, ggml_op_name(node->op), node->name,
+        GGML_LOG_DEBUG("node #%3d (%10.10s): %20.20s (%5.5s) [%5.5s %8.8s]:", i, ggml_op_name(node->op), node->name,
             fmt_size(ggml_nbytes(node)), tensor_backend ? ggml_backend_name(tensor_backend) : "NULL", GET_CAUSE(node));
         for (int j = 0; j < GGML_MAX_SRC; j++) {
             struct ggml_tensor * src = node->src[j];
@@ -1576,10 +1586,10 @@ static void ggml_backend_sched_print_assignments(ggml_backend_sched_t sched, str
                 continue;
             }
             ggml_backend_t src_backend = ggml_backend_sched_get_tensor_backend(sched, src);
-            fprintf(stderr, " %20.20s (%5.5s) [%5.5s %8.8s]", src->name,
+            GGML_LOG_DEBUG(" %20.20s (%5.5s) [%5.5s %8.8s]", src->name,
                 fmt_size(ggml_nbytes(src)), src_backend ? ggml_backend_name(src_backend) : "NULL", GET_CAUSE(src));
         }
-        fprintf(stderr, "\n");
+        GGML_LOG_DEBUG("\n");
     }
 }
 
@@ -2093,11 +2103,11 @@ static bool ggml_backend_sched_alloc_splits(ggml_backend_sched_t sched) {
         // the re-allocation may cause the split inputs to be moved to a different address
         ggml_backend_sched_synchronize(sched);
 #ifndef NDEBUG
-        fprintf(stderr, "%s: failed to allocate graph, reserving (backend_ids_changed = %d)\n", __func__, backend_ids_changed);
+        GGML_LOG_DEBUG("%s: failed to allocate graph, reserving (backend_ids_changed = %d)\n", __func__, backend_ids_changed);
 #endif
         ggml_gallocr_reserve_n(sched->galloc, &sched->graph, sched->node_backend_ids, sched->leaf_backend_ids);
         if (!ggml_gallocr_alloc_graph(sched->galloc, &sched->graph)) {
-            fprintf(stderr, "%s: failed to allocate graph\n", __func__);
+            GGML_LOG_ERROR("%s: failed to allocate graph\n", __func__);
             return false;
         }
     }
@@ -2491,7 +2501,7 @@ struct ggml_backend_graph_copy ggml_backend_graph_copy(ggml_backend_t backend, s
     struct ggml_context * ctx_unallocated = ggml_init(params);
 
     if (ctx_allocated == NULL || ctx_unallocated == NULL) {
-        fprintf(stderr, "failed to allocate context for graph copy\n");
+        GGML_LOG_ERROR("%s: failed to allocate context for graph copy\n", __func__);
         ggml_hash_set_free(&hash_set);
         free(node_copies);
         free(node_init);
@@ -2514,7 +2524,7 @@ struct ggml_backend_graph_copy ggml_backend_graph_copy(ggml_backend_t backend, s
     // allocate nodes
     ggml_backend_buffer_t buffer = ggml_backend_alloc_ctx_tensors(ctx_allocated, backend);
     if (buffer == NULL) {
-        fprintf(stderr, "failed to allocate buffer for graph copy\n");
+        GGML_LOG_ERROR("%s: failed to allocate buffer for graph copy\n", __func__);
         ggml_hash_set_free(&hash_set);
         free(node_copies);
         free(node_init);
diff --git a/ggml/src/ggml-blas.cpp b/ggml/src/ggml-blas.cpp
index 55f724586..7875ec86d 100644
--- a/ggml/src/ggml-blas.cpp
+++ b/ggml/src/ggml-blas.cpp
@@ -297,14 +297,14 @@ ggml_backend_t ggml_backend_blas_init(void) {
         /* .context   = */ ctx,
     };
 
-#if !defined(NDEBUG) && defined(OPENBLAS_VERSION) && defined(GGML_USE_OPENMP)
+#if defined(OPENBLAS_VERSION) && defined(GGML_USE_OPENMP)
     if (openblas_get_parallel() != OPENBLAS_OPENMP) {
-        fprintf(stderr, "%s: warning: ggml is using OpenMP, but OpenBLAS was compiled without OpenMP support\n", __func__);
+        GGML_LOG_DEBUG("%s: warning: ggml is using OpenMP, but OpenBLAS was compiled without OpenMP support\n", __func__);
     }
 #endif
 
-#if !defined(NDEBUG) && defined(BLIS_ENABLE_CBLAS) && defined(GGML_USE_OPENMP) && !defined(BLIS_ENABLE_OPENMP)
-    fprintf(stderr, "%s: warning: ggml is using OpenMP, but BLIS was compiled without OpenMP support\n", __func__);
+#if defined(BLIS_ENABLE_CBLAS) && defined(GGML_USE_OPENMP) && !defined(BLIS_ENABLE_OPENMP)
+    GGML_LOG_DEBUG("%s: warning: ggml is using OpenMP, but BLIS was compiled without OpenMP support\n", __func__);
 #endif
 
     return backend;
diff --git a/ggml/src/ggml-cann.cpp b/ggml/src/ggml-cann.cpp
index db5f8f186..ec3c0a688 100644
--- a/ggml/src/ggml-cann.cpp
+++ b/ggml/src/ggml-cann.cpp
@@ -1148,6 +1148,7 @@ ggml_backend_cann_buffer_type(int32_t device) {
         for (int32_t i = 0; i < GGML_CANN_MAX_DEVICES; i++) {
             ggml_backend_cann_buffer_types[i] = {
                 /* .iface    = */ ggml_backend_cann_buffer_type_interface,
+                /* .device    = */ nullptr,
                 /* .context  = */
                  new ggml_backend_cann_buffer_type_context{
                     i, "CANN" + std::to_string(i)},
@@ -1868,7 +1869,7 @@ static ggml_backend_event_t ggml_backend_cann_event_new(
     ACL_CHECK(aclrtCreateEvent(&event));
 
     return new ggml_backend_event{
-        /* .backend = */ backend,
+        /* .device = */ nullptr,
         /* .context = */ event,
     };
 }
@@ -1895,10 +1896,9 @@ static void ggml_backend_cann_event_free(ggml_backend_event_t event) {
  *
  * @param event Pointer to the event structure to be recorded.
  */
-static void ggml_backend_cann_event_record(ggml_backend_event_t event) {
+static void ggml_backend_cann_event_record(ggml_backend_t backend, ggml_backend_event_t event) {
     ggml_backend_cann_context* cann_ctx =
-        (ggml_backend_cann_context*)event->backend->context;
-
+        (ggml_backend_cann_context*)backend->context;
     ACL_CHECK(aclrtRecordEvent((aclrtEvent)event->context, cann_ctx->stream()));
 }
 
@@ -1916,8 +1916,7 @@ static void ggml_backend_cann_event_wait(ggml_backend_t backend,
                                          ggml_backend_event_t event) {
     ggml_backend_cann_context* cann_ctx =
         (ggml_backend_cann_context*)backend->context;
-
-    if (ggml_backend_is_cann(event->backend)) {
+    if (ggml_backend_is_cann(backend)) {
         ACL_CHECK(aclrtStreamWaitEvent(cann_ctx->stream(),
                                        (aclrtEvent)event->context));
     } else {
diff --git a/ggml/src/ggml-cuda.cu b/ggml/src/ggml-cuda.cu
index e547984da..cec3c5e9a 100644
--- a/ggml/src/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda.cu
@@ -291,7 +291,7 @@ struct ggml_cuda_pool_leg : public ggml_cuda_pool {
                 return;
             }
         }
-        GGML_LOG_WARN(GGML_CUDA_NAME " buffer pool full, increase MAX_CUDA_BUFFERS\n");
+        GGML_LOG_DEBUG(GGML_CUDA_NAME " buffer pool full, increase MAX_CUDA_BUFFERS\n");
         ggml_cuda_set_device(device);
         CUDA_CHECK(cudaFree(ptr));
         pool_size -= size;
@@ -980,7 +980,7 @@ static void * ggml_cuda_host_malloc(size_t size) {
     if (err != cudaSuccess) {
         // clear the error
         cudaGetLastError();
-        GGML_LOG_WARN("%s: failed to allocate %.2f MiB of pinned memory: %s\n", __func__,
+        GGML_LOG_DEBUG("%s: failed to allocate %.2f MiB of pinned memory: %s\n", __func__,
                            size / 1024.0 / 1024.0, cudaGetErrorString(err));
         return nullptr;
     }
@@ -2410,7 +2410,7 @@ static bool ggml_backend_cuda_cpy_tensor_async(ggml_backend_t backend_src, ggml_
 
     if (cuda_ctx_src->device != buf_ctx_src->device || cuda_ctx_dst->device != buf_ctx_dst->device) {
 #ifndef NDEBUG
-        GGML_LOG_WARN("%s: backend and buffer devices do not match\n", __func__);
+        GGML_LOG_DEBUG("%s: backend and buffer devices do not match\n", __func__);
 #endif
         return false;
     }
@@ -2528,7 +2528,7 @@ static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend,
         if (ggml_cuda_info().devices[cuda_ctx->device].cc < CC_AMPERE) {
             cuda_ctx->cuda_graph->disable_due_to_gpu_arch = true;
 #ifndef NDEBUG
-            GGML_LOG_WARN("%s: disabling CUDA graphs due to GPU architecture\n", __func__);
+            GGML_LOG_DEBUG("%s: disabling CUDA graphs due to GPU architecture\n", __func__);
 #endif
         }
     }
@@ -2579,14 +2579,14 @@ static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend,
             if (node->src[0] && node->src[0]->buffer && ggml_backend_buffer_is_cuda_split(node->src[0]->buffer)) {
                 use_cuda_graph = false; // Split buffers are not supported by CUDA graph capture
 #ifndef NDEBUG
-                GGML_LOG_WARN("%s: disabling CUDA graphs due to split buffer\n", __func__);
+                GGML_LOG_DEBUG("%s: disabling CUDA graphs due to split buffer\n", __func__);
 #endif
             }
 
             if (node->op == GGML_OP_MUL_MAT_ID) {
                 use_cuda_graph = false; // This node type is not supported by CUDA graph capture
 #ifndef NDEBUG
-                GGML_LOG_WARN("%s: disabling CUDA graphs due to mul_mat_id\n", __func__);
+                GGML_LOG_DEBUG("%s: disabling CUDA graphs due to mul_mat_id\n", __func__);
 #endif
             }
 
@@ -2595,7 +2595,7 @@ static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend,
                 // Changes in batch size or context size can cause changes to the grid size of some kernels.
                 use_cuda_graph = false;
 #ifndef NDEBUG
-                GGML_LOG_WARN("%s: disabling CUDA graphs due to batch size > 1 [%s] [%ld %ld %ld %ld]\n", __func__, node->name, node->ne[0], node->ne[1], node->ne[2], node->ne[3]);
+                GGML_LOG_DEBUG("%s: disabling CUDA graphs due to batch size > 1 [%s] [%ld %ld %ld %ld]\n", __func__, node->name, node->ne[0], node->ne[1], node->ne[2], node->ne[3]);
 #endif
             }
 
@@ -2607,7 +2607,7 @@ static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend,
                 if (!ptr) {
                     use_cuda_graph = false;
 #ifndef NDEBUG
-                    GGML_LOG_WARN("%s: disabling CUDA graphs due to unsupported copy op\n", __func__);
+                    GGML_LOG_DEBUG("%s: disabling CUDA graphs due to unsupported copy op\n", __func__);
 #endif
                 } else {
                     if (std::find(ggml_cuda_cpy_fn_ptrs.begin(), ggml_cuda_cpy_fn_ptrs.end(), ptr) == ggml_cuda_cpy_fn_ptrs.end()) {
@@ -2631,7 +2631,7 @@ static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend,
         if (cuda_ctx->cuda_graph->number_consecutive_updates >= 4) {
             cuda_ctx->cuda_graph->disable_due_to_too_many_updates = true;
 #ifndef NDEBUG
-            GGML_LOG_WARN("%s: disabling CUDA graphs due to too many consecutive updates\n", __func__);
+            GGML_LOG_DEBUG("%s: disabling CUDA graphs due to too many consecutive updates\n", __func__);
 #endif
         }
     }
@@ -2689,7 +2689,7 @@ static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend,
                 use_cuda_graph = false;
                 cuda_ctx->cuda_graph->disable_due_to_failed_graph_capture = true;
 #ifndef NDEBUG
-                GGML_LOG_WARN("%s: disabling CUDA graphs due to failed graph capture\n", __func__);
+                GGML_LOG_DEBUG("%s: disabling CUDA graphs due to failed graph capture\n", __func__);
 #endif
             } else {
                 graph_evaluated_or_captured = true; // CUDA graph has been captured
@@ -2858,7 +2858,7 @@ bool ggml_backend_cuda_register_host_buffer(void * buffer, size_t size) {
         // clear the error
         cudaGetLastError();
 
-        GGML_LOG_WARN("%s: failed to register %.2f MiB of pinned memory: %s\n", __func__,
+        GGML_LOG_DEBUG("%s: failed to register %.2f MiB of pinned memory: %s\n", __func__,
                            size / 1024.0 / 1024.0, cudaGetErrorString(err));
         return false;
     }
diff --git a/ggml/src/ggml-cuda/dmmv.cu b/ggml/src/ggml-cuda/dmmv.cu
index 96a5adef5..00e21b5d7 100644
--- a/ggml/src/ggml-cuda/dmmv.cu
+++ b/ggml/src/ggml-cuda/dmmv.cu
@@ -416,10 +416,11 @@ static __global__ void dequantize_mul_mat_vec_q6_k(const void * __restrict__ vx,
 
 static __device__ void convert_f16(const void * vx, const int64_t ib, const int iqs, dfloat2 & v){
     const half * x = (const half *) vx;
-
+    // load 2 halfs into register in a single instruction
+    const half2 x_reg = *((half2 *) &(x[ib + iqs]));
     // automatic half -> float type cast if dfloat == float
-    v.x = x[ib + iqs + 0];
-    v.y = x[ib + iqs + 1];
+    v.x = __low2float(x_reg);
+    v.y = __high2float(x_reg);
 }
 
 static constexpr __device__ dequantize_kernel_t get_dequantize_kernel(ggml_type type) {
@@ -476,13 +477,28 @@ static __global__ void dequantize_mul_mat_vec(const void * __restrict__ vx, cons
             // matrix multiplication
             // for qr = 2 the y index needs to increase by 1 per j iter because of y_offset = qk/2
 #ifdef GGML_CUDA_F16
-            tmp += __hmul2(v, {
-                y[iybs + iqs + j/qr + 0],
-                y[iybs + iqs + j/qr + y_offset]
-            });
+            if ( y_offset == 1 ) {
+                // load 2 dfloats into register in a single instruction
+                const dfloat2 y_reg = *((dfloat2 *) &(y[iybs + iqs + j/qr]));
+                tmp += __hmul2(v, y_reg);
+            }
+            else {
+                tmp += __hmul2(v, {
+                        y[iybs + iqs + j/qr + 0],
+                        y[iybs + iqs + j/qr + y_offset]
+                    });
+            }
 #else
-            tmp += v.x * y[iybs + iqs + j/qr + 0];
-            tmp += v.y * y[iybs + iqs + j/qr + y_offset];
+            if ( y_offset == 1 ) {
+                // load 2 dfloats into register in a single instruction
+                const dfloat2 y_reg = *((dfloat2 *) &(y[iybs + iqs + j/qr]));
+                tmp += v.x * y_reg.x;
+                tmp += v.y * y_reg.y;
+            }
+            else {
+                tmp += v.x * y[iybs + iqs + j/qr + 0];
+                tmp += v.y * y[iybs + iqs + j/qr + y_offset];
+            }
 #endif // GGML_CUDA_F16
         }
     }
diff --git a/ggml/src/ggml-impl.h b/ggml/src/ggml-impl.h
index d3f4bad8c..65c4f8119 100644
--- a/ggml/src/ggml-impl.h
+++ b/ggml/src/ggml-impl.h
@@ -19,6 +19,9 @@ extern "C" {
 #define MIN(a, b) ((a) < (b) ? (a) : (b))
 #define MAX(a, b) ((a) > (b) ? (a) : (b))
 
