common : move arg parser code to `arg.cpp` (#9388)

* common : move arg parser to arg.cpp

* better categorize args

* add cmake

* missing climits

* missing cstdarg

* common : more explicit includes

* fix build

* refactor gpt_params_parse

* update server readme

* fix test

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

Makefile

@@ -925,6 +925,7 @@ OBJ_LLAMA = \
 
 OBJ_COMMON = \
 	common/common.o \
+	common/arg.o \
 	common/console.o \
 	common/ngram-cache.o \
 	common/sampling.o \
@@ -1157,6 +1158,11 @@ common/common.o: \
 	include/llama.h
 	$(CXX) $(CXXFLAGS) -c $< -o $@
 
+common/arg.o: \
+	common/arg.cpp \
+	common/arg.h
+	$(CXX) $(CXXFLAGS) -c $< -o $@
+
 common/sampling.o: \
 	common/sampling.cpp \
 	common/sampling.h \

common/CMakeLists.txt

@@ -54,6 +54,8 @@ add_library(${TARGET} STATIC
     base64.hpp
     common.h
     common.cpp
+    arg.h
+    arg.cpp
     sampling.h
     sampling.cpp
     console.h

common/arg.cpp

@@ -0,0 +1,1994 @@
+#include "arg.h"
+
+#include "sampling.h"
+
+#include <algorithm>
+#include <string>
+#include <vector>
+#include <set>
+#include <fstream>
+#include <regex>
+#include <cstdarg>
+#include <climits>
+
+#include "json-schema-to-grammar.h"
+
+using json = nlohmann::ordered_json;
+
+llama_arg & llama_arg::set_examples(std::initializer_list<enum llama_example> examples) {
+    this->examples = std::move(examples);
+    return *this;
+}
+
+llama_arg & llama_arg::set_env(const char * env) {
+    help = help + "\n(env: " + env + ")";
+    this->env = env;
+    return *this;
+}
+
+llama_arg & llama_arg::set_sparam() {
+    is_sparam = true;
+    return *this;
+}
+
+bool llama_arg::in_example(enum llama_example ex) {
+    return examples.find(ex) != examples.end();
+}
+
+bool llama_arg::get_value_from_env(std::string & output) {
+    if (env == nullptr) return false;
+    char * value = std::getenv(env);
+    if (value) {
+        output = value;
+        return true;
+    }
+    return false;
+}
+
+bool llama_arg::has_value_from_env() {
+    return env != nullptr && std::getenv(env);
+}
+
+static std::vector<std::string> break_str_into_lines(std::string input, size_t max_char_per_line) {
+    std::vector<std::string> result;
+    std::istringstream iss(input);
+    std::string line;
+    auto add_line = [&](const std::string& l) {
+        if (l.length() <= max_char_per_line) {
+            result.push_back(l);
+        } else {
+            std::istringstream line_stream(l);
+            std::string word, current_line;
+            while (line_stream >> word) {
+                if (current_line.length() + !current_line.empty() + word.length() > max_char_per_line) {
+                    if (!current_line.empty()) result.push_back(current_line);
+                    current_line = word;
+                } else {
+                    current_line += (!current_line.empty() ? " " : "") + word;
+                }
+            }
+            if (!current_line.empty()) result.push_back(current_line);
+        }
+    };
+    while (std::getline(iss, line)) {
+        add_line(line);
+    }
+    return result;
+}
+
+std::string llama_arg::to_string() {
+    // params for printing to console
+    const static int n_leading_spaces = 40;
+    const static int n_char_per_line_help = 70; // TODO: detect this based on current console
+    std::string leading_spaces(n_leading_spaces, ' ');
+
+    std::ostringstream ss;
+    for (const auto arg : args) {
+        if (arg == args.front()) {
+            if (args.size() == 1) {
+                ss << arg;
+            } else {
+                // first arg is usually abbreviation, we need padding to make it more beautiful
+                auto tmp = std::string(arg) + ", ";
+                auto spaces = std::string(std::max(0, 7 - (int)tmp.size()), ' ');
+                ss << tmp << spaces;
+            }
+        } else {
+            ss << arg << (arg != args.back() ? ", " : "");
+        }
+    }
+    if (value_hint) ss << " " << value_hint;
+    if (value_hint_2) ss << " " << value_hint_2;
+    if (ss.tellp() > n_leading_spaces - 3) {
+        // current line is too long, add new line
+        ss << "\n" << leading_spaces;
+    } else {
+        // padding between arg and help, same line
+        ss << std::string(leading_spaces.size() - ss.tellp(), ' ');
+    }
+    const auto help_lines = break_str_into_lines(help, n_char_per_line_help);
+    for (const auto & line : help_lines) {
+        ss << (&line == &help_lines.front() ? "" : leading_spaces) << line << "\n";
+    }
+    return ss.str();
+}
+
+//
+// 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 gpt_params_handle_model_default(gpt_params & params) {
+    if (!params.hf_repo.empty()) {
+        // short-hand to avoid specifying --hf-file -> default it to --model
+        if (params.hf_file.empty()) {
+            if (params.model.empty()) {
+                throw std::invalid_argument("error: --hf-repo requires either --hf-file or --model\n");
+            }
+            params.hf_file = params.model;
+        } else if (params.model.empty()) {
+            params.model = fs_get_cache_file(string_split(params.hf_file, '/').back());
+        }
+    } else if (!params.model_url.empty()) {
+        if (params.model.empty()) {
+            auto f = string_split(params.model_url, '#').front();
+            f = string_split(f, '?').front();
+            params.model = fs_get_cache_file(string_split(f, '/').back());
+        }
+    } else if (params.model.empty()) {
+        params.model = DEFAULT_MODEL_PATH;
+    }
+}
+
+//
+// CLI argument parsing functions
+//
+
+static bool gpt_params_parse_ex(int argc, char ** argv, gpt_params_context & ctx_arg) {
+    std::string arg;
+    const std::string arg_prefix = "--";
+    gpt_params & params = ctx_arg.params;
+    gpt_sampler_params & sparams = params.sparams;
+
+    std::unordered_map<std::string, llama_arg *> arg_to_options;
+    for (auto & opt : ctx_arg.options) {
+        for (const auto & arg : opt.args) {
+            arg_to_options[arg] = &opt;
+        }
+    }
+
+    // handle environment variables
+    for (auto & opt : ctx_arg.options) {
+        std::string value;
+        if (opt.get_value_from_env(value)) {
+            try {
+                if (opt.handler_void && (value == "1" || value == "true")) {
+                    opt.handler_void(params);
+                }
+                if (opt.handler_int) {
+                    opt.handler_int(params, std::stoi(value));
+                }
+                if (opt.handler_string) {
+                    opt.handler_string(params, value);
+                    continue;
+                }
+            } catch (std::exception & e) {
+                throw std::invalid_argument(format(
+                    "error while handling environment variable \"%s\": %s\n\n", opt.env, e.what()));
+            }
+        }
+    }
+
+    // handle command line arguments
+    auto check_arg = [&](int i) {
+        if (i+1 >= argc) {
+            throw std::invalid_argument("expected value for argument");
+        }
+    };
+
+    for (int i = 1; i < argc; i++) {
+        const std::string arg_prefix = "--";
+
+        std::string arg = argv[i];
+        if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) {
+            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()));
+        }
+        auto opt = *arg_to_options[arg];
+        if (opt.has_value_from_env()) {
+            fprintf(stderr, "warn: %s environment variable is set, but will be overwritten by command line argument %s\n", opt.env, arg.c_str());
+        }
+        try {
+            if (opt.handler_void) {
+                opt.handler_void(params);
+                continue;
+            }
+
+            // arg with single value
+            check_arg(i);
+            std::string val = argv[++i];
+            if (opt.handler_int) {
+                opt.handler_int(params, std::stoi(val));
+                continue;
+            }
+            if (opt.handler_string) {
+                opt.handler_string(params, val);
+                continue;
+            }
+
+            // arg with 2 values
+            check_arg(i);
+            std::string val2 = argv[++i];
+            if (opt.handler_str_str) {
+                opt.handler_str_str(params, val, val2);
+                continue;
+            }
+        } catch (std::exception & e) {
+            throw std::invalid_argument(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()));
+        }
+    }
+
+    postprocess_cpu_params(params.cpuparams, nullptr);
+    postprocess_cpu_params(params.cpuparams_batch, &params.cpuparams);
+    postprocess_cpu_params(params.draft_cpuparams, &params.cpuparams);
+    postprocess_cpu_params(params.draft_cpuparams_batch, &params.cpuparams_batch);
+
+    if (params.prompt_cache_all && (params.interactive || params.interactive_first)) {
+        throw std::invalid_argument("error: --prompt-cache-all not supported in interactive mode yet\n");
+    }
+
+    gpt_params_handle_model_default(params);
+
+    if (params.escape) {
+        string_process_escapes(params.prompt);
+        string_process_escapes(params.input_prefix);
+        string_process_escapes(params.input_suffix);
+        for (auto & antiprompt : params.antiprompt) {
+            string_process_escapes(antiprompt);
+        }
+    }
+
+    if (!params.kv_overrides.empty()) {
+        params.kv_overrides.emplace_back();
+        params.kv_overrides.back().key[0] = 0;
+    }
+
+    if (sparams.seed == LLAMA_DEFAULT_SEED) {
+        sparams.seed = time(NULL);
+    }
+
+    return true;
+}
+
+static void gpt_params_print_usage(gpt_params_context & ctx_arg) {
+    auto print_options = [](std::vector<llama_arg *> & options) {
+        for (llama_arg * opt : options) {
+            printf("%s", opt->to_string().c_str());
+        }
+    };
+
+    std::vector<llama_arg *> common_options;
+    std::vector<llama_arg *> sparam_options;
+    std::vector<llama_arg *> specific_options;
+    for (auto & opt : ctx_arg.options) {
+        // in case multiple LLAMA_EXAMPLE_* are set, we prioritize the LLAMA_EXAMPLE_* matching current example
+        if (opt.is_sparam) {
+            sparam_options.push_back(&opt);
+        } else if (opt.in_example(ctx_arg.ex)) {
+            specific_options.push_back(&opt);
+        } else {
+            common_options.push_back(&opt);
+        }
+    }
+    printf("----- common params -----\n\n");
+    print_options(common_options);
+    printf("\n\n----- sampling params -----\n\n");
+    print_options(sparam_options);
+    // TODO: maybe convert enum llama_example to string
+    printf("\n\n----- example-specific params -----\n\n");
+    print_options(specific_options);
+}
+
+bool gpt_params_parse(int argc, char ** argv, gpt_params & params, llama_example ex, void(*print_usage)(int, char **)) {
+    auto ctx_arg = gpt_params_parser_init(params, ex, print_usage);
+    const gpt_params params_org = ctx_arg.params; // the example can modify the default params
+
+    try {
+        if (!gpt_params_parse_ex(argc, argv, ctx_arg)) {
+            ctx_arg.params = params_org;
+            return false;
+        }
+        if (ctx_arg.params.usage) {
+            gpt_params_print_usage(ctx_arg);
+            if (ctx_arg.print_usage) {
+                ctx_arg.print_usage(argc, argv);
+            }
+            exit(0);
+        }
+    } catch (const std::invalid_argument & ex) {
+        fprintf(stderr, "%s\n", ex.what());
+        ctx_arg.params = params_org;
+        return false;
+    }
+
+    return true;
+}
+
+gpt_params_context gpt_params_parser_init(gpt_params & params, llama_example ex, void(*print_usage)(int, char **)) {
+    gpt_params_context ctx_arg(params);
+    ctx_arg.print_usage = print_usage;
+    ctx_arg.ex          = ex;
+
+    std::string sampler_type_chars;
+    std::string sampler_type_names;
+    for (const auto & sampler : params.sparams.samplers) {
+        sampler_type_chars += gpt_sampler_type_to_chr(sampler);
+        sampler_type_names += gpt_sampler_type_to_str(sampler) + ";";
+    }
+    sampler_type_names.pop_back();
+
+
+    /**
+     * filter options by example
+     * rules:
+     * - all examples inherit options from LLAMA_EXAMPLE_COMMON
+     * - if LLAMA_EXAMPLE_* is set (other than COMMON), we only show the option in the corresponding example
+     * - if both {LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_*,} are set, we will prioritize the LLAMA_EXAMPLE_* matching current example
+     */
+    auto add_opt = [&](llama_arg arg) {
+        if (arg.in_example(ex) || arg.in_example(LLAMA_EXAMPLE_COMMON)) {
+            ctx_arg.options.push_back(std::move(arg));
+        }
+    };
+
+
+    add_opt(llama_arg(
+        {"-h", "--help", "--usage"},
+        "print usage and exit",
+        [](gpt_params & params) {
+            params.usage = true;
+        }
+    ));
+    add_opt(llama_arg(
+        {"--version"},
+        "show version and build info",
+        [](gpt_params &) {
+            fprintf(stderr, "version: %d (%s)\n", LLAMA_BUILD_NUMBER, LLAMA_COMMIT);
+            fprintf(stderr, "built with %s for %s\n", LLAMA_COMPILER, LLAMA_BUILD_TARGET);
+            exit(0);
+        }
+    ));
+    add_opt(llama_arg(
+        {"-v", "--verbose"},
+        "print verbose information",
+        [](gpt_params & params) {
+            params.verbosity = 1;
+        }
+    ));
+    add_opt(llama_arg(
+        {"--verbosity"}, "N",
+        format("set specific verbosity level (default: %d)", params.verbosity),
+        [](gpt_params & params, int value) {
+            params.verbosity = value;
+        }
+    ));
+    add_opt(llama_arg(
+        {"--verbose-prompt"},
+        format("print a verbose prompt before generation (default: %s)", params.verbose_prompt ? "true" : "false"),
+        [](gpt_params & params) {
+            params.verbose_prompt = true;
+        }
+    ).set_examples({LLAMA_EXAMPLE_MAIN}));
+    add_opt(llama_arg(
+        {"--no-display-prompt"},
+        format("don't print prompt at generation (default: %s)", !params.display_prompt ? "true" : "false"),
+        [](gpt_params & params) {
+            params.display_prompt = false;
+        }
+    ).set_examples({LLAMA_EXAMPLE_MAIN}));
+    add_opt(llama_arg(
+        {"-co", "--color"},
+        format("colorise output to distinguish prompt and user input from generations (default: %s)", params.use_color ? "true" : "false"),
+        [](gpt_params & params) {
+            params.use_color = true;
+        }
+    ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_INFILL}));
+    add_opt(llama_arg(
+        {"-t", "--threads"}, "N",
+        format("number of threads to use during generation (default: %d)", params.cpuparams.n_threads),
+        [](gpt_params & params, int value) {
+            params.cpuparams.n_threads = value;
+            if (params.cpuparams.n_threads <= 0) {
+                params.cpuparams.n_threads = std::thread::hardware_concurrency();
+            }
+        }
+    ).set_env("LLAMA_ARG_THREADS"));
+    add_opt(llama_arg(
+        {"-tb", "--threads-batch"}, "N",
+        "number of threads to use during batch and prompt processing (default: same as --threads)",
+        [](gpt_params & params, int value) {
+            params.cpuparams_batch.n_threads = value;
+            if (params.cpuparams_batch.n_threads <= 0) {
+                params.cpuparams_batch.n_threads = std::thread::hardware_concurrency();
+            }
+        }
+    ));
+    add_opt(llama_arg(
+        {"-td", "--threads-draft"}, "N",
+        "number of threads to use during generation (default: same as --threads)",
+        [](gpt_params & params, int value) {
+            params.draft_cpuparams.n_threads = value;
+            if (params.draft_cpuparams.n_threads <= 0) {
+                params.draft_cpuparams.n_threads = std::thread::hardware_concurrency();
+            }
+        }
+    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
+    add_opt(llama_arg(
+        {"-tbd", "--threads-batch-draft"}, "N",
+        "number of threads to use during batch and prompt processing (default: same as --threads-draft)",
+        [](gpt_params & params, int value) {
+            params.draft_cpuparams_batch.n_threads = value;
+            if (params.draft_cpuparams_batch.n_threads <= 0) {
+                params.draft_cpuparams_batch.n_threads = std::thread::hardware_concurrency();
+            }
+        }
+    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
+    add_opt(llama_arg(
+        {"-C", "--cpu-mask"}, "M",
+        "CPU affinity mask: arbitrarily long hex. Complements cpu-range (default: \"\")",
+        [](gpt_params & params, const std::string & mask) {
+            params.cpuparams.mask_valid = true;
+            if (!parse_cpu_mask(mask, params.cpuparams.cpumask)) {
+                throw std::invalid_argument("invalid cpumask");
+            }
+        }
+    ));
+    add_opt(llama_arg(
+        {"-Cr", "--cpu-range"}, "lo-hi",
+        "range of CPUs for affinity. Complements --cpu-mask",
+        [](gpt_params & params, const std::string & range) {
+            params.cpuparams.mask_valid = true;
+            if (!parse_cpu_range(range, params.cpuparams.cpumask)) {
+                throw std::invalid_argument("invalid range");
+            }
+        }
+    ));
+    add_opt(llama_arg(
+        {"--cpu-strict"}, "<0|1>",
+        format("use strict CPU placement (default: %u)\n", (unsigned) params.cpuparams.strict_cpu),
+        [](gpt_params & params, const std::string & value) {
+            params.cpuparams.strict_cpu = std::stoul(value);
+        }
+    ));
+    add_opt(llama_arg(
+        {"--prio"}, "N",
+        format("set process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: %d)\n", params.cpuparams.priority),
+        [](gpt_params & params, int prio) {
+            if (prio < 0 || prio > 3) {
+                throw std::invalid_argument("invalid value");
+            }
+            params.cpuparams.priority = (enum ggml_sched_priority) prio;
+        }
+    ));
+    add_opt(llama_arg(
+        {"--poll"}, "<0...100>",
+        format("use polling level to wait for work (0 - no polling, default: %u)\n", (unsigned) params.cpuparams.poll),
+        [](gpt_params & params, const std::string & value) {
+            params.cpuparams.poll = std::stoul(value);
+        }
+    ));
+    add_opt(llama_arg(
+        {"-Cb", "--cpu-mask-batch"}, "M",
+        "CPU affinity mask: arbitrarily long hex. Complements cpu-range-batch (default: same as --cpu-mask)",
+        [](gpt_params & params, const std::string & mask) {
+            params.cpuparams_batch.mask_valid = true;
+            if (!parse_cpu_mask(mask, params.cpuparams_batch.cpumask)) {
+                throw std::invalid_argument("invalid cpumask");
+            }
+        }
+    ));
+    add_opt(llama_arg(
+        {"-Crb", "--cpu-range-batch"}, "lo-hi",
+        "ranges of CPUs for affinity. Complements --cpu-mask-batch",
+        [](gpt_params & params, const std::string & range) {
+            params.cpuparams_batch.mask_valid = true;
+            if (!parse_cpu_range(range, params.cpuparams_batch.cpumask)) {
+                throw std::invalid_argument("invalid range");
+            }
+        }
+    ));
+    add_opt(llama_arg(
+        {"--cpu-strict-batch"}, "<0|1>",
+        "use strict CPU placement (default: same as --cpu-strict)",
+        [](gpt_params & params, int value) {
+            params.cpuparams_batch.strict_cpu = value;
+        }
+    ));
+    add_opt(llama_arg(
+        {"--prio-batch"}, "N",
+        format("set process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: %d)\n", params.cpuparams_batch.priority),
+        [](gpt_params & params, int prio) {
+            if (prio < 0 || prio > 3) {
+                throw std::invalid_argument("invalid value");
+            }
+            params.cpuparams_batch.priority = (enum ggml_sched_priority) prio;
+        }
+    ));
+    add_opt(llama_arg(
+        {"--poll-batch"}, "<0|1>",
+        "use polling to wait for work (default: same as --poll)",
+        [](gpt_params & params, int value) {
+            params.cpuparams_batch.poll = value;
+        }
+    ));
+    add_opt(llama_arg(
+        {"-Cd", "--cpu-mask-draft"}, "M",
+        "Draft model CPU affinity mask. Complements cpu-range-draft (default: same as --cpu-mask)",
+        [](gpt_params & params, const std::string & mask) {
+            params.draft_cpuparams.mask_valid = true;
+            if (!parse_cpu_mask(mask, params.draft_cpuparams.cpumask)) {
+                throw std::invalid_argument("invalid cpumask");
+            }
+        }
+    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
+    add_opt(llama_arg(
+        {"-Crd", "--cpu-range-draft"}, "lo-hi",
+        "Ranges of CPUs for affinity. Complements --cpu-mask-draft",
+        [](gpt_params & params, const std::string & range) {
+            params.draft_cpuparams.mask_valid = true;
+            if (!parse_cpu_range(range, params.draft_cpuparams.cpumask)) {
+                throw std::invalid_argument("invalid range");
+            }
+        }
+    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
+    add_opt(llama_arg(
+        {"--cpu-strict-draft"}, "<0|1>",
+        "Use strict CPU placement for draft model (default: same as --cpu-strict)",
+        [](gpt_params & params, int value) {
+            params.draft_cpuparams.strict_cpu = value;
+        }
+    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
+    add_opt(llama_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),
+        [](gpt_params & params, int prio) {
+            if (prio < 0 || prio > 3) {
+                throw std::invalid_argument("invalid value");
+            }
+            params.draft_cpuparams.priority = (enum ggml_sched_priority) prio;
+        }
+    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
+    add_opt(llama_arg(
+        {"--poll-draft"}, "<0|1>",
+        "Use polling to wait for draft model work (default: same as --poll])",
+        [](gpt_params & params, int value) {
+            params.draft_cpuparams.poll = value;
+        }
+    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
+    add_opt(llama_arg(
+        {"-Cbd", "--cpu-mask-batch-draft"}, "M",
+        "Draft model CPU affinity mask. Complements cpu-range-draft (default: same as --cpu-mask)",
+        [](gpt_params & params, const std::string & mask) {
+            params.draft_cpuparams_batch.mask_valid = true;
+            if (!parse_cpu_mask(mask, params.draft_cpuparams_batch.cpumask)) {
+                throw std::invalid_argument("invalid cpumask");
+            }
+        }
+    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
+    add_opt(llama_arg(
+        {"-Crbd", "--cpu-range-batch-draft"}, "lo-hi",
+        "Ranges of CPUs for affinity. Complements --cpu-mask-draft-batch)",
+        [](gpt_params & params, const std::string & range) {
+            params.draft_cpuparams_batch.mask_valid = true;
+            if (!parse_cpu_range(range, params.draft_cpuparams_batch.cpumask)) {
+                throw std::invalid_argument("invalid cpumask");
+            }
+        }
+    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
+    add_opt(llama_arg(
+        {"--cpu-strict-batch-draft"}, "<0|1>",
+        "Use strict CPU placement for draft model (default: --cpu-strict-draft)",
+        [](gpt_params & params, int value) {
+            params.draft_cpuparams_batch.strict_cpu = value;
+        }
+    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
+    add_opt(llama_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),
+        [](gpt_params & params, int prio) {
+            if (prio < 0 || prio > 3) {
+                throw std::invalid_argument("invalid value");
+            }
+            params.draft_cpuparams_batch.priority = (enum ggml_sched_priority) prio;
+        }
+    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
+    add_opt(llama_arg(
+        {"--poll-batch-draft"}, "<0|1>",
+        "Use polling to wait for draft model work (default: --poll-draft)",
+        [](gpt_params & params, int value) {
+            params.draft_cpuparams_batch.poll = value;
+        }
+    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
+    add_opt(llama_arg(
+        {"--draft"}, "N",
+        format("number of tokens to draft for speculative decoding (default: %d)", params.n_draft),
+        [](gpt_params & params, int value) {
+            params.n_draft = value;
+        }
+    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP}));
+    add_opt(llama_arg(
+        {"-ps", "--p-split"}, "N",
+        format("speculative decoding split probability (default: %.1f)", (double)params.p_split),
+        [](gpt_params & params, const std::string & value) {
+            params.p_split = std::stof(value);
+        }
+    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
+    add_opt(llama_arg(
+        {"-lcs", "--lookup-cache-static"}, "FNAME",
+        "path to static lookup cache to use for lookup decoding (not updated by generation)",
+        [](gpt_params & params, const std::string & value) {
+            params.lookup_cache_static = value;
+        }
+    ).set_examples({LLAMA_EXAMPLE_LOOKUP}));
+    add_opt(llama_arg(
+        {"-lcd", "--lookup-cache-dynamic"}, "FNAME",
+        "path to dynamic lookup cache to use for lookup decoding (updated by generation)",
+        [](gpt_params & params, const std::string & value) {
+            params.lookup_cache_dynamic = value;
+        }
+    ).set_examples({LLAMA_EXAMPLE_LOOKUP}));
+    add_opt(llama_arg(
+        {"-c", "--ctx-size"}, "N",
+        format("size of the prompt context (default: %d, 0 = loaded from model)", params.n_ctx),
+        [](gpt_params & params, int value) {
+            params.n_ctx = value;
+        }
+    ).set_env("LLAMA_ARG_CTX_SIZE"));
+    add_opt(llama_arg(
+        {"-n", "--predict", "--n-predict"}, "N",
+        format("number of tokens to predict (default: %d, -1 = infinity, -2 = until context filled)", params.n_predict),
+        [](gpt_params & params, int value) {
+            params.n_predict = value;
+        }
+    ).set_env("LLAMA_ARG_N_PREDICT"));
+    add_opt(llama_arg(
+        {"-b", "--batch-size"}, "N",
+        format("logical maximum batch size (default: %d)", params.n_batch),
+        [](gpt_params & params, int value) {
+            params.n_batch = value;
+        }
+    ).set_env("LLAMA_ARG_BATCH"));
+    add_opt(llama_arg(
+        {"-ub", "--ubatch-size"}, "N",
+        format("physical maximum batch size (default: %d)", params.n_ubatch),
+        [](gpt_params & params, int value) {
+            params.n_ubatch = value;
+        }
+    ).set_env("LLAMA_ARG_UBATCH"));
+    add_opt(llama_arg(
+        {"--keep"}, "N",
+        format("number of tokens to keep from the initial prompt (default: %d, -1 = all)", params.n_keep),
+        [](gpt_params & params, int value) {
+            params.n_keep = value;
+        }
+    ));
+    add_opt(llama_arg(
+        {"--chunks"}, "N",
+        format("max number of chunks to process (default: %d, -1 = all)", params.n_chunks),
+        [](gpt_params & params, int value) {
+            params.n_chunks = value;
+        }
+    ).set_examples({LLAMA_EXAMPLE_IMATRIX, LLAMA_EXAMPLE_PERPLEXITY, LLAMA_EXAMPLE_RETRIEVAL}));
+    add_opt(llama_arg(
+        {"-fa", "--flash-attn"},
+        format("enable Flash Attention (default: %s)", params.flash_attn ? "enabled" : "disabled"),
+        [](gpt_params & params) {
+            params.flash_attn = true;
+        }
+    ).set_env("LLAMA_ARG_FLASH_ATTN"));
+    add_opt(llama_arg(
+        {"-p", "--prompt"}, "PROMPT",
+        ex == LLAMA_EXAMPLE_MAIN
+            ? "prompt to start generation with\nif -cnv is set, this will be used as system prompt"
+            : "prompt to start generation with",
+        [](gpt_params & params, const std::string & value) {
+            params.prompt = value;
+        }
+    ));
+    add_opt(llama_arg(
+        {"-f", "--file"}, "FNAME",
+        "a file containing the prompt (default: none)",
+        [](gpt_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()));
+            }
+            // store the external file name in params
+            params.prompt_file = value;
+            std::copy(std::istreambuf_iterator<char>(file), std::istreambuf_iterator<char>(), back_inserter(params.prompt));
+            if (!params.prompt.empty() && params.prompt.back() == '\n') {
+                params.prompt.pop_back();
+            }
+        }
+    ));
+    add_opt(llama_arg(
+        {"--in-file"}, "FNAME",
+        "an input file (repeat to specify multiple files)",
+        [](gpt_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()));
+            }
+            params.in_files.push_back(value);
+        }
+    ).set_examples({LLAMA_EXAMPLE_IMATRIX}));
+    add_opt(llama_arg(
+        {"-bf", "--binary-file"}, "FNAME",
+        "binary file containing the prompt (default: none)",
+        [](gpt_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()));
+            }
+            // store the external file name in params
+            params.prompt_file = value;
+            std::ostringstream ss;
+            ss << file.rdbuf();
+            params.prompt = ss.str();
+            fprintf(stderr, "Read %zu bytes from binary file %s\n", params.prompt.size(), value.c_str());
+        }
+    ));
+    add_opt(llama_arg(
+        {"-e", "--escape"},
+        format("process escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\) (default: %s)", params.escape ? "true" : "false"),
+        [](gpt_params & params) {
+            params.escape = true;
+        }
+    ));
+    add_opt(llama_arg(
+        {"--no-escape"},
+        "do not process escape sequences",
+        [](gpt_params & params) {
+            params.escape = false;
+        }
+    ));
+    add_opt(llama_arg(
+        {"-ptc", "--print-token-count"}, "N",
+        format("print token count every N tokens (default: %d)", params.n_print),
+        [](gpt_params & params, int value) {
+            params.n_print = value;
+        }
+    ).set_examples({LLAMA_EXAMPLE_MAIN}));
+    add_opt(llama_arg(
+        {"--prompt-cache"}, "FNAME",
+        "file to cache prompt state for faster startup (default: none)",
+        [](gpt_params & params, const std::string & value) {
+            params.path_prompt_cache = value;
+        }
+    ).set_examples({LLAMA_EXAMPLE_MAIN}));
+    add_opt(llama_arg(
+        {"--prompt-cache-all"},
+        "if specified, saves user input and generations to cache as well\n",
+        [](gpt_params & params) {
+            params.prompt_cache_all = true;
+        }
+    ).set_examples({LLAMA_EXAMPLE_MAIN}));
+    add_opt(llama_arg(
+        {"--prompt-cache-ro"},
+        "if specified, uses the prompt cache but does not update it",
+        [](gpt_params & params) {
+            params.prompt_cache_ro = true;
+        }
+    ).set_examples({LLAMA_EXAMPLE_MAIN}));
+    add_opt(llama_arg(
+        {"-r", "--reverse-prompt"}, "PROMPT",
+        "halt generation at PROMPT, return control in interactive mode\n",
+        [](gpt_params & params, const std::string & value) {
+            params.antiprompt.emplace_back(value);
+        }
+    ).set_examples({LLAMA_EXAMPLE_MAIN}));
+    add_opt(llama_arg(
+        {"-sp", "--special"},
+        format("special tokens output enabled (default: %s)", params.special ? "true" : "false"),
+        [](gpt_params & params) {
+            params.special = true;
+        }
+    ).set_examples({LLAMA_EXAMPLE_MAIN}));
+    add_opt(llama_arg(
+        {"-cnv", "--conversation"},
+        format(
+            "run in conversation mode:\n"
+            "- does not print special tokens and suffix/prefix\n"
+            "- interactive mode is also enabled\n"
+            "(default: %s)",
+            params.conversation ? "true" : "false"
+        ),
+        [](gpt_params & params) {
+            params.conversation = true;
+        }
+    ).set_examples({LLAMA_EXAMPLE_MAIN}));
+    add_opt(llama_arg(
+        {"-i", "--interactive"},
+        format("run in interactive mode (default: %s)", params.interactive ? "true" : "false"),
+        [](gpt_params & params) {
+            params.interactive = true;
+        }
+    ).set_examples({LLAMA_EXAMPLE_MAIN}));
+    add_opt(llama_arg(
+        {"-if", "--interactive-first"},
+        format("run in interactive mode and wait for input right away (default: %s)", params.interactive_first ? "true" : "false"),
+        [](gpt_params & params) {
+            params.interactive_first = true;
+        }
+    ).set_examples({LLAMA_EXAMPLE_MAIN}));
+    add_opt(llama_arg(
+        {"-mli", "--multiline-input"},
+        "allows you to write or paste multiple lines without ending each in '\\'",
+        [](gpt_params & params) {
+            params.multiline_input = true;
+        }
+    ).set_examples({LLAMA_EXAMPLE_MAIN}));
+    add_opt(llama_arg(
+        {"--in-prefix-bos"},
+        "prefix BOS to user inputs, preceding the `--in-prefix` string",
+        [](gpt_params & params) {
+            params.input_prefix_bos = true;
+            params.enable_chat_template = false;
+        }
+    ).set_examples({LLAMA_EXAMPLE_MAIN}));
+    add_opt(llama_arg(
+        {"--in-prefix"}, "STRING",
+        "string to prefix user inputs with (default: empty)",
+        [](gpt_params & params, const std::string & value) {
+            params.input_prefix = value;
+            params.enable_chat_template = false;
+        }
+    ).set_examples({LLAMA_EXAMPLE_MAIN}));
+    add_opt(llama_arg(
+        {"--in-suffix"}, "STRING",
+        "string to suffix after user inputs with (default: empty)",
+        [](gpt_params & params, const std::string & value) {
+            params.input_suffix = value;
+            params.enable_chat_template = false;
+        }
+    ).set_examples({LLAMA_EXAMPLE_MAIN}));
+    add_opt(llama_arg(
+        {"--no-warmup"},
+        "skip warming up the model with an empty run",
+        [](gpt_params & params) {
+            params.warmup = false;
+        }
+    ).set_examples({LLAMA_EXAMPLE_MAIN}));
+    add_opt(llama_arg(
+        {"--spm-infill"},
+        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"
+        ),
+        [](gpt_params & params) {
+            params.spm_infill = true;
+        }
+    ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_INFILL}));
+    add_opt(llama_arg(
+        {"--samplers"}, "SAMPLERS",
+        format("samplers that will be used for generation in the order, separated by \';\'\n(default: %s)", sampler_type_names.c_str()),
+        [](gpt_params & params, const std::string & value) {
+            const auto sampler_names = string_split(value, ';');
+            params.sparams.samplers = gpt_sampler_types_from_names(sampler_names, true);
+        }
+    ).set_sparam());
+    add_opt(llama_arg(
+        {"-s", "--seed"}, "SEED",
+        format("RNG seed (default: %d, use random seed for < 0)", params.sparams.seed),
+        [](gpt_params & params, const std::string & value) {
+            params.sparams.seed = std::stoul(value);
+        }
+    ).set_sparam());
+    add_opt(llama_arg(
+        {"--sampling-seq"}, "SEQUENCE",
+        format("simplified sequence for samplers that will be used (default: %s)", sampler_type_chars.c_str()),
+        [](gpt_params & params, const std::string & value) {
+            params.sparams.samplers = gpt_sampler_types_from_chars(value);
+        }
+    ).set_sparam());
+    add_opt(llama_arg(
+        {"--ignore-eos"},
+        "ignore end of stream token and continue generating (implies --logit-bias EOS-inf)",
+        [](gpt_params & params) {
+            params.sparams.ignore_eos = true;
+        }
+    ).set_sparam());
+    add_opt(llama_arg(
+        {"--penalize-nl"},
+        format("penalize newline tokens (default: %s)", params.sparams.penalize_nl ? "true" : "false"),
+        [](gpt_params & params) {
+            params.sparams.penalize_nl = true;
+        }
+    ).set_sparam());
+    add_opt(llama_arg(
+        {"--temp"}, "N",
+        format("temperature (default: %.1f)", (double)params.sparams.temp),
+        [](gpt_params & params, const std::string & value) {
+            params.sparams.temp = std::stof(value);
+            params.sparams.temp = std::max(params.sparams.temp, 0.0f);
+        }
+    ).set_sparam());
+    add_opt(llama_arg(
+        {"--top-k"}, "N",
+        format("top-k sampling (default: %d, 0 = disabled)", params.sparams.top_k),
+        [](gpt_params & params, int value) {
+            params.sparams.top_k = value;
+        }
+    ).set_sparam());
+    add_opt(llama_arg(
+        {"--top-p"}, "N",
+        format("top-p sampling (default: %.1f, 1.0 = disabled)", (double)params.sparams.top_p),
+        [](gpt_params & params, const std::string & value) {
+            params.sparams.top_p = std::stof(value);
+        }
+    ).set_sparam());
+    add_opt(llama_arg(
+        {"--min-p"}, "N",
+        format("min-p sampling (default: %.1f, 0.0 = disabled)", (double)params.sparams.min_p),
+        [](gpt_params & params, const std::string & value) {
+            params.sparams.min_p = std::stof(value);
+        }
+    ).set_sparam());
+    add_opt(llama_arg(
+        {"--tfs"}, "N",
+        format("tail free sampling, parameter z (default: %.1f, 1.0 = disabled)", (double)params.sparams.tfs_z),
+        [](gpt_params & params, const std::string & value) {
+            params.sparams.tfs_z = std::stof(value);
+        }
+    ).set_sparam());
+    add_opt(llama_arg(
+        {"--typical"}, "N",
+        format("locally typical sampling, parameter p (default: %.1f, 1.0 = disabled)", (double)params.sparams.typ_p),
+        [](gpt_params & params, const std::string & value) {
+            params.sparams.typ_p = std::stof(value);
+        }
+    ).set_sparam());
+    add_opt(llama_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),
+        [](gpt_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);
+        }
+    ).set_sparam());
+    add_opt(llama_arg(
+        {"--repeat-penalty"}, "N",
+        format("penalize repeat sequence of tokens (default: %.1f, 1.0 = disabled)", (double)params.sparams.penalty_repeat),
+        [](gpt_params & params, const std::string & value) {
+            params.sparams.penalty_repeat = std::stof(value);
+        }
+    ).set_sparam());
+    add_opt(llama_arg(
+        {"--presence-penalty"}, "N",
+        format("repeat alpha presence penalty (default: %.1f, 0.0 = disabled)", (double)params.sparams.penalty_present),
+        [](gpt_params & params, const std::string & value) {
+            params.sparams.penalty_present = std::stof(value);
+        }
+    ).set_sparam());
+    add_opt(llama_arg(
+        {"--frequency-penalty"}, "N",
+        format("repeat alpha frequency penalty (default: %.1f, 0.0 = disabled)", (double)params.sparams.penalty_freq),
+        [](gpt_params & params, const std::string & value) {
+            params.sparams.penalty_freq = std::stof(value);
+        }
+    ).set_sparam());
+    add_opt(llama_arg(
+        {"--dynatemp-range"}, "N",
+        format("dynamic temperature range (default: %.1f, 0.0 = disabled)", (double)params.sparams.dynatemp_range),
+        [](gpt_params & params, const std::string & value) {
+            params.sparams.dynatemp_range = std::stof(value);
+        }
+    ).set_sparam());
+    add_opt(llama_arg(
+        {"--dynatemp-exp"}, "N",
+        format("dynamic temperature exponent (default: %.1f)", (double)params.sparams.dynatemp_exponent),
+        [](gpt_params & params, const std::string & value) {
+            params.sparams.dynatemp_exponent = std::stof(value);
+        }
+    ).set_sparam());
+    add_opt(llama_arg(
+        {"--mirostat"}, "N",
+        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),
+        [](gpt_params & params, int value) {
+            params.sparams.mirostat = value;
+        }
+    ).set_sparam());
+    add_opt(llama_arg(
+        {"--mirostat-lr"}, "N",
+        format("Mirostat learning rate, parameter eta (default: %.1f)", (double)params.sparams.mirostat_eta),
+        [](gpt_params & params, const std::string & value) {
+            params.sparams.mirostat_eta = std::stof(value);
+        }
+    ).set_sparam());
+    add_opt(llama_arg(
+        {"--mirostat-ent"}, "N",
+        format("Mirostat target entropy, parameter tau (default: %.1f)", (double)params.sparams.mirostat_tau),
+        [](gpt_params & params, const std::string & value) {
+            params.sparams.mirostat_tau = std::stof(value);
+        }
+    ).set_sparam());
+    add_opt(llama_arg(
+        {"-l", "--logit-bias"}, "TOKEN_ID(+/-)BIAS",
+        "modifies the likelihood of token appearing in the completion,\n"
+        "i.e. `--logit-bias 15043+1` to increase likelihood of token ' Hello',\n"
+        "or `--logit-bias 15043-1` to decrease likelihood of token ' Hello'",
+        [](gpt_params & params, const std::string & value) {
+            std::stringstream ss(value);
+            llama_token key;
+            char sign;
+            std::string value_str;
+            try {
+                if (ss >> key && ss >> sign && std::getline(ss, value_str) && (sign == '+' || sign == '-')) {
+                    const float bias = std::stof(value_str) * ((sign == '-') ? -1.0f : 1.0f);
+                    params.sparams.logit_bias.push_back({key, bias});
+                } else {
+                    throw std::invalid_argument("invalid input format");
+                }
+            } catch (const std::exception&) {
+                throw std::invalid_argument("invalid input format");
+            }
+        }
+    ).set_sparam());
+    add_opt(llama_arg(
+        {"--grammar"}, "GRAMMAR",
+        format("BNF-like grammar to constrain generations (see samples in grammars/ dir) (default: '%s')", params.sparams.grammar.c_str()),
+        [](gpt_params & params, const std::string & value) {
+            params.sparams.grammar = value;
+        }
+    ).set_sparam());
+    add_opt(llama_arg(
+        {"--grammar-file"}, "FNAME",
+        "file to read grammar from",
+        [](gpt_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::copy(
+                std::istreambuf_iterator<char>(file),
+                std::istreambuf_iterator<char>(),
+                std::back_inserter(params.sparams.grammar)
+            );
+        }
+    ).set_sparam());
+    add_opt(llama_arg(
+        {"-j", "--json-schema"}, "SCHEMA",
+        "JSON schema to constrain generations (https://json-schema.org/), e.g. `{}` for any JSON object\nFor schemas w/ external $refs, use --grammar + example/json_schema_to_grammar.py instead",
+        [](gpt_params & params, const std::string & value) {
+            params.sparams.grammar = json_schema_to_grammar(json::parse(value));
+        }
+    ).set_sparam());
+    add_opt(llama_arg(
+        {"--pooling"}, "{none,mean,cls,last}",
+        "pooling type for embeddings, use model default if unspecified",
+        [](gpt_params & params, const std::string & value) {
+            /**/ if (value == "none") { params.pooling_type = LLAMA_POOLING_TYPE_NONE; }
+            else if (value == "mean") { params.pooling_type = LLAMA_POOLING_TYPE_MEAN; }
+            else if (value == "cls") { params.pooling_type = LLAMA_POOLING_TYPE_CLS; }
+            else if (value == "last") { params.pooling_type = LLAMA_POOLING_TYPE_LAST; }
+            else { throw std::invalid_argument("invalid value"); }
+        }
+    ).set_examples({LLAMA_EXAMPLE_EMBEDDING}));
+    add_opt(llama_arg(
+        {"--attention"}, "{causal,non,causal}",
+        "attention type for embeddings, use model default if unspecified",
+        [](gpt_params & params, const std::string & value) {
+            /**/ if (value == "causal") { params.attention_type = LLAMA_ATTENTION_TYPE_CAUSAL; }
+            else if (value == "non-causal") { params.attention_type = LLAMA_ATTENTION_TYPE_NON_CAUSAL; }
+            else { throw std::invalid_argument("invalid value"); }
+        }
+    ).set_examples({LLAMA_EXAMPLE_EMBEDDING}));
+    add_opt(llama_arg(
+        {"--rope-scaling"}, "{none,linear,yarn}",
+        "RoPE frequency scaling method, defaults to linear unless specified by the model",
+        [](gpt_params & params, const std::string & value) {
+            /**/ if (value == "none") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_NONE; }
+            else if (value == "linear") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_LINEAR; }
+            else if (value == "yarn") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_YARN; }
+            else { throw std::invalid_argument("invalid value"); }
+        }
+    ));
+    add_opt(llama_arg(
+        {"--rope-scale"}, "N",
+        "RoPE context scaling factor, expands context by a factor of N",
+        [](gpt_params & params, const std::string & value) {
+            params.rope_freq_scale = 1.0f / std::stof(value);
+        }
+    ));
+    add_opt(llama_arg(
+        {"--rope-freq-base"}, "N",
+        "RoPE base frequency, used by NTK-aware scaling (default: loaded from model)",
+        [](gpt_params & params, const std::string & value) {
+            params.rope_freq_base = std::stof(value);
+        }
+    ));
+    add_opt(llama_arg(
+        {"--rope-freq-scale"}, "N",
+        "RoPE frequency scaling factor, expands context by a factor of 1/N",
+        [](gpt_params & params, const std::string & value) {
+            params.rope_freq_scale = std::stof(value);
+        }
+    ));
+    add_opt(llama_arg(
+        {"--yarn-orig-ctx"}, "N",
+        format("YaRN: original context size of model (default: %d = model training context size)", params.yarn_orig_ctx),
+        [](gpt_params & params, int value) {
+            params.yarn_orig_ctx = value;
+        }
+    ));
+    add_opt(llama_arg(
+        {"--yarn-ext-factor"}, "N",
+        format("YaRN: extrapolation mix factor (default: %.1f, 0.0 = full interpolation)", (double)params.yarn_ext_factor),
+        [](gpt_params & params, const std::string & value) {
+            params.yarn_ext_factor = std::stof(value);
+        }
+    ));
+    add_opt(llama_arg(
+        {"--yarn-attn-factor"}, "N",
+        format("YaRN: scale sqrt(t) or attention magnitude (default: %.1f)", (double)params.yarn_attn_factor),
+        [](gpt_params & params, const std::string & value) {
+            params.yarn_attn_factor = std::stof(value);
+        }
+    ));
+    add_opt(llama_arg(
+        {"--yarn-beta-slow"}, "N",
+        format("YaRN: high correction dim or alpha (default: %.1f)", (double)params.yarn_beta_slow),
+        [](gpt_params & params, const std::string & value) {
+            params.yarn_beta_slow = std::stof(value);
+        }
+    ));
+    add_opt(llama_arg(
+        {"--yarn-beta-fast"}, "N",
+        format("YaRN: low correction dim or beta (default: %.1f)", (double)params.yarn_beta_fast),
+        [](gpt_params & params, const std::string & value) {
+            params.yarn_beta_fast = std::stof(value);
+        }
+    ));
+    add_opt(llama_arg(
+        {"-gan", "--grp-attn-n"}, "N",
+        format("group-attention factor (default: %d)", params.grp_attn_n),
+        [](gpt_params & params, int value) {
+            params.grp_attn_n = value;
+        }
+    ));
+    add_opt(llama_arg(
+        {"-gaw", "--grp-attn-w"}, "N",
+        format("group-attention width (default: %.1f)", (double)params.grp_attn_w),
+        [](gpt_params & params, int value) {
+            params.grp_attn_w = value;
+        }
+    ));
+    add_opt(llama_arg(
+        {"-dkvc", "--dump-kv-cache"},
+        "verbose print of the KV cache",
+        [](gpt_params & params) {
+            params.dump_kv_cache = true;
+        }
+    ));
+    add_opt(llama_arg(
+        {"-nkvo", "--no-kv-offload"},
+        "disable KV offload",
+        [](gpt_params & params) {
+            params.no_kv_offload = true;
+        }
+    ));
+    add_opt(llama_arg(
+        {"-ctk", "--cache-type-k"}, "TYPE",
+        format("KV cache data type for K (default: %s)", params.cache_type_k.c_str()),
+        [](gpt_params & params, const std::string & value) {
+            // TODO: get the type right here
+            params.cache_type_k = value;
+        }
+    ));
+    add_opt(llama_arg(
+        {"-ctv", "--cache-type-v"}, "TYPE",
+        format("KV cache data type for V (default: %s)", params.cache_type_v.c_str()),
+        [](gpt_params & params, const std::string & value) {
+            // TODO: get the type right here
+            params.cache_type_v = value;
+        }
+    ));
+    add_opt(llama_arg(
+        {"--perplexity", "--all-logits"},
+        format("return logits for all tokens in the batch (default: %s)", params.logits_all ? "true" : "false"),
+        [](gpt_params & params) {
+            params.logits_all = true;
+        }
+    ).set_examples({LLAMA_EXAMPLE_PERPLEXITY}));
+    add_opt(llama_arg(
+        {"--hellaswag"},
+        "compute HellaSwag score over random tasks from datafile supplied with -f",
+        [](gpt_params & params) {
+            params.hellaswag = true;
+        }
+    ).set_examples({LLAMA_EXAMPLE_PERPLEXITY}));
+    add_opt(llama_arg(
+        {"--hellaswag-tasks"}, "N",
+        format("number of tasks to use when computing the HellaSwag score (default: %zu)", params.hellaswag_tasks),
+        [](gpt_params & params, int value) {
+            params.hellaswag_tasks = value;
+        }
+    ).set_examples({LLAMA_EXAMPLE_PERPLEXITY}));
+    add_opt(llama_arg(
+        {"--winogrande"},
+        "compute Winogrande score over random tasks from datafile supplied with -f",
+        [](gpt_params & params) {
+            params.winogrande = true;
+        }
+    ).set_examples({LLAMA_EXAMPLE_PERPLEXITY}));
+    add_opt(llama_arg(
+        {"--winogrande-tasks"}, "N",
+        format("number of tasks to use when computing the Winogrande score (default: %zu)", params.winogrande_tasks),
+        [](gpt_params & params, int value) {
+            params.winogrande_tasks = value;
+        }
+    ).set_examples({LLAMA_EXAMPLE_PERPLEXITY}));
+    add_opt(llama_arg(
+        {"--multiple-choice"},
+        "compute multiple choice score over random tasks from datafile supplied with -f",
+        [](gpt_params & params) {
+            params.multiple_choice = true;
+        }
+    ).set_examples({LLAMA_EXAMPLE_PERPLEXITY}));
+    add_opt(llama_arg(
+        {"--multiple-choice-tasks"}, "N",
+        format("number of tasks to use when computing the multiple choice score (default: %zu)", params.multiple_choice_tasks),
+        [](gpt_params & params, int value) {
+            params.multiple_choice_tasks = value;
+        }
+    ).set_examples({LLAMA_EXAMPLE_PERPLEXITY}));
+    add_opt(llama_arg(
+        {"--kl-divergence"},
+        "computes KL-divergence to logits provided via --kl-divergence-base",
+        [](gpt_params & params) {
+            params.kl_divergence = true;
+        }
+    ).set_examples({LLAMA_EXAMPLE_PERPLEXITY}));
+    add_opt(llama_arg(
+        {"--save-all-logits", "--kl-divergence-base"}, "FNAME",
+        "set logits file",
+        [](gpt_params & params, const std::string & value) {
+            params.logits_file = value;
+        }
+    ).set_examples({LLAMA_EXAMPLE_PERPLEXITY}));
+    add_opt(llama_arg(
+        {"--ppl-stride"}, "N",
+        format("stride for perplexity calculation (default: %d)", params.ppl_stride),
+        [](gpt_params & params, int value) {
+            params.ppl_stride = value;
+        }
+    ).set_examples({LLAMA_EXAMPLE_PERPLEXITY}));
+    add_opt(llama_arg(
+        {"--ppl-output-type"}, "<0|1>",
+        format("output type for perplexity calculation (default: %d)", params.ppl_output_type),
+        [](gpt_params & params, int value) {
+            params.ppl_output_type = value;
+        }
+    ).set_examples({LLAMA_EXAMPLE_PERPLEXITY}));
+    add_opt(llama_arg(
+        {"-dt", "--defrag-thold"}, "N",
+        format("KV cache defragmentation threshold (default: %.1f, < 0 - disabled)", (double)params.defrag_thold),
+        [](gpt_params & params, const std::string & value) {
+            params.defrag_thold = std::stof(value);
+        }
+    ).set_env("LLAMA_ARG_DEFRAG_THOLD"));
+    add_opt(llama_arg(
+        {"-np", "--parallel"}, "N",
+        format("number of parallel sequences to decode (default: %d)", params.n_parallel),
+        [](gpt_params & params, int value) {
+            params.n_parallel = value;
+        }
+    ));
+    add_opt(llama_arg(
+        {"-ns", "--sequences"}, "N",
+        format("number of sequences to decode (default: %d)", params.n_sequences),
+        [](gpt_params & params, int value) {
+            params.n_sequences = value;
+        }
+    ).set_examples({LLAMA_EXAMPLE_PARALLEL}));
+    add_opt(llama_arg(
+        {"-cb", "--cont-batching"},
+        format("enable continuous batching (a.k.a dynamic batching) (default: %s)", params.cont_batching ? "enabled" : "disabled"),
+        [](gpt_params & params) {
+            params.cont_batching = true;
+        }
+    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_CONT_BATCHING"));
+    add_opt(llama_arg(
+        {"-nocb", "--no-cont-batching"},
+        "disable continuous batching",
+        [](gpt_params & params) {
+            params.cont_batching = false;
+        }
+    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_NO_CONT_BATCHING"));
+    add_opt(llama_arg(
+        {"--mmproj"}, "FILE",
+        "path to a multimodal projector file for LLaVA. see examples/llava/README.md",
+        [](gpt_params & params, const std::string & value) {
+            params.mmproj = value;
+        }
+    ).set_examples({LLAMA_EXAMPLE_LLAVA}));
+    add_opt(llama_arg(
+        {"--image"}, "FILE",
+        "path to an image file. use with multimodal models. Specify multiple times for batching",
+        [](gpt_params & params, const std::string & value) {
+            params.image.emplace_back(value);
+        }
+    ).set_examples({LLAMA_EXAMPLE_LLAVA}));
+#ifdef GGML_USE_RPC