+// required for mmap as gguf only guarantees 32-byte alignment
+#define TENSOR_ALIGNMENT 32
+
 // static_assert should be a #define, but if it's not,
 // fall back to the _Static_assert C11 keyword.
 // if C99 - static_assert is noop
@@ -196,6 +199,11 @@ struct ggml_cgraph {
 
 struct ggml_cgraph ggml_graph_view(struct ggml_cgraph * cgraph, int i0, int i1);
 
+// Memory allocation
+
+void * ggml_aligned_malloc(size_t size);
+void ggml_aligned_free(void * ptr, size_t size);
+
 #ifdef __cplusplus
 }
 #endif
diff --git a/ggml/src/ggml-vulkan.cpp b/ggml/src/ggml-vulkan.cpp
index 4a2345971..306c25be4 100644
--- a/ggml/src/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan.cpp
@@ -1941,7 +1941,7 @@ static vk_device ggml_vk_get_device(size_t idx) {
         if (device->fp16) {
             device_extensions.push_back("VK_KHR_shader_float16_int8");
         }
-        device->name = device->properties.deviceName.data();
+        device->name = GGML_VK_NAME + std::to_string(idx);
 
         device_create_info = {
             vk::DeviceCreateFlags(),
@@ -1968,7 +1968,7 @@ static vk_device ggml_vk_get_device(size_t idx) {
 
         device->buffer_type = {
             /* .iface    = */ ggml_backend_vk_buffer_type_interface,
-            /* .device   = */ nullptr,
+            /* .device   = */ ggml_backend_reg_dev_get(ggml_backend_vk_reg(), idx),
             /* .context  = */ new ggml_backend_vk_buffer_type_context{ device->name, device },
         };
 
@@ -6378,7 +6378,7 @@ ggml_backend_buffer_type_t ggml_backend_vk_host_buffer_type() {
             /* .get_alloc_size   = */ ggml_backend_cpu_buffer_type()->iface.get_alloc_size,
             /* .is_host          = */ ggml_backend_cpu_buffer_type()->iface.is_host,
         },
-        /* .device   = */ nullptr,
+        /* .device   = */ ggml_backend_reg_dev_get(ggml_backend_vk_reg(), 0),
         /* .context  = */ nullptr,
     };
 
@@ -6581,9 +6581,135 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg
     UNUSED(backend);
 }
 
-static bool ggml_backend_vk_supports_op(ggml_backend_t backend, const ggml_tensor * op) {
-    // ggml_backend_vk_context * ctx = (ggml_backend_vk_context *) backend->context;
+// TODO: enable async and synchronize
+static ggml_backend_i ggml_backend_vk_interface = {
+    /* .get_name                = */ ggml_backend_vk_name,
+    /* .free                    = */ ggml_backend_vk_free,
+    /* .get_default_buffer_type = */ ggml_backend_vk_get_default_buffer_type,
+    /* .set_tensor_async        = */ NULL,  // ggml_backend_vk_set_tensor_async,
+    /* .get_tensor_async        = */ NULL,  // ggml_backend_vk_get_tensor_async,
+    /* .cpy_tensor_async        = */ NULL,  // ggml_backend_vk_cpy_tensor_async,
+    /* .synchronize             = */ NULL,  // ggml_backend_vk_synchronize,
+    /* .graph_plan_create       = */ NULL,
+    /* .graph_plan_free         = */ NULL,
+    /* .graph_plan_update       = */ NULL,
+    /* .graph_plan_compute      = */ NULL,
+    /* .graph_compute           = */ ggml_backend_vk_graph_compute,
+    /* .supports_op             = */ NULL,
+    /* .supports_buft           = */ NULL,
+    /* .offload_op              = */ NULL,
+    /* .event_record            = */ NULL,
+    /* .event_wait              = */ NULL,
+};
 
+static ggml_guid_t ggml_backend_vk_guid() {
+    static ggml_guid guid = { 0xb8, 0xf7, 0x4f, 0x86, 0x40, 0x3c, 0xe1, 0x02, 0x91, 0xc8, 0xdd, 0xe9, 0x02, 0x3f, 0xc0, 0x2b };
+    return &guid;
+}
+
+ggml_backend_t ggml_backend_vk_init(size_t dev_num) {
+    VK_LOG_DEBUG("ggml_backend_vk_init(" << dev_num << ")");
+
+    ggml_backend_vk_context * ctx = new ggml_backend_vk_context;
+    ggml_vk_init(ctx, dev_num);
+
+    ggml_backend_t vk_backend = new ggml_backend {
+        /* .guid      = */ ggml_backend_vk_guid(),
+        /* .interface = */ ggml_backend_vk_interface,
+        /* .device    = */ ggml_backend_reg_dev_get(ggml_backend_vk_reg(), dev_num),
+        /* .context   = */ ctx,
+    };
+
+    return vk_backend;
+}
+
+bool ggml_backend_is_vk(ggml_backend_t backend) {
+    return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_vk_guid());
+}
+
+int ggml_backend_vk_get_device_count() {
+    return ggml_vk_get_device_count();
+}
+
+void ggml_backend_vk_get_device_description(int device, char * description, size_t description_size) {
+    GGML_ASSERT(device < (int) vk_instance.device_indices.size());
+    int dev_idx = vk_instance.device_indices[device];
+    ggml_vk_get_device_description(dev_idx, description, description_size);
+}
+
+void ggml_backend_vk_get_device_memory(int device, size_t * free, size_t * total) {
+    GGML_ASSERT(device < (int) vk_instance.device_indices.size());
+
+    vk::PhysicalDevice vkdev = vk_instance.instance.enumeratePhysicalDevices()[vk_instance.device_indices[device]];
+
+    vk::PhysicalDeviceMemoryProperties memprops = vkdev.getMemoryProperties();
+
+    for (const vk::MemoryHeap& heap : memprops.memoryHeaps) {
+        if (heap.flags & vk::MemoryHeapFlagBits::eDeviceLocal) {
+            *total = heap.size;
+            *free = heap.size;
+            break;
+        }
+    }
+}
+
+//////////////////////////
+
+struct ggml_backend_vk_device_context {
+    int device;
+    std::string name;
+    std::string description;
+};
+
+static const char * ggml_backend_vk_device_get_name(ggml_backend_dev_t dev) {
+    ggml_backend_vk_device_context * ctx = (ggml_backend_vk_device_context *)dev->context;
+    return ctx->name.c_str();
+}
+
+static const char * ggml_backend_vk_device_get_description(ggml_backend_dev_t dev) {
+    ggml_backend_vk_device_context * ctx = (ggml_backend_vk_device_context *)dev->context;
+    return ctx->description.c_str();
+}
+
+static void ggml_backend_vk_device_get_memory(ggml_backend_dev_t device, size_t * free, size_t * total) {
+    ggml_backend_vk_device_context * ctx = (ggml_backend_vk_device_context *)device->context;
+    ggml_backend_vk_get_device_memory(ctx->device, free, total);
+}
+
+static ggml_backend_buffer_type_t ggml_backend_vk_device_get_buffer_type(ggml_backend_dev_t dev) {
+    ggml_backend_vk_device_context * ctx = (ggml_backend_vk_device_context *)dev->context;
+    return ggml_backend_vk_buffer_type(ctx->device);
+}
+
+static ggml_backend_buffer_type_t ggml_backend_vk_device_get_host_buffer_type(ggml_backend_dev_t dev) {
+    UNUSED(dev);
+    return ggml_backend_vk_host_buffer_type();
+}
+
+static enum ggml_backend_dev_type ggml_backend_vk_device_get_type(ggml_backend_dev_t dev) {
+    UNUSED(dev);
+    return GGML_BACKEND_DEVICE_TYPE_GPU_FULL;
+}
+
+static void ggml_backend_vk_device_get_props(ggml_backend_dev_t dev, struct ggml_backend_dev_props * props) {
+    props->name        = ggml_backend_vk_device_get_name(dev);
+    props->description = ggml_backend_vk_device_get_description(dev);
+    props->type        = ggml_backend_vk_device_get_type(dev);
+    ggml_backend_vk_device_get_memory(dev, &props->memory_free, &props->memory_total);
+    props->caps = {
+        /* async       */ false,
+        /* host_buffer */ true,
+        /* events      */ false,
+    };
+}
+
+static ggml_backend_t ggml_backend_vk_device_init(ggml_backend_dev_t dev, const char * params) {
+    UNUSED(params);
+    ggml_backend_vk_device_context * ctx = (ggml_backend_vk_device_context *)dev->context;
+    return ggml_backend_vk_init(ctx->device);
+}
+
+static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggml_tensor * op) {
     switch (op->op) {
         case GGML_OP_UNARY:
             switch (ggml_get_unary_op(op)) {
@@ -6701,97 +6827,101 @@ static bool ggml_backend_vk_supports_op(ggml_backend_t backend, const ggml_tenso
             return false;
     }
 
-    UNUSED(backend);
+    UNUSED(dev);
 }
 
-static bool ggml_backend_vk_offload_op(ggml_backend_t backend, const ggml_tensor * op) {
+static bool ggml_backend_vk_device_supports_buft(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) {
+    if (buft->iface.get_name != ggml_backend_vk_buffer_type_name) {
+        return false;
+    }
+
+    ggml_backend_vk_device_context * ctx = (ggml_backend_vk_device_context *)dev->context;
+    ggml_backend_vk_buffer_type_context * buft_ctx = (ggml_backend_vk_buffer_type_context *)buft->context;
+
+    return buft_ctx->device->idx == ctx->device;
+}
+
+static bool ggml_backend_vk_device_offload_op(ggml_backend_dev_t dev, const ggml_tensor * op) {
     const int min_batch_size = 32;
 
     return (op->ne[1] >= min_batch_size && op->op != GGML_OP_GET_ROWS) ||
            (op->ne[2] >= min_batch_size && op->op == GGML_OP_MUL_MAT_ID);
 
-    UNUSED(backend);
+    UNUSED(dev);
 }
 
-static bool ggml_backend_vk_supports_buft(ggml_backend_t backend, ggml_backend_buffer_type_t buft) {
-    if (buft->iface.get_name != ggml_backend_vk_buffer_type_name) {
-        return false;
-    }
-
-    ggml_backend_vk_buffer_type_context * buft_ctx = (ggml_backend_vk_buffer_type_context *)buft->context;
-    ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context;
-
-    return buft_ctx->device == ctx->device;
-}
-
-// TODO: enable async and synchronize
-static ggml_backend_i ggml_backend_vk_interface = {
-    /* .get_name                = */ ggml_backend_vk_name,
-    /* .free                    = */ ggml_backend_vk_free,
-    /* .get_default_buffer_type = */ ggml_backend_vk_get_default_buffer_type,
-    /* .set_tensor_async        = */ NULL,  // ggml_backend_vk_set_tensor_async,
-    /* .get_tensor_async        = */ NULL,  // ggml_backend_vk_get_tensor_async,
-    /* .cpy_tensor_async        = */ NULL,  // ggml_backend_vk_cpy_tensor_async,
-    /* .synchronize             = */ NULL,  // ggml_backend_vk_synchronize,
-    /* .graph_plan_create       = */ NULL,
-    /* .graph_plan_free         = */ NULL,
-    /* .graph_plan_update       = */ NULL,
-    /* .graph_plan_compute      = */ NULL,
-    /* .graph_compute           = */ ggml_backend_vk_graph_compute,
-    /* .supports_op             = */ ggml_backend_vk_supports_op,
-    /* .supports_buft           = */ ggml_backend_vk_supports_buft,
-    /* .offload_op              = */ ggml_backend_vk_offload_op,
-    /* .event_record            = */ NULL,
-    /* .event_wait              = */ NULL,
+static const struct ggml_backend_device_i ggml_backend_vk_device_i = {
+    /* .get_name             = */ ggml_backend_vk_device_get_name,
+    /* .get_description      = */ ggml_backend_vk_device_get_description,
+    /* .get_memory           = */ ggml_backend_vk_device_get_memory,
+    /* .get_type             = */ ggml_backend_vk_device_get_type,
+    /* .get_props            = */ ggml_backend_vk_device_get_props,
+    /* .init_backend         = */ ggml_backend_vk_device_init,
+    /* .get_buffer_type      = */ ggml_backend_vk_device_get_buffer_type,
+    /* .get_host_buffer_type = */ ggml_backend_vk_device_get_host_buffer_type,
+    /* .buffer_from_host_ptr = */ NULL,
+    /* .supports_op          = */ ggml_backend_vk_device_supports_op,
+    /* .supports_buft        = */ ggml_backend_vk_device_supports_buft,
+    /* .offload_op           = */ ggml_backend_vk_device_offload_op,
+    /* .event_new            = */ NULL,
+    /* .event_free           = */ NULL,
+    /* .event_synchronize    = */ NULL,
 };
 
-static ggml_guid_t ggml_backend_vk_guid() {
-    static ggml_guid guid = { 0xb8, 0xf7, 0x4f, 0x86, 0x40, 0x3c, 0xe1, 0x02, 0x91, 0xc8, 0xdd, 0xe9, 0x02, 0x3f, 0xc0, 0x2b };
-    return &guid;
+static const char * ggml_backend_vk_reg_get_name(ggml_backend_reg_t reg) {
+    UNUSED(reg);
+    return GGML_VK_NAME;
 }
 
-ggml_backend_t ggml_backend_vk_init(size_t dev_num) {
-    VK_LOG_DEBUG("ggml_backend_vk_init(" << dev_num << ")");
-
-    ggml_backend_vk_context * ctx = new ggml_backend_vk_context;
-    ggml_vk_init(ctx, dev_num);
-
-    ggml_backend_t vk_backend = new ggml_backend {
-        /* .guid      = */ ggml_backend_vk_guid(),
-        /* .interface = */ ggml_backend_vk_interface,
-        /* .device    = */ nullptr,
-        /* .context   = */ ctx,
-    };
-
-    return vk_backend;
+static size_t ggml_backend_vk_reg_get_device_count(ggml_backend_reg_t reg) {
+    UNUSED(reg);
+    return ggml_backend_vk_get_device_count();
 }
 
-bool ggml_backend_is_vk(ggml_backend_t backend) {
-    return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_vk_guid());
-}
+static ggml_backend_dev_t ggml_backend_vk_reg_get_device(ggml_backend_reg_t reg, size_t device) {
+    static std::vector<ggml_backend_dev_t> devices;
 
-int ggml_backend_vk_get_device_count() {
-    return ggml_vk_get_device_count();
-}
+    static bool initialized = false;
 
-void ggml_backend_vk_get_device_description(int device, char * description, size_t description_size) {
-    ggml_vk_get_device_description(device, description, description_size);
-}
-
-void ggml_backend_vk_get_device_memory(int device, size_t * free, size_t * total) {
-    GGML_ASSERT(device < (int) vk_instance.device_indices.size());
-
-    vk::PhysicalDevice vkdev = vk_instance.instance.enumeratePhysicalDevices()[vk_instance.device_indices[device]];
-
-    vk::PhysicalDeviceMemoryProperties memprops = vkdev.getMemoryProperties();
-
-    for (const vk::MemoryHeap& heap : memprops.memoryHeaps) {
-        if (heap.flags & vk::MemoryHeapFlagBits::eDeviceLocal) {
-            *total = heap.size;
-            *free = heap.size;
-            break;
+    {
+        static std::mutex mutex;
+        std::lock_guard<std::mutex> lock(mutex);
+        if (!initialized) {
+            for (size_t i = 0; i < ggml_backend_vk_get_device_count(); i++) {
+                ggml_backend_vk_device_context * ctx = new ggml_backend_vk_device_context;
+                char desc[256];
+                ggml_backend_vk_get_device_description(i, desc, sizeof(desc));
+                ctx->device = i;
+                ctx->name = GGML_VK_NAME + std::to_string(i);
+                ctx->description = desc;
+                devices.push_back(new ggml_backend_device {
+                    /* .iface   = */ ggml_backend_vk_device_i,
+                    /* .reg     = */ reg,
+                    /* .context = */ ctx,
+                });
+            }
+            initialized = true;
         }
     }
+
+    GGML_ASSERT(device < devices.size());
+    return devices[device];
+}
+
+static const struct ggml_backend_reg_i ggml_backend_vk_reg_i = {
+    /* .get_name         = */ ggml_backend_vk_reg_get_name,
+    /* .get_device_count = */ ggml_backend_vk_reg_get_device_count,
+    /* .get_device       = */ ggml_backend_vk_reg_get_device,
+    /* .get_proc_address = */ NULL,
+};
+
+ggml_backend_reg_t ggml_backend_vk_reg() {
+    static ggml_backend_reg reg = {
+        /* .iface   = */ ggml_backend_vk_reg_i,
+        /* .context = */ nullptr,
+    };
+
+    return &reg;
 }
 
 // Extension availability
diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c
index cd8b3c0f1..85e8dce27 100644
--- a/ggml/src/ggml.c
+++ b/ggml/src/ggml.c
@@ -35,10 +35,6 @@
 #include <omp.h>
 #endif
 
-#ifdef GGML_USE_METAL
-#include <unistd.h>
-#endif
-
 #if defined(__ARM_FEATURE_SVE) || defined(__ARM_FEATURE_MATMUL_INT8)
 #undef GGML_USE_LLAMAFILE
 #endif
@@ -189,6 +185,8 @@ typedef pthread_t ggml_thread_t;
 #endif
 
 #if defined(__APPLE__)
+#include <unistd.h>
+#include <mach/mach.h>
 #include <TargetConditionals.h>
 #endif
 
@@ -386,22 +384,40 @@ void ggml_log_callback_default(enum ggml_log_level level, const char * text, voi
 //#define GGML_SOFT_MAX_ACCELERATE
 #endif
 
+
+void * ggml_aligned_malloc(size_t size) {
 #if defined(_MSC_VER) || defined(__MINGW32__)
-#define GGML_ALIGNED_MALLOC(size) _aligned_malloc(size, GGML_MEM_ALIGN)
-#define GGML_ALIGNED_FREE(ptr)    _aligned_free(ptr)
+    return _aligned_malloc(size, TENSOR_ALIGNMENT);
 #else
-inline static void * ggml_aligned_malloc(size_t size) {
     if (size == 0) {
         GGML_LOG_WARN("Behavior may be unexpected when allocating 0 bytes for ggml_aligned_malloc!\n");
         return NULL;
     }
     void * aligned_memory = NULL;
 #ifdef GGML_USE_CPU_HBM
-    int result = hbw_posix_memalign(&aligned_memory, 16, size);
+    int result = hbw_posix_memalign(&aligned_memory, TENSOR_ALIGNMENT, size);
+#elif TARGET_OS_OSX
+    kern_return_t alloc_status = vm_allocate((vm_map_t) mach_task_self(), (vm_address_t *) &aligned_memory, size, VM_FLAGS_ANYWHERE);
+    int result = EFAULT;
+    switch (alloc_status) {
+        case KERN_SUCCESS:
+            result = 0;
+            break;
+        case KERN_INVALID_ADDRESS:
+            result = EINVAL;
+            break;
+        case KERN_NO_SPACE:
+            result = ENOMEM;
+            break;
+        default:
+            result = EFAULT;
+            break;
+    }
 #elif GGML_USE_METAL
-    int result = posix_memalign(&aligned_memory, sysconf(_SC_PAGESIZE), size);
+    const long page_size = sysconf(_SC_PAGESIZE);
+    int result = posix_memalign(&aligned_memory, MAX(TENSOR_ALIGNMENT, page_size), size);
 #else
-    int result = posix_memalign(&aligned_memory, GGML_MEM_ALIGN, size);
+    int result = posix_memalign(&aligned_memory, TENSOR_ALIGNMENT, size);
 #endif
     if (result != 0) {
         // Handle allocation failure
@@ -419,14 +435,26 @@ inline static void * ggml_aligned_malloc(size_t size) {
         return NULL;
     }
     return aligned_memory;
+#endif
 }
-#define GGML_ALIGNED_MALLOC(size) ggml_aligned_malloc(size)
-#ifdef GGML_USE_CPU_HBM
-#define GGML_ALIGNED_FREE(ptr)    if(NULL != ptr) hbw_free(ptr)
+
+void ggml_aligned_free(void * ptr, size_t size) {
+    GGML_UNUSED(size);
+#if defined(_MSC_VER) || defined(__MINGW32__)
+    _aligned_free(ptr);
+#elif GGML_USE_CPU_HBM
+    if (ptr != NULL) {
+        hbw_free(ptr);
+    }
+#elif TARGET_OS_OSX
+    if (ptr != NULL) {
+        vm_deallocate((vm_map_t)mach_task_self(), (vm_address_t)ptr, size);
+    }
 #else
-#define GGML_ALIGNED_FREE(ptr)    free(ptr)
-#endif
+    free(ptr);
 #endif
+}
+
 
 inline static void * ggml_malloc(size_t size) {
     if (size == 0) {
@@ -3894,7 +3922,7 @@ struct ggml_context * ggml_init(struct ggml_init_params params) {
 
     *ctx = (struct ggml_context) {
         /*.mem_size           =*/ mem_size,
-        /*.mem_buffer         =*/ params.mem_buffer ? params.mem_buffer : GGML_ALIGNED_MALLOC(mem_size),
+        /*.mem_buffer         =*/ params.mem_buffer ? params.mem_buffer : ggml_aligned_malloc(mem_size),
         /*.mem_buffer_owned   =*/ params.mem_buffer ? false : true,
         /*.no_alloc           =*/ params.no_alloc,
         /*.no_alloc_save      =*/ params.no_alloc,
@@ -3934,7 +3962,7 @@ void ggml_free(struct ggml_context * ctx) {
                     __func__, i, ggml_used_mem(ctx));
 
             if (ctx->mem_buffer_owned) {
-                GGML_ALIGNED_FREE(ctx->mem_buffer);
+                ggml_aligned_free(ctx->mem_buffer, ctx->mem_size);
             }
 
             found = true;
@@ -19663,9 +19691,10 @@ static void ggml_thread_cpumask_next(const bool * global_mask, bool * local_mask
 void ggml_threadpool_free(struct ggml_threadpool* threadpool) {
     if (!threadpool) return;
 
+    const int n_threads = threadpool->n_threads_max;
+
 #ifndef GGML_USE_OPENMP
     struct ggml_compute_state* workers = threadpool->workers;
-    const int n_threads = threadpool->n_threads_max;
 
     ggml_mutex_lock(&threadpool->mutex);
 
@@ -19685,8 +19714,9 @@ void ggml_threadpool_free(struct ggml_threadpool* threadpool) {
     ggml_cond_destroy(&threadpool->cond);
 #endif // GGML_USE_OPENMP
 
-    GGML_ALIGNED_FREE(threadpool->workers);
-    GGML_ALIGNED_FREE(threadpool);
+    const size_t workers_size = sizeof(struct ggml_compute_state) * n_threads;
+    ggml_aligned_free(threadpool->workers, workers_size);
+    ggml_aligned_free(threadpool, sizeof(struct ggml_threadpool));
 }
 
 #ifndef GGML_USE_OPENMP
@@ -20123,7 +20153,7 @@ static struct ggml_threadpool * ggml_threadpool_new_impl(
                 struct ggml_cplan * cplan) {
 
     struct ggml_threadpool * threadpool =
-        GGML_ALIGNED_MALLOC(sizeof(struct ggml_threadpool));
+        ggml_aligned_malloc(sizeof(struct ggml_threadpool));
     {
         threadpool->cgraph           = cgraph;
         threadpool->cplan            = cplan;
@@ -20144,7 +20174,7 @@ static struct ggml_threadpool * ggml_threadpool_new_impl(
 
     // Allocate and init workers state
     const size_t workers_size = sizeof(struct ggml_compute_state) * tpp->n_threads;
-    struct ggml_compute_state * workers = GGML_ALIGNED_MALLOC(workers_size);
+    struct ggml_compute_state * workers = ggml_aligned_malloc(workers_size);
 
     memset(workers, 0, workers_size);
     for (int j = 0; j < tpp->n_threads; j++) {
diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py
index e08617ba2..7ab08b036 100644
--- a/gguf-py/gguf/constants.py
+++ b/gguf-py/gguf/constants.py
@@ -152,6 +152,8 @@ class Keys:
         MERGES               = "tokenizer.ggml.merges"
         BOS_ID               = "tokenizer.ggml.bos_token_id"
         EOS_ID               = "tokenizer.ggml.eos_token_id"
+        EOT_ID               = "tokenizer.ggml.eot_token_id"
+        EOM_ID               = "tokenizer.ggml.eom_token_id"
         UNK_ID               = "tokenizer.ggml.unknown_token_id"
         SEP_ID               = "tokenizer.ggml.seperator_token_id"
         PAD_ID               = "tokenizer.ggml.padding_token_id"
@@ -168,11 +170,16 @@ class Keys:
         CHAT_TEMPLATE_N      = "tokenizer.chat_template.{name}"
         CHAT_TEMPLATES       = "tokenizer.chat_templates"
         # FIM/Infill special tokens constants
+        FIM_PRE_ID           = "tokenizer.ggml.fim_pre_token_id"
+        FIM_SUF_ID           = "tokenizer.ggml.fim_suf_token_id"
+        FIM_MID_ID           = "tokenizer.ggml.fim_mid_token_id"
+        FIM_PAD_ID           = "tokenizer.ggml.fim_pad_token_id"
+        FIM_REP_ID           = "tokenizer.ggml.fim_rep_token_id"
+        FIM_SEP_ID           = "tokenizer.ggml.fim_sep_token_id"
+        # deprecated:
         PREFIX_ID            = "tokenizer.ggml.prefix_token_id"
         SUFFIX_ID            = "tokenizer.ggml.suffix_token_id"
         MIDDLE_ID            = "tokenizer.ggml.middle_token_id"
-        EOT_ID               = "tokenizer.ggml.eot_token_id"
-        EOM_ID               = "tokenizer.ggml.eom_token_id"
 
     class Adapter:
         TYPE       = "adapter.type"
@@ -1579,6 +1586,8 @@ KEY_TOKENIZER_SCORES     = Keys.Tokenizer.SCORES
 KEY_TOKENIZER_MERGES     = Keys.Tokenizer.MERGES
 KEY_TOKENIZER_BOS_ID     = Keys.Tokenizer.BOS_ID
 KEY_TOKENIZER_EOS_ID     = Keys.Tokenizer.EOS_ID
+KEY_TOKENIZER_EOT_ID     = Keys.Tokenizer.EOT_ID
+KEY_TOKENIZER_EOM_ID     = Keys.Tokenizer.EOM_ID
 KEY_TOKENIZER_UNK_ID     = Keys.Tokenizer.UNK_ID
 KEY_TOKENIZER_SEP_ID     = Keys.Tokenizer.SEP_ID
 KEY_TOKENIZER_PAD_ID     = Keys.Tokenizer.PAD_ID
@@ -1586,8 +1595,15 @@ KEY_TOKENIZER_CLS_ID     = Keys.Tokenizer.CLS_ID
 KEY_TOKENIZER_MASK_ID    = Keys.Tokenizer.MASK_ID
 KEY_TOKENIZER_HF_JSON    = Keys.Tokenizer.HF_JSON
 KEY_TOKENIZER_RWKV       = Keys.Tokenizer.RWKV
-KEY_TOKENIZER_PRIFIX_ID  = Keys.Tokenizer.PREFIX_ID
+
+KEY_TOKENIZER_FIM_PRE_ID = Keys.Tokenizer.FIM_PRE_ID
+KEY_TOKENIZER_FIM_SUF_ID = Keys.Tokenizer.FIM_SUF_ID
+KEY_TOKENIZER_FIM_MID_ID = Keys.Tokenizer.FIM_MID_ID
+KEY_TOKENIZER_FIM_PAD_ID = Keys.Tokenizer.FIM_PAD_ID
+KEY_TOKENIZER_FIM_REP_ID = Keys.Tokenizer.FIM_REP_ID
+KEY_TOKENIZER_FIM_SEP_ID = Keys.Tokenizer.FIM_SEP_ID
+
+# deprecated
+KEY_TOKENIZER_PREFIX_ID  = Keys.Tokenizer.PREFIX_ID
 KEY_TOKENIZER_SUFFIX_ID  = Keys.Tokenizer.SUFFIX_ID
 KEY_TOKENIZER_MIDDLE_ID  = Keys.Tokenizer.MIDDLE_ID
-KEY_TOKENIZER_EOT_ID     = Keys.Tokenizer.EOT_ID
-KEY_TOKENIZER_EOM_ID     = Keys.Tokenizer.EOM_ID
diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py
index 5c460ef1b..0d8d8a0b0 100644
--- a/gguf-py/gguf/gguf_writer.py
+++ b/gguf-py/gguf/gguf_writer.py
@@ -843,15 +843,6 @@ class GGUFWriter:
 
         self.add_string(Keys.Tokenizer.CHAT_TEMPLATE, value)
 
-    def add_prefix_token_id(self, id: int) -> None:
-        self.add_uint32(Keys.Tokenizer.PREFIX_ID, id)
-
-    def add_suffix_token_id(self, id: int) -> None:
-        self.add_uint32(Keys.Tokenizer.SUFFIX_ID, id)
-
-    def add_middle_token_id(self, id: int) -> None:
-        self.add_uint32(Keys.Tokenizer.MIDDLE_ID, id)
-
     def add_eot_token_id(self, id: int) -> None:
         self.add_uint32(Keys.Tokenizer.EOT_ID, id)
 
diff --git a/include/llama.h b/include/llama.h
index c1537c85e..361df855b 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -899,6 +899,7 @@ extern "C" {
     // Special tokens
     LLAMA_API llama_token llama_token_bos(const struct llama_model * model); // beginning-of-sentence
     LLAMA_API llama_token llama_token_eos(const struct llama_model * model); // end-of-sentence
+    LLAMA_API llama_token llama_token_eot(const struct llama_model * model); // end-of-turn
     LLAMA_API llama_token llama_token_cls(const struct llama_model * model); // classification
     LLAMA_API llama_token llama_token_sep(const struct llama_model * model); // sentence separator
     LLAMA_API llama_token llama_token_nl (const struct llama_model * model); // next-line
@@ -907,11 +908,17 @@ extern "C" {
     LLAMA_API bool llama_add_bos_token(const struct llama_model * model);
     LLAMA_API bool llama_add_eos_token(const struct llama_model * model);
 