+    add_opt(llama_arg(
+        {"--rpc"}, "SERVERS",
+        "comma separated list of RPC servers",
+        [](gpt_params & params, const std::string & value) {
+            params.rpc_servers = value;
+        }
+    ));
+#endif
+    add_opt(llama_arg(
+        {"--mlock"},
+        "force system to keep model in RAM rather than swapping or compressing",
+        [](gpt_params & params) {
+            params.use_mlock = true;
+        }
+    ));
+    add_opt(llama_arg(
+        {"--no-mmap"},
+        "do not memory-map model (slower load but may reduce pageouts if not using mlock)",
+        [](gpt_params & params) {
+            params.use_mmap = false;
+        }
+    ));
+    add_opt(llama_arg(
+        {"--numa"}, "TYPE",
+        "attempt optimizations that help on some NUMA systems\n"
+        "- distribute: spread execution evenly over all nodes\n"
+        "- isolate: only spawn threads on CPUs on the node that execution started on\n"
+        "- numactl: use the CPU map provided by numactl\n"
+        "if run without this previously, it is recommended to drop the system page cache before using this\n"
+        "see https://github.com/ggerganov/llama.cpp/issues/1437",
+        [](gpt_params & params, const std::string & value) {
+            /**/ if (value == "distribute" || value == "") { params.numa = GGML_NUMA_STRATEGY_DISTRIBUTE; }
+            else if (value == "isolate") { params.numa = GGML_NUMA_STRATEGY_ISOLATE; }
+            else if (value == "numactl") { params.numa = GGML_NUMA_STRATEGY_NUMACTL; }
+            else { throw std::invalid_argument("invalid value"); }
+        }
+    ));
+    add_opt(llama_arg(
+        {"-ngl", "--gpu-layers", "--n-gpu-layers"}, "N",
+        "number of layers to store in VRAM",
+        [](gpt_params & params, int value) {
+            params.n_gpu_layers = value;
+            if (!llama_supports_gpu_offload()) {
+                fprintf(stderr, "warning: not compiled with GPU offload support, --gpu-layers option will be ignored\n");
+                fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n");
+            }
+        }
+    ).set_env("LLAMA_ARG_N_GPU_LAYERS"));
+    add_opt(llama_arg(
+        {"-ngld", "--gpu-layers-draft", "--n-gpu-layers-draft"}, "N",
+        "number of layers to store in VRAM for the draft model",
+        [](gpt_params & params, int value) {
+            params.n_gpu_layers_draft = value;
+            if (!llama_supports_gpu_offload()) {
+                fprintf(stderr, "warning: not compiled with GPU offload support, --gpu-layers-draft option will be ignored\n");
+                fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n");
+            }
+        }
+    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
+    add_opt(llama_arg(
+        {"-sm", "--split-mode"}, "{none,layer,row}",
+        "how to split the model across multiple GPUs, one of:\n"
+        "- none: use one GPU only\n"
+        "- layer (default): split layers and KV across GPUs\n"
+        "- row: split rows across GPUs",
+        [](gpt_params & params, const std::string & value) {
+            std::string arg_next = value;
+            if (arg_next == "none") {
+                params.split_mode = LLAMA_SPLIT_MODE_NONE;
+            } else if (arg_next == "layer") {
+                params.split_mode = LLAMA_SPLIT_MODE_LAYER;
+            }
+            else if (arg_next == "row") {
+#ifdef GGML_USE_SYCL
+                fprintf(stderr, "warning: The split mode value:[row] is not supported by llama.cpp with SYCL. It's developing.\nExit!\n");
+                exit(1);
+#endif // GGML_USE_SYCL
+                params.split_mode = LLAMA_SPLIT_MODE_ROW;
+            }
+            else {
+                throw std::invalid_argument("invalid value");
+            }
+#ifndef GGML_USE_CUDA_SYCL_VULKAN
+            fprintf(stderr, "warning: llama.cpp was compiled without CUDA/SYCL/Vulkan. Setting the split mode has no effect.\n");
+#endif // GGML_USE_CUDA_SYCL_VULKAN
+        }
+    ));
+    add_opt(llama_arg(
+        {"-ts", "--tensor-split"}, "N0,N1,N2,...",
+        "fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1",
+        [](gpt_params & params, const std::string & value) {
+            std::string arg_next = value;
+
+            // split string by , and /
+            const std::regex regex{ R"([,/]+)" };
+            std::sregex_token_iterator it{ arg_next.begin(), arg_next.end(), regex, -1 };
+            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())
+                );
+            }
+            for (size_t i = 0; i < llama_max_devices(); ++i) {
+                if (i < split_arg.size()) {
+                        params.tensor_split[i] = std::stof(split_arg[i]);
+                } else {
+                        params.tensor_split[i] = 0.0f;
+                }
+            }
+#ifndef GGML_USE_CUDA_SYCL_VULKAN
+            fprintf(stderr, "warning: llama.cpp was compiled without CUDA/SYCL/Vulkan. Setting a tensor split has no effect.\n");
+#endif // GGML_USE_CUDA_SYCL_VULKAN
+        }
+    ));
+    add_opt(llama_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),
+        [](gpt_params & params, int value) {
+            params.main_gpu = value;
+#ifndef GGML_USE_CUDA_SYCL_VULKAN
+            fprintf(stderr, "warning: llama.cpp was compiled without CUDA/SYCL/Vulkan. Setting the main GPU has no effect.\n");
+#endif // GGML_USE_CUDA_SYCL_VULKAN
+        }
+    ));
+    add_opt(llama_arg(
+        {"--check-tensors"},
+        format("check model tensor data for invalid values (default: %s)", params.check_tensors ? "true" : "false"),
+        [](gpt_params & params) {
+            params.check_tensors = true;
+        }
+    ));
+    add_opt(llama_arg(
+        {"--override-kv"}, "KEY=TYPE:VALUE",
+        "advanced option to override model metadata by key. may be specified multiple times.\n"
+        "types: int, float, bool, str. example: --override-kv tokenizer.ggml.add_bos_token=bool:false",
+        [](gpt_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()));
+            }
+        }
+    ));
+    add_opt(llama_arg(
+        {"--lora"}, "FNAME",
+        "path to LoRA adapter (can be repeated to use multiple adapters)",
+        [](gpt_params & params, const std::string & value) {
+            params.lora_adapters.push_back({ std::string(value), 1.0 });
+        }
+        // we define this arg on both COMMON and EXPORT_LORA, so when showing help message of export-lora, it will be categorized as "example-specific" arg
+    ).set_examples({LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_EXPORT_LORA}));
+    add_opt(llama_arg(
+        {"--lora-scaled"}, "FNAME", "SCALE",
+        "path to LoRA adapter with user defined scaling (can be repeated to use multiple adapters)",
+        [](gpt_params & params, const std::string & fname, const std::string & scale) {
+            params.lora_adapters.push_back({ fname, std::stof(scale) });
+        }
+        // we define this arg on both COMMON and EXPORT_LORA, so when showing help message of export-lora, it will be categorized as "example-specific" arg
+    ).set_examples({LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_EXPORT_LORA}));
+    add_opt(llama_arg(
+        {"--control-vector"}, "FNAME",
+        "add a control vector\nnote: this argument can be repeated to add multiple control vectors",
+        [](gpt_params & params, const std::string & value) {
+            params.control_vectors.push_back({ 1.0f, value, });
+        }
+    ));
+    add_opt(llama_arg(
+        {"--control-vector-scaled"}, "FNAME", "SCALE",
+        "add a control vector with user defined scaling SCALE\n"
+        "note: this argument can be repeated to add multiple scaled control vectors",
+        [](gpt_params & params, const std::string & fname, const std::string & scale) {
+            params.control_vectors.push_back({ std::stof(scale), fname });
+        }
+    ));
+    add_opt(llama_arg(
+        {"--control-vector-layer-range"}, "START", "END",
+        "layer range to apply the control vector(s) to, start and end inclusive",
+        [](gpt_params & params, const std::string & start, const std::string & end) {
+            params.control_vector_layer_start = std::stoi(start);
+            params.control_vector_layer_end = std::stoi(end);
+        }
+    ));
+    add_opt(llama_arg(
+        {"-a", "--alias"}, "STRING",
+        "set alias for model name (to be used by REST API)",
+        [](gpt_params & params, const std::string & value) {
+            params.model_alias = value;
+        }
+    ).set_examples({LLAMA_EXAMPLE_SERVER}));
+    add_opt(llama_arg(
+        {"-m", "--model"}, "FNAME",
+        ex == LLAMA_EXAMPLE_EXPORT_LORA
+            ? std::string("model path from which to load base model")
+            : format(
+                "model path (default: `models/$filename` with filename from `--hf-file` "
+                "or `--model-url` if set, otherwise %s)", DEFAULT_MODEL_PATH
+            ),
+        [](gpt_params & params, const std::string & value) {
+            params.model = value;
+        }
+    ).set_examples({LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_EXPORT_LORA}).set_env("LLAMA_ARG_MODEL"));
+    add_opt(llama_arg(
+        {"-md", "--model-draft"}, "FNAME",
+        "draft model for speculative decoding (default: unused)",
+        [](gpt_params & params, const std::string & value) {
+            params.model_draft = value;
+        }
+    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
+    add_opt(llama_arg(
+        {"-mu", "--model-url"}, "MODEL_URL",
+        "model download url (default: unused)",
+        [](gpt_params & params, const std::string & value) {
+            params.model_url = value;
+        }
+    ).set_env("LLAMA_ARG_MODEL_URL"));
+    add_opt(llama_arg(
+        {"-hfr", "--hf-repo"}, "REPO",
+        "Hugging Face model repository (default: unused)",
+        [](gpt_params & params, const std::string & value) {
+            params.hf_repo = value;
+        }
+    ).set_env("LLAMA_ARG_HF_REPO"));
+    add_opt(llama_arg(
+        {"-hff", "--hf-file"}, "FILE",
+        "Hugging Face model file (default: unused)",
+        [](gpt_params & params, const std::string & value) {
+            params.hf_file = value;
+        }
+    ).set_env("LLAMA_ARG_HF_FILE"));
+    add_opt(llama_arg(
+        {"-hft", "--hf-token"}, "TOKEN",
+        "Hugging Face access token (default: value from HF_TOKEN environment variable)",
+        [](gpt_params & params, const std::string & value) {
+            params.hf_token = value;
+        }
+    ).set_env("HF_TOKEN"));
+    add_opt(llama_arg(
+        {"--context-file"}, "FNAME",
+        "file to load context from (repeat to specify multiple files)",
+        [](gpt_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()));
+            }
+            params.context_files.push_back(value);
+        }
+    ).set_examples({LLAMA_EXAMPLE_RETRIEVAL}));
+    add_opt(llama_arg(
+        {"--chunk-size"}, "N",
+        format("minimum length of embedded text chunks (default: %d)", params.chunk_size),
+        [](gpt_params & params, int value) {
+            params.chunk_size = value;
+        }
+    ).set_examples({LLAMA_EXAMPLE_RETRIEVAL}));
+    add_opt(llama_arg(
+        {"--chunk-separator"}, "STRING",
+        format("separator between chunks (default: '%s')", params.chunk_separator.c_str()),
+        [](gpt_params & params, const std::string & value) {
+            params.chunk_separator = value;
+        }
+    ).set_examples({LLAMA_EXAMPLE_RETRIEVAL}));
+    add_opt(llama_arg(
+        {"--junk"}, "N",
+        format("number of times to repeat the junk text (default: %d)", params.n_junk),
+        [](gpt_params & params, int value) {
+            params.n_junk = value;
+        }
+    ).set_examples({LLAMA_EXAMPLE_PASSKEY}));
+    add_opt(llama_arg(
+        {"--pos"}, "N",
+        format("position of the passkey in the junk text (default: %d)", params.i_pos),
+        [](gpt_params & params, int value) {
+            params.i_pos = value;
+        }
+    ).set_examples({LLAMA_EXAMPLE_PASSKEY}));
+    add_opt(llama_arg(
+        {"-o", "--output", "--output-file"}, "FNAME",
+        format("output file (default: '%s')",
+            ex == LLAMA_EXAMPLE_EXPORT_LORA
+                ? params.lora_outfile.c_str()
+                : ex == LLAMA_EXAMPLE_CVECTOR_GENERATOR
+                    ? params.cvector_outfile.c_str()
+                    : params.out_file.c_str()),
+        [](gpt_params & params, const std::string & value) {
+            params.out_file = value;
+            params.cvector_outfile = value;
+            params.lora_outfile = value;
+        }
+    ).set_examples({LLAMA_EXAMPLE_IMATRIX, LLAMA_EXAMPLE_CVECTOR_GENERATOR, LLAMA_EXAMPLE_EXPORT_LORA}));
+    add_opt(llama_arg(
+        {"-ofreq", "--output-frequency"}, "N",
+        format("output the imatrix every N iterations (default: %d)", params.n_out_freq),
+        [](gpt_params & params, int value) {
+            params.n_out_freq = value;
+        }
+    ).set_examples({LLAMA_EXAMPLE_IMATRIX}));
+    add_opt(llama_arg(
+        {"--save-frequency"}, "N",
+        format("save an imatrix copy every N iterations (default: %d)", params.n_save_freq),
+        [](gpt_params & params, int value) {
+            params.n_save_freq = value;
+        }
+    ).set_examples({LLAMA_EXAMPLE_IMATRIX}));
+    add_opt(llama_arg(
+        {"--process-output"},
+        format("collect data for the output tensor (default: %s)", params.process_output ? "true" : "false"),
+        [](gpt_params & params) {
+            params.process_output = true;
+        }
+    ).set_examples({LLAMA_EXAMPLE_IMATRIX}));
+    add_opt(llama_arg(
+        {"--no-ppl"},
+        format("do not compute perplexity (default: %s)", params.compute_ppl ? "true" : "false"),
+        [](gpt_params & params) {
+            params.compute_ppl = false;
+        }
+    ).set_examples({LLAMA_EXAMPLE_IMATRIX}));
+    add_opt(llama_arg(
+        {"--chunk", "--from-chunk"}, "N",
+        format("start processing the input from chunk N (default: %d)", params.i_chunk),
+        [](gpt_params & params, int value) {
+            params.i_chunk = value;
+        }
+    ).set_examples({LLAMA_EXAMPLE_IMATRIX}));
+    add_opt(llama_arg(
+        {"-pps"},
+        format("is the prompt shared across parallel sequences (default: %s)", params.is_pp_shared ? "true" : "false"),
+        [](gpt_params & params) {
+            params.is_pp_shared = true;
+        }
+    ).set_examples({LLAMA_EXAMPLE_BENCH}));
+    add_opt(llama_arg(
+        {"-npp"}, "n0,n1,...",
+        "number of prompt tokens",
+        [](gpt_params & params, const std::string & value) {
+            auto p = string_split<int>(value, ',');
+            params.n_pp.insert(params.n_pp.end(), p.begin(), p.end());
+        }
+    ).set_examples({LLAMA_EXAMPLE_BENCH}));
+    add_opt(llama_arg(
+        {"-ntg"}, "n0,n1,...",
+        "number of text generation tokens",
+        [](gpt_params & params, const std::string & value) {
+            auto p = string_split<int>(value, ',');
+            params.n_tg.insert(params.n_tg.end(), p.begin(), p.end());
+        }
+    ).set_examples({LLAMA_EXAMPLE_BENCH}));
+    add_opt(llama_arg(
+        {"-npl"}, "n0,n1,...",
+        "number of parallel prompts",
+        [](gpt_params & params, const std::string & value) {
+            auto p = string_split<int>(value, ',');
+            params.n_pl.insert(params.n_pl.end(), p.begin(), p.end());
+        }
+    ).set_examples({LLAMA_EXAMPLE_BENCH}));
+    add_opt(llama_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),
+        [](gpt_params & params, int value) {
+            params.embd_normalize = value;
+        }
+    ).set_examples({LLAMA_EXAMPLE_EMBEDDING}));
+    add_opt(llama_arg(
+        {"--embd-output-format"}, "FORMAT",
+        "empty = default, \"array\" = [[],[]...], \"json\" = openai style, \"json+\" = same \"json\" + cosine similarity matrix",
+        [](gpt_params & params, const std::string & value) {
+            params.embd_out = value;
+        }
+    ).set_examples({LLAMA_EXAMPLE_EMBEDDING}));
+    add_opt(llama_arg(
+        {"--embd-separator"}, "STRING",
+        "separator of embendings (default \\n) for example \"<#sep#>\"",
+        [](gpt_params & params, const std::string & value) {
+            params.embd_sep = value;
+        }
+    ).set_examples({LLAMA_EXAMPLE_EMBEDDING}));
+    add_opt(llama_arg(
+        {"--host"}, "HOST",
+        format("ip address to listen (default: %s)", params.hostname.c_str()),
+        [](gpt_params & params, const std::string & value) {
+            params.hostname = value;
+        }
+    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_HOST"));
+    add_opt(llama_arg(
+        {"--port"}, "PORT",
+        format("port to listen (default: %d)", params.port),
+        [](gpt_params & params, int value) {
+            params.port = value;
+        }
+    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_PORT"));
+    add_opt(llama_arg(
+        {"--path"}, "PATH",
+        format("path to serve static files from (default: %s)", params.public_path.c_str()),
+        [](gpt_params & params, const std::string & value) {
+            params.public_path = value;
+        }
+    ).set_examples({LLAMA_EXAMPLE_SERVER}));
+    add_opt(llama_arg(
+        {"--embedding", "--embeddings"},
+        format("restrict to only support embedding use case; use only with dedicated embedding models (default: %s)", params.embedding ? "enabled" : "disabled"),
+        [](gpt_params & params) {
+            params.embedding = true;
+        }
+    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_EMBEDDINGS"));
+    add_opt(llama_arg(
+        {"--api-key"}, "KEY",
+        "API key to use for authentication (default: none)",
+        [](gpt_params & params, const std::string & value) {
+            params.api_keys.push_back(value);
+        }
+    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_API_KEY"));
+    add_opt(llama_arg(
+        {"--api-key-file"}, "FNAME",
+        "path to file containing API keys (default: none)",
+        [](gpt_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()));
+            }
+            std::string key;
+            while (std::getline(key_file, key)) {
+                if (!key.empty()) {
+                        params.api_keys.push_back(key);
+                }
+            }
+            key_file.close();
+        }
+    ).set_examples({LLAMA_EXAMPLE_SERVER}));
+    add_opt(llama_arg(
+        {"--ssl-key-file"}, "FNAME",
+        "path to file a PEM-encoded SSL private key",
+        [](gpt_params & params, const std::string & value) {
+            params.ssl_file_key = value;
+        }
+    ).set_examples({LLAMA_EXAMPLE_SERVER}));
+    add_opt(llama_arg(
+        {"--ssl-cert-file"}, "FNAME",
+        "path to file a PEM-encoded SSL certificate",
+        [](gpt_params & params, const std::string & value) {
+            params.ssl_file_cert = value;
+        }
+    ).set_examples({LLAMA_EXAMPLE_SERVER}));
+    add_opt(llama_arg(
+        {"-to", "--timeout"}, "N",
+        format("server read/write timeout in seconds (default: %d)", params.timeout_read),
+        [](gpt_params & params, int value) {
+            params.timeout_read  = value;
+            params.timeout_write = value;
+        }
+    ).set_examples({LLAMA_EXAMPLE_SERVER}));
+    add_opt(llama_arg(
+        {"--threads-http"}, "N",
+        format("number of threads used to process HTTP requests (default: %d)", params.n_threads_http),
+        [](gpt_params & params, int value) {
+            params.n_threads_http = value;
+        }
+    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_THREADS_HTTP"));
+    add_opt(llama_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",
+        [](gpt_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;
+        }
+    ).set_examples({LLAMA_EXAMPLE_SERVER}));
+    add_opt(llama_arg(
+        {"--log-format"}, "{text, json}",
+        "log output format: json or text (default: json)",
+        [](gpt_params & params, const std::string & value) {
+            if (value == "json") {
+                params.log_json = true;
+            } else if (value == "text") {
+                params.log_json = false;
+            } else {
+                throw std::invalid_argument("invalid value");
+            }
+        }
+    ).set_examples({LLAMA_EXAMPLE_SERVER}));
+    add_opt(llama_arg(
+        {"--metrics"},
+        format("enable prometheus compatible metrics endpoint (default: %s)", params.endpoint_metrics ? "enabled" : "disabled"),
+        [](gpt_params & params) {
+            params.endpoint_metrics = true;
+        }
+    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_ENDPOINT_METRICS"));
+    add_opt(llama_arg(
+        {"--no-slots"},
+        format("disables slots monitoring endpoint (default: %s)", params.endpoint_slots ? "enabled" : "disabled"),
+        [](gpt_params & params) {
+            params.endpoint_slots = false;
+        }
+    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_NO_ENDPOINT_SLOTS"));
+    add_opt(llama_arg(
+        {"--slot-save-path"}, "PATH",
+        "path to save slot kv cache (default: disabled)",
+        [](gpt_params & params, const std::string & value) {
+            params.slot_save_path = value;
+            // if doesn't end with DIRECTORY_SEPARATOR, add it
+            if (!params.slot_save_path.empty() && params.slot_save_path[params.slot_save_path.size() - 1] != DIRECTORY_SEPARATOR) {
+                params.slot_save_path += DIRECTORY_SEPARATOR;
+            }
+        }
+    ).set_examples({LLAMA_EXAMPLE_SERVER}));
+    add_opt(llama_arg(
+        {"--chat-template"}, "JINJA_TEMPLATE",
+        "set custom jinja chat template (default: template taken from model's metadata)\n"
+        "if suffix/prefix are specified, template will be disabled\n"
+        "only commonly used templates are accepted:\nhttps://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template",
+        [](gpt_params & params, const std::string & value) {
+            if (!llama_chat_verify_template(value)) {
+                throw std::runtime_error(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()
+                ));
+            }
+            params.chat_template = value;
+        }
+    ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_CHAT_TEMPLATE"));
+    add_opt(llama_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),
+        [](gpt_params & params, const std::string & value) {
+            params.slot_prompt_similarity = std::stof(value);
+        }
+    ).set_examples({LLAMA_EXAMPLE_SERVER}));
+    add_opt(llama_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"),
+        [](gpt_params & params) {
+            params.lora_init_without_apply = true;
+        }
+    ).set_examples({LLAMA_EXAMPLE_SERVER}));
+    add_opt(llama_arg(
+        {"--simple-io"},
+        "use basic IO for better compatibility in subprocesses and limited consoles",
+        [](gpt_params & params) {
+            params.simple_io = true;
+        }
+    ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_INFILL}));
+    add_opt(llama_arg(
+        {"-ld", "--logdir"}, "LOGDIR",
+        "path under which to save YAML logs (no logging if unset)",
+        [](gpt_params & params, const std::string & value) {
+            params.logdir = value;
+
+            if (params.logdir.back() != DIRECTORY_SEPARATOR) {
+                params.logdir += DIRECTORY_SEPARATOR;
+            }
+        }
+    ));
+    add_opt(llama_arg(
+        {"--positive-file"}, "FNAME",
+        format("positive prompts file, one prompt per line (default: '%s')", params.cvector_positive_file.c_str()),
+        [](gpt_params & params, const std::string & value) {
+            params.cvector_positive_file = value;
+        }
+    ).set_examples({LLAMA_EXAMPLE_CVECTOR_GENERATOR}));
+    add_opt(llama_arg(
+        {"--negative-file"}, "FNAME",
+        format("negative prompts file, one prompt per line (default: '%s')", params.cvector_negative_file.c_str()),
+        [](gpt_params & params, const std::string & value) {
+            params.cvector_negative_file = value;
+        }
+    ).set_examples({LLAMA_EXAMPLE_CVECTOR_GENERATOR}));
+    add_opt(llama_arg(
+        {"--pca-batch"}, "N",
+        format("batch size used for PCA. Larger batch runs faster, but uses more memory (default: %d)", params.n_pca_batch),
+        [](gpt_params & params, int value) {
+            params.n_pca_batch = value;
+        }
+    ).set_examples({LLAMA_EXAMPLE_CVECTOR_GENERATOR}));
+    add_opt(llama_arg(
+        {"--pca-iter"}, "N",
+        format("number of iterations used for PCA (default: %d)", params.n_pca_iterations),
+        [](gpt_params & params, int value) {
+            params.n_pca_iterations = value;
+        }
+    ).set_examples({LLAMA_EXAMPLE_CVECTOR_GENERATOR}));
+    add_opt(llama_arg(
+        {"--method"}, "{pca, mean}",
+        "dimensionality reduction method to be used (default: pca)",
+        [](gpt_params & params, const std::string & value) {
+            /**/ if (value == "pca") { params.cvector_dimre_method = DIMRE_METHOD_PCA; }
+            else if (value == "mean") { params.cvector_dimre_method = DIMRE_METHOD_MEAN; }
+            else { throw std::invalid_argument("invalid value"); }
+        }
+    ).set_examples({LLAMA_EXAMPLE_CVECTOR_GENERATOR}));
+    add_opt(llama_arg(
+        {"--output-format"}, "{md,jsonl}",
+        "output format for batched-bench results (default: md)",
+        [](gpt_params & params, const std::string & value) {
+            /**/ if (value == "jsonl") { params.batched_bench_output_jsonl = true; }
+            else if (value == "md") { params.batched_bench_output_jsonl = false; }
+            else { std::invalid_argument("invalid value"); }
+        }
+    ).set_examples({LLAMA_EXAMPLE_BENCH}));
+#ifndef LOG_DISABLE_LOGS
+    // TODO: make this looks less weird
+    add_opt(llama_arg(
+        {"--log-test"},
+        "Log test",
+        [](gpt_params &) { log_param_single_parse("--log-test"); }
+    ));
+    add_opt(llama_arg(
+        {"--log-disable"},
+        "Log disable",
+        [](gpt_params &) { log_param_single_parse("--log-disable"); }
+    ));
+    add_opt(llama_arg(
+        {"--log-enable"},
+        "Log enable",
+        [](gpt_params &) { log_param_single_parse("--log-enable"); }
+    ));
+    add_opt(llama_arg(
+        {"--log-new"},
+        "Log new",
+        [](gpt_params &) { log_param_single_parse("--log-new"); }
+    ));
+    add_opt(llama_arg(
+        {"--log-append"},
+        "Log append",
+        [](gpt_params &) { log_param_single_parse("--log-append"); }
+    ));
+    add_opt(llama_arg(
+        {"--log-file"}, "FNAME",
+        "Log file",
+        [](gpt_params &, const std::string & value) { log_param_pair_parse(false, "--log-file", value); }
+    ));
+#endif // LOG_DISABLE_LOGS
+
+    return ctx_arg;
+}
+