-    // Codellama infill tokens
-    LLAMA_API llama_token llama_token_prefix(const struct llama_model * model); // Beginning of infill prefix
-    LLAMA_API llama_token llama_token_middle(const struct llama_model * model); // Beginning of infill middle
-    LLAMA_API llama_token llama_token_suffix(const struct llama_model * model); // Beginning of infill suffix
-    LLAMA_API llama_token llama_token_eot   (const struct llama_model * model); // End of infill middle
+    // infill tokens
+    DEPRECATED(LLAMA_API llama_token llama_token_prefix(const struct llama_model * model), "use llama_token_fim_pre instead");
+    DEPRECATED(LLAMA_API llama_token llama_token_middle(const struct llama_model * model), "use llama_token_fim_mid instead");
+    DEPRECATED(LLAMA_API llama_token llama_token_suffix(const struct llama_model * model), "use llama_token_fim_suf instead");
+
+    LLAMA_API llama_token llama_token_fim_pre(const struct llama_model * model);
+    LLAMA_API llama_token llama_token_fim_suf(const struct llama_model * model);
+    LLAMA_API llama_token llama_token_fim_mid(const struct llama_model * model);
+    LLAMA_API llama_token llama_token_fim_pad(const struct llama_model * model);
+    LLAMA_API llama_token llama_token_fim_rep(const struct llama_model * model);
+    LLAMA_API llama_token llama_token_fim_sep(const struct llama_model * model);
 
     //
     // Tokenization
@@ -948,6 +955,12 @@ extern "C" {
                                int32_t   lstrip,
                                   bool   special);
 
+    // check if token0 is contained as a prefix in token1
+    LLAMA_API bool llama_token_is_prefix(
+              const struct llama_model * model,
+                           llama_token   token0,
+                           llama_token   token1);
+
     /// @details Convert the provided tokens into text (inverse of llama_tokenize()).
     /// @param text The char pointer must be large enough to hold the resulting text.
     /// @return Returns the number of chars/bytes on success, no more than text_len_max.
@@ -1096,6 +1109,9 @@ extern "C" {
     /// @details Dynamic temperature implementation (a.k.a. entropy) described in the paper https://arxiv.org/abs/2309.02772.
     LLAMA_API struct llama_sampler * llama_sampler_init_temp_ext   (float   t, float   delta, float exponent);
 
+    /// @details XTC sampler as described in https://github.com/oobabooga/text-generation-webui/pull/6335
+    LLAMA_API struct llama_sampler * llama_sampler_init_xtc        (float   p, float   t,     size_t min_keep, uint32_t seed);
+
     /// @details Mirostat 1.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words.
     /// @param candidates A vector of `llama_token_data` containing the candidate tokens, their probabilities (p), and log-odds (logit) for the current position in the generated text.
     /// @param tau  The target cross-entropy (or surprise) value you want to achieve for the generated text. A higher value corresponds to more surprising or less predictable text, while a lower value corresponds to less surprising or more predictable text.
@@ -1140,6 +1156,28 @@ extern "C" {
                              int32_t   n_logit_bias,
               const llama_logit_bias * logit_bias);
 
+    // this sampler is meant to be used for fill-in-the-middle infilling
+    // it's supposed to be used after top_k + top_p sampling
+    //
+    // 1. if the sum of the EOG probs times the number of candidates is higher than the sum of the other probs -> pick EOG
+    // 2. combine probs of tokens that have the same prefix
+    //
+    // example:
+    //
+    // - before:
+    //   "hel":   0.5
+    //   "hell":  0.2
+    //   "hello": 0.1
+    //   "dummy": 0.1
+    //
+    // - after:
+    //   "hel":   0.8
+    //   "dummy": 0.1
+    //
+    // 3. discard non-EOG tokens with low prob
+    // 4. if no tokens are left -> pick EOT
+    //
+    LLAMA_API struct llama_sampler * llama_sampler_init_infill(const struct llama_model * model);
 
     // Returns the seed used by the sampler if applicable, LLAMA_DEFAULT_SEED otherwise
     LLAMA_API uint32_t llama_sampler_get_seed(const struct llama_sampler * smpl);
diff --git a/koboldcpp.py b/koboldcpp.py
index 42feb4b50..7c4a9fb07 100644
--- a/koboldcpp.py
+++ b/koboldcpp.py
@@ -44,7 +44,7 @@ maxhordelen = 400
 modelbusy = threading.Lock()
 requestsinqueue = 0
 defaultport = 5001
-KcppVersion = "1.76"
+KcppVersion = "1.77"
 showdebug = True
 guimode = False
 showsamplerwarning = True
@@ -2250,8 +2250,8 @@ def show_gui():
 
     import customtkinter as ctk
     nextstate = 0 #0=exit, 1=launch
-    original_windowwidth = 550
-    original_windowheight = 550
+    original_windowwidth = 580
+    original_windowheight = 560
     windowwidth = original_windowwidth
     windowheight = original_windowheight
     ctk.set_appearance_mode("dark")
diff --git a/src/llama-sampling.cpp b/src/llama-sampling.cpp
index e255a8fc4..2e6550682 100644
--- a/src/llama-sampling.cpp
+++ b/src/llama-sampling.cpp
@@ -1059,6 +1059,101 @@ struct llama_sampler * llama_sampler_init_temp_ext(float temp, float delta, floa
     };
 }
 
+// xtc
+
+struct llama_sampler_xtc {
+    const float    probability;
+    const float    threshold;
+    const size_t   min_keep;
+
+    const uint32_t seed;
+    uint32_t       seed_cur;
+
+    std::mt19937   rng;
+};
+
+static const char * llama_sampler_xtc_name(const struct llama_sampler * /*smpl*/) {
+    return "xtc";
+}
+
+static void llama_sample_xtc_apply(struct llama_sampler * smpl, llama_token_data_array * cur_p) {
+    auto * ctx = (llama_sampler_xtc *) smpl->ctx;
+
+    if (ctx->probability <= 0.0f
+        || ctx->threshold > 0.5f
+        || cur_p->size < 2) {
+        return;
+    }
+
+    std::uniform_real_distribution<float> distribution(0.0f, 1.0f);
+    float chance = distribution(ctx->rng);
+    if (chance > ctx->probability) return;
+
+    // in case it's not sorted/recalculated yet
+    llama_sampler_softmax_impl(cur_p);
+
+    int pos_last = 0;
+
+    for (size_t i = 0; i < cur_p->size; ++i) {
+        if (cur_p->data[i].p >= ctx->threshold) {
+            pos_last = i;
+        } else break;
+    }
+
+    if (cur_p->size - pos_last >= ctx->min_keep && pos_last > 0) {
+        cur_p->data += pos_last;
+        cur_p->size -= pos_last;
+    }
+}
+
+static struct llama_sampler * llama_sampler_xtc_clone(const struct llama_sampler * smpl) {
+    const auto * ctx = (const llama_sampler_xtc *) smpl->ctx;
+    auto * result = llama_sampler_init_xtc(ctx->probability, ctx->threshold, ctx->min_keep, ctx->seed);
+
+    // copy the state
+    {
+        auto * result_ctx = (llama_sampler_xtc *) result->ctx;
+
+        result_ctx->rng = ctx->rng;
+    }
+
+    return result;
+}
+
+static void llama_sampler_xtc_free(struct llama_sampler * smpl) {
+    delete (llama_sampler_xtc *) smpl->ctx;
+}
+
+static void llama_sampler_xtc_reset(struct llama_sampler * smpl) {
+    auto * ctx = (llama_sampler_xtc *) smpl->ctx;
+    ctx->seed_cur = get_rng_seed(ctx->seed);
+    ctx->rng.seed(ctx->seed_cur);
+}
+
+static struct llama_sampler_i llama_sampler_xtc_i = {
+    /* .name   = */ llama_sampler_xtc_name,
+    /* .accept = */ nullptr,
+    /* .apply  = */ llama_sample_xtc_apply,
+    /* .reset  = */ llama_sampler_xtc_reset,
+    /* .clone  = */ llama_sampler_xtc_clone,
+    /* .free   = */ llama_sampler_xtc_free,
+};
+
+struct llama_sampler * llama_sampler_init_xtc(float p, float t, size_t min_keep, uint32_t seed) {
+    auto seed_cur = get_rng_seed(seed);
+    return new llama_sampler {
+        /* .iface = */ &llama_sampler_xtc_i,
+        /* .ctx   = */ new llama_sampler_xtc {
+            /* .probability   = */ p,
+            /* .threshold     = */ t,
+            /* .min_keep      = */ min_keep,
+            /* .seed          = */ seed,
+            /* .seed_cur      = */ seed_cur,
+            /* .rng           = */ std::mt19937(seed_cur),
+        },
+    };
+}
+
 // mirostat
 
 struct llama_sampler_mirostat {
@@ -1644,6 +1739,207 @@ struct llama_sampler * llama_sampler_init_logit_bias(
     };
 }
 
+// infill
+
+//#define GGML_DEBUG_SAMPLER_INFILL
+
+struct llama_sampler_infill {
+    const struct llama_vocab * vocab;
+};
+
+static const char * llama_sampler_infill_name(const struct llama_sampler * /*smpl*/) {
+    return "infill";
+}
+
+static void llama_sampler_infill_apply(struct llama_sampler * smpl, llama_token_data_array * cur_p) {
+    auto * ctx = (llama_sampler_infill *) smpl->ctx;
+
+    llama_sampler_softmax_impl(cur_p);
+
+#if defined(GGML_DEBUG_SAMPLER_INFILL)
+#define LOG_DBG_CUR LLAMA_LOG_DEBUG
+#else
+#define LOG_DBG_CUR(...)
+#endif
+
+    for (size_t i = 0; i < cur_p->size; ++i) {
+        LOG_DBG_CUR("%s: cur_p[%3zu] = { id: %6d, p: %.6f, logit: %6.3f }\n", __func__, i, cur_p->data[i].id, cur_p->data[i].p, cur_p->data[i].logit);
+    }
+
+    float p_txt_sum = 0.0f;
+    float p_eog_sum = 0.0f;
+
+    for (size_t i = 0; i < cur_p->size; ++i) {
+        if (llama_token_is_eog_impl(*ctx->vocab, cur_p->data[i].id)) {
+            p_eog_sum += cur_p->data[i].p;
+        } else {
+            p_txt_sum += cur_p->data[i].p;
+        }
+    }
+
+    const float rat = p_eog_sum == 0.0 ? INFINITY : p_txt_sum / p_eog_sum; GGML_UNUSED(rat);
+
+    LOG_DBG_CUR("%s: p_txt_sum = %.2f, p_eog_sum = %.2f, rat = %.2f, n = %zu\n", __func__, p_txt_sum, p_eog_sum, rat, cur_p->size);
+
+    if (3*p_eog_sum*cur_p->size > p_txt_sum) {
+        LOG_DBG_CUR("%s: the ratio p_txt/p_eog = %.2f is too low -> sampling EOG\n", __func__, p_txt_sum/p_eog_sum);
+
+        // keep just the EOG tokens
+        const auto size_org = cur_p->size;
+
+        cur_p->size = 0;
+
+        float p_sum = 0.0f;
+
+        for (size_t i = 0; i < size_org; ++i) {
+            if (llama_token_is_eog_impl(*ctx->vocab, cur_p->data[i].id)) {
+                p_sum += cur_p->data[i].p;
+
+                cur_p->data[cur_p->size++] = cur_p->data[i];
+            }
+        }
+
+        // normalize probs
+        for (size_t i = 0; i < cur_p->size; ++i) {
+            cur_p->data[i].p /= p_sum;
+        }
+
+        return;
+    }
+
+    size_t n_combined = 0; GGML_UNUSED(n_combined);
+
+    // combine tokens with common prefix
+    for (size_t i = 0; i < cur_p->size; ++i) {
+        for (size_t j = 0; j < cur_p->size; ++j) {
+            if (cur_p->data[i].logit == -INFINITY) {
+                break;
+            }
+
+            if (i == j || cur_p->data[j].logit == -INFINITY) {
+                continue;
+            }
+
+            if (llama_token_is_prefix_impl(*ctx->vocab, cur_p->data[i].id, cur_p->data[j].id)) {
+                if (cur_p->data[i].p >  cur_p->data[j].p) {
+                    cur_p->data[i].p += cur_p->data[j].p;
+                    cur_p->data[j].logit = -INFINITY;
+                    cur_p->data[j].p     = 0.0f;
+                } else {
+                    cur_p->data[j].p += cur_p->data[i].p;
+                    cur_p->data[i].logit = -INFINITY;
+                    cur_p->data[i].p     = 0.0f;
+                }
+
+                n_combined++;
+            }
+        }
+    }
+
+    size_t n_non_eog = 0;
+
+    size_t size_org = cur_p->size;
+
+    float p_sum = 0.0f;
+    float thold = 0.2f;
+
+    cur_p->size = 0;
+
+    LOG_DBG_CUR("%s: n_combined = %zu, applying thold = %.3f\n", __func__, n_combined, thold);
+
+    for (size_t i = 0; i < size_org; ++i) {
+        const bool is_eog = llama_token_is_eog_impl(*ctx->vocab, cur_p->data[i].id);
+
+        if (cur_p->data[i].p < thold && !is_eog) {
+            continue;
+        }
+
+        if (!is_eog) {
+            ++n_non_eog;
+        }
+
+        p_sum += cur_p->data[i].p;
+
+        // keep this token
+        cur_p->data[cur_p->size++] = cur_p->data[i];
+    }
+
+    LOG_DBG_CUR("%s: n_non_eog = %zu\n", __func__, n_non_eog);
+
+    // if no non-EOG tokens are left -> reduce cur_p to single EOT token
+    if (n_non_eog == 0) {
+        cur_p->size = 1;
+        cur_p->data[0].id = llama_token_eot_impl(*ctx->vocab);
+        cur_p->data[0].logit = 1.0f;
+
+        return;
+    }
+
+    // normalize probs
+    for (size_t i = 0; i < cur_p->size; ++i) {
+        cur_p->data[i].p /= p_sum;
+
+        LOG_DBG_CUR("%s: cur_p[%3zu] = { id: %6d, p: %.6f, logit: %6.3f }\n", __func__, i, cur_p->data[i].id, cur_p->data[i].p, cur_p->data[i].logit);
+    }
+
+    size_org = cur_p->size;
+    p_sum = 0.0f;
+    thold = 1.0/(n_non_eog + 1);
+
+    cur_p->size = 0;
+
+    LOG_DBG_CUR("%s: applying thold = %.3f\n", __func__, thold);
+
+    for (size_t i = 0; i < size_org; ++i) {
+        const bool is_eog = llama_token_is_eog_impl(*ctx->vocab, cur_p->data[i].id);
+
+        if (cur_p->data[i].p < thold && !is_eog) {
+            continue;
+        }
+
+        p_sum += cur_p->data[i].p;
+
+        cur_p->data[cur_p->size++] = cur_p->data[i];
+    }
+
+    // normalize probs
+    for (size_t i = 0; i < cur_p->size; ++i) {
+        cur_p->data[i].p /= p_sum;
+
+        LOG_DBG_CUR("%s: cur_p[%3zu] = { id: %6d, p: %.6f, logit: %6.3f }\n", __func__, i, cur_p->data[i].id, cur_p->data[i].p, cur_p->data[i].logit);
+    }
+
+#undef LOG_DBG_CUR
+}
+
+static struct llama_sampler * llama_sampler_infill_clone(const struct llama_sampler * smpl) {
+    const auto * ctx = (const llama_sampler_infill *) smpl->ctx;
+    return llama_sampler_init_infill_impl(*ctx->vocab);
+}
+
+static void llama_sampler_infill_free(struct llama_sampler * smpl) {
+    delete (llama_sampler_infill *) smpl->ctx;
+}
+
+static struct llama_sampler_i llama_sampler_infill_i = {
+    /* .name   = */ llama_sampler_infill_name,
+    /* .accept = */ nullptr,
+    /* .apply  = */ llama_sampler_infill_apply,
+    /* .reset  = */ nullptr,
+    /* .clone  = */ llama_sampler_infill_clone,
+    /* .free   = */ llama_sampler_infill_free,
+};
+
+struct llama_sampler * llama_sampler_init_infill_impl(
+        const struct llama_vocab & vocab) {
+    return new llama_sampler {
+        /* .iface = */ &llama_sampler_infill_i,
+        /* .ctx   = */ new llama_sampler_infill {
+            /* .vocab = */ &vocab,
+        },
+    };
+}
+
 // utils
 
 uint32_t llama_sampler_get_seed(const struct llama_sampler * smpl) {
diff --git a/src/llama-sampling.h b/src/llama-sampling.h
index d90b14713..2683f1b92 100644
--- a/src/llama-sampling.h
+++ b/src/llama-sampling.h
@@ -4,8 +4,6 @@
 