common/arg.h

@@ -0,0 +1,77 @@
+#pragma once
+
+#include "common.h"
+
+#include <set>
+#include <string>
+#include <vector>
+
+//
+// CLI argument parsing
+//
+
+struct llama_arg {
+    std::set<enum llama_example> examples = {LLAMA_EXAMPLE_COMMON};
+    std::vector<const char *> args;
+    const char * value_hint   = nullptr; // help text or example for arg value
+    const char * value_hint_2 = nullptr; // for second arg value
+    const char * env          = nullptr;
+    std::string help;
+    bool is_sparam = false; // is current arg a sampling param?
+    void (*handler_void)   (gpt_params & params) = nullptr;
+    void (*handler_string) (gpt_params & params, const std::string &) = nullptr;
+    void (*handler_str_str)(gpt_params & params, const std::string &, const std::string &) = nullptr;
+    void (*handler_int)    (gpt_params & params, int) = nullptr;
+
+    llama_arg(
+        const std::initializer_list<const char *> & args,
+        const char * value_hint,
+        const std::string & help,
+        void (*handler)(gpt_params & params, const std::string &)
+    ) : args(args), value_hint(value_hint), help(help), handler_string(handler) {}
+
+    llama_arg(
+        const std::initializer_list<const char *> & args,
+        const char * value_hint,
+        const std::string & help,
+        void (*handler)(gpt_params & params, int)
+    ) : args(args), value_hint(value_hint), help(help), handler_int(handler) {}
+
+    llama_arg(
+        const std::initializer_list<const char *> & args,
+        const std::string & help,
+        void (*handler)(gpt_params & params)
+    ) : args(args), help(help), handler_void(handler) {}
+
+    // support 2 values for arg
+    llama_arg(
+        const std::initializer_list<const char *> & args,
+        const char * value_hint,
+        const char * value_hint_2,
+        const std::string & help,
+        void (*handler)(gpt_params & params, const std::string &, const std::string &)
+    ) : args(args), value_hint(value_hint), value_hint_2(value_hint_2), help(help), handler_str_str(handler) {}
+
+    llama_arg & set_examples(std::initializer_list<enum llama_example> examples);
+    llama_arg & set_env(const char * env);
+    llama_arg & set_sparam();
+    bool in_example(enum llama_example ex);
+    bool get_value_from_env(std::string & output);
+    bool has_value_from_env();
+    std::string to_string();
+};
+
+struct gpt_params_context {
+    enum llama_example ex = LLAMA_EXAMPLE_COMMON;
+    gpt_params & params;
+    std::vector<llama_arg> options;
+    void(*print_usage)(int, char **) = nullptr;
+    gpt_params_context(gpt_params & params) : params(params) {}
+};
+
+// parse input arguments from CLI
+// if one argument has invalid value, it will automatically display usage of the specific argument (and not the full usage message)
+bool gpt_params_parse(int argc, char ** argv, gpt_params & params, llama_example ex, void(*print_usage)(int, char **) = nullptr);
+
+// function to be used by test-arg-parser
+gpt_params_context gpt_params_parser_init(gpt_params & params, llama_example ex, void(*print_usage)(int, char **) = nullptr);

common/common.cpp

@@ -25,7 +25,6 @@
 #include <unordered_map>
 #include <unordered_set>
 #include <vector>
-#include <climits>
 
 #if defined(__APPLE__) && defined(__MACH__)
 #include <sys/types.h>
@@ -272,53 +271,6 @@ bool set_process_priority(enum ggml_sched_priority prio) {
 // CLI argument parsing
 //
 
-#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 gpt_params_handle_model_default(gpt_params & params) {
-    if (!params.hf_repo.empty()) {
-        // short-hand to avoid specifying --hf-file -> default it to --model
-        if (params.hf_file.empty()) {
-            if (params.model.empty()) {
-                throw std::invalid_argument("error: --hf-repo requires either --hf-file or --model\n");
-            }
-            params.hf_file = params.model;
-        } else if (params.model.empty()) {
-            params.model = fs_get_cache_file(string_split(params.hf_file, '/').back());
-        }
-    } else if (!params.model_url.empty()) {
-        if (params.model.empty()) {
-            auto f = string_split(params.model_url, '#').front();
-            f = string_split(f, '?').front();
-            params.model = fs_get_cache_file(string_split(f, '/').back());
-        }
-    } else if (params.model.empty()) {
-        params.model = DEFAULT_MODEL_PATH;
-    }
-}
 
 void postprocess_cpu_params(cpu_params& cpuparams, const cpu_params* role_model) {
     int32_t n_set = 0;
@@ -344,150 +296,6 @@ void postprocess_cpu_params(cpu_params& cpuparams, const cpu_params* role_model)
     }
 }
 
-bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params, std::vector<llama_arg> & options) {
-    std::string arg;
-    const std::string arg_prefix = "--";
-    gpt_sampler_params & sparams = params.sparams;
-
-    std::unordered_map<std::string, llama_arg *> arg_to_options;
-    for (auto & opt : options) {
-        for (const auto & arg : opt.args) {
-            arg_to_options[arg] = &opt;
-        }
-    }
-
-    // handle environment variables
-    for (auto & opt : options) {
-        std::string value;
-        if (opt.get_value_from_env(value)) {
-            try {
-                if (opt.handler_void && (value == "1" || value == "true")) {
-                    opt.handler_void(params);
-                }
-                if (opt.handler_int) {
-                    opt.handler_int(params, std::stoi(value));
-                }
-                if (opt.handler_string) {
-                    opt.handler_string(params, value);
-                    continue;
-                }
-            } catch (std::exception & e) {
-                throw std::invalid_argument(format(
-                    "error while handling environment variable \"%s\": %s\n\n", opt.env, e.what()));
-            }
-        }
-    }
-
-    // handle command line arguments
-    auto check_arg = [&](int i) {
-        if (i+1 >= argc) {
-            throw std::invalid_argument("expected value for argument");
-        }
-    };
-
-    for (int i = 1; i < argc; i++) {
-        const std::string arg_prefix = "--";
-
-        std::string arg = argv[i];
-        if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) {
-            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()));
-        }
-        auto opt = *arg_to_options[arg];
-        if (opt.has_value_from_env()) {
-            fprintf(stderr, "warn: %s environment variable is set, but will be overwritten by command line argument %s\n", opt.env, arg.c_str());
-        }
-        try {
-            if (opt.handler_void) {
-                opt.handler_void(params);
-                continue;
-            }
-
-            // arg with single value
-            check_arg(i);
-            std::string val = argv[++i];
-            if (opt.handler_int) {
-                opt.handler_int(params, std::stoi(val));
-                continue;
-            }
-            if (opt.handler_string) {
-                opt.handler_string(params, val);
-                continue;
-            }
-
-            // arg with 2 values
-            check_arg(i);
-            std::string val2 = argv[++i];
-            if (opt.handler_str_str) {
-                opt.handler_str_str(params, val, val2);
-                continue;
-            }
-        } catch (std::exception & e) {
-            throw std::invalid_argument(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()));
-        }
-    }
-
-    postprocess_cpu_params(params.cpuparams, nullptr);
-    postprocess_cpu_params(params.cpuparams_batch, &params.cpuparams);
-    postprocess_cpu_params(params.draft_cpuparams, &params.cpuparams);
-    postprocess_cpu_params(params.draft_cpuparams_batch, &params.cpuparams_batch);
-
-    if (params.prompt_cache_all && (params.interactive || params.interactive_first)) {
-        throw std::invalid_argument("error: --prompt-cache-all not supported in interactive mode yet\n");
-    }
-
-    gpt_params_handle_model_default(params);
-
-    if (params.escape) {
-        string_process_escapes(params.prompt);
-        string_process_escapes(params.input_prefix);
-        string_process_escapes(params.input_suffix);
-        for (auto & antiprompt : params.antiprompt) {
-            string_process_escapes(antiprompt);
-        }
-    }
-
-    if (!params.kv_overrides.empty()) {
-        params.kv_overrides.emplace_back();
-        params.kv_overrides.back().key[0] = 0;
-    }
-
-    if (sparams.seed == LLAMA_DEFAULT_SEED) {
-        sparams.seed = time(NULL);
-    }
-
-    return true;
-}
-
-bool gpt_params_parse(int argc, char ** argv, gpt_params & params, std::vector<llama_arg> & options) {
-    const auto params_org = params; // the example can modify the default params
-
-    try {
-        if (!gpt_params_parse_ex(argc, argv, params, options)) {
-            params = params_org;
-            return false;
-        }
-        if (params.usage) {
-            gpt_params_print_usage(params, options);
-            if (params.print_usage) {
-                params.print_usage(argc, argv);
-            }
-            exit(0);
-        }
-    } catch (const std::invalid_argument & ex) {
-        fprintf(stderr, "%s\n", ex.what());
-        params = params_org;
-        return false;
-    }
-
-    return true;
-}
-
 bool parse_cpu_range(const std::string & range, bool (&boolmask)[GGML_MAX_N_THREADS]) {
     size_t dash_loc = range.find('-');
     if (dash_loc == std::string::npos) {
@@ -561,1743 +369,6 @@ bool parse_cpu_mask(const std::string & mask, bool (&boolmask)[GGML_MAX_N_THREAD
     return true;
 }
 