 #include "llama-grammar.h"
 
-#include <unordered_map>
-
 struct llama_vocab;
 struct llama_grammar;
 
@@ -27,3 +25,6 @@ struct llama_sampler * llama_sampler_init_grammar_impl(
         const struct llama_vocab & vocab,
                       const char * grammar_str,
                       const char * grammar_root);
+
+struct llama_sampler * llama_sampler_init_infill_impl(
+        const struct llama_vocab & vocab);
diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp
index d8acd584a..be9ac9d6b 100644
--- a/src/llama-vocab.cpp
+++ b/src/llama-vocab.cpp
@@ -230,7 +230,7 @@ struct llm_tokenizer_spm_session {
         }
 
         // seed the work queue with all possible 2-character tokens.
-        for (size_t i = 1; i < symbols.size(); ++i) {
+        for (int i = 1; i < (int) symbols.size(); ++i) {
             try_add_bigram(i - 1, i);
         }
 
@@ -790,7 +790,7 @@ struct llm_tokenizer_bpe_session {
                 index++;
                 symbols.emplace_back(sym);
             }
-            for (size_t i = 1; i < symbols.size(); ++i) {
+            for (int i = 1; i < (int) symbols.size(); ++i) {
                 add_new_bigram(i - 1, i);
             }
 
@@ -1918,6 +1918,14 @@ llama_token llama_token_eos_impl(const struct llama_vocab & vocab) {
     return vocab.special_eos_id;
 }
 
+llama_token llama_token_eot_impl(const struct llama_vocab & vocab) {
+    return vocab.special_eot_id;
+}
+
+llama_token llama_token_eom_impl(const struct llama_vocab & vocab) {
+    return vocab.special_eom_id;
+}
+
 llama_token llama_token_cls_impl(const struct llama_vocab & vocab) {
     return vocab.special_cls_id;
 }
@@ -1943,23 +1951,39 @@ bool llama_add_eos_token_impl(const struct llama_vocab & vocab) {
 }
 
 llama_token llama_token_prefix_impl(const struct llama_vocab & vocab) {
-    return vocab.special_prefix_id;
+    return vocab.special_fim_pre_id;
 }
 
 llama_token llama_token_middle_impl(const struct llama_vocab & vocab) {
-    return vocab.special_middle_id;
+    return vocab.special_fim_mid_id;
 }
 
 llama_token llama_token_suffix_impl(const struct llama_vocab & vocab) {
-    return vocab.special_suffix_id;
+    return vocab.special_fim_suf_id;
 }
 
-llama_token llama_token_eot_impl(const struct llama_vocab & vocab) {
-    return vocab.special_eot_id;
+llama_token llama_token_fim_pre_impl(const struct llama_vocab & vocab) {
+    return vocab.special_fim_pre_id;
 }
 
-llama_token llama_token_eom_impl(const struct llama_vocab & vocab) {
-    return vocab.special_eom_id;
+llama_token llama_token_fim_suf_impl(const struct llama_vocab & vocab) {
+    return vocab.special_fim_suf_id;
+}
+
+llama_token llama_token_fim_mid_impl(const struct llama_vocab & vocab) {
+    return vocab.special_fim_mid_id;
+}
+
+llama_token llama_token_fim_pad_impl(const struct llama_vocab & vocab) {
+    return vocab.special_fim_pad_id;
+}
+
+llama_token llama_token_fim_rep_impl(const struct llama_vocab & vocab) {
+    return vocab.special_fim_rep_id;
+}
+
+llama_token llama_token_fim_sep_impl(const struct llama_vocab & vocab) {
+    return vocab.special_fim_sep_id;
 }
 
 int32_t llama_tokenize_impl(
@@ -2094,6 +2118,23 @@ int32_t llama_token_to_piece_impl(const struct llama_vocab & vocab, llama_token
     return 0;
 }
 
+bool llama_token_is_prefix_impl(
+        const struct llama_vocab & vocab,
+                     llama_token   token0,
+                     llama_token   token1) {
+    char text_buf_0[128];
+    char text_buf_1[128];
+
+    const int32_t len0 = llama_token_to_piece_impl(vocab, token0, text_buf_0, sizeof(text_buf_0) - 1, 0, false);
+    const int32_t len1 = llama_token_to_piece_impl(vocab, token1, text_buf_1, sizeof(text_buf_1) - 1, 0, false);
+
+    if (len0 <= 0 || len1 <= 0) {
+        return false;
+    }
+
+    return len0 <= len1 && memcmp(text_buf_0, text_buf_1, len0) == 0;
+}
+
 int32_t llama_detokenize_impl(
         const struct llama_vocab & vocab,
                const llama_token * tokens,
diff --git a/src/llama-vocab.h b/src/llama-vocab.h
index 28bad9135..d958d0073 100644
--- a/src/llama-vocab.h
+++ b/src/llama-vocab.h
@@ -37,20 +37,26 @@ struct llama_vocab {
     std::map<std::pair<std::string, std::string>, int> bpe_ranks;
 
     // default LLaMA special tokens
+    // TODO: should we set all of these to LLAMA_TOKEN_NULL?
     id special_bos_id  = 1;
     id special_eos_id  = 2;
+    id special_eot_id  = LLAMA_TOKEN_NULL;
+    id special_eom_id  = LLAMA_TOKEN_NULL;
     id special_unk_id  = 0;
     id special_sep_id  = LLAMA_TOKEN_NULL;
     id special_pad_id  = LLAMA_TOKEN_NULL;
     id special_cls_id  = LLAMA_TOKEN_NULL;
     id special_mask_id = LLAMA_TOKEN_NULL;
 
-    id linefeed_id       = 13;
-    id special_prefix_id = LLAMA_TOKEN_NULL;
-    id special_suffix_id = LLAMA_TOKEN_NULL;
-    id special_middle_id = LLAMA_TOKEN_NULL;
-    id special_eot_id    = LLAMA_TOKEN_NULL; // TODO: move above after "eos_id", and here add "file separator" token
-    id special_eom_id    = LLAMA_TOKEN_NULL;
+    id linefeed_id = 13;
+
+    // fim tokens
+    id special_fim_pre_id = LLAMA_TOKEN_NULL;
+    id special_fim_suf_id = LLAMA_TOKEN_NULL;
+    id special_fim_mid_id = LLAMA_TOKEN_NULL;
+    id special_fim_pad_id = LLAMA_TOKEN_NULL;
+    id special_fim_rep_id = LLAMA_TOKEN_NULL; // repo
+    id special_fim_sep_id = LLAMA_TOKEN_NULL; // file separator
 
     // set of all tokens that cause "end of generation"
     std::set<id> special_eog_ids;
@@ -104,19 +110,26 @@ bool llama_token_is_control_impl(const struct llama_vocab & vocab, llama_token t
 
 llama_token llama_token_bos_impl(const struct llama_vocab & vocab);
 llama_token llama_token_eos_impl(const struct llama_vocab & vocab);
+llama_token llama_token_eot_impl(const struct llama_vocab & vocab);
+llama_token llama_token_eom_impl(const struct llama_vocab & vocab);
 llama_token llama_token_cls_impl(const struct llama_vocab & vocab);
 llama_token llama_token_sep_impl(const struct llama_vocab & vocab);
 llama_token llama_token_nl_impl (const struct llama_vocab & vocab);
 llama_token llama_token_pad_impl(const struct llama_vocab & vocab);
 
-bool llama_add_bos_token_impl(const struct llama_vocab & vocab);
-bool llama_add_eos_token_impl(const struct llama_vocab & vocab);
-
 llama_token llama_token_prefix_impl(const struct llama_vocab & vocab);
 llama_token llama_token_middle_impl(const struct llama_vocab & vocab);
 llama_token llama_token_suffix_impl(const struct llama_vocab & vocab);
-llama_token llama_token_eot_impl   (const struct llama_vocab & vocab);
-llama_token llama_token_eom_impl   (const struct llama_vocab & vocab);
+
+llama_token llama_token_fim_pre_impl(const struct llama_vocab & vocab);
+llama_token llama_token_fim_suf_impl(const struct llama_vocab & vocab);
+llama_token llama_token_fim_mid_impl(const struct llama_vocab & vocab);
+llama_token llama_token_fim_pad_impl(const struct llama_vocab & vocab);
+llama_token llama_token_fim_rep_impl(const struct llama_vocab & vocab);
+llama_token llama_token_fim_sep_impl(const struct llama_vocab & vocab);
+
+bool llama_add_bos_token_impl(const struct llama_vocab & vocab);
+bool llama_add_eos_token_impl(const struct llama_vocab & vocab);
 
 int32_t llama_tokenize_impl(
         const struct llama_vocab & vocab,
@@ -136,6 +149,12 @@ int32_t llama_token_to_piece_impl(
                          int32_t   lstrip,
                             bool   special);
 
+// check if token0 is contained as a prefix in token1
+bool llama_token_is_prefix_impl(
+        const struct llama_vocab & vocab,
+                     llama_token   token0,
+                     llama_token   token1);
+
 int32_t llama_detokenize_impl(
         const struct llama_vocab & vocab,
                const llama_token * tokens,
diff --git a/src/llama.cpp b/src/llama.cpp
index 974da63ce..963c44b76 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -15,8 +15,6 @@
 #  include "ggml-cuda.h"
 #elif defined(GGML_USE_CLBLAST)
 #  include "ggml-opencl.h"
-#elif defined(GGML_USE_VULKAN)
-#  include "ggml-vulkan.h"
 #elif defined(GGML_USE_SYCL)
 #  include "ggml-sycl.h"
 #elif defined(GGML_USE_KOMPUTE)
@@ -356,6 +354,8 @@ enum llm_kv {
     LLM_KV_TOKENIZER_MERGES,
     LLM_KV_TOKENIZER_BOS_ID,
     LLM_KV_TOKENIZER_EOS_ID,
+    LLM_KV_TOKENIZER_EOT_ID,
+    LLM_KV_TOKENIZER_EOM_ID,
     LLM_KV_TOKENIZER_UNK_ID,
     LLM_KV_TOKENIZER_SEP_ID,
     LLM_KV_TOKENIZER_PAD_ID,
@@ -368,14 +368,20 @@ enum llm_kv {
     LLM_KV_TOKENIZER_PRECOMPILED_CHARSMAP,
     LLM_KV_TOKENIZER_HF_JSON,
     LLM_KV_TOKENIZER_RWKV,
-    LLM_KV_TOKENIZER_PREFIX_ID,
-    LLM_KV_TOKENIZER_SUFFIX_ID,
-    LLM_KV_TOKENIZER_MIDDLE_ID,
-    LLM_KV_TOKENIZER_EOT_ID,
-    LLM_KV_TOKENIZER_EOM_ID,
+    LLM_KV_TOKENIZER_FIM_PRE_ID,
+    LLM_KV_TOKENIZER_FIM_SUF_ID,
+    LLM_KV_TOKENIZER_FIM_MID_ID,
+    LLM_KV_TOKENIZER_FIM_PAD_ID,
+    LLM_KV_TOKENIZER_FIM_REP_ID,
+    LLM_KV_TOKENIZER_FIM_SEP_ID,
 
     LLM_KV_ADAPTER_TYPE,
     LLM_KV_ADAPTER_LORA_ALPHA,
+
+    // deprecated:
+    LLM_KV_TOKENIZER_PREFIX_ID,
+    LLM_KV_TOKENIZER_SUFFIX_ID,
+    LLM_KV_TOKENIZER_MIDDLE_ID,
 };
 
 static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
@@ -433,57 +439,65 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
     { LLM_KV_ATTENTION_SLIDING_WINDOW,         "%s.attention.sliding_window"         },
     { LLM_KV_ATTENTION_SCALE,                  "%s.attention.scale"                  },
 
-    { LLM_KV_ROPE_DIMENSION_COUNT,          "%s.rope.dimension_count"                 },
-    { LLM_KV_ROPE_FREQ_BASE,                "%s.rope.freq_base"                       },
-    { LLM_KV_ROPE_SCALE_LINEAR,             "%s.rope.scale_linear"                    },
-    { LLM_KV_ROPE_SCALING_TYPE,             "%s.rope.scaling.type"                    },
-    { LLM_KV_ROPE_SCALING_FACTOR,           "%s.rope.scaling.factor"                  },
-    { LLM_KV_ROPE_SCALING_ATTN_FACTOR,      "%s.rope.scaling.attn_factor"             },
-    { LLM_KV_ROPE_SCALING_ORIG_CTX_LEN,     "%s.rope.scaling.original_context_length" },
-    { LLM_KV_ROPE_SCALING_FINETUNED,        "%s.rope.scaling.finetuned"               },
-    { LLM_KV_ROPE_SCALING_YARN_LOG_MUL,     "%s.rope.scaling.yarn_log_multiplier"     },
+    { LLM_KV_ROPE_DIMENSION_COUNT,             "%s.rope.dimension_count"                 },
+    { LLM_KV_ROPE_FREQ_BASE,                   "%s.rope.freq_base"                       },
+    { LLM_KV_ROPE_SCALE_LINEAR,                "%s.rope.scale_linear"                    },
+    { LLM_KV_ROPE_SCALING_TYPE,                "%s.rope.scaling.type"                    },
+    { LLM_KV_ROPE_SCALING_FACTOR,              "%s.rope.scaling.factor"                  },
+    { LLM_KV_ROPE_SCALING_ATTN_FACTOR,         "%s.rope.scaling.attn_factor"             },
+    { LLM_KV_ROPE_SCALING_ORIG_CTX_LEN,        "%s.rope.scaling.original_context_length" },
+    { LLM_KV_ROPE_SCALING_FINETUNED,           "%s.rope.scaling.finetuned"               },
+    { LLM_KV_ROPE_SCALING_YARN_LOG_MUL,        "%s.rope.scaling.yarn_log_multiplier"     },
 