-static std::vector<std::string> break_str_into_lines(std::string input, size_t max_char_per_line) {
-    std::vector<std::string> result;
-    std::istringstream iss(input);
-    std::string line;
-    auto add_line = [&](const std::string& l) {
-        if (l.length() <= max_char_per_line) {
-            result.push_back(l);
-        } else {
-            std::istringstream line_stream(l);
-            std::string word, current_line;
-            while (line_stream >> word) {
-                if (current_line.length() + !current_line.empty() + word.length() > max_char_per_line) {
-                    if (!current_line.empty()) result.push_back(current_line);
-                    current_line = word;
-                } else {
-                    current_line += (!current_line.empty() ? " " : "") + word;
-                }
-            }
-            if (!current_line.empty()) result.push_back(current_line);
-        }
-    };
-    while (std::getline(iss, line)) {
-        add_line(line);
-    }
-    return result;
-}
-
-std::string llama_arg::to_string() {
-    // params for printing to console
-    const static int n_leading_spaces = 40;
-    const static int n_char_per_line_help = 70; // TODO: detect this based on current console
-    std::string leading_spaces(n_leading_spaces, ' ');
-
-    std::ostringstream ss;
-    for (const auto arg : args) {
-        if (arg == args.front()) {
-            if (args.size() == 1) {
-                ss << arg;
-            } else {
-                // first arg is usually abbreviation, we need padding to make it more beautiful
-                auto tmp = std::string(arg) + ", ";
-                ss << format("%-7s", tmp.c_str());
-            }
-        } else {
-            ss << arg << (arg != args.back() ? ", " : "");
-        }
-    }
-    if (value_hint) ss << " " << value_hint;
-    if (value_hint_2) ss << " " << value_hint_2;
-    if (ss.tellp() > n_leading_spaces - 3) {
-        // current line is too long, add new line
-        ss << "\n" << leading_spaces;
-    } else {
-        // padding between arg and help, same line
-        ss << std::string(leading_spaces.size() - ss.tellp(), ' ');
-    }
-    const auto help_lines = break_str_into_lines(help, n_char_per_line_help);
-    for (const auto & line : help_lines) {
-        ss << (&line == &help_lines.front() ? "" : leading_spaces) << line << "\n";
-    }
-    return ss.str();
-}
-
-void gpt_params_print_usage(gpt_params & params, std::vector<llama_arg> & options) {
-    auto print_options = [](std::vector<llama_arg *> & options) {
-        for (llama_arg * opt : options) {
-            printf("%s", opt->to_string().c_str());
-        }
-    };
-
-    std::vector<llama_arg *> common_options;
-    std::vector<llama_arg *> specific_options;
-    for (auto & opt : options) {
-        // in case multiple LLAMA_EXAMPLE_* are set, we prioritize the LLAMA_EXAMPLE_* matching current example
-        if (opt.in_example(params.curr_ex)) {
-            specific_options.push_back(&opt);
-        } else {
-            common_options.push_back(&opt);
-        }
-    }
-    printf("----- common options -----\n\n");
-    print_options(common_options);
-    // TODO: maybe convert enum llama_example to string
-    printf("\n\n----- example-specific options -----\n\n");
-    print_options(specific_options);
-}
-
-std::vector<llama_arg> gpt_params_parser_init(gpt_params & params, llama_example ex) {
-    return gpt_params_parser_init(params, ex, nullptr);
-}
-
-std::vector<llama_arg> gpt_params_parser_init(gpt_params & params, llama_example ex, std::function<void(int, char **)> print_usage) {
-    std::vector<llama_arg> options;
-    params.print_usage = print_usage;
-    params.curr_ex     = ex;
-
-    std::string sampler_type_chars;
-    std::string sampler_type_names;
-    for (const auto & sampler : params.sparams.samplers) {
-        sampler_type_chars += gpt_sampler_type_to_chr(sampler);
-        sampler_type_names += gpt_sampler_type_to_str(sampler) + ";";
-    }
-    sampler_type_names.pop_back();
-
-
-    /**
-     * filter options by example
-     * rules:
-     * - all examples inherit options from LLAMA_EXAMPLE_COMMON
-     * - if LLAMA_EXAMPLE_* is set (other than COMMON), we only show the option in the corresponding example
-     * - if both {LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_*,} are set, we will prioritize the LLAMA_EXAMPLE_* matching current example
-     */
-    auto add_opt = [&](llama_arg arg) {
-        if (arg.in_example(ex) || arg.in_example(LLAMA_EXAMPLE_COMMON)) {
-            options.push_back(std::move(arg));
-        }
-    };
-
-
-    add_opt(llama_arg(
-        {"-h", "--help", "--usage"},
-        "print usage and exit",
-        [](gpt_params & params) {
-            params.usage = true;
-        }
-    ));
-    add_opt(llama_arg(
-        {"--version"},
-        "show version and build info",
-        [](gpt_params &) {
-            fprintf(stderr, "version: %d (%s)\n", LLAMA_BUILD_NUMBER, LLAMA_COMMIT);
-            fprintf(stderr, "built with %s for %s\n", LLAMA_COMPILER, LLAMA_BUILD_TARGET);
-            exit(0);
-        }
-    ));
-    add_opt(llama_arg(
-        {"-v", "--verbose"},
-        "print verbose information",
-        [](gpt_params & params) {
-            params.verbosity = 1;
-        }
-    ));
-    add_opt(llama_arg(
-        {"--verbosity"}, "N",
-        format("set specific verbosity level (default: %d)", params.verbosity),
-        [](gpt_params & params, int value) {
-            params.verbosity = value;
-        }
-    ));
-    add_opt(llama_arg(
-        {"--verbose-prompt"},
-        format("print a verbose prompt before generation (default: %s)", params.verbose_prompt ? "true" : "false"),
-        [](gpt_params & params) {
-            params.verbose_prompt = true;
-        }
-    ).set_examples({LLAMA_EXAMPLE_MAIN}));
-    add_opt(llama_arg(
-        {"--no-display-prompt"},
-        format("don't print prompt at generation (default: %s)", !params.display_prompt ? "true" : "false"),
-        [](gpt_params & params) {
-            params.display_prompt = false;
-        }
-    ).set_examples({LLAMA_EXAMPLE_MAIN}));
-    add_opt(llama_arg(
-        {"-co", "--color"},
-        format("colorise output to distinguish prompt and user input from generations (default: %s)", params.use_color ? "true" : "false"),
-        [](gpt_params & params) {
-            params.use_color = true;
-        }
-    ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_INFILL}));
-    add_opt(llama_arg(
-        {"-s", "--seed"}, "SEED",
-        format("RNG seed (default: %d, use random seed for < 0)", params.sparams.seed),
-        [](gpt_params & params, const std::string & value) {
-            params.sparams.seed = std::stoul(value);
-        }
-    ));
-    add_opt(llama_arg(
-        {"-t", "--threads"}, "N",
-        format("number of threads to use during generation (default: %d)", params.cpuparams.n_threads),
-        [](gpt_params & params, int value) {
-            params.cpuparams.n_threads = value;
-            if (params.cpuparams.n_threads <= 0) {
-                params.cpuparams.n_threads = std::thread::hardware_concurrency();
-            }
-        }
-    ).set_env("LLAMA_ARG_THREADS"));
-    add_opt(llama_arg(
-        {"-tb", "--threads-batch"}, "N",
-        "number of threads to use during batch and prompt processing (default: same as --threads)",
-        [](gpt_params & params, int value) {
-            params.cpuparams_batch.n_threads = value;
-            if (params.cpuparams_batch.n_threads <= 0) {
-                params.cpuparams_batch.n_threads = std::thread::hardware_concurrency();
-            }
-        }
-    ));
-    add_opt(llama_arg(
-        {"-td", "--threads-draft"}, "N",
-        "number of threads to use during generation (default: same as --threads)",
-        [](gpt_params & params, int value) {
-            params.draft_cpuparams.n_threads = value;
-            if (params.draft_cpuparams.n_threads <= 0) {
-                params.draft_cpuparams.n_threads = std::thread::hardware_concurrency();
-            }
-        }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
-    add_opt(llama_arg(
-        {"-tbd", "--threads-batch-draft"}, "N",
-        "number of threads to use during batch and prompt processing (default: same as --threads-draft)",
-        [](gpt_params & params, int value) {
-            params.draft_cpuparams_batch.n_threads = value;
-            if (params.draft_cpuparams_batch.n_threads <= 0) {
-                params.draft_cpuparams_batch.n_threads = std::thread::hardware_concurrency();
-            }
-        }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
-    add_opt(llama_arg(
-        {"-C", "--cpu-mask"}, "M",
-        "CPU affinity mask: arbitrarily long hex. Complements cpu-range (default: \"\")",
-        [](gpt_params & params, const std::string & mask) {
-            params.cpuparams.mask_valid = true;
-            if (!parse_cpu_mask(mask, params.cpuparams.cpumask)) {
-                throw std::invalid_argument("invalid cpumask");
-            }
-        }
-    ));
-    add_opt(llama_arg(
-        {"-Cr", "--cpu-range"}, "lo-hi",
-        "range of CPUs for affinity. Complements --cpu-mask",
-        [](gpt_params & params, const std::string & range) {
-            params.cpuparams.mask_valid = true;
-            if (!parse_cpu_range(range, params.cpuparams.cpumask)) {
-                throw std::invalid_argument("invalid range");
-            }
-        }
-    ));
-    add_opt(llama_arg(
-        {"--cpu-strict"}, "<0|1>",
-        format("use strict CPU placement (default: %u)\n", (unsigned) params.cpuparams.strict_cpu),
-        [](gpt_params & params, const std::string & value) {
-            params.cpuparams.strict_cpu = std::stoul(value);
-        }
-    ));
-    add_opt(llama_arg(
-        {"--prio"}, "N",
-        format("set process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: %d)\n", params.cpuparams.priority),
-        [](gpt_params & params, int prio) {
-            if (prio < 0 || prio > 3) {
-                throw std::invalid_argument("invalid value");
-            }
-            params.cpuparams.priority = (enum ggml_sched_priority) prio;
-        }
-    ));
-    add_opt(llama_arg(
-        {"--poll"}, "<0...100>",
-        format("use polling level to wait for work (0 - no polling, default: %u)\n", (unsigned) params.cpuparams.poll),
-        [](gpt_params & params, const std::string & value) {
-            params.cpuparams.poll = std::stoul(value);
-        }
-    ));
-    add_opt(llama_arg(
-        {"-Cb", "--cpu-mask-batch"}, "M",
-        "CPU affinity mask: arbitrarily long hex. Complements cpu-range-batch (default: same as --cpu-mask)",
-        [](gpt_params & params, const std::string & mask) {
-            params.cpuparams_batch.mask_valid = true;
-            if (!parse_cpu_mask(mask, params.cpuparams_batch.cpumask)) {
-                throw std::invalid_argument("invalid cpumask");
-            }
-        }
-    ));
-    add_opt(llama_arg(
-        {"-Crb", "--cpu-range-batch"}, "lo-hi",
-        "ranges of CPUs for affinity. Complements --cpu-mask-batch",
-        [](gpt_params & params, const std::string & range) {
-            params.cpuparams_batch.mask_valid = true;
-            if (!parse_cpu_range(range, params.cpuparams_batch.cpumask)) {
-                throw std::invalid_argument("invalid range");
-            }
-        }
-    ));
-    add_opt(llama_arg(
-        {"--cpu-strict-batch"}, "<0|1>",
-        "use strict CPU placement (default: same as --cpu-strict)",
-        [](gpt_params & params, int value) {
-            params.cpuparams_batch.strict_cpu = value;
-        }
-    ));
-    add_opt(llama_arg(
-        {"--prio-batch"}, "N",
-        format("set process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: %d)\n", params.cpuparams_batch.priority),
-        [](gpt_params & params, int prio) {
-            if (prio < 0 || prio > 3) {
-                throw std::invalid_argument("invalid value");
-            }
-            params.cpuparams_batch.priority = (enum ggml_sched_priority) prio;
-        }
-    ));
-    add_opt(llama_arg(
-        {"--poll-batch"}, "<0|1>",
-        "use polling to wait for work (default: same as --poll)",
-        [](gpt_params & params, int value) {
-            params.cpuparams_batch.poll = value;
-        }
-    ));
-    add_opt(llama_arg(
-        {"-Cd", "--cpu-mask-draft"}, "M",
-        "Draft model CPU affinity mask. Complements cpu-range-draft (default: same as --cpu-mask)",
-        [](gpt_params & params, const std::string & mask) {
-            params.draft_cpuparams.mask_valid = true;
-            if (!parse_cpu_mask(mask, params.draft_cpuparams.cpumask)) {
-                throw std::invalid_argument("invalid cpumask");
-            }
-        }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
-    add_opt(llama_arg(
-        {"-Crd", "--cpu-range-draft"}, "lo-hi",
-        "Ranges of CPUs for affinity. Complements --cpu-mask-draft",
-        [](gpt_params & params, const std::string & range) {
-            params.draft_cpuparams.mask_valid = true;
-            if (!parse_cpu_range(range, params.draft_cpuparams.cpumask)) {
-                throw std::invalid_argument("invalid range");
-            }
-        }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
-    add_opt(llama_arg(
-        {"--cpu-strict-draft"}, "<0|1>",
-        "Use strict CPU placement for draft model (default: same as --cpu-strict)",
-        [](gpt_params & params, int value) {
-            params.draft_cpuparams.strict_cpu = value;
-        }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
-    add_opt(llama_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),
-        [](gpt_params & params, int prio) {
-            if (prio < 0 || prio > 3) {
-                throw std::invalid_argument("invalid value");
-            }
-            params.draft_cpuparams.priority = (enum ggml_sched_priority) prio;
-        }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
-    add_opt(llama_arg(
-        {"--poll-draft"}, "<0|1>",
-        "Use polling to wait for draft model work (default: same as --poll])",
-        [](gpt_params & params, int value) {
-            params.draft_cpuparams.poll = value;
-        }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
-    add_opt(llama_arg(
-        {"-Cbd", "--cpu-mask-batch-draft"}, "M",
-        "Draft model CPU affinity mask. Complements cpu-range-draft (default: same as --cpu-mask)",
-        [](gpt_params & params, const std::string & mask) {
-            params.draft_cpuparams_batch.mask_valid = true;
-            if (!parse_cpu_mask(mask, params.draft_cpuparams_batch.cpumask)) {
-                throw std::invalid_argument("invalid cpumask");
-            }
-        }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
-    add_opt(llama_arg(
-        {"-Crbd", "--cpu-range-batch-draft"}, "lo-hi",
-        "Ranges of CPUs for affinity. Complements --cpu-mask-draft-batch)",
-        [](gpt_params & params, const std::string & range) {
-            params.draft_cpuparams_batch.mask_valid = true;
-            if (!parse_cpu_range(range, params.draft_cpuparams_batch.cpumask)) {
-                throw std::invalid_argument("invalid cpumask");
-            }
-        }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
-    add_opt(llama_arg(
-        {"--cpu-strict-batch-draft"}, "<0|1>",
-        "Use strict CPU placement for draft model (default: --cpu-strict-draft)",
-        [](gpt_params & params, int value) {
-            params.draft_cpuparams_batch.strict_cpu = value;
-        }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
-    add_opt(llama_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),
-        [](gpt_params & params, int prio) {
-            if (prio < 0 || prio > 3) {
-                throw std::invalid_argument("invalid value");
-            }
-            params.draft_cpuparams_batch.priority = (enum ggml_sched_priority) prio;
-        }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
-    add_opt(llama_arg(
-        {"--poll-batch-draft"}, "<0|1>",
-        "Use polling to wait for draft model work (default: --poll-draft)",
-        [](gpt_params & params, int value) {
-            params.draft_cpuparams_batch.poll = value;
-        }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
-    add_opt(llama_arg(
-        {"--draft"}, "N",
-        format("number of tokens to draft for speculative decoding (default: %d)", params.n_draft),
-        [](gpt_params & params, int value) {
-            params.n_draft = value;
-        }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
-    add_opt(llama_arg(
-        {"-ps", "--p-split"}, "N",
-        format("speculative decoding split probability (default: %.1f)", (double)params.p_split),
-        [](gpt_params & params, const std::string & value) {
-            params.p_split = std::stof(value);
-        }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
-    add_opt(llama_arg(
-        {"-lcs", "--lookup-cache-static"}, "FNAME",
-        "path to static lookup cache to use for lookup decoding (not updated by generation)",
-        [](gpt_params & params, const std::string & value) {
-            params.lookup_cache_static = value;
-        }
-    ));
-    add_opt(llama_arg(
-        {"-lcd", "--lookup-cache-dynamic"}, "FNAME",
-        "path to dynamic lookup cache to use for lookup decoding (updated by generation)",
-        [](gpt_params & params, const std::string & value) {
-            params.lookup_cache_dynamic = value;
-        }
-    ));
-    add_opt(llama_arg(
-        {"-c", "--ctx-size"}, "N",
-        format("size of the prompt context (default: %d, 0 = loaded from model)", params.n_ctx),
-        [](gpt_params & params, int value) {
-            params.n_ctx = value;
-        }
-    ).set_env("LLAMA_ARG_CTX_SIZE"));
-    add_opt(llama_arg(
-        {"-n", "--predict", "--n-predict"}, "N",
-        format("number of tokens to predict (default: %d, -1 = infinity, -2 = until context filled)", params.n_predict),
-        [](gpt_params & params, int value) {
-            params.n_predict = value;
-        }
-    ).set_env("LLAMA_ARG_N_PREDICT"));
-    add_opt(llama_arg(
-        {"-b", "--batch-size"}, "N",
-        format("logical maximum batch size (default: %d)", params.n_batch),
-        [](gpt_params & params, int value) {
-            params.n_batch = value;
-        }
-    ).set_env("LLAMA_ARG_BATCH"));
-    add_opt(llama_arg(
-        {"-ub", "--ubatch-size"}, "N",
-        format("physical maximum batch size (default: %d)", params.n_ubatch),
-        [](gpt_params & params, int value) {
-            params.n_ubatch = value;
-        }
-    ).set_env("LLAMA_ARG_UBATCH"));
-    add_opt(llama_arg(
-        {"--keep"}, "N",
-        format("number of tokens to keep from the initial prompt (default: %d, -1 = all)", params.n_keep),
-        [](gpt_params & params, int value) {
-            params.n_keep = value;
-        }
-    ));
-    add_opt(llama_arg(
-        {"--chunks"}, "N",
-        format("max number of chunks to process (default: %d, -1 = all)", params.n_chunks),
-        [](gpt_params & params, int value) {
-            params.n_chunks = value;
-        }
-    ));
-    add_opt(llama_arg(
-        {"-fa", "--flash-attn"},
-        format("enable Flash Attention (default: %s)", params.flash_attn ? "enabled" : "disabled"),
-        [](gpt_params & params) {
-            params.flash_attn = true;
-        }
-    ).set_env("LLAMA_ARG_FLASH_ATTN"));
-    add_opt(llama_arg(
-        {"-p", "--prompt"}, "PROMPT",
-        ex == LLAMA_EXAMPLE_MAIN
-            ? "prompt to start generation with\nif -cnv is set, this will be used as system prompt"
-            : "prompt to start generation with",
-        [](gpt_params & params, const std::string & value) {
-            params.prompt = value;
-        }
-    ));
-    add_opt(llama_arg(
-        {"-f", "--file"}, "FNAME",
-        "a file containing the prompt (default: none)",
-        [](gpt_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()));
-            }
-            // store the external file name in params
-            params.prompt_file = value;
-            std::copy(std::istreambuf_iterator<char>(file), std::istreambuf_iterator<char>(), back_inserter(params.prompt));
-            if (!params.prompt.empty() && params.prompt.back() == '\n') {
-                params.prompt.pop_back();
-            }
-        }
-    ));
-    add_opt(llama_arg(
-        {"--in-file"}, "FNAME",
-        "an input file (repeat to specify multiple files)",
-        [](gpt_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()));
-            }
-            params.in_files.push_back(value);
-        }
-    ));
-    add_opt(llama_arg(
-        {"-bf", "--binary-file"}, "FNAME",
-        "binary file containing the prompt (default: none)",
-        [](gpt_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()));
-            }
-            // store the external file name in params
-            params.prompt_file = value;
-            std::ostringstream ss;
-            ss << file.rdbuf();
-            params.prompt = ss.str();
-            fprintf(stderr, "Read %zu bytes from binary file %s\n", params.prompt.size(), value.c_str());
-        }
-    ));
-    add_opt(llama_arg(
-        {"-e", "--escape"},
-        format("process escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\) (default: %s)", params.escape ? "true" : "false"),
-        [](gpt_params & params) {
-            params.escape = true;
-        }
-    ));
-    add_opt(llama_arg(
-        {"--no-escape"},
-        "do not process escape sequences",
-        [](gpt_params & params) {
-            params.escape = false;
-        }
-    ));
-    add_opt(llama_arg(
-        {"-ptc", "--print-token-count"}, "N",
-        format("print token count every N tokens (default: %d)", params.n_print),
-        [](gpt_params & params, int value) {
-            params.n_print = value;
-        }
-    ).set_examples({LLAMA_EXAMPLE_MAIN}));
-    add_opt(llama_arg(
-        {"--prompt-cache"}, "FNAME",
-        "file to cache prompt state for faster startup (default: none)",
-        [](gpt_params & params, const std::string & value) {
-            params.path_prompt_cache = value;
-        }
-    ).set_examples({LLAMA_EXAMPLE_MAIN}));
-    add_opt(llama_arg(
-        {"--prompt-cache-all"},
-        "if specified, saves user input and generations to cache as well\n",
-        [](gpt_params & params) {
-            params.prompt_cache_all = true;
-        }
-    ).set_examples({LLAMA_EXAMPLE_MAIN}));
-    add_opt(llama_arg(
-        {"--prompt-cache-ro"},
-        "if specified, uses the prompt cache but does not update it",
-        [](gpt_params & params) {
-            params.prompt_cache_ro = true;
-        }
-    ).set_examples({LLAMA_EXAMPLE_MAIN}));
-    add_opt(llama_arg(
-        {"-r", "--reverse-prompt"}, "PROMPT",
-        "halt generation at PROMPT, return control in interactive mode\n",
-        [](gpt_params & params, const std::string & value) {
-            params.antiprompt.emplace_back(value);
-        }
-    ).set_examples({LLAMA_EXAMPLE_MAIN}));
-    add_opt(llama_arg(
-        {"-sp", "--special"},
-        format("special tokens output enabled (default: %s)", params.special ? "true" : "false"),
-        [](gpt_params & params) {
-            params.special = true;
-        }
-    ).set_examples({LLAMA_EXAMPLE_MAIN}));
-    add_opt(llama_arg(
-        {"-cnv", "--conversation"},
-        format(
-            "run in conversation mode:\n"
-            "- does not print special tokens and suffix/prefix\n"
-            "- interactive mode is also enabled\n"
-            "(default: %s)",
-            params.conversation ? "true" : "false"
-        ),
-        [](gpt_params & params) {
-            params.conversation = true;
-        }
-    ).set_examples({LLAMA_EXAMPLE_MAIN}));
-    add_opt(llama_arg(
-        {"-i", "--interactive"},
-        format("run in interactive mode (default: %s)", params.interactive ? "true" : "false"),
-        [](gpt_params & params) {
-            params.interactive = true;
-        }
-    ).set_examples({LLAMA_EXAMPLE_MAIN}));
-    add_opt(llama_arg(
-        {"-if", "--interactive-first"},
-        format("run in interactive mode and wait for input right away (default: %s)", params.interactive_first ? "true" : "false"),
-        [](gpt_params & params) {
-            params.interactive_first = true;
-        }
-    ).set_examples({LLAMA_EXAMPLE_MAIN}));
-    add_opt(llama_arg(
-        {"-mli", "--multiline-input"},
-        "allows you to write or paste multiple lines without ending each in '\\'",
-        [](gpt_params & params) {
-            params.multiline_input = true;
-        }
-    ).set_examples({LLAMA_EXAMPLE_MAIN}));
-    add_opt(llama_arg(
-        {"--in-prefix-bos"},
-        "prefix BOS to user inputs, preceding the `--in-prefix` string",
-        [](gpt_params & params) {
-            params.input_prefix_bos = true;
-            params.enable_chat_template = false;
-        }
-    ).set_examples({LLAMA_EXAMPLE_MAIN}));
-    add_opt(llama_arg(
-        {"--in-prefix"}, "STRING",
-        "string to prefix user inputs with (default: empty)",
-        [](gpt_params & params, const std::string & value) {
-            params.input_prefix = value;
-            params.enable_chat_template = false;
-        }
-    ).set_examples({LLAMA_EXAMPLE_MAIN}));
-    add_opt(llama_arg(
-        {"--in-suffix"}, "STRING",
-        "string to suffix after user inputs with (default: empty)",
-        [](gpt_params & params, const std::string & value) {
-            params.input_suffix = value;
-            params.enable_chat_template = false;
-        }
-    ).set_examples({LLAMA_EXAMPLE_MAIN}));
-    add_opt(llama_arg(
-        {"--no-warmup"},
-        "skip warming up the model with an empty run",
-        [](gpt_params & params) {
-            params.warmup = false;
-        }
-    ).set_examples({LLAMA_EXAMPLE_MAIN}));
-    add_opt(llama_arg(
-        {"--spm-infill"},
-        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"
-        ),
-        [](gpt_params & params) {
-            params.spm_infill = true;
-        }
-    ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_INFILL}));
-    add_opt(llama_arg(
-        {"--samplers"}, "SAMPLERS",
-        format("samplers that will be used for generation in the order, separated by \';\'\n(default: %s)", sampler_type_names.c_str()),
-        [](gpt_params & params, const std::string & value) {
-            const auto sampler_names = string_split(value, ';');
-            params.sparams.samplers = gpt_sampler_types_from_names(sampler_names, true);
-        }
-    ));
-    add_opt(llama_arg(
-        {"--sampling-seq"}, "SEQUENCE",
-        format("simplified sequence for samplers that will be used (default: %s)", sampler_type_chars.c_str()),
-        [](gpt_params & params, const std::string & value) {
-            params.sparams.samplers = gpt_sampler_types_from_chars(value);
-        }
-    ));
-    add_opt(llama_arg(
-        {"--ignore-eos"},
-        "ignore end of stream token and continue generating (implies --logit-bias EOS-inf)",
-        [](gpt_params & params) {
-            params.sparams.ignore_eos = true;
-        }
-    ));
-    add_opt(llama_arg(
-        {"--penalize-nl"},
-        format("penalize newline tokens (default: %s)", params.sparams.penalize_nl ? "true" : "false"),
-        [](gpt_params & params) {
-            params.sparams.penalize_nl = true;
-        }
-    ));
-    add_opt(llama_arg(
-        {"--temp"}, "N",
-        format("temperature (default: %.1f)", (double)params.sparams.temp),
-        [](gpt_params & params, const std::string & value) {
-            params.sparams.temp = std::stof(value);
-            params.sparams.temp = std::max(params.sparams.temp, 0.0f);
-        }
-    ));
-    add_opt(llama_arg(
-        {"--top-k"}, "N",
-        format("top-k sampling (default: %d, 0 = disabled)", params.sparams.top_k),
-        [](gpt_params & params, int value) {
-            params.sparams.top_k = value;
-        }
-    ));
-    add_opt(llama_arg(
-        {"--top-p"}, "N",
-        format("top-p sampling (default: %.1f, 1.0 = disabled)", (double)params.sparams.top_p),
-        [](gpt_params & params, const std::string & value) {
-            params.sparams.top_p = std::stof(value);
-        }
-    ));
-    add_opt(llama_arg(
-        {"--min-p"}, "N",
-        format("min-p sampling (default: %.1f, 0.0 = disabled)", (double)params.sparams.min_p),
-        [](gpt_params & params, const std::string & value) {
-            params.sparams.min_p = std::stof(value);
-        }
-    ));
-    add_opt(llama_arg(
-        {"--tfs"}, "N",
-        format("tail free sampling, parameter z (default: %.1f, 1.0 = disabled)", (double)params.sparams.tfs_z),
-        [](gpt_params & params, const std::string & value) {
-            params.sparams.tfs_z = std::stof(value);
-        }
-    ));
-    add_opt(llama_arg(
-        {"--typical"}, "N",
-        format("locally typical sampling, parameter p (default: %.1f, 1.0 = disabled)", (double)params.sparams.typ_p),
-        [](gpt_params & params, const std::string & value) {
-            params.sparams.typ_p = std::stof(value);
-        }
-    ));
-    add_opt(llama_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),
-        [](gpt_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);
-        }
-    ));
-    add_opt(llama_arg(
-        {"--repeat-penalty"}, "N",
-        format("penalize repeat sequence of tokens (default: %.1f, 1.0 = disabled)", (double)params.sparams.penalty_repeat),
-        [](gpt_params & params, const std::string & value) {
-            params.sparams.penalty_repeat = std::stof(value);
-        }
-    ));
-    add_opt(llama_arg(
-        {"--presence-penalty"}, "N",
-        format("repeat alpha presence penalty (default: %.1f, 0.0 = disabled)", (double)params.sparams.penalty_present),
-        [](gpt_params & params, const std::string & value) {
-            params.sparams.penalty_present = std::stof(value);
-        }
-    ));
-    add_opt(llama_arg(
-        {"--frequency-penalty"}, "N",
-        format("repeat alpha frequency penalty (default: %.1f, 0.0 = disabled)", (double)params.sparams.penalty_freq),
-        [](gpt_params & params, const std::string & value) {
-            params.sparams.penalty_freq = std::stof(value);
-        }
-    ));
-    add_opt(llama_arg(
-        {"--dynatemp-range"}, "N",
-        format("dynamic temperature range (default: %.1f, 0.0 = disabled)", (double)params.sparams.dynatemp_range),
-        [](gpt_params & params, const std::string & value) {
-            params.sparams.dynatemp_range = std::stof(value);
-        }
-    ));
-    add_opt(llama_arg(
-        {"--dynatemp-exp"}, "N",
-        format("dynamic temperature exponent (default: %.1f)", (double)params.sparams.dynatemp_exponent),
-        [](gpt_params & params, const std::string & value) {
-            params.sparams.dynatemp_exponent = std::stof(value);
-        }
-    ));
-    add_opt(llama_arg(
-        {"--mirostat"}, "N",
-        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),
-        [](gpt_params & params, int value) {
-            params.sparams.mirostat = value;
-        }
-    ));
-    add_opt(llama_arg(
-        {"--mirostat-lr"}, "N",
-        format("Mirostat learning rate, parameter eta (default: %.1f)", (double)params.sparams.mirostat_eta),
-        [](gpt_params & params, const std::string & value) {
-            params.sparams.mirostat_eta = std::stof(value);
-        }
-    ));
-    add_opt(llama_arg(
-        {"--mirostat-ent"}, "N",
-        format("Mirostat target entropy, parameter tau (default: %.1f)", (double)params.sparams.mirostat_tau),
-        [](gpt_params & params, const std::string & value) {
-            params.sparams.mirostat_tau = std::stof(value);
-        }
-    ));
-    add_opt(llama_arg(
-        {"-l", "--logit-bias"}, "TOKEN_ID(+/-)BIAS",
-        "modifies the likelihood of token appearing in the completion,\n"
-        "i.e. `--logit-bias 15043+1` to increase likelihood of token ' Hello',\n"
-        "or `--logit-bias 15043-1` to decrease likelihood of token ' Hello'",
-        [](gpt_params & params, const std::string & value) {
-            std::stringstream ss(value);
-            llama_token key;
-            char sign;
-            std::string value_str;
-            try {
-                if (ss >> key && ss >> sign && std::getline(ss, value_str) && (sign == '+' || sign == '-')) {
-                    const float bias = std::stof(value_str) * ((sign == '-') ? -1.0f : 1.0f);
-                    params.sparams.logit_bias.push_back({key, bias});
-                } else {
-                    throw std::invalid_argument("invalid input format");
-                }
-            } catch (const std::exception&) {
-                throw std::invalid_argument("invalid input format");
-            }
-        }
-    ));
-    add_opt(llama_arg(
-        {"--grammar"}, "GRAMMAR",
-        format("BNF-like grammar to constrain generations (see samples in grammars/ dir) (default: '%s')", params.sparams.grammar.c_str()),
-        [](gpt_params & params, const std::string & value) {
-            params.sparams.grammar = value;
-        }
-    ));
-    add_opt(llama_arg(
-        {"--grammar-file"}, "FNAME",
-        "file to read grammar from",
-        [](gpt_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::copy(
-                std::istreambuf_iterator<char>(file),
-                std::istreambuf_iterator<char>(),
-                std::back_inserter(params.sparams.grammar)
-            );
-        }
-    ));
-    add_opt(llama_arg(
-        {"-j", "--json-schema"}, "SCHEMA",
-        "JSON schema to constrain generations (https://json-schema.org/), e.g. `{}` for any JSON object\nFor schemas w/ external $refs, use --grammar + example/json_schema_to_grammar.py instead",
-        [](gpt_params & params, const std::string & value) {
-            params.sparams.grammar = json_schema_to_grammar(json::parse(value));
-        }
-    ));
-    add_opt(llama_arg(
-        {"--pooling"}, "{none,mean,cls,last}",
-        "pooling type for embeddings, use model default if unspecified",
-        [](gpt_params & params, const std::string & value) {
-            /**/ if (value == "none") { params.pooling_type = LLAMA_POOLING_TYPE_NONE; }
-            else if (value == "mean") { params.pooling_type = LLAMA_POOLING_TYPE_MEAN; }
-            else if (value == "cls") { params.pooling_type = LLAMA_POOLING_TYPE_CLS; }
-            else if (value == "last") { params.pooling_type = LLAMA_POOLING_TYPE_LAST; }
-            else { throw std::invalid_argument("invalid value"); }
-        }
-    ).set_examples({LLAMA_EXAMPLE_EMBEDDING}));
-    add_opt(llama_arg(
-        {"--attention"}, "{causal,non,causal}",
-        "attention type for embeddings, use model default if unspecified",
-        [](gpt_params & params, const std::string & value) {
-            /**/ if (value == "causal") { params.attention_type = LLAMA_ATTENTION_TYPE_CAUSAL; }
-            else if (value == "non-causal") { params.attention_type = LLAMA_ATTENTION_TYPE_NON_CAUSAL; }
-            else { throw std::invalid_argument("invalid value"); }
-        }
-    ).set_examples({LLAMA_EXAMPLE_EMBEDDING}));
-    add_opt(llama_arg(
-        {"--rope-scaling"}, "{none,linear,yarn}",
-        "RoPE frequency scaling method, defaults to linear unless specified by the model",
-        [](gpt_params & params, const std::string & value) {
-            /**/ if (value == "none") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_NONE; }
-            else if (value == "linear") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_LINEAR; }
-            else if (value == "yarn") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_YARN; }
-            else { throw std::invalid_argument("invalid value"); }
-        }
-    ));
-    add_opt(llama_arg(
-        {"--rope-scale"}, "N",
-        "RoPE context scaling factor, expands context by a factor of N",
-        [](gpt_params & params, const std::string & value) {
-            params.rope_freq_scale = 1.0f / std::stof(value);
-        }
-    ));
-    add_opt(llama_arg(
-        {"--rope-freq-base"}, "N",
-        "RoPE base frequency, used by NTK-aware scaling (default: loaded from model)",
-        [](gpt_params & params, const std::string & value) {
-            params.rope_freq_base = std::stof(value);
-        }
-    ));
-    add_opt(llama_arg(
-        {"--rope-freq-scale"}, "N",
-        "RoPE frequency scaling factor, expands context by a factor of 1/N",
-        [](gpt_params & params, const std::string & value) {
-            params.rope_freq_scale = std::stof(value);
-        }
-    ));
-    add_opt(llama_arg(
-        {"--yarn-orig-ctx"}, "N",
-        format("YaRN: original context size of model (default: %d = model training context size)", params.yarn_orig_ctx),
-        [](gpt_params & params, int value) {
-            params.yarn_orig_ctx = value;
-        }
-    ));
-    add_opt(llama_arg(
-        {"--yarn-ext-factor"}, "N",
-        format("YaRN: extrapolation mix factor (default: %.1f, 0.0 = full interpolation)", (double)params.yarn_ext_factor),
-        [](gpt_params & params, const std::string & value) {
-            params.yarn_ext_factor = std::stof(value);
-        }
-    ));
-    add_opt(llama_arg(
-        {"--yarn-attn-factor"}, "N",
-        format("YaRN: scale sqrt(t) or attention magnitude (default: %.1f)", (double)params.yarn_attn_factor),
-        [](gpt_params & params, const std::string & value) {
-            params.yarn_attn_factor = std::stof(value);
-        }
-    ));
-    add_opt(llama_arg(
-        {"--yarn-beta-slow"}, "N",
-        format("YaRN: high correction dim or alpha (default: %.1f)", (double)params.yarn_beta_slow),
-        [](gpt_params & params, const std::string & value) {
-            params.yarn_beta_slow = std::stof(value);
-        }
-    ));
-    add_opt(llama_arg(
-        {"--yarn-beta-fast"}, "N",
-        format("YaRN: low correction dim or beta (default: %.1f)", (double)params.yarn_beta_fast),
-        [](gpt_params & params, const std::string & value) {
-            params.yarn_beta_fast = std::stof(value);
-        }
-    ));
-    add_opt(llama_arg(
-        {"-gan", "--grp-attn-n"}, "N",
-        format("group-attention factor (default: %d)", params.grp_attn_n),
-        [](gpt_params & params, int value) {
-            params.grp_attn_n = value;
-        }
-    ));
-    add_opt(llama_arg(
-        {"-gaw", "--grp-attn-w"}, "N",
-        format("group-attention width (default: %.1f)", (double)params.grp_attn_w),
-        [](gpt_params & params, int value) {
-            params.grp_attn_w = value;
-        }
-    ));
-    add_opt(llama_arg(
-        {"-dkvc", "--dump-kv-cache"},
-        "verbose print of the KV cache",
-        [](gpt_params & params) {
-            params.dump_kv_cache = true;
-        }
-    ));
-    add_opt(llama_arg(
-        {"-nkvo", "--no-kv-offload"},
-        "disable KV offload",
-        [](gpt_params & params) {
-            params.no_kv_offload = true;
-        }
-    ));
-    add_opt(llama_arg(
-        {"-ctk", "--cache-type-k"}, "TYPE",
-        format("KV cache data type for K (default: %s)", params.cache_type_k.c_str()),
-        [](gpt_params & params, const std::string & value) {
-            // TODO: get the type right here
-            params.cache_type_k = value;
-        }
-    ));
-    add_opt(llama_arg(
-        {"-ctv", "--cache-type-v"}, "TYPE",
-        format("KV cache data type for V (default: %s)", params.cache_type_v.c_str()),
-        [](gpt_params & params, const std::string & value) {
-            // TODO: get the type right here
-            params.cache_type_v = value;
-        }
-    ));
-    add_opt(llama_arg(
-        {"--perplexity", "--all-logits"},
-        format("return logits for all tokens in the batch (default: %s)", params.logits_all ? "true" : "false"),
-        [](gpt_params & params) {
-            params.logits_all = true;
-        }
-    ).set_examples({LLAMA_EXAMPLE_PERPLEXITY}));
-    add_opt(llama_arg(
-        {"--hellaswag"},
-        "compute HellaSwag score over random tasks from datafile supplied with -f",
-        [](gpt_params & params) {
-            params.hellaswag = true;
-        }
-    ).set_examples({LLAMA_EXAMPLE_PERPLEXITY}));
-    add_opt(llama_arg(
-        {"--hellaswag-tasks"}, "N",
-        format("number of tasks to use when computing the HellaSwag score (default: %zu)", params.hellaswag_tasks),
-        [](gpt_params & params, int value) {
-            params.hellaswag_tasks = value;
-        }
-    ).set_examples({LLAMA_EXAMPLE_PERPLEXITY}));
-    add_opt(llama_arg(
-        {"--winogrande"},
-        "compute Winogrande score over random tasks from datafile supplied with -f",
-        [](gpt_params & params) {
-            params.winogrande = true;
-        }
-    ).set_examples({LLAMA_EXAMPLE_PERPLEXITY}));
-    add_opt(llama_arg(
-        {"--winogrande-tasks"}, "N",
-        format("number of tasks to use when computing the Winogrande score (default: %zu)", params.winogrande_tasks),
-        [](gpt_params & params, int value) {
-            params.winogrande_tasks = value;
-        }
-    ).set_examples({LLAMA_EXAMPLE_PERPLEXITY}));
-    add_opt(llama_arg(
-        {"--multiple-choice"},
-        "compute multiple choice score over random tasks from datafile supplied with -f",
-        [](gpt_params & params) {
-            params.multiple_choice = true;
-        }
-    ).set_examples({LLAMA_EXAMPLE_PERPLEXITY}));
-    add_opt(llama_arg(
-        {"--multiple-choice-tasks"}, "N",
-        format("number of tasks to use when computing the multiple choice score (default: %zu)", params.multiple_choice_tasks),
-        [](gpt_params & params, int value) {
-            params.multiple_choice_tasks = value;
-        }
-    ).set_examples({LLAMA_EXAMPLE_PERPLEXITY}));
-    add_opt(llama_arg(
-        {"--kl-divergence"},
-        "computes KL-divergence to logits provided via --kl-divergence-base",
-        [](gpt_params & params) {
-            params.kl_divergence = true;
-        }
-    ).set_examples({LLAMA_EXAMPLE_PERPLEXITY}));
-    add_opt(llama_arg(
-        {"--save-all-logits", "--kl-divergence-base"}, "FNAME",
-        "set logits file",
-        [](gpt_params & params, const std::string & value) {
-            params.logits_file = value;
-        }
-    ).set_examples({LLAMA_EXAMPLE_PERPLEXITY}));
-    add_opt(llama_arg(
-        {"--ppl-stride"}, "N",
-        format("stride for perplexity calculation (default: %d)", params.ppl_stride),
-        [](gpt_params & params, int value) {
-            params.ppl_stride = value;
-        }
-    ).set_examples({LLAMA_EXAMPLE_PERPLEXITY}));
-    add_opt(llama_arg(
-        {"--ppl-output-type"}, "<0|1>",
-        format("output type for perplexity calculation (default: %d)", params.ppl_output_type),
-        [](gpt_params & params, int value) {
-            params.ppl_output_type = value;
-        }
-    ).set_examples({LLAMA_EXAMPLE_PERPLEXITY}));
-    add_opt(llama_arg(
-        {"-dt", "--defrag-thold"}, "N",
-        format("KV cache defragmentation threshold (default: %.1f, < 0 - disabled)", (double)params.defrag_thold),
-        [](gpt_params & params, const std::string & value) {
-            params.defrag_thold = std::stof(value);
-        }
-    ).set_env("LLAMA_ARG_DEFRAG_THOLD"));
-    add_opt(llama_arg(
-        {"-np", "--parallel"}, "N",
-        format("number of parallel sequences to decode (default: %d)", params.n_parallel),
-        [](gpt_params & params, int value) {
-            params.n_parallel = value;
-        }
-    ));
-    add_opt(llama_arg(
-        {"-ns", "--sequences"}, "N",
-        format("number of sequences to decode (default: %d)", params.n_sequences),
-        [](gpt_params & params, int value) {
-            params.n_sequences = value;
-        }
-    ));
-    add_opt(llama_arg(
-        {"-cb", "--cont-batching"},
-        format("enable continuous batching (a.k.a dynamic batching) (default: %s)", params.cont_batching ? "enabled" : "disabled"),
-        [](gpt_params & params) {
-            params.cont_batching = true;
-        }
-    ).set_env("LLAMA_ARG_CONT_BATCHING"));
-    add_opt(llama_arg(
-        {"-nocb", "--no-cont-batching"},
-        "disable continuous batching",
-        [](gpt_params & params) {
-            params.cont_batching = false;
-        }
-    ).set_env("LLAMA_ARG_NO_CONT_BATCHING"));
-    add_opt(llama_arg(
-        {"--mmproj"}, "FILE",
-        "path to a multimodal projector file for LLaVA. see examples/llava/README.md",
-        [](gpt_params & params, const std::string & value) {
-            params.mmproj = value;
-        }
-    ).set_examples({LLAMA_EXAMPLE_LLAVA}));
-    add_opt(llama_arg(
-        {"--image"}, "FILE",
-        "path to an image file. use with multimodal models. Specify multiple times for batching",
-        [](gpt_params & params, const std::string & value) {
-            params.image.emplace_back(value);
-        }
-    ).set_examples({LLAMA_EXAMPLE_LLAVA}));
-#ifdef GGML_USE_RPC
-    add_opt(llama_arg(
-        {"--rpc"}, "SERVERS",
-        "comma separated list of RPC servers",
-        [](gpt_params & params, const std::string & value) {
-            params.rpc_servers = value;
-        }
-    ));
-#endif
-    add_opt(llama_arg(
-        {"--mlock"},
-        "force system to keep model in RAM rather than swapping or compressing",
-        [](gpt_params & params) {
-            params.use_mlock = true;
-        }
-    ));
-    add_opt(llama_arg(
-        {"--no-mmap"},
-        "do not memory-map model (slower load but may reduce pageouts if not using mlock)",
-        [](gpt_params & params) {
-            params.use_mmap = false;
-        }
-    ));
-    add_opt(llama_arg(
-        {"--numa"}, "TYPE",
-        "attempt optimizations that help on some NUMA systems\n"
-        "- distribute: spread execution evenly over all nodes\n"
-        "- isolate: only spawn threads on CPUs on the node that execution started on\n"
-        "- numactl: use the CPU map provided by numactl\n"
-        "if run without this previously, it is recommended to drop the system page cache before using this\n"
-        "see https://github.com/ggerganov/llama.cpp/issues/1437",
-        [](gpt_params & params, const std::string & value) {
-            /**/ if (value == "distribute" || value == "") { params.numa = GGML_NUMA_STRATEGY_DISTRIBUTE; }
-            else if (value == "isolate") { params.numa = GGML_NUMA_STRATEGY_ISOLATE; }
-            else if (value == "numactl") { params.numa = GGML_NUMA_STRATEGY_NUMACTL; }
-            else { throw std::invalid_argument("invalid value"); }
-        }
-    ));
-    add_opt(llama_arg(
-        {"-ngl", "--gpu-layers", "--n-gpu-layers"}, "N",
-        "number of layers to store in VRAM",
-        [](gpt_params & params, int value) {
-            params.n_gpu_layers = value;
-            if (!llama_supports_gpu_offload()) {
-                fprintf(stderr, "warning: not compiled with GPU offload support, --gpu-layers option will be ignored\n");
-                fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n");
-            }
-        }
-    ).set_env("LLAMA_ARG_N_GPU_LAYERS"));
-    add_opt(llama_arg(
-        {"-ngld", "--gpu-layers-draft", "--n-gpu-layers-draft"}, "N",
-        "number of layers to store in VRAM for the draft model",
-        [](gpt_params & params, int value) {
-            params.n_gpu_layers_draft = value;
-            if (!llama_supports_gpu_offload()) {
-                fprintf(stderr, "warning: not compiled with GPU offload support, --gpu-layers-draft option will be ignored\n");
-                fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n");
-            }
-        }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
-    add_opt(llama_arg(
-        {"-sm", "--split-mode"}, "{none,layer,row}",
-        "how to split the model across multiple GPUs, one of:\n"
-        "- none: use one GPU only\n"
-        "- layer (default): split layers and KV across GPUs\n"
-        "- row: split rows across GPUs",
-        [](gpt_params & params, const std::string & value) {
-            std::string arg_next = value;
-            if (arg_next == "none") {
-                params.split_mode = LLAMA_SPLIT_MODE_NONE;
-            } else if (arg_next == "layer") {
-                params.split_mode = LLAMA_SPLIT_MODE_LAYER;
-            }
-            else if (arg_next == "row") {
-#ifdef GGML_USE_SYCL
-                fprintf(stderr, "warning: The split mode value:[row] is not supported by llama.cpp with SYCL. It's developing.\nExit!\n");
-                exit(1);
-#endif // GGML_USE_SYCL
-                params.split_mode = LLAMA_SPLIT_MODE_ROW;
-            }
-            else {
-                throw std::invalid_argument("invalid value");
-            }
-#ifndef GGML_USE_CUDA_SYCL_VULKAN
-            fprintf(stderr, "warning: llama.cpp was compiled without CUDA/SYCL/Vulkan. Setting the split mode has no effect.\n");
-#endif // GGML_USE_CUDA_SYCL_VULKAN
-        }
-    ));
-    add_opt(llama_arg(
-        {"-ts", "--tensor-split"}, "N0,N1,N2,...",
-        "fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1",
-        [](gpt_params & params, const std::string & value) {
-            std::string arg_next = value;
-
-            // split string by , and /
-            const std::regex regex{ R"([,/]+)" };
-            std::sregex_token_iterator it{ arg_next.begin(), arg_next.end(), regex, -1 };
-            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())
-                );
-            }
-            for (size_t i = 0; i < llama_max_devices(); ++i) {
-                if (i < split_arg.size()) {
-                        params.tensor_split[i] = std::stof(split_arg[i]);
-                } else {
-                        params.tensor_split[i] = 0.0f;
-                }
-            }
-#ifndef GGML_USE_CUDA_SYCL_VULKAN
-            fprintf(stderr, "warning: llama.cpp was compiled without CUDA/SYCL/Vulkan. Setting a tensor split has no effect.\n");
-#endif // GGML_USE_CUDA_SYCL_VULKAN
-        }
-    ));
-    add_opt(llama_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),
-        [](gpt_params & params, int value) {
-            params.main_gpu = value;
-#ifndef GGML_USE_CUDA_SYCL_VULKAN
-            fprintf(stderr, "warning: llama.cpp was compiled without CUDA/SYCL/Vulkan. Setting the main GPU has no effect.\n");
-#endif // GGML_USE_CUDA_SYCL_VULKAN
-        }
-    ));
-    add_opt(llama_arg(
-        {"--check-tensors"},
-        format("check model tensor data for invalid values (default: %s)", params.check_tensors ? "true" : "false"),
-        [](gpt_params & params) {
-            params.check_tensors = true;
-        }
-    ));
-    add_opt(llama_arg(
-        {"--override-kv"}, "KEY=TYPE:VALUE",
-        "advanced option to override model metadata by key. may be specified multiple times.\n"
-        "types: int, float, bool, str. example: --override-kv tokenizer.ggml.add_bos_token=bool:false",
-        [](gpt_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()));
-            }
-        }
-    ));
-    add_opt(llama_arg(
-        {"--lora"}, "FNAME",
-        "path to LoRA adapter (can be repeated to use multiple adapters)",
-        [](gpt_params & params, const std::string & value) {
-            params.lora_adapters.push_back({ std::string(value), 1.0 });
-        }
-    ).set_examples({LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_EXPORT_LORA}));
-    add_opt(llama_arg(
-        {"--lora-scaled"}, "FNAME", "SCALE",
-        "path to LoRA adapter with user defined scaling (can be repeated to use multiple adapters)",
-        [](gpt_params & params, const std::string & fname, const std::string & scale) {
-            params.lora_adapters.push_back({ fname, std::stof(scale) });
-        }
-    ).set_examples({LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_EXPORT_LORA}));
-    add_opt(llama_arg(
-        {"--control-vector"}, "FNAME",
-        "add a control vector\nnote: this argument can be repeated to add multiple control vectors",
-        [](gpt_params & params, const std::string & value) {
-            params.control_vectors.push_back({ 1.0f, value, });
-        }
-    ));
-    add_opt(llama_arg(
-        {"--control-vector-scaled"}, "FNAME", "SCALE",
-        "add a control vector with user defined scaling SCALE\n"
-        "note: this argument can be repeated to add multiple scaled control vectors",
-        [](gpt_params & params, const std::string & fname, const std::string & scale) {
-            params.control_vectors.push_back({ std::stof(scale), fname });
-        }
-    ));
-    add_opt(llama_arg(
-        {"--control-vector-layer-range"}, "START", "END",
-        "layer range to apply the control vector(s) to, start and end inclusive",
-        [](gpt_params & params, const std::string & start, const std::string & end) {
-            params.control_vector_layer_start = std::stoi(start);
-            params.control_vector_layer_end = std::stoi(end);
-        }
-    ));
-    add_opt(llama_arg(
-        {"-a", "--alias"}, "STRING",
-        "set alias for model name (to be used by REST API)",
-        [](gpt_params & params, const std::string & value) {
-            params.model_alias = value;
-        }
-    ).set_examples({LLAMA_EXAMPLE_SERVER}));
-    add_opt(llama_arg(
-        {"-m", "--model"}, "FNAME",
-        ex == LLAMA_EXAMPLE_EXPORT_LORA
-            ? std::string("model path from which to load base model")
-            : format(
-                "model path (default: `models/$filename` with filename from `--hf-file` "
-                "or `--model-url` if set, otherwise %s)", DEFAULT_MODEL_PATH
-            ),
-        [](gpt_params & params, const std::string & value) {
-            params.model = value;
-        }
-    ).set_examples({LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_EXPORT_LORA}).set_env("LLAMA_ARG_MODEL"));
-    add_opt(llama_arg(
-        {"-md", "--model-draft"}, "FNAME",
-        "draft model for speculative decoding (default: unused)",
-        [](gpt_params & params, const std::string & value) {
-            params.model_draft = value;
-        }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
-    add_opt(llama_arg(
-        {"-mu", "--model-url"}, "MODEL_URL",
-        "model download url (default: unused)",
-        [](gpt_params & params, const std::string & value) {
-            params.model_url = value;
-        }
-    ).set_env("LLAMA_ARG_MODEL_URL"));
-    add_opt(llama_arg(
-        {"-hfr", "--hf-repo"}, "REPO",
-        "Hugging Face model repository (default: unused)",
-        [](gpt_params & params, const std::string & value) {
-            params.hf_repo = value;
-        }
-    ).set_env("LLAMA_ARG_HF_REPO"));
-    add_opt(llama_arg(
-        {"-hff", "--hf-file"}, "FILE",
-        "Hugging Face model file (default: unused)",
-        [](gpt_params & params, const std::string & value) {
-            params.hf_file = value;
-        }
-    ).set_env("LLAMA_ARG_HF_FILE"));
-    add_opt(llama_arg(
-        {"-hft", "--hf-token"}, "TOKEN",
-        "Hugging Face access token (default: value from HF_TOKEN environment variable)",
-        [](gpt_params & params, const std::string & value) {
-            params.hf_token = value;
-        }
-    ).set_env("HF_TOKEN"));
-    add_opt(llama_arg(
-        {"--context-file"}, "FNAME",
-        "file to load context from (repeat to specify multiple files)",
-        [](gpt_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()));
-            }
-            params.context_files.push_back(value);
-        }
-    ).set_examples({LLAMA_EXAMPLE_RETRIEVAL}));
-    add_opt(llama_arg(
-        {"--chunk-size"}, "N",
-        format("minimum length of embedded text chunks (default: %d)", params.chunk_size),
-        [](gpt_params & params, int value) {
-            params.chunk_size = value;
-        }
-    ).set_examples({LLAMA_EXAMPLE_RETRIEVAL}));
-    add_opt(llama_arg(
-        {"--chunk-separator"}, "STRING",
-        format("separator between chunks (default: '%s')", params.chunk_separator.c_str()),
-        [](gpt_params & params, const std::string & value) {
-            params.chunk_separator = value;
-        }
-    ).set_examples({LLAMA_EXAMPLE_RETRIEVAL}));
-    add_opt(llama_arg(
-        {"--junk"}, "N",
-        format("number of times to repeat the junk text (default: %d)", params.n_junk),
-        [](gpt_params & params, int value) {
-            params.n_junk = value;
-        }
-    ).set_examples({LLAMA_EXAMPLE_PASSKEY}));
-    add_opt(llama_arg(
-        {"--pos"}, "N",
-        format("position of the passkey in the junk text (default: %d)", params.i_pos),
-        [](gpt_params & params, int value) {
-            params.i_pos = value;
-        }
-    ).set_examples({LLAMA_EXAMPLE_PASSKEY}));
-    add_opt(llama_arg(
-        {"-o", "--output", "--output-file"}, "FNAME",
-        format("output file (default: '%s')",
-            ex == LLAMA_EXAMPLE_EXPORT_LORA
-                ? params.lora_outfile.c_str()
-                : ex == LLAMA_EXAMPLE_CVECTOR_GENERATOR
-                    ? params.cvector_outfile.c_str()
-                    : params.out_file.c_str()),
-        [](gpt_params & params, const std::string & value) {
-            params.out_file = value;
-            params.cvector_outfile = value;
-            params.lora_outfile = value;
-        }
-    ).set_examples({LLAMA_EXAMPLE_IMATRIX, LLAMA_EXAMPLE_CVECTOR_GENERATOR, LLAMA_EXAMPLE_EXPORT_LORA}));
-    add_opt(llama_arg(
-        {"-ofreq", "--output-frequency"}, "N",
-        format("output the imatrix every N iterations (default: %d)", params.n_out_freq),
-        [](gpt_params & params, int value) {
-            params.n_out_freq = value;
-        }
-    ).set_examples({LLAMA_EXAMPLE_IMATRIX}));
-    add_opt(llama_arg(
-        {"--save-frequency"}, "N",
-        format("save an imatrix copy every N iterations (default: %d)", params.n_save_freq),
-        [](gpt_params & params, int value) {
-            params.n_save_freq = value;
-        }
-    ).set_examples({LLAMA_EXAMPLE_IMATRIX}));
-    add_opt(llama_arg(
-        {"--process-output"},
-        format("collect data for the output tensor (default: %s)", params.process_output ? "true" : "false"),
-        [](gpt_params & params) {
-            params.process_output = true;
-        }
-    ).set_examples({LLAMA_EXAMPLE_IMATRIX}));
-    add_opt(llama_arg(
-        {"--no-ppl"},
-        format("do not compute perplexity (default: %s)", params.compute_ppl ? "true" : "false"),
-        [](gpt_params & params) {
-            params.compute_ppl = false;
-        }
-    ).set_examples({LLAMA_EXAMPLE_IMATRIX}));
-    add_opt(llama_arg(
-        {"--chunk", "--from-chunk"}, "N",
-        format("start processing the input from chunk N (default: %d)", params.i_chunk),
-        [](gpt_params & params, int value) {
-            params.i_chunk = value;
-        }
-    ).set_examples({LLAMA_EXAMPLE_IMATRIX}));
-    add_opt(llama_arg(
-        {"-pps"},
-        format("is the prompt shared across parallel sequences (default: %s)", params.is_pp_shared ? "true" : "false"),
-        [](gpt_params & params) {
-            params.is_pp_shared = true;
-        }
-    ).set_examples({LLAMA_EXAMPLE_BENCH}));
-    add_opt(llama_arg(
-        {"-npp"}, "n0,n1,...",
-        "number of prompt tokens",
-        [](gpt_params & params, const std::string & value) {
-            auto p = string_split<int>(value, ',');
-            params.n_pp.insert(params.n_pp.end(), p.begin(), p.end());
-        }
-    ).set_examples({LLAMA_EXAMPLE_BENCH}));
-    add_opt(llama_arg(
-        {"-ntg"}, "n0,n1,...",
-        "number of text generation tokens",
-        [](gpt_params & params, const std::string & value) {
-            auto p = string_split<int>(value, ',');
-            params.n_tg.insert(params.n_tg.end(), p.begin(), p.end());
-        }
-    ).set_examples({LLAMA_EXAMPLE_BENCH}));
-    add_opt(llama_arg(
-        {"-npl"}, "n0,n1,...",
-        "number of parallel prompts",
-        [](gpt_params & params, const std::string & value) {
-            auto p = string_split<int>(value, ',');
-            params.n_pl.insert(params.n_pl.end(), p.begin(), p.end());
-        }
-    ).set_examples({LLAMA_EXAMPLE_BENCH}));
-    add_opt(llama_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),
-        [](gpt_params & params, int value) {
-            params.embd_normalize = value;
-        }
-    ).set_examples({LLAMA_EXAMPLE_EMBEDDING}));
-    add_opt(llama_arg(
-        {"--embd-output-format"}, "FORMAT",
-        "empty = default, \"array\" = [[],[]...], \"json\" = openai style, \"json+\" = same \"json\" + cosine similarity matrix",
-        [](gpt_params & params, const std::string & value) {
-            params.embd_out = value;
-        }
-    ).set_examples({LLAMA_EXAMPLE_EMBEDDING}));
-    add_opt(llama_arg(
-        {"--embd-separator"}, "STRING",
-        "separator of embendings (default \\n) for example \"<#sep#>\"",
-        [](gpt_params & params, const std::string & value) {
-            params.embd_sep = value;
-        }
-    ).set_examples({LLAMA_EXAMPLE_EMBEDDING}));
-    add_opt(llama_arg(
-        {"--host"}, "HOST",
-        format("ip address to listen (default: %s)", params.hostname.c_str()),
-        [](gpt_params & params, const std::string & value) {
-            params.hostname = value;
-        }
-    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_HOST"));
-    add_opt(llama_arg(
-        {"--port"}, "PORT",
-        format("port to listen (default: %d)", params.port),
-        [](gpt_params & params, int value) {
-            params.port = value;
-        }
-    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_PORT"));
-    add_opt(llama_arg(
-        {"--path"}, "PATH",
-        format("path to serve static files from (default: %s)", params.public_path.c_str()),
-        [](gpt_params & params, const std::string & value) {
-            params.public_path = value;
-        }
-    ).set_examples({LLAMA_EXAMPLE_SERVER}));
-    add_opt(llama_arg(
-        {"--embedding", "--embeddings"},
-        format("restrict to only support embedding use case; use only with dedicated embedding models (default: %s)", params.embedding ? "enabled" : "disabled"),
-        [](gpt_params & params) {
-            params.embedding = true;
-        }
-    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_EMBEDDINGS"));
-    add_opt(llama_arg(
-        {"--api-key"}, "KEY",
-        "API key to use for authentication (default: none)",
-        [](gpt_params & params, const std::string & value) {
-            params.api_keys.push_back(value);
-        }
-    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_API_KEY"));
-    add_opt(llama_arg(
-        {"--api-key-file"}, "FNAME",
-        "path to file containing API keys (default: none)",
-        [](gpt_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()));
-            }
-            std::string key;
-            while (std::getline(key_file, key)) {
-                if (!key.empty()) {
-                        params.api_keys.push_back(key);
-                }
-            }
-            key_file.close();
-        }
-    ).set_examples({LLAMA_EXAMPLE_SERVER}));
-    add_opt(llama_arg(
-        {"--ssl-key-file"}, "FNAME",
-        "path to file a PEM-encoded SSL private key",
-        [](gpt_params & params, const std::string & value) {
-            params.ssl_file_key = value;
-        }
-    ).set_examples({LLAMA_EXAMPLE_SERVER}));
-    add_opt(llama_arg(
-        {"--ssl-cert-file"}, "FNAME",
-        "path to file a PEM-encoded SSL certificate",
-        [](gpt_params & params, const std::string & value) {
-            params.ssl_file_cert = value;
-        }
-    ).set_examples({LLAMA_EXAMPLE_SERVER}));
-    add_opt(llama_arg(
-        {"-to", "--timeout"}, "N",
-        format("server read/write timeout in seconds (default: %d)", params.timeout_read),
-        [](gpt_params & params, int value) {
-            params.timeout_read  = value;
-            params.timeout_write = value;
-        }
-    ).set_examples({LLAMA_EXAMPLE_SERVER}));
-    add_opt(llama_arg(
-        {"--threads-http"}, "N",
-        format("number of threads used to process HTTP requests (default: %d)", params.n_threads_http),
-        [](gpt_params & params, int value) {
-            params.n_threads_http = value;
-        }
-    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_THREADS_HTTP"));
-    add_opt(llama_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",
-        [](gpt_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;
-        }
-    ).set_examples({LLAMA_EXAMPLE_SERVER}));
-    add_opt(llama_arg(
-        {"--log-format"}, "{text, json}",
-        "log output format: json or text (default: json)",
-        [](gpt_params & params, const std::string & value) {
-            if (value == "json") {
-                params.log_json = true;
-            } else if (value == "text") {
-                params.log_json = false;
-            } else {
-                throw std::invalid_argument("invalid value");
-            }
-        }
-    ).set_examples({LLAMA_EXAMPLE_SERVER}));
-    add_opt(llama_arg(
-        {"--metrics"},
-        format("enable prometheus compatible metrics endpoint (default: %s)", params.endpoint_metrics ? "enabled" : "disabled"),
-        [](gpt_params & params) {
-            params.endpoint_metrics = true;
-        }
-    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_ENDPOINT_METRICS"));
-    add_opt(llama_arg(
-        {"--no-slots"},
-        format("disables slots monitoring endpoint (default: %s)", params.endpoint_slots ? "enabled" : "disabled"),
-        [](gpt_params & params) {
-            params.endpoint_slots = false;
-        }
-    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_NO_ENDPOINT_SLOTS"));
-    add_opt(llama_arg(
-        {"--slot-save-path"}, "PATH",
-        "path to save slot kv cache (default: disabled)",
-        [](gpt_params & params, const std::string & value) {
-            params.slot_save_path = value;
-            // if doesn't end with DIRECTORY_SEPARATOR, add it
-            if (!params.slot_save_path.empty() && params.slot_save_path[params.slot_save_path.size() - 1] != DIRECTORY_SEPARATOR) {
-                params.slot_save_path += DIRECTORY_SEPARATOR;
-            }
-        }
-    ).set_examples({LLAMA_EXAMPLE_SERVER}));
-    add_opt(llama_arg(
-        {"--chat-template"}, "JINJA_TEMPLATE",
-        "set custom jinja chat template (default: template taken from model's metadata)\n"
-        "if suffix/prefix are specified, template will be disabled\n"
-        "only commonly used templates are accepted:\nhttps://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template",
-        [](gpt_params & params, const std::string & value) {
-            if (!llama_chat_verify_template(value)) {
-                throw std::runtime_error(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()
-                ));
-            }
-            params.chat_template = value;
-        }
-    ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_CHAT_TEMPLATE"));
-    add_opt(llama_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),
-        [](gpt_params & params, const std::string & value) {
-            params.slot_prompt_similarity = std::stof(value);
-        }
-    ).set_examples({LLAMA_EXAMPLE_SERVER}));
-    add_opt(llama_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"),
-        [](gpt_params & params) {
-            params.lora_init_without_apply = true;
-        }
-    ).set_examples({LLAMA_EXAMPLE_SERVER}));
-    add_opt(llama_arg(
-        {"--simple-io"},
-        "use basic IO for better compatibility in subprocesses and limited consoles",
-        [](gpt_params & params) {
-            params.simple_io = true;
-        }
-    ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_INFILL}));
-    add_opt(llama_arg(
-        {"-ld", "--logdir"}, "LOGDIR",
-        "path under which to save YAML logs (no logging if unset)",
-        [](gpt_params & params, const std::string & value) {
-            params.logdir = value;
-
-            if (params.logdir.back() != DIRECTORY_SEPARATOR) {
-                params.logdir += DIRECTORY_SEPARATOR;
-            }
-        }
-    ));
-    add_opt(llama_arg(
-        {"--positive-file"}, "FNAME",
-        format("positive prompts file, one prompt per line (default: '%s')", params.cvector_positive_file.c_str()),
-        [](gpt_params & params, const std::string & value) {
-            params.cvector_positive_file = value;
-        }
-    ).set_examples({LLAMA_EXAMPLE_CVECTOR_GENERATOR}));
-    add_opt(llama_arg(
-        {"--negative-file"}, "FNAME",
-        format("negative prompts file, one prompt per line (default: '%s')", params.cvector_negative_file.c_str()),
-        [](gpt_params & params, const std::string & value) {
-            params.cvector_negative_file = value;
-        }
-    ).set_examples({LLAMA_EXAMPLE_CVECTOR_GENERATOR}));
-    add_opt(llama_arg(
-        {"--pca-batch"}, "N",
-        format("batch size used for PCA. Larger batch runs faster, but uses more memory (default: %d)", params.n_pca_batch),
-        [](gpt_params & params, int value) {
-            params.n_pca_batch = value;
-        }
-    ).set_examples({LLAMA_EXAMPLE_CVECTOR_GENERATOR}));
-    add_opt(llama_arg(
-        {"--pca-iter"}, "N",
-        format("number of iterations used for PCA (default: %d)", params.n_pca_iterations),
-        [](gpt_params & params, int value) {
-            params.n_pca_iterations = value;
-        }
-    ).set_examples({LLAMA_EXAMPLE_CVECTOR_GENERATOR}));
-    add_opt(llama_arg(
-        {"--method"}, "{pca, mean}",
-        "dimensionality reduction method to be used (default: pca)",
-        [](gpt_params & params, const std::string & value) {
-            /**/ if (value == "pca") { params.cvector_dimre_method = DIMRE_METHOD_PCA; }
-            else if (value == "mean") { params.cvector_dimre_method = DIMRE_METHOD_MEAN; }
-            else { throw std::invalid_argument("invalid value"); }
-        }
-    ).set_examples({LLAMA_EXAMPLE_CVECTOR_GENERATOR}));
-    add_opt(llama_arg(
-        {"--output-format"}, "{md,jsonl}",
-        "output format for batched-bench results (default: md)",
-        [](gpt_params & params, const std::string & value) {
-            /**/ if (value == "jsonl") { params.batched_bench_output_jsonl = true; }
-            else if (value == "md") { params.batched_bench_output_jsonl = false; }
-            else { std::invalid_argument("invalid value"); }
-        }
-    ).set_examples({LLAMA_EXAMPLE_BENCH}));
-#ifndef LOG_DISABLE_LOGS
-    // TODO: make this looks less weird
-    add_opt(llama_arg(
-        {"--log-test"},
-        "Log test",
-        [](gpt_params &) { log_param_single_parse("--log-test"); }
-    ));
-    add_opt(llama_arg(
-        {"--log-disable"},
-        "Log disable",
-        [](gpt_params &) { log_param_single_parse("--log-disable"); }
-    ));
-    add_opt(llama_arg(
-        {"--log-enable"},
-        "Log enable",
-        [](gpt_params &) { log_param_single_parse("--log-enable"); }
-    ));
-    add_opt(llama_arg(
-        {"--log-new"},
-        "Log new",
-        [](gpt_params &) { log_param_single_parse("--log-new"); }
-    ));
-    add_opt(llama_arg(
-        {"--log-append"},
-        "Log append",
-        [](gpt_params &) { log_param_single_parse("--log-append"); }
-    ));
-    add_opt(llama_arg(
-        {"--log-file"}, "FNAME",
-        "Log file",
-        [](gpt_params &, const std::string & value) { log_param_pair_parse(false, "--log-file", value); }
-    ));
-#endif // LOG_DISABLE_LOGS
-
-    return options;
-}
-
 std::string gpt_params_get_system_info(const gpt_params & params) {
     std::ostringstream os;
 