-    { LLM_KV_SPLIT_NO,                      "split.no"            },
-    { LLM_KV_SPLIT_COUNT,                   "split.count"         },
-    { LLM_KV_SPLIT_TENSORS_COUNT,           "split.tensors.count" },
+    { LLM_KV_SPLIT_NO,                         "split.no"            },
+    { LLM_KV_SPLIT_COUNT,                      "split.count"         },
+    { LLM_KV_SPLIT_TENSORS_COUNT,              "split.tensors.count" },
 
-    { LLM_KV_SSM_CONV_KERNEL,               "%s.ssm.conv_kernel"    },
-    { LLM_KV_SSM_INNER_SIZE,                "%s.ssm.inner_size"     },
-    { LLM_KV_SSM_STATE_SIZE,                "%s.ssm.state_size"     },
-    { LLM_KV_SSM_TIME_STEP_RANK,            "%s.ssm.time_step_rank" },
-    { LLM_KV_SSM_DT_B_C_RMS,                "%s.ssm.dt_b_c_rms" },
+    { LLM_KV_SSM_CONV_KERNEL,                  "%s.ssm.conv_kernel"    },
+    { LLM_KV_SSM_INNER_SIZE,                   "%s.ssm.inner_size"     },
+    { LLM_KV_SSM_STATE_SIZE,                   "%s.ssm.state_size"     },
+    { LLM_KV_SSM_TIME_STEP_RANK,               "%s.ssm.time_step_rank" },
+    { LLM_KV_SSM_DT_B_C_RMS,                   "%s.ssm.dt_b_c_rms"     },
 
-    { LLM_KV_WKV_HEAD_SIZE,                 "%s.wkv.head_size" },
+    { LLM_KV_WKV_HEAD_SIZE,                    "%s.wkv.head_size" },
 
-    { LLM_KV_TOKENIZER_MODEL,                "tokenizer.ggml.model"                    },
-    { LLM_KV_TOKENIZER_PRE,                  "tokenizer.ggml.pre"                      },
-    { LLM_KV_TOKENIZER_LIST,                 "tokenizer.ggml.tokens"                   },
-    { LLM_KV_TOKENIZER_TOKEN_TYPE,           "tokenizer.ggml.token_type"               },
-    { LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT,     "tokenizer.ggml.token_type_count"         },
-    { LLM_KV_TOKENIZER_SCORES,               "tokenizer.ggml.scores"                   },
-    { LLM_KV_TOKENIZER_MERGES,               "tokenizer.ggml.merges"                   },
-    { LLM_KV_TOKENIZER_BOS_ID,               "tokenizer.ggml.bos_token_id"             },
-    { LLM_KV_TOKENIZER_EOS_ID,               "tokenizer.ggml.eos_token_id"             },
-    { LLM_KV_TOKENIZER_UNK_ID,               "tokenizer.ggml.unknown_token_id"         },
-    { LLM_KV_TOKENIZER_SEP_ID,               "tokenizer.ggml.seperator_token_id"       },
-    { LLM_KV_TOKENIZER_PAD_ID,               "tokenizer.ggml.padding_token_id"         },
-    { LLM_KV_TOKENIZER_CLS_ID,               "tokenizer.ggml.cls_token_id"             },
-    { LLM_KV_TOKENIZER_MASK_ID,              "tokenizer.ggml.mask_token_id"            },
-    { LLM_KV_TOKENIZER_ADD_BOS,              "tokenizer.ggml.add_bos_token"            },
-    { LLM_KV_TOKENIZER_ADD_EOS,              "tokenizer.ggml.add_eos_token"            },
-    { LLM_KV_TOKENIZER_ADD_PREFIX,           "tokenizer.ggml.add_space_prefix"         },
-    { LLM_KV_TOKENIZER_REMOVE_EXTRA_WS,      "tokenizer.ggml.remove_extra_whitespaces" },
-    { LLM_KV_TOKENIZER_PRECOMPILED_CHARSMAP, "tokenizer.ggml.precompiled_charsmap"     },
-    { LLM_KV_TOKENIZER_HF_JSON,              "tokenizer.huggingface.json"              },
-    { LLM_KV_TOKENIZER_RWKV,                 "tokenizer.rwkv.world"                    },
-    { LLM_KV_TOKENIZER_PREFIX_ID,            "tokenizer.ggml.prefix_token_id"          },
-    { LLM_KV_TOKENIZER_SUFFIX_ID,            "tokenizer.ggml.suffix_token_id"          },
-    { LLM_KV_TOKENIZER_MIDDLE_ID,            "tokenizer.ggml.middle_token_id"          },
-    { LLM_KV_TOKENIZER_EOT_ID,               "tokenizer.ggml.eot_token_id"             },
-    { LLM_KV_TOKENIZER_EOM_ID,               "tokenizer.ggml.eom_token_id"             },
+    { LLM_KV_TOKENIZER_MODEL,                  "tokenizer.ggml.model"                    },
+    { LLM_KV_TOKENIZER_PRE,                    "tokenizer.ggml.pre"                      },
+    { LLM_KV_TOKENIZER_LIST,                   "tokenizer.ggml.tokens"                   },
+    { LLM_KV_TOKENIZER_TOKEN_TYPE,             "tokenizer.ggml.token_type"               },
+    { LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT,       "tokenizer.ggml.token_type_count"         },
+    { LLM_KV_TOKENIZER_SCORES,                 "tokenizer.ggml.scores"                   },
+    { LLM_KV_TOKENIZER_MERGES,                 "tokenizer.ggml.merges"                   },
+    { LLM_KV_TOKENIZER_BOS_ID,                 "tokenizer.ggml.bos_token_id"             },
+    { LLM_KV_TOKENIZER_EOS_ID,                 "tokenizer.ggml.eos_token_id"             },
+    { LLM_KV_TOKENIZER_EOT_ID,                 "tokenizer.ggml.eot_token_id"             },
+    { LLM_KV_TOKENIZER_EOM_ID,                 "tokenizer.ggml.eom_token_id"             },
+    { LLM_KV_TOKENIZER_UNK_ID,                 "tokenizer.ggml.unknown_token_id"         },
+    { LLM_KV_TOKENIZER_SEP_ID,                 "tokenizer.ggml.seperator_token_id"       },
+    { LLM_KV_TOKENIZER_PAD_ID,                 "tokenizer.ggml.padding_token_id"         },
+    { LLM_KV_TOKENIZER_CLS_ID,                 "tokenizer.ggml.cls_token_id"             },
+    { LLM_KV_TOKENIZER_MASK_ID,                "tokenizer.ggml.mask_token_id"            },
+    { LLM_KV_TOKENIZER_ADD_BOS,                "tokenizer.ggml.add_bos_token"            },
+    { LLM_KV_TOKENIZER_ADD_EOS,                "tokenizer.ggml.add_eos_token"            },
+    { LLM_KV_TOKENIZER_ADD_PREFIX,             "tokenizer.ggml.add_space_prefix"         },
+    { LLM_KV_TOKENIZER_REMOVE_EXTRA_WS,        "tokenizer.ggml.remove_extra_whitespaces" },
+    { LLM_KV_TOKENIZER_PRECOMPILED_CHARSMAP,   "tokenizer.ggml.precompiled_charsmap"     },
+    { LLM_KV_TOKENIZER_HF_JSON,                "tokenizer.huggingface.json"              },
+    { LLM_KV_TOKENIZER_RWKV,                   "tokenizer.rwkv.world"                    },
+    { LLM_KV_TOKENIZER_FIM_PRE_ID,             "tokenizer.ggml.fim_pre_token_id"         },
+    { LLM_KV_TOKENIZER_FIM_SUF_ID,             "tokenizer.ggml.fim_suf_token_id"         },
+    { LLM_KV_TOKENIZER_FIM_MID_ID,             "tokenizer.ggml.fim_mid_token_id"         },
+    { LLM_KV_TOKENIZER_FIM_PAD_ID,             "tokenizer.ggml.fim_pad_token_id"         },
+    { LLM_KV_TOKENIZER_FIM_REP_ID,             "tokenizer.ggml.fim_rep_token_id"         },
+    { LLM_KV_TOKENIZER_FIM_SEP_ID,             "tokenizer.ggml.fim_sep_token_id"         },
 
-    { LLM_KV_ADAPTER_TYPE,                  "adapter.type"       },
-    { LLM_KV_ADAPTER_LORA_ALPHA,            "adapter.lora.alpha" },
+    { LLM_KV_ADAPTER_TYPE,                     "adapter.type"       },
+    { LLM_KV_ADAPTER_LORA_ALPHA,               "adapter.lora.alpha" },
+
+    // deprecated
+    { LLM_KV_TOKENIZER_PREFIX_ID,              "tokenizer.ggml.prefix_token_id" },
+    { LLM_KV_TOKENIZER_SUFFIX_ID,              "tokenizer.ggml.suffix_token_id" },
+    { LLM_KV_TOKENIZER_MIDDLE_ID,              "tokenizer.ggml.middle_token_id" },
 };
 
 struct LLM_KV {
@@ -3417,8 +3431,6 @@ static int llama_get_device_count(const llama_model & model) {
 
 #if defined(GGML_USE_SYCL)
     count += ggml_backend_sycl_get_device_count();
-#elif defined(GGML_USE_VULKAN)
-    count += ggml_backend_vk_get_device_count();
 #elif defined(GGML_USE_CANN)
     count += ggml_backend_cann_get_device_count();
 #endif
@@ -3450,10 +3462,6 @@ static ggml_backend_buffer_type_t llama_default_buffer_type_cpu(const llama_mode
     }
 #elif defined(GGML_USE_CPU_HBM)
     buft = ggml_backend_cpu_hbm_buffer_type();
-#elif defined(GGML_USE_VULKAN)
-    if (host_buffer) {
-        buft = ggml_backend_vk_host_buffer_type();
-    }
 #endif
 
     if (buft == nullptr) {
@@ -3472,9 +3480,7 @@ static ggml_backend_buffer_type_t llama_default_buffer_type_offload(const llama_
     }
     device -= (int)model.devices.size();
 
-#if defined(GGML_USE_VULKAN)
-    buft = ggml_backend_vk_buffer_type(device);
-#elif defined(GGML_USE_SYCL)
+#if defined(GGML_USE_SYCL)
     buft = ggml_backend_sycl_buffer_type(device);
 #elif defined(GGML_USE_CLBLAST)
     buft = ggml_backend_opencl_buffer_type();
@@ -3536,11 +3542,6 @@ static size_t llama_get_device_memory(const llama_model & model, int device) {
     size_t free;
     ggml_backend_sycl_get_device_memory(device, &free, &total);
     return free;
-#elif defined(GGML_USE_VULKAN)
-    size_t total;
-    size_t free;
-    ggml_backend_vk_get_device_memory(device, &free, &total);
-    return free;
 #elif defined(GGML_USE_CANN)
     size_t total;
     size_t free;
@@ -6197,14 +6198,14 @@ static void llm_load_vocab(
             vocab.type = LLAMA_VOCAB_TYPE_NONE;
 
             // default special tokens
-            vocab.special_bos_id  = -1;
-            vocab.special_eos_id  = -1;
-            vocab.special_unk_id  = -1;
-            vocab.special_sep_id  = -1;
-            vocab.special_pad_id  = -1;
-            vocab.special_cls_id  = -1;
-            vocab.special_mask_id = -1;
-            vocab.linefeed_id     = -1;
+            vocab.special_bos_id  = LLAMA_TOKEN_NULL;
+            vocab.special_eos_id  = LLAMA_TOKEN_NULL;
+            vocab.special_unk_id  = LLAMA_TOKEN_NULL;
+            vocab.special_sep_id  = LLAMA_TOKEN_NULL;
+            vocab.special_pad_id  = LLAMA_TOKEN_NULL;
+            vocab.special_cls_id  = LLAMA_TOKEN_NULL;
+            vocab.special_mask_id = LLAMA_TOKEN_NULL;
+            vocab.linefeed_id     = LLAMA_TOKEN_NULL;
 
             // read vocab size from metadata
             if (!ml.get_key(LLM_KV_VOCAB_SIZE, vocab.n_vocab, false)) {
@@ -6221,16 +6222,16 @@ static void llm_load_vocab(
             vocab.special_bos_id  = 1;
             vocab.special_eos_id  = 2;
             vocab.special_unk_id  = 0;
-            vocab.special_sep_id  = -1;
-            vocab.special_pad_id  = -1;
-            vocab.special_cls_id  = -1;
-            vocab.special_mask_id = -1;
+            vocab.special_sep_id  = LLAMA_TOKEN_NULL;
+            vocab.special_pad_id  = LLAMA_TOKEN_NULL;
+            vocab.special_cls_id  = LLAMA_TOKEN_NULL;
+            vocab.special_mask_id = LLAMA_TOKEN_NULL;
         } else if (tokenizer_model == "bert") {
             vocab.type = LLAMA_VOCAB_TYPE_WPM;
 
             // default special tokens
-            vocab.special_bos_id  = -1;
-            vocab.special_eos_id  = -1;
+            vocab.special_bos_id  = LLAMA_TOKEN_NULL;
+            vocab.special_eos_id  = LLAMA_TOKEN_NULL;
             vocab.special_unk_id  = 100;
             vocab.special_sep_id  = 102;
             vocab.special_pad_id  = 0;
@@ -6275,22 +6276,22 @@ static void llm_load_vocab(
             // default special tokens
             vocab.special_bos_id  = 11;
             vocab.special_eos_id  = 11;
-            vocab.special_unk_id  = -1;
-            vocab.special_sep_id  = -1;
-            vocab.special_pad_id  = -1;
-            vocab.special_cls_id  = -1;
-            vocab.special_mask_id = -1;
+            vocab.special_unk_id  = LLAMA_TOKEN_NULL;
+            vocab.special_sep_id  = LLAMA_TOKEN_NULL;
+            vocab.special_pad_id  = LLAMA_TOKEN_NULL;
+            vocab.special_cls_id  = LLAMA_TOKEN_NULL;
+            vocab.special_mask_id = LLAMA_TOKEN_NULL;
         } else if (tokenizer_model == "t5") {
             vocab.type = LLAMA_VOCAB_TYPE_UGM;
 
             // default special tokens
-            vocab.special_bos_id  = -1;
+            vocab.special_bos_id  = LLAMA_TOKEN_NULL;
             vocab.special_eos_id  = 1;
             vocab.special_unk_id  = 2;
-            vocab.special_sep_id  = -1;
+            vocab.special_sep_id  = LLAMA_TOKEN_NULL;
             vocab.special_pad_id  = 0;
-            vocab.special_cls_id  = -1;
-            vocab.special_mask_id = -1;
+            vocab.special_cls_id  = LLAMA_TOKEN_NULL;
+            vocab.special_mask_id = LLAMA_TOKEN_NULL;
 
             const int precompiled_charsmap_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_PRECOMPILED_CHARSMAP).c_str());
             if (precompiled_charsmap_keyidx != -1) {
@@ -6313,11 +6314,11 @@ static void llm_load_vocab(
             vocab.type = LLAMA_VOCAB_TYPE_RWKV;
 
             // default special tokens
-            vocab.special_bos_id = -1;
-            vocab.special_eos_id = -1;
-            vocab.special_unk_id = -1;
-            vocab.special_sep_id = -1;
-            vocab.special_pad_id = -1;
+            vocab.special_bos_id = LLAMA_TOKEN_NULL;
+            vocab.special_eos_id = LLAMA_TOKEN_NULL;
+            vocab.special_unk_id = LLAMA_TOKEN_NULL;
+            vocab.special_sep_id = LLAMA_TOKEN_NULL;
+            vocab.special_pad_id = LLAMA_TOKEN_NULL;
         } else {
             throw std::runtime_error(format("unknown tokenizer: '%s'", tokenizer_model.c_str()));
         }
@@ -6401,7 +6402,7 @@ static void llm_load_vocab(
             } else if (
                 tokenizer_pre == "chatglm-bpe") {
                 vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_CHATGLM4;
-                vocab.special_bos_id  = -1;
+                vocab.special_bos_id = LLAMA_TOKEN_NULL;
             } else if (
                 tokenizer_pre == "viking") {
                 vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_VIKING;
@@ -6528,44 +6529,6 @@ static void llm_load_vocab(
 
     // determine the newline token: LLaMA "<0x0A>" == 10 == '\n', Falcon 193 == '\n'
     if (vocab.type == LLAMA_VOCAB_TYPE_SPM) {
-        // For Fill-In-the-Middle (FIM)/infill models which where converted
-        // prior to support of FIM special tokens in GGUF, the following
-        // will allow those models to continue to work. The general names
-        // of the known models are currently CodeLlama (LLM_ARCH_LLAMA) and
-        // CodeGemma (LLM_ARCH_GEMMA). This can potentially be removed once
-        // new versions of these models have been published.
-        std::string gen_name;
-        ml.get_key(LLM_KV_GENERAL_NAME, gen_name, false);
-
-        std::transform(gen_name.begin(), gen_name.end(), gen_name.begin(),
-            [](unsigned char c){ return std::tolower(c); });
-
-        if (gen_name.find("code") != std::string::npos) {
-            if (model.arch == LLM_ARCH_LLAMA
-              && 32010 < vocab.id_to_token.size()
-              && vocab.id_to_token[32007].text.find("<PRE>") != std::string::npos
-              && vocab.id_to_token[32008].text.find("<SUF>") != std::string::npos
-              && vocab.id_to_token[32009].text.find("<MID>") != std::string::npos
-              && vocab.id_to_token[32010].text.find("<EOT>") != std::string::npos) {
-                vocab.special_prefix_id = 32007;
-                vocab.special_suffix_id = 32008;
-                vocab.special_middle_id = 32009;
-                vocab.special_eot_id    = 32010;
-            } else if (model.arch == LLM_ARCH_GEMMA
-              && 107 < vocab.id_to_token.size()
-              && vocab.id_to_token[67].text == "<|fim_prefix|>"
-              && vocab.id_to_token[69].text == "<|fim_suffix|>"
-              && vocab.id_to_token[68].text == "<|fim_middle|>"
-              && vocab.id_to_token[107].text == "<end_of_turn>") {
-                vocab.special_prefix_id = 67;
-                vocab.special_suffix_id = 69;
-                vocab.special_middle_id = 68;
-                // TODO: this is not EOT, it is "file separator" token, needs fix
-                //       https://huggingface.co/google/codegemma-7b-it/blob/9b1d9231388358c04d90bd003458f5070d97db44/tokenizer_config.json#L565-L572
-                //vocab.special_eot_id    = 70;
-                vocab.special_eot_id    = 107;
-            }
-        }
         try {
             vocab.linefeed_id = llama_byte_to_token_impl(vocab, '\n');
         } catch (const std::exception & e) {
@@ -6593,18 +6556,26 @@ static void llm_load_vocab(
     // special tokens
     {
         const std::vector<std::pair<enum llm_kv, int32_t &>> special_token_types = {
-            { LLM_KV_TOKENIZER_BOS_ID,    vocab.special_bos_id    },
-            { LLM_KV_TOKENIZER_EOS_ID,    vocab.special_eos_id    },
-            { LLM_KV_TOKENIZER_UNK_ID,    vocab.special_unk_id    },
-            { LLM_KV_TOKENIZER_SEP_ID,    vocab.special_sep_id    },
-            { LLM_KV_TOKENIZER_PAD_ID,    vocab.special_pad_id    },
-            { LLM_KV_TOKENIZER_CLS_ID,    vocab.special_cls_id    },
-            { LLM_KV_TOKENIZER_MASK_ID,   vocab.special_mask_id   },
-            { LLM_KV_TOKENIZER_PREFIX_ID, vocab.special_prefix_id },
-            { LLM_KV_TOKENIZER_SUFFIX_ID, vocab.special_suffix_id },
-            { LLM_KV_TOKENIZER_MIDDLE_ID, vocab.special_middle_id },
-            { LLM_KV_TOKENIZER_EOT_ID,    vocab.special_eot_id    },
-            { LLM_KV_TOKENIZER_EOM_ID,    vocab.special_eom_id    },
+            { LLM_KV_TOKENIZER_BOS_ID,     vocab.special_bos_id     },
+            { LLM_KV_TOKENIZER_EOS_ID,     vocab.special_eos_id     },
+            { LLM_KV_TOKENIZER_EOT_ID,     vocab.special_eot_id     },
+            { LLM_KV_TOKENIZER_EOM_ID,     vocab.special_eom_id     },
+            { LLM_KV_TOKENIZER_UNK_ID,     vocab.special_unk_id     },
+            { LLM_KV_TOKENIZER_SEP_ID,     vocab.special_sep_id     },
+            { LLM_KV_TOKENIZER_PAD_ID,     vocab.special_pad_id     },
+            { LLM_KV_TOKENIZER_CLS_ID,     vocab.special_cls_id     },
+            { LLM_KV_TOKENIZER_MASK_ID,    vocab.special_mask_id    },
+            { LLM_KV_TOKENIZER_FIM_PRE_ID, vocab.special_fim_pre_id },
+            { LLM_KV_TOKENIZER_FIM_SUF_ID, vocab.special_fim_suf_id },
+            { LLM_KV_TOKENIZER_FIM_MID_ID, vocab.special_fim_mid_id },
+            { LLM_KV_TOKENIZER_FIM_PAD_ID, vocab.special_fim_pad_id },
+            { LLM_KV_TOKENIZER_FIM_REP_ID, vocab.special_fim_rep_id },
+            { LLM_KV_TOKENIZER_FIM_SEP_ID, vocab.special_fim_sep_id },
+
+            // deprecated
+            { LLM_KV_TOKENIZER_PREFIX_ID, vocab.special_fim_pre_id },
+            { LLM_KV_TOKENIZER_SUFFIX_ID, vocab.special_fim_suf_id },
+            { LLM_KV_TOKENIZER_MIDDLE_ID, vocab.special_fim_mid_id },
         };
 
         for (const auto & it : special_token_types) {
@@ -6635,46 +6606,140 @@ static void llm_load_vocab(
             }
         }
 
-        // find EOT token: "<|eot_id|>", "<|im_end|>", "<end_of_turn>", etc.
-        //
-        // TODO: convert scripts should provide this token through the KV metadata LLAMA_KV_TOKENIZER_EOT_ID
-        //       for now, we apply this workaround to find the EOT token based on its text
-        if (vocab.special_eot_id == -1) {
-            for (const auto & t : vocab.token_to_id) {
+        // auto-detect special tokens by text
+        // TODO: convert scripts should provide these tokens through the KV metadata LLM_KV_TOKENIZER_...
+        //       for now, we apply this workaround to find the tokens based on their text
+
+        for (const auto & t : vocab.token_to_id) {
+            // find EOT token: "<|eot_id|>", "<|im_end|>", "<end_of_turn>", etc.
+            if (vocab.special_eot_id == LLAMA_TOKEN_NULL) {
                 if (false
-                        // TODO: gemma "<end_of_turn>" is exported as a normal token, so the following check does not work
-                        //       need to fix convert script
-                        //vocab.id_to_token[t.second].type == LLAMA_TOKEN_TYPE_CONTROL &&
                         || t.first == "<|eot_id|>"
                         || t.first == "<|im_end|>"
                         || t.first == "<|end|>"
                         || t.first == "<end_of_turn>"
                         || t.first == "<|endoftext|>"
                         || t.first == "<EOT>"
+                        || t.first == "<｜end▁of▁sentence｜>" // DeepSeek
                    ) {
                     vocab.special_eot_id = t.second;
                     if ((vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
-                        LLAMA_LOG_WARN("%s: control-looking token: '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
-                                __func__, t.first.c_str());
+                        LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
+                                __func__, t.second, t.first.c_str());
                         vocab.id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
                     }
-                    break;
                 }
             }
-        }
 
-        // find EOM token: "<|eom_id|>"
-        //
-        // TODO: convert scripts should provide this token through the KV metadata LLAMA_KV_TOKENIZER_EOM_ID
-        //       for now, we apply this workaround to find the EOM token based on its text
-        if (vocab.special_eom_id == -1) {
-            const auto & t = vocab.token_to_id.find("<|eom_id|>");
-            if (t != vocab.token_to_id.end()) {
-                vocab.special_eom_id = t->second;
-                if ((vocab.id_to_token[t->second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
-                    LLAMA_LOG_WARN("%s: control-looking token: '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
-                        __func__, t->first.c_str());
-                    vocab.id_to_token[t->second].attr = LLAMA_TOKEN_ATTR_CONTROL;
+            // find EOM token: "<|eom_id|>"
+            if (vocab.special_eom_id == LLAMA_TOKEN_NULL) {
+                if (false
+                        || t.first == "<|eom_id|>"
+                        ) {
+                    vocab.special_eom_id = t.second;
+                    if ((vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
+                        LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
+                                __func__, t.second, t.first.c_str());
+                        vocab.id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
+                    }
+                }
+            }
+
+            // find FIM_PRE token: "<|fim_prefix|>", "<fim-prefix>", "<PRE>", etc.
+            if (vocab.special_fim_pre_id == LLAMA_TOKEN_NULL) {
+                if (false
+                        || t.first == "<|fim_prefix|>"  // Qwen
+                        || t.first == "<fim-prefix>"
+                        || t.first == "<｜fim▁begin｜>" // DeepSeek
+                        || t.first == "<PRE>"
+                        ) {
+                    vocab.special_fim_pre_id = t.second;
+                    if ((vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
+                        LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
+                                __func__, t.second, t.first.c_str());
+                        vocab.id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
+                    }
+                }
+            }
+
+            // find FIM_SUF token: "<|fim_suffix|>", "<fim-suffix>", "<SUF>", etc.
+            if (vocab.special_fim_suf_id == LLAMA_TOKEN_NULL) {
+                if (false
+                        || t.first == "<|fim_suffix|>" // Qwen
+                        || t.first == "<fim-suffix>"
+                        || t.first == "<｜fim▁hole｜>" // DeepSeek
+                        || t.first == "<SUF>"
+                        ) {
+                    vocab.special_fim_suf_id = t.second;
+                    if ((vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
+                        LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
+                                __func__, t.second, t.first.c_str());
+                        vocab.id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
+                    }
+                }
+            }
+
+            // find FIM_MID token: "<|fim_middle|>", "<fim-middle>", "<MID>", etc.
+            if (vocab.special_fim_mid_id == LLAMA_TOKEN_NULL) {
+                if (false
+                        || t.first == "<|fim_middle|>" // Qwen
+                        || t.first == "<fim-middle>"
+                        || t.first == "<｜fim▁end｜>"  // DeepSeek
+                        || t.first == "<MID>"
+                        ) {
+                    vocab.special_fim_mid_id = t.second;
+                    if ((vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
+                        LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
+                                __func__, t.second, t.first.c_str());
+                        vocab.id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
+                    }
+                }
+            }
+
+            // find FIM_PAD token: "<|fim_pad|>", "<fim-pad>", "<PAD>", etc.
+            if (vocab.special_fim_pad_id == LLAMA_TOKEN_NULL) {
+                if (false
+                        || t.first == "<|fim_pad|>" // Qwen
+                        || t.first == "<fim-pad>"
+                        || t.first == "<PAD>"
+                        ) {
+                    vocab.special_fim_pad_id = t.second;
+                    if ((vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
+                        LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
+                                __func__, t.second, t.first.c_str());
+                        vocab.id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
+                    }
+                }
+            }
+
+            // find FIM_REP token: "<|fim_repo|>", "<fim-repo>", "<REP>", etc.
+            if (vocab.special_fim_rep_id == LLAMA_TOKEN_NULL) {
+                if (false
+                        || t.first == "<|fim_repo|>"  // Qwen
+                        || t.first == "<|repo_name|>"
+                        || t.first == "<fim-repo>"
+                        || t.first == "<REPO>"
+                        ) {
+                    vocab.special_fim_rep_id = t.second;
+                    if ((vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
+                        LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
+                                __func__, t.second, t.first.c_str());
+                        vocab.id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
+                    }
+                }
+            }
+
+            // find FIM_SEP token: "<|file_sep|>"
+            if (vocab.special_fim_sep_id == LLAMA_TOKEN_NULL) {
+                if (false
+                        || t.first == "<|file_sep|>" // Qwen
+                        ) {
+                    vocab.special_fim_sep_id = t.second;
+                    if ((vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
+                        LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
+                                __func__, t.second, t.first.c_str());
+                        vocab.id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
+                    }
                 }
             }
         }
@@ -6683,6 +6748,19 @@ static void llm_load_vocab(
         // this is currently determined based on the token text, which is obviously not ideal
         // ref: https://github.com/ggerganov/llama.cpp/issues/9606
         vocab.special_eog_ids.clear();
+
+        if (vocab.special_fim_pad_id != LLAMA_TOKEN_NULL && vocab.special_eog_ids.count(vocab.special_fim_pad_id) == 0) {
+            vocab.special_eog_ids.insert(vocab.special_fim_pad_id);
+        }
+
+        if (vocab.special_fim_rep_id != LLAMA_TOKEN_NULL && vocab.special_eog_ids.count(vocab.special_fim_rep_id) == 0) {
+            vocab.special_eog_ids.insert(vocab.special_fim_rep_id);
+        }
+
+        if (vocab.special_fim_sep_id != LLAMA_TOKEN_NULL && vocab.special_eog_ids.count(vocab.special_fim_sep_id) == 0) {
+            vocab.special_eog_ids.insert(vocab.special_fim_sep_id);
+        }
+
         for (const auto & t : vocab.token_to_id) {
             if (false
                     || t.first == "<|eot_id|>"
@@ -6695,24 +6773,31 @@ static void llm_load_vocab(
                ) {
                 vocab.special_eog_ids.insert(t.second);
                 if ((vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
-                    LLAMA_LOG_WARN("%s: control-looking token: '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
-                            __func__, t.first.c_str());
+                    LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
+                            __func__, t.second, t.first.c_str());
                     vocab.id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
                 }
+            } else {
+                // token is control, but not marked as EOG -> print a warning
+                if (vocab.id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL && vocab.special_eog_ids.count(t.second) == 0) {
+                    LLAMA_LOG_WARN("%s: control token: %6d '%s' is not marked as EOG\n",
+                            __func__, t.second, t.first.c_str());
+                }
             }
         }
 