common/common.h

@@ -4,20 +4,11 @@
 
 #include "llama.h"
 
-#include "sampling.h"
-
 #define LOG_NO_FILE_LINE_FUNCTION
 #include "log.h"
 
-#include <cmath>
 #include <string>
 #include <vector>
-#include <random>
-#include <thread>
-#include <set>
-#include <unordered_map>
-#include <tuple>
-#include <functional>
 
 #ifdef _WIN32
 #define DIRECTORY_SEPARATOR '\\'
@@ -56,11 +47,20 @@ struct llama_control_vector_load_info;
 // CPU utils
 //
 
+struct cpu_params {
+    int      n_threads                   = -1;
+    bool     cpumask[GGML_MAX_N_THREADS] = {false}; // CPU affinity mask.
+    bool     mask_valid                  = false;   // Default: any CPU
+    enum ggml_sched_priority  priority   = GGML_SCHED_PRIO_NORMAL;  // Scheduling prio : (0 - normal, 1 - medium, 2 - high, 3 - realtime)
+    bool     strict_cpu                  = false;   // Use strict CPU placement
+    uint32_t poll                        = 50;      // Polling (busywait) level (0 - no polling, 100 - mostly polling)
+};
+
 int32_t cpu_get_num_physical_cores();
 int32_t cpu_get_num_math();
 
 //
-// CLI argument parsing
+// Common params
 //
 
 enum llama_example {
@@ -78,28 +78,71 @@ enum llama_example {
     LLAMA_EXAMPLE_CVECTOR_GENERATOR,
     LLAMA_EXAMPLE_EXPORT_LORA,
     LLAMA_EXAMPLE_LLAVA,
+    LLAMA_EXAMPLE_LOOKUP,
+    LLAMA_EXAMPLE_PARALLEL,
 
     LLAMA_EXAMPLE_COUNT,
 };
 
+enum gpt_sampler_type {
+    GPT_SAMPLER_TYPE_NONE        = 0,
+    GPT_SAMPLER_TYPE_TOP_K       = 1,
+    GPT_SAMPLER_TYPE_TOP_P       = 2,
+    GPT_SAMPLER_TYPE_MIN_P       = 3,
+    GPT_SAMPLER_TYPE_TFS_Z       = 4,
+    GPT_SAMPLER_TYPE_TYPICAL_P   = 5,
+    GPT_SAMPLER_TYPE_TEMPERATURE = 6,
+};
+
 // dimensionality reduction methods, used by cvector-generator
 enum dimre_method {
     DIMRE_METHOD_PCA,
     DIMRE_METHOD_MEAN,
 };
 
-struct cpu_params {
-    int      n_threads                   = -1;
-    bool     cpumask[GGML_MAX_N_THREADS] = {false}; // CPU affinity mask.
-    bool     mask_valid                  = false;   // Default: any CPU
-    enum ggml_sched_priority  priority   = GGML_SCHED_PRIO_NORMAL;  // Scheduling prio : (0 - normal, 1 - medium, 2 - high, 3 - realtime)
-    bool     strict_cpu                  = false;   // Use strict CPU placement
-    uint32_t poll                        = 50;      // Polling (busywait) level (0 - no polling, 100 - mostly polling)
+// sampler parameters
+struct gpt_sampler_params {
+    uint32_t seed = LLAMA_DEFAULT_SEED; // the seed used to initialize llama_sampler
+
+    int32_t n_prev            = 64;    // number of previous tokens to remember
+    int32_t n_probs           = 0;     // if greater than 0, output the probabilities of top n_probs tokens.
+    int32_t min_keep          = 0;     // 0 = disabled, otherwise samplers should return at least min_keep tokens
+    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   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
+    float   dynatemp_range    = 0.00f; // 0.0 = disabled
+    float   dynatemp_exponent = 1.00f; // controls how entropy maps to temperature in dynamic temperature sampler
+    int32_t penalty_last_n    = 64;    // last n tokens to penalize (0 = disable penalty, -1 = context size)
+    float   penalty_repeat    = 1.00f; // 1.0 = disabled
+    float   penalty_freq      = 0.00f; // 0.0 = disabled
+    float   penalty_present   = 0.00f; // 0.0 = disabled
+    int32_t mirostat          = 0;     // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0
+    float   mirostat_tau      = 5.00f; // target entropy
+    float   mirostat_eta      = 0.10f; // learning rate
+    bool    penalize_nl       = false; // consider newlines as a repeatable token
+    bool    ignore_eos        = false;
+
+    std::vector<enum gpt_sampler_type> samplers = {
+        GPT_SAMPLER_TYPE_TOP_K,
+        GPT_SAMPLER_TYPE_TFS_Z,
+        GPT_SAMPLER_TYPE_TYPICAL_P,
+        GPT_SAMPLER_TYPE_TOP_P,
+        GPT_SAMPLER_TYPE_MIN_P,
+        GPT_SAMPLER_TYPE_TEMPERATURE
+    };
+
+    std::string grammar; // optional BNF-like grammar to constrain sampling
+
+    std::vector<llama_logit_bias> logit_bias; // logit biases to apply
+
+    // print the parameters into a string
+    std::string print() const;
 };
 
 struct gpt_params {
-    enum llama_example curr_ex    = LLAMA_EXAMPLE_COMMON;
-
     int32_t n_predict             =    -1; // new tokens to predict
     int32_t n_ctx                 =     0; // context size
     int32_t n_batch               =  2048; // logical batch size for prompt processing (must be >=32 to use BLAS)
@@ -143,23 +186,23 @@ struct gpt_params {
 
     struct gpt_sampler_params sparams;
 
-    std::string model                = ""; // model path
-    std::string model_draft          = ""; // draft model for speculative decoding
-    std::string model_alias          = "unknown"; // model alias
-    std::string model_url            = ""; // model url to download
-    std::string hf_token             = ""; // HF token
-    std::string hf_repo              = ""; // HF repo
-    std::string hf_file              = ""; // HF file
-    std::string prompt               = "";
-    std::string prompt_file          = ""; // store the external prompt file name
-    std::string path_prompt_cache    = ""; // path to file for saving/loading prompt eval state
-    std::string input_prefix         = ""; // string to prefix user inputs with
-    std::string input_suffix         = ""; // string to suffix user inputs with
-    std::string logdir               = ""; // directory in which to save YAML log files
-    std::string lookup_cache_static  = ""; // path of static ngram cache file for lookup decoding
-    std::string lookup_cache_dynamic = ""; // path of dynamic ngram cache file for lookup decoding
-    std::string logits_file          = ""; // file for saving *all* logits
-    std::string rpc_servers          = ""; // comma separated list of RPC servers
+    std::string model                = ""; // model path                                                    // NOLINT
+    std::string model_draft          = ""; // draft model for speculative decoding                          // NOLINT
+    std::string model_alias          = "unknown"; // model alias                                            // NOLINT
+    std::string model_url            = ""; // model url to download                                         // NOLINT
+    std::string hf_token             = ""; // HF token                                                      // NOLINT
+    std::string hf_repo              = ""; // HF repo                                                       // NOLINT
+    std::string hf_file              = ""; // HF file                                                       // NOLINT
+    std::string prompt               = "";                                                                  // NOLINT
+    std::string prompt_file          = ""; // store the external prompt file name                           // NOLINT
+    std::string path_prompt_cache    = ""; // path to file for saving/loading prompt eval state             // NOLINT
+    std::string input_prefix         = ""; // string to prefix user inputs with                             // NOLINT
+    std::string input_suffix         = ""; // string to suffix user inputs with                             // NOLINT
+    std::string logdir               = ""; // directory in which to save YAML log files                     // NOLINT
+    std::string lookup_cache_static  = ""; // path of static ngram cache file for lookup decoding           // NOLINT
+    std::string lookup_cache_dynamic = ""; // path of dynamic ngram cache file for lookup decoding          // NOLINT
+    std::string logits_file          = ""; // file for saving *all* logits                                  // NOLINT
+    std::string rpc_servers          = ""; // comma separated list of RPC servers                           // NOLINT
 
     std::vector<std::string> in_files;   // all input files
     std::vector<std::string> antiprompt; // strings upon which more user input is prompted (a.k.a. reverse prompts)
@@ -189,7 +232,6 @@ struct gpt_params {
 
     bool   kl_divergence    = false; // compute KL divergence
 
-    std::function<void(int, char **)> print_usage = nullptr; // print example-specific usage and example
     bool usage             = false; // print usage
     bool use_color         = false; // use color to distinguish generations and inputs
     bool special           = false; // enable special token output
@@ -220,7 +262,7 @@ struct gpt_params {
     std::string cache_type_v = "f16"; // KV cache data type for the V
 
     // multimodal models (see examples/llava)
-    std::string mmproj = "";        // path to multimodal projector
+    std::string mmproj = "";        // path to multimodal projector                                         // NOLINT
     std::vector<std::string> image; // path to image file(s)
 
     // embedding
@@ -236,15 +278,15 @@ struct gpt_params {
     int     n_threads_http = -1;           // number of threads to process HTTP requests (TODO: support threadpool)
 
     std::string hostname      = "127.0.0.1";
-    std::string public_path   = "";
-    std::string chat_template = "";
-    std::string system_prompt = "";
+    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;
 
-    std::string ssl_file_key  = "";
-    std::string ssl_file_cert = "";
+    std::string ssl_file_key  = "";                                                                         // NOLINT
+    std::string ssl_file_cert = "";                                                                         // NOLINT
 
     bool endpoint_slots   = true;
     bool endpoint_metrics = false;
@@ -299,92 +341,6 @@ struct gpt_params {
     bool batched_bench_output_jsonl = false;
 };
 