-        if (vocab.special_eos_id != -1 && vocab.special_eog_ids.count(vocab.special_eos_id) == 0) {
+        // sanity checks
+        if (vocab.special_eos_id != LLAMA_TOKEN_NULL && vocab.special_eog_ids.count(vocab.special_eos_id) == 0) {
             vocab.special_eog_ids.insert(vocab.special_eos_id);
             LLAMA_LOG_WARN("%s: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect\n", __func__);
         }
 
-        if (vocab.special_eot_id != -1 && vocab.special_eog_ids.count(vocab.special_eot_id) == 0) {
+        if (vocab.special_eot_id != LLAMA_TOKEN_NULL && vocab.special_eog_ids.count(vocab.special_eot_id) == 0) {
             vocab.special_eog_ids.insert(vocab.special_eot_id);
             LLAMA_LOG_WARN("%s: special_eot_id is not in special_eog_ids - the tokenizer config may be incorrect\n", __func__);
         }
 
-        if (vocab.special_eom_id != -1 && vocab.special_eog_ids.count(vocab.special_eom_id) == 0) {
+        if (vocab.special_eom_id != LLAMA_TOKEN_NULL && vocab.special_eog_ids.count(vocab.special_eom_id) == 0) {
             vocab.special_eog_ids.insert(vocab.special_eom_id);
             LLAMA_LOG_WARN("%s: special_eom_id is not in special_eog_ids - the tokenizer config may be incorrect\n", __func__);
         }
@@ -6906,20 +6991,24 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
     LLAMA_LOG_INFO("%s: general.name     = %s\n",    __func__, model.name.c_str());
 
     // special tokens
-    if (vocab.special_bos_id    != -1) { LLAMA_LOG_INFO( "%s: BOS token        = %d '%s'\n", __func__, vocab.special_bos_id,  vocab.id_to_token[vocab.special_bos_id].text.c_str() );  }
-    if (vocab.special_eos_id    != -1) { LLAMA_LOG_INFO( "%s: EOS token        = %d '%s'\n", __func__, vocab.special_eos_id,  vocab.id_to_token[vocab.special_eos_id].text.c_str() );  }
-    if (vocab.special_unk_id    != -1) { LLAMA_LOG_INFO( "%s: UNK token        = %d '%s'\n", __func__, vocab.special_unk_id,  vocab.id_to_token[vocab.special_unk_id].text.c_str() );  }
-    if (vocab.special_sep_id    != -1) { LLAMA_LOG_INFO( "%s: SEP token        = %d '%s'\n", __func__, vocab.special_sep_id,  vocab.id_to_token[vocab.special_sep_id].text.c_str() );  }
-    if (vocab.special_pad_id    != -1) { LLAMA_LOG_INFO( "%s: PAD token        = %d '%s'\n", __func__, vocab.special_pad_id,  vocab.id_to_token[vocab.special_pad_id].text.c_str() );  }
-    if (vocab.special_cls_id    != -1) { LLAMA_LOG_INFO( "%s: CLS token        = %d '%s'\n", __func__, vocab.special_cls_id,  vocab.id_to_token[vocab.special_cls_id].text.c_str() );  }
-    if (vocab.special_mask_id   != -1) { LLAMA_LOG_INFO( "%s: MASK token       = %d '%s'\n", __func__, vocab.special_mask_id, vocab.id_to_token[vocab.special_mask_id].text.c_str() ); }
+    if (vocab.special_bos_id  != -1)    { LLAMA_LOG_INFO( "%s: BOS token        = %d '%s'\n", __func__, vocab.special_bos_id,     vocab.id_to_token[vocab.special_bos_id].text.c_str() );  }
+    if (vocab.special_eos_id  != -1)    { LLAMA_LOG_INFO( "%s: EOS token        = %d '%s'\n", __func__, vocab.special_eos_id,     vocab.id_to_token[vocab.special_eos_id].text.c_str() );  }
+    if (vocab.special_eot_id  != -1)    { LLAMA_LOG_INFO( "%s: EOT token        = %d '%s'\n", __func__, vocab.special_eot_id,     vocab.id_to_token[vocab.special_eot_id].text.c_str() );  }
+    if (vocab.special_eom_id  != -1)    { LLAMA_LOG_INFO( "%s: EOM token        = %d '%s'\n", __func__, vocab.special_eom_id,     vocab.id_to_token[vocab.special_eom_id].text.c_str() );  }
+    if (vocab.special_unk_id  != -1)    { LLAMA_LOG_INFO( "%s: UNK token        = %d '%s'\n", __func__, vocab.special_unk_id,     vocab.id_to_token[vocab.special_unk_id].text.c_str() );  }
+    if (vocab.special_sep_id  != -1)    { LLAMA_LOG_INFO( "%s: SEP token        = %d '%s'\n", __func__, vocab.special_sep_id,     vocab.id_to_token[vocab.special_sep_id].text.c_str() );  }
+    if (vocab.special_pad_id  != -1)    { LLAMA_LOG_INFO( "%s: PAD token        = %d '%s'\n", __func__, vocab.special_pad_id,     vocab.id_to_token[vocab.special_pad_id].text.c_str() );  }
+    if (vocab.special_cls_id  != -1)    { LLAMA_LOG_INFO( "%s: CLS token        = %d '%s'\n", __func__, vocab.special_cls_id,     vocab.id_to_token[vocab.special_cls_id].text.c_str() );  }
+    if (vocab.special_mask_id != -1)    { LLAMA_LOG_INFO( "%s: MASK token       = %d '%s'\n", __func__, vocab.special_mask_id,    vocab.id_to_token[vocab.special_mask_id].text.c_str() ); }
 
-    if (vocab.linefeed_id       != -1) { LLAMA_LOG_INFO( "%s: LF token         = %d '%s'\n", __func__, vocab.linefeed_id,       vocab.id_to_token[vocab.linefeed_id].text.c_str() );       }
-    if (vocab.special_prefix_id != -1) { LLAMA_LOG_INFO( "%s: PRE token        = %d '%s'\n", __func__, vocab.special_prefix_id, vocab.id_to_token[vocab.special_prefix_id].text.c_str() ); }
-    if (vocab.special_suffix_id != -1) { LLAMA_LOG_INFO( "%s: SUF token        = %d '%s'\n", __func__, vocab.special_suffix_id, vocab.id_to_token[vocab.special_suffix_id].text.c_str() ); }
-    if (vocab.special_middle_id != -1) { LLAMA_LOG_INFO( "%s: MID token        = %d '%s'\n", __func__, vocab.special_middle_id, vocab.id_to_token[vocab.special_middle_id].text.c_str() ); }
-    if (vocab.special_eot_id    != -1) { LLAMA_LOG_INFO( "%s: EOT token        = %d '%s'\n", __func__, vocab.special_eot_id,    vocab.id_to_token[vocab.special_eot_id].text.c_str() );    }
-    if (vocab.special_eom_id    != -1) { LLAMA_LOG_INFO( "%s: EOM token        = %d '%s'\n", __func__, vocab.special_eom_id,    vocab.id_to_token[vocab.special_eom_id].text.c_str() );    }
+    if (vocab.linefeed_id != -1)        { LLAMA_LOG_INFO( "%s: LF token         = %d '%s'\n", __func__, vocab.linefeed_id,        vocab.id_to_token[vocab.linefeed_id].text.c_str() ); }
+
+    if (vocab.special_fim_pre_id != -1) { LLAMA_LOG_INFO( "%s: FIM PRE token    = %d '%s'\n", __func__, vocab.special_fim_pre_id, vocab.id_to_token[vocab.special_fim_pre_id].text.c_str() ); }
+    if (vocab.special_fim_suf_id != -1) { LLAMA_LOG_INFO( "%s: FIM SUF token    = %d '%s'\n", __func__, vocab.special_fim_suf_id, vocab.id_to_token[vocab.special_fim_suf_id].text.c_str() ); }
+    if (vocab.special_fim_mid_id != -1) { LLAMA_LOG_INFO( "%s: FIM MID token    = %d '%s'\n", __func__, vocab.special_fim_mid_id, vocab.id_to_token[vocab.special_fim_mid_id].text.c_str() ); }
+    if (vocab.special_fim_pad_id != -1) { LLAMA_LOG_INFO( "%s: FIM PAD token    = %d '%s'\n", __func__, vocab.special_fim_pad_id, vocab.id_to_token[vocab.special_fim_pad_id].text.c_str() ); }
+    if (vocab.special_fim_rep_id != -1) { LLAMA_LOG_INFO( "%s: FIM REP token    = %d '%s'\n", __func__, vocab.special_fim_rep_id, vocab.id_to_token[vocab.special_fim_rep_id].text.c_str() ); }
+    if (vocab.special_fim_sep_id != -1) { LLAMA_LOG_INFO( "%s: FIM SEP token    = %d '%s'\n", __func__, vocab.special_fim_sep_id, vocab.id_to_token[vocab.special_fim_sep_id].text.c_str() ); }
 
     for (const auto & id : vocab.special_eog_ids) {
         LLAMA_LOG_INFO( "%s: EOG token        = %d '%s'\n", __func__, id, vocab.id_to_token[id].text.c_str() );
@@ -16061,9 +16150,11 @@ struct llm_build_context {
         cur = ggml_get_rows(ctx0, cur, inp_out_ids);
 
         cur = llm_build_norm(ctx0, cur, hparams, model.output_norm, model.output_norm_b, LLM_NORM, cb, -1);
-        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
+        cb(cur, "result_norm", -1);
 
+        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
         cb(cur, "result_output", -1);
+
         ggml_build_forward_expand(gf, cur);
 
         return gf;
@@ -19059,7 +19150,7 @@ bool llama_supports_mlock(void) {
 }
 
 bool llama_supports_gpu_offload(void) {
-#if defined(GGML_USE_CLBLAST) || defined(GGML_USE_VULKAN) || \
+#if defined(GGML_USE_CLBLAST) || \
     defined(GGML_USE_SYCL) || defined(GGML_USE_KOMPUTE)
     // Defined when llama.cpp is compiled with support for offloading model layers to GPU.
     return true;
@@ -19191,8 +19282,13 @@ struct llama_model * llama_load_model_from_file(
 
             case GGML_BACKEND_DEVICE_TYPE_GPU:
             case GGML_BACKEND_DEVICE_TYPE_GPU_FULL:
+            {
+                size_t free, total; // NOLINT
+                ggml_backend_dev_memory(dev, &free, &total);
+                LLAMA_LOG_INFO("%s: using device %s (%s) - %zu MiB free\n", __func__, ggml_backend_dev_name(dev), ggml_backend_dev_description(dev), free/1024/1024);
                 model->devices.push_back(dev);
                 break;
+            }
         }
     }
 
@@ -19387,32 +19483,7 @@ struct llama_context * llama_new_context_with_model(
             main_gpu -= (int)model->devices.size();
         }
 
-#if defined(GGML_USE_VULKAN)
-        if (model->split_mode == LLAMA_SPLIT_MODE_ROW) {
-            LLAMA_LOG_ERROR("%s: Row split not supported. Failed to initialize Vulkan backend\n", __func__);
-            llama_free(ctx);
-            return nullptr;
-        }
-        if (model->split_mode == LLAMA_SPLIT_MODE_NONE) {
-            ggml_backend_t backend = ggml_backend_vk_init(main_gpu);
-            if (backend == nullptr) {
-                LLAMA_LOG_ERROR("%s: failed to initialize Vulkan backend\n", __func__);
-                llama_free(ctx);
-                return nullptr;
-            }
-            ctx->backends.push_back(backend);
-        } else {
-            for (int device = 0; device < ggml_backend_vk_get_device_count(); ++device) {
-                ggml_backend_t backend = ggml_backend_vk_init(device);
-                if (backend == nullptr) {
-                    LLAMA_LOG_ERROR("%s: failed to initialize Vulkan%d backend\n", __func__, device);
-                    llama_free(ctx);
-                    return nullptr;
-                }
-                ctx->backends.push_back(backend);
-            }
-        }
-#elif defined(GGML_USE_SYCL)
+#if defined(GGML_USE_SYCL)
         // with split_mode LLAMA_SPLIT_MODE_NONE or LLAMA_SPLIT_MODE_ROW, only the main GPU backend is used
         if (model->split_mode == LLAMA_SPLIT_MODE_NONE || model->split_mode == LLAMA_SPLIT_MODE_ROW) {
             ggml_backend_t backend = ggml_backend_sycl_init(main_gpu);
@@ -19523,7 +19594,7 @@ struct llama_context * llama_new_context_with_model(
             }
 
             LLAMA_LOG_INFO("%s: KV self size  = %7.2f MiB, K (%s): %7.2f MiB, V (%s): %7.2f MiB\n", __func__,
-                (float)(memory_size_k + memory_size_v) / (1024.0f * 1024.0f),
+                      (float)(memory_size_k + memory_size_v) / (1024.0f * 1024.0f),
                 ggml_type_name(type_k), (float)memory_size_k / (1024.0f * 1024.0f),
                 ggml_type_name(type_v), (float)memory_size_v / (1024.0f * 1024.0f));
         }
@@ -21387,6 +21458,10 @@ llama_token llama_token_eos(const struct llama_model * model) {
     return llama_token_eos_impl(model->vocab);
 }
 
+llama_token llama_token_eot(const struct llama_model * model) {
+    return llama_token_eot_impl(model->vocab);
+}
+
 llama_token llama_token_cls(const struct llama_model * model) {
     return llama_token_cls_impl(model->vocab);
 }
@@ -21423,8 +21498,28 @@ llama_token llama_token_suffix(const struct llama_model * model) {
     return llama_token_suffix_impl(model->vocab);
 }
 
-llama_token llama_token_eot(const struct llama_model * model) {
-    return llama_token_eot_impl(model->vocab);
+llama_token llama_token_fim_pre(const struct llama_model * model) {
+    return llama_token_fim_pre_impl(model->vocab);
+}
+
+llama_token llama_token_fim_suf(const struct llama_model * model) {
+    return llama_token_fim_suf_impl(model->vocab);
+}
+
+llama_token llama_token_fim_mid(const struct llama_model * model) {
+    return llama_token_fim_mid_impl(model->vocab);
+}
+
+llama_token llama_token_fim_pad(const struct llama_model * model) {
+    return llama_token_fim_pad_impl(model->vocab);
+}
+
+llama_token llama_token_fim_rep(const struct llama_model * model) {
+    return llama_token_fim_rep_impl(model->vocab);
+}
+
+llama_token llama_token_fim_sep(const struct llama_model * model) {
+    return llama_token_fim_sep_impl(model->vocab);
 }
 
 //
@@ -21452,6 +21547,13 @@ int32_t llama_token_to_piece(
     return llama_token_to_piece_impl(model->vocab, token, buf, length, lstrip, special);
 }
 
+bool llama_token_is_prefix(
+    const struct llama_model * model,
+                 llama_token   token0,
+                 llama_token   token1) {
+    return llama_token_is_prefix_impl(model->vocab, token0, token1);
+}
+
 int32_t llama_detokenize(
     const struct llama_model * model,
            const llama_token * tokens,
@@ -21782,6 +21884,10 @@ struct llama_sampler * llama_sampler_init_grammar(const struct llama_model * mod
     return llama_sampler_init_grammar_impl(model->vocab, grammar_str, grammar_root);
 }
 
+struct llama_sampler * llama_sampler_init_infill(const struct llama_model * model) {
+    return llama_sampler_init_infill_impl(model->vocab);
+}
+
 //
 // model split
 //