-struct llama_arg {
-    std::set<enum llama_example> examples = {LLAMA_EXAMPLE_COMMON};
-    std::vector<const char *> args;
-    const char * value_hint   = nullptr; // help text or example for arg value
-    const char * value_hint_2 = nullptr; // for second arg value
-    const char * env          = nullptr;
-    std::string help;
-    void (*handler_void)   (gpt_params & params) = nullptr;
-    void (*handler_string) (gpt_params & params, const std::string &) = nullptr;
-    void (*handler_str_str)(gpt_params & params, const std::string &, const std::string &) = nullptr;
-    void (*handler_int)    (gpt_params & params, int) = nullptr;
-
-    llama_arg(
-        const std::initializer_list<const char *> & args,
-        const char * value_hint,
-        const std::string & help,
-        void (*handler)(gpt_params & params, const std::string &)
-    ) : args(args), value_hint(value_hint), help(help), handler_string(handler) {}
-
-    llama_arg(
-        const std::initializer_list<const char *> & args,
-        const char * value_hint,
-        const std::string & help,
-        void (*handler)(gpt_params & params, int)
-    ) : args(args), value_hint(value_hint), help(help), handler_int(handler) {}
-
-    llama_arg(
-        const std::initializer_list<const char *> & args,
-        const std::string & help,
-        void (*handler)(gpt_params & params)
-    ) : args(args), help(help), handler_void(handler) {}
-
-    // support 2 values for arg
-    llama_arg(
-        const std::initializer_list<const char *> & args,
-        const char * value_hint,
-        const char * value_hint_2,
-        const std::string & help,
-        void (*handler)(gpt_params & params, const std::string &, const std::string &)
-    ) : args(args), value_hint(value_hint), value_hint_2(value_hint_2), help(help), handler_str_str(handler) {}
-
-    llama_arg & set_examples(std::initializer_list<enum llama_example> examples) {
-        this->examples = std::move(examples);
-        return *this;
-    }
-
-    llama_arg & set_env(const char * env) {
-        help = help + "\n(env: " + env + ")";
-        this->env = env;
-        return *this;
-    }
-
-    bool in_example(enum llama_example ex) {
-        return examples.find(ex) != examples.end();
-    }
-
-    bool get_value_from_env(std::string & output) const {
-        if (env == nullptr) return false;
-        char * value = std::getenv(env);
-        if (value) {
-            output = value;
-            return true;
-        }
-        return false;
-    }
-
-    bool has_value_from_env() const {
-        return env != nullptr && std::getenv(env);
-    }
-
-    std::string to_string();
-};
-
-// initialize list of options (arguments) that can be used by the current example
-std::vector<llama_arg> gpt_params_parser_init(gpt_params & params, llama_example ex);
-// optionally, we can provide "print_usage" to print example usage
-std::vector<llama_arg> gpt_params_parser_init(gpt_params & params, llama_example ex, std::function<void(int, char **)> print_usage);
-
-// parse input arguments from CLI
-// if one argument has invalid value, it will automatically display usage of the specific argument (and not the full usage message)
-bool gpt_params_parse   (int argc, char ** argv, gpt_params & params, std::vector<llama_arg> & options);
-bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params, std::vector<llama_arg> & options);
-
-// print full usage message; it will be called internally by gpt_params_parse() if "-h" is set
-void gpt_params_print_usage(gpt_params & params, std::vector<llama_arg> & options);
-
 std::string gpt_params_get_system_info(const gpt_params & params);
 
 bool parse_cpu_range(const std::string& range, bool(&boolmask)[GGML_MAX_N_THREADS]);

common/sampling.cpp

@@ -2,6 +2,9 @@
 
 #include "common.h"
 
+#include <cmath>
+#include <unordered_map>
+
 // the ring buffer works similarly to std::deque, but with a fixed capacity
 // TODO: deduplicate with llama-impl.h
 template<typename T>
@@ -420,7 +423,7 @@ std::vector<gpt_sampler_type> gpt_sampler_types_from_names(const std::vector<std
 }
 
 std::vector<gpt_sampler_type> gpt_sampler_types_from_chars(const std::string & chars) {
-    std::unordered_map<char, gpt_sampler_type> sampler_name_map {
+    std::unordered_map<char, gpt_sampler_type> sampler_name_map = {
         { gpt_sampler_type_to_chr(GPT_SAMPLER_TYPE_TOP_K),       GPT_SAMPLER_TYPE_TOP_K },
         { gpt_sampler_type_to_chr(GPT_SAMPLER_TYPE_TFS_Z),       GPT_SAMPLER_TYPE_TFS_Z },
         { gpt_sampler_type_to_chr(GPT_SAMPLER_TYPE_TYPICAL_P),   GPT_SAMPLER_TYPE_TYPICAL_P },

common/sampling.h

@@ -2,61 +2,11 @@
 
 #include "llama.h"
 
+#include "common.h"
+
 #include <string>
 #include <vector>
 
-enum gpt_sampler_type {
-    GPT_SAMPLER_TYPE_NONE        = 0,
-    GPT_SAMPLER_TYPE_TOP_K       = 1,
-    GPT_SAMPLER_TYPE_TOP_P       = 2,
-    GPT_SAMPLER_TYPE_MIN_P       = 3,
-    GPT_SAMPLER_TYPE_TFS_Z       = 4,
-    GPT_SAMPLER_TYPE_TYPICAL_P   = 5,
-    GPT_SAMPLER_TYPE_TEMPERATURE = 6,
-};
-
-// sampling parameters
-struct gpt_sampler_params {
-    uint32_t seed = LLAMA_DEFAULT_SEED; // the seed used to initialize llama_sampler
-
-    int32_t n_prev            = 64;    // number of previous tokens to remember
-    int32_t n_probs           = 0;     // if greater than 0, output the probabilities of top n_probs tokens.
-    int32_t min_keep          = 0;     // 0 = disabled, otherwise samplers should return at least min_keep tokens
-    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   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
-    float   dynatemp_range    = 0.00f; // 0.0 = disabled
-    float   dynatemp_exponent = 1.00f; // controls how entropy maps to temperature in dynamic temperature sampler
-    int32_t penalty_last_n    = 64;    // last n tokens to penalize (0 = disable penalty, -1 = context size)
-    float   penalty_repeat    = 1.00f; // 1.0 = disabled
-    float   penalty_freq      = 0.00f; // 0.0 = disabled
-    float   penalty_present   = 0.00f; // 0.0 = disabled
-    int32_t mirostat          = 0;     // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0
-    float   mirostat_tau      = 5.00f; // target entropy
-    float   mirostat_eta      = 0.10f; // learning rate
-    bool    penalize_nl       = false; // consider newlines as a repeatable token
-    bool    ignore_eos        = false;
-
-    std::vector<enum gpt_sampler_type> samplers = {
-        GPT_SAMPLER_TYPE_TOP_K,
-        GPT_SAMPLER_TYPE_TFS_Z,
-        GPT_SAMPLER_TYPE_TYPICAL_P,
-        GPT_SAMPLER_TYPE_TOP_P,
-        GPT_SAMPLER_TYPE_MIN_P,
-        GPT_SAMPLER_TYPE_TEMPERATURE
-    };
-
-    std::string grammar; // optional BNF-like grammar to constrain sampling
-
-    std::vector<llama_logit_bias> logit_bias; // logit biases to apply
-
-    // print the parameters into a string
-    std::string print() const;
-};
-
 // gpt_sampler extends llama_sampler with additional functionality:
 //
 //  - grammar support

examples/batched-bench/batched-bench.cpp

@@ -1,3 +1,4 @@
+#include "arg.h"
 #include "common.h"
 #include "llama.h"
 
@@ -37,8 +38,7 @@ static void print_usage(int, char ** argv) {
 int main(int argc, char ** argv) {
     gpt_params params;
 
-    auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_BENCH, print_usage);
-    if (!gpt_params_parse(argc, argv, params, options)) {
+    if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_BENCH, print_usage)) {
         return 1;
     }
 

examples/batched/batched.cpp

@@ -1,3 +1,4 @@
+#include "arg.h"
 #include "common.h"
 #include "llama.h"
 
@@ -18,8 +19,7 @@ int main(int argc, char ** argv) {
     params.prompt = "Hello my name is";
     params.n_predict = 32;
 
-    auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_COMMON, print_usage);
-    if (!gpt_params_parse(argc, argv, params, options)) {
+    if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON, print_usage)) {
         return 1;
     }
 

examples/cvector-generator/cvector-generator.cpp

@@ -1,3 +1,4 @@
+#include "arg.h"
 #include "common.h"
 #include "llama.h"
 #include "ggml.h"
@@ -388,8 +389,7 @@ static int prepare_entries(gpt_params & params, train_context & ctx_train) {
 int main(int argc, char ** argv) {
     gpt_params params;
 
-    auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_CVECTOR_GENERATOR, print_usage);
-    if (!gpt_params_parse(argc, argv, params, options)) {
+    if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_CVECTOR_GENERATOR, print_usage)) {
         return 1;
     }
 

examples/cvector-generator/pca.hpp

@@ -12,12 +12,9 @@
 
 #include <cstdio>
 #include <ctime>
+#include <random>
 #include <string>
-#include <tuple>
 #include <vector>
-#include <algorithm>
-#include <iostream>
-#include <fstream>
 
 #define DEBUG_POS 5
 

examples/embedding/embedding.cpp

@@ -1,3 +1,4 @@
+#include "arg.h"
 #include "common.h"
 #include "llama.h"
 
@@ -79,8 +80,7 @@ static void batch_decode(llama_context * ctx, llama_batch & batch, float * outpu
 int main(int argc, char ** argv) {
     gpt_params params;
 
-    auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_EMBEDDING);
-    if (!gpt_params_parse(argc, argv, params, options)) {
+    if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_EMBEDDING)) {
         return 1;
     }
 

examples/eval-callback/eval-callback.cpp

@@ -1,3 +1,4 @@
+#include "arg.h"
 #include "common.h"
 #include "llama.h"
 #include "ggml.h"
@@ -144,8 +145,7 @@ int main(int argc, char ** argv) {
 
     gpt_params params;
 
-    auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_COMMON);
-    if (!gpt_params_parse(argc, argv, params, options)) {
+    if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON)) {
         return 1;
     }
 

examples/export-lora/export-lora.cpp

@@ -1,3 +1,4 @@
+#include "arg.h"
 #include "common.h"
 #include "ggml.h"
 #include "ggml-alloc.h"
@@ -401,8 +402,7 @@ static void print_usage(int, char ** argv) {
 int main(int argc, char ** argv) {
     gpt_params params;
 
-    auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_EXPORT_LORA, print_usage);
-    if (!gpt_params_parse(argc, argv, params, options)) {
+    if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_EXPORT_LORA, print_usage)) {
         return 1;
     }
 

examples/gen-docs/gen-docs.cpp

@@ -1,3 +1,4 @@
+#include "arg.h"
 #include "common.h"
 
 #include <fstream>
@@ -9,11 +10,11 @@ static void export_md(std::string fname, llama_example ex) {
     std::ofstream file(fname, std::ofstream::out | std::ofstream::trunc);
 
     gpt_params params;
-    auto options = gpt_params_parser_init(params, ex);
+    auto ctx_arg = gpt_params_parser_init(params, ex);
 
     file << "| Argument | Explanation |\n";
     file << "| -------- | ----------- |\n";
-    for (auto & opt : options) {
+    for (auto & opt : ctx_arg.options) {
         file << "| `";
         // args
         for (const auto & arg : opt.args) {

examples/gritlm/gritlm.cpp

@@ -1,3 +1,4 @@
+#include "arg.h"
 #include "common.h"
 #include "llama.h"
 
@@ -153,8 +154,7 @@ static std::string gritlm_instruction(const std::string & instruction) {
 int main(int argc, char * argv[]) {
     gpt_params params;
 
-    auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_COMMON);
-    if (!gpt_params_parse(argc, argv, params, options)) {
+    if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON)) {
         return 1;
     }
 

examples/imatrix/imatrix.cpp

@@ -1,3 +1,4 @@
+#include "arg.h"
 #include "common.h"
 #include "llama.h"
 
@@ -577,8 +578,7 @@ int main(int argc, char ** argv) {
     params.logits_all = true;
     params.verbosity = 1;
 
-    auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_IMATRIX, print_usage);
-    if (!gpt_params_parse(argc, argv, params, options)) {
+    if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_IMATRIX, print_usage)) {
         return 1;
     }
 

examples/infill/infill.cpp

@@ -1,6 +1,7 @@
+#include "arg.h"
 #include "common.h"
-
 #include "console.h"
+#include "sampling.h"
 #include "llama.h"
 
 #include <cassert>
@@ -105,8 +106,7 @@ int main(int argc, char ** argv) {
     gpt_params params;
     g_params = &params;
 
-    auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_INFILL);
-    if (!gpt_params_parse(argc, argv, params, options)) {
+    if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_INFILL)) {
         return 1;
     }
 

examples/llava/llava-cli.cpp

@@ -1,11 +1,12 @@
-#include "ggml.h"
+#include "arg.h"
+#include "base64.hpp"
 #include "log.h"
 #include "common.h"
+#include "sampling.h"
 #include "clip.h"
 #include "llava.h"
 #include "llama.h"
-
-#include "base64.hpp"
+#include "ggml.h"
 
 #include <cstdio>
 #include <cstdlib>
@@ -278,8 +279,7 @@ int main(int argc, char ** argv) {
 
     gpt_params params;
 
-    auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_LLAVA, print_usage);
-    if (!gpt_params_parse(argc, argv, params, options)) {
+    if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_LLAVA, print_usage)) {
         return 1;
     }
 

examples/llava/minicpmv-cli.cpp

@@ -1,9 +1,11 @@
-#include "ggml.h"
+#include "arg.h"
 #include "log.h"
 #include "common.h"
+#include "sampling.h"
 #include "clip.h"
 #include "llava.h"
 #include "llama.h"
+#include "ggml.h"
 
 #include <cstdio>
 #include <cstdlib>
@@ -253,8 +255,7 @@ int main(int argc, char ** argv) {
 
     gpt_params params;
 
-    auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_COMMON, show_additional_info);
-    if (!gpt_params_parse(argc, argv, params, options)) {
+    if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON, show_additional_info)) {
         return 1;
     }
 

examples/lookahead/lookahead.cpp

@@ -1,4 +1,6 @@
+#include "arg.h"
 #include "common.h"
+#include "sampling.h"
 #include "llama.h"
 
 #include <cstdio>
@@ -36,8 +38,7 @@ struct ngram_container {
 int main(int argc, char ** argv) {
     gpt_params params;
 
-    auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_COMMON);
-    if (!gpt_params_parse(argc, argv, params, options)) {
+    if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON)) {
         return 1;
     }
 

examples/lookup/lookup-create.cpp

@@ -1,7 +1,8 @@
-#include "ggml.h"
-#include "llama.h"
+#include "arg.h"
 #include "common.h"
 #include "ngram-cache.h"
+#include "ggml.h"
+#include "llama.h"
 
 #include <cstdint>
 #include <fstream>
@@ -13,8 +14,7 @@
 int main(int argc, char ** argv){
     gpt_params params;
 
-    auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_COMMON);
-    if (!gpt_params_parse(argc, argv, params, options)) {
+    if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_LOOKUP)) {
         return 1;
     }
 
@@ -40,4 +40,6 @@ int main(int argc, char ** argv){
     fprintf(stderr, "%s: hashing done, writing file to %s\n", __func__, params.lookup_cache_static.c_str());
 
     llama_ngram_cache_save(ngram_cache, params.lookup_cache_static);
+
+    return 0;
 }

examples/lookup/lookup-stats.cpp

@@ -1,8 +1,9 @@
-#include "ggml.h"
+#include "arg.h"
 #include "common.h"
-#include "llama.h"
 #include "log.h"
 #include "ngram-cache.h"
+#include "llama.h"
+#include "ggml.h"
 
 #include <cmath>
 #include <cstdint>
@@ -15,8 +16,7 @@
 int main(int argc, char ** argv){
     gpt_params params;
 
-    auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_COMMON);
-    if (!gpt_params_parse(argc, argv, params, options)) {
+    if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_LOOKUP)) {
         return 1;
     }
 

examples/lookup/lookup.cpp

@@ -1,7 +1,9 @@
+#include "arg.h"
 #include "ggml.h"
-#include "llama.h"
 #include "common.h"
 #include "ngram-cache.h"
+#include "sampling.h"
+#include "llama.h"
 
 #include <cstdint>
 #include <cstdio>
@@ -12,8 +14,7 @@
 int main(int argc, char ** argv){
     gpt_params params;
 
-    auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_COMMON);
-    if (!gpt_params_parse(argc, argv, params, options)) {
+    if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_LOOKUP)) {
         return 1;
     }
 

examples/main/main.cpp

@@ -1,6 +1,7 @@
+#include "arg.h"
 #include "common.h"
-
 #include "console.h"
+#include "sampling.h"
 #include "llama.h"
 
 #include <cassert>
@@ -138,9 +139,7 @@ static std::string chat_add_and_format(struct llama_model * model, std::vector<l
 int main(int argc, char ** argv) {
     gpt_params params;
     g_params = &params;
-    auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_MAIN, print_usage);
-
-    if (!gpt_params_parse(argc, argv, params, options)) {
+    if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_MAIN, print_usage)) {
         return 1;
     }
 

examples/parallel/parallel.cpp

@@ -1,7 +1,9 @@
 // A basic application simulating a server with multiple clients.
 // The clients submit requests to the server and they are processed in parallel.
 
+#include "arg.h"
 #include "common.h"
+#include "sampling.h"
 #include "llama.h"
 
 #include <cmath>
@@ -100,8 +102,7 @@ int main(int argc, char ** argv) {
 
     gpt_params params;
 
-    auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_COMMON);
-    if (!gpt_params_parse(argc, argv, params, options)) {
+    if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_PARALLEL)) {
         return 1;
     }
 

examples/passkey/passkey.cpp

@@ -1,3 +1,4 @@
+#include "arg.h"
 #include "common.h"
 #include "llama.h"
 
@@ -19,8 +20,7 @@ int main(int argc, char ** argv) {
     params.n_keep = 32;
     params.i_pos  = -1;
 
-    auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_PASSKEY, print_usage);
-    if (!gpt_params_parse(argc, argv, params, options)) {
+    if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_PASSKEY, print_usage)) {
         return 1;
     }
 

examples/perplexity/perplexity.cpp

@@ -1,18 +1,19 @@
+#include "arg.h"
 #include "common.h"
 #include "llama.h"
 
+#include <array>
+#include <atomic>
 #include <cmath>
 #include <cstdio>
 #include <cstring>
 #include <ctime>
+#include <fstream>
+#include <mutex>
+#include <random>
 #include <sstream>
 #include <thread>
-#include <mutex>
-#include <atomic>
 #include <vector>
-#include <array>
-#include <fstream>
-#include <sstream>
 
 #if defined(_MSC_VER)
 #pragma warning(disable: 4244 4267) // possible loss of data
@@ -1967,8 +1968,7 @@ int main(int argc, char ** argv) {
     params.n_ctx = 512;
     params.logits_all = true;
 
-    auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_PERPLEXITY);
-    if (!gpt_params_parse(argc, argv, params, options)) {
+    if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_PERPLEXITY)) {
         return 1;
     }
 

examples/retrieval/retrieval.cpp

@@ -1,3 +1,4 @@
+#include "arg.h"
 #include "common.h"
 #include "llama.h"
 
@@ -111,8 +112,7 @@ static void batch_decode(llama_context * ctx, llama_batch & batch, float * outpu
 int main(int argc, char ** argv) {
     gpt_params params;
 
-    auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_RETRIEVAL, print_usage);
-    if (!gpt_params_parse(argc, argv, params, options)) {
+    if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_RETRIEVAL, print_usage)) {
         return 1;
     }
 

examples/save-load-state/save-load-state.cpp

@@ -1,3 +1,4 @@
+#include "arg.h"
 #include "common.h"
 #include "llama.h"
 
@@ -10,8 +11,7 @@ int main(int argc, char ** argv) {
     params.prompt = "The quick brown fox";
     params.sparams.seed = 1234;
 
-    auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_COMMON);
-    if (!gpt_params_parse(argc, argv, params, options)) {
+    if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON)) {
         return 1;
     }
 

examples/server/README.md

@@ -23,36 +23,32 @@ The project is under active development, and we are [looking for feedback and co
 | `--version` | show version and build info |
 | `-v, --verbose` | print verbose information |
 | `--verbosity N` | set specific verbosity level (default: 0) |
-| `--verbose-prompt` | print a verbose prompt before generation (default: false) |
-| `--no-display-prompt` | don't print prompt at generation (default: false) |
-| `-s, --seed SEED` | RNG seed (default: -1, use random seed for < 0) |
 | `-t, --threads N` | number of threads to use during generation (default: -1)<br/>(env: LLAMA_ARG_THREADS) |
 | `-tb, --threads-batch N` | number of threads to use during batch and prompt processing (default: same as --threads) |
 | `-C, --cpu-mask M` | CPU affinity mask: arbitrarily long hex. Complements cpu-range (default: "") |
 | `-Cr, --cpu-range lo-hi` | range of CPUs for affinity. Complements --cpu-mask |
 | `--cpu-strict <0\|1>` | use strict CPU placement (default: 0)<br/> |
+| `--prio N` | set process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: 0)<br/> |
 | `--poll <0...100>` | use polling level to wait for work (0 - no polling, default: 50)<br/> |
 | `-Cb, --cpu-mask-batch M` | CPU affinity mask: arbitrarily long hex. Complements cpu-range-batch (default: same as --cpu-mask) |
 | `-Crb, --cpu-range-batch lo-hi` | ranges of CPUs for affinity. Complements --cpu-mask-batch |
 | `--cpu-strict-batch <0\|1>` | use strict CPU placement (default: same as --cpu-strict) |
+| `--prio-batch N` | set process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: 0)<br/> |
 | `--poll-batch <0\|1>` | use polling to wait for work (default: same as --poll) |
-| `-lcs, --lookup-cache-static FNAME` | path to static lookup cache to use for lookup decoding (not updated by generation) |
-| `-lcd, --lookup-cache-dynamic FNAME` | path to dynamic lookup cache to use for lookup decoding (updated by generation) |
 | `-c, --ctx-size N` | size of the prompt context (default: 0, 0 = loaded from model)<br/>(env: LLAMA_ARG_CTX_SIZE) |
 | `-n, --predict, --n-predict N` | number of tokens to predict (default: -1, -1 = infinity, -2 = until context filled)<br/>(env: LLAMA_ARG_N_PREDICT) |
 | `-b, --batch-size N` | logical maximum batch size (default: 2048)<br/>(env: LLAMA_ARG_BATCH) |
 | `-ub, --ubatch-size N` | physical maximum batch size (default: 512)<br/>(env: LLAMA_ARG_UBATCH) |
 | `--keep N` | number of tokens to keep from the initial prompt (default: 0, -1 = all) |
-| `--chunks N` | max number of chunks to process (default: -1, -1 = all) |
 | `-fa, --flash-attn` | enable Flash Attention (default: disabled)<br/>(env: LLAMA_ARG_FLASH_ATTN) |
 | `-p, --prompt PROMPT` | prompt to start generation with |
 | `-f, --file FNAME` | a file containing the prompt (default: none) |
-| `--in-file FNAME` | an input file (repeat to specify multiple files) |
 | `-bf, --binary-file FNAME` | binary file containing the prompt (default: none) |
 | `-e, --escape` | process escapes sequences (\n, \r, \t, \', \", \\) (default: true) |
 | `--no-escape` | do not process escape sequences |
 | `--spm-infill` | use Suffix/Prefix/Middle pattern for infill (instead of Prefix/Suffix/Middle) as some models prefer this. (default: disabled) |
-| `--samplers SAMPLERS` | samplers that will be used for generation in the order, separated by ';'<br/>(default: top_k;tfs_z;typical_p;top_p;min_p;temperature) |
+| `--samplers SAMPLERS` | samplers that will be used for generation in the order, separated by ';'<br/>(default: top_k;tfs_z;typ_p;top_p;min_p;temperature) |
+| `-s, --seed SEED` | RNG seed (default: -1, use random seed for < 0) |
 | `--sampling-seq SEQUENCE` | simplified sequence for samplers that will be used (default: kfypmt) |
 | `--ignore-eos` | ignore end of stream token and continue generating (implies --logit-bias EOS-inf) |
 | `--penalize-nl` | penalize newline tokens (default: false) |
@@ -92,13 +88,12 @@ The project is under active development, and we are [looking for feedback and co
 | `-ctv, --cache-type-v TYPE` | KV cache data type for V (default: f16) |
 | `-dt, --defrag-thold N` | KV cache defragmentation threshold (default: -1.0, < 0 - disabled)<br/>(env: LLAMA_ARG_DEFRAG_THOLD) |
 | `-np, --parallel N` | number of parallel sequences to decode (default: 1) |
-| `-ns, --sequences N` | number of sequences to decode (default: 1) |
 | `-cb, --cont-batching` | enable continuous batching (a.k.a dynamic batching) (default: enabled)<br/>(env: LLAMA_ARG_CONT_BATCHING) |
 | `-nocb, --no-cont-batching` | disable continuous batching<br/>(env: LLAMA_ARG_NO_CONT_BATCHING) |
 | `--mlock` | force system to keep model in RAM rather than swapping or compressing |
 | `--no-mmap` | do not memory-map model (slower load but may reduce pageouts if not using mlock) |
 | `--numa TYPE` | attempt optimizations that help on some NUMA systems<br/>- distribute: spread execution evenly over all nodes<br/>- isolate: only spawn threads on CPUs on the node that execution started on<br/>- numactl: use the CPU map provided by numactl<br/>if run without this previously, it is recommended to drop the system page cache before using this<br/>see https://github.com/ggerganov/llama.cpp/issues/1437 |
-| `-ngl, --gpu-layers N` | number of layers to store in VRAM<br/>(env: LLAMA_ARG_N_GPU_LAYERS) |
+| `-ngl, --gpu-layers, --n-gpu-layers N` | number of layers to store in VRAM<br/>(env: LLAMA_ARG_N_GPU_LAYERS) |
 | `-sm, --split-mode {none,layer,row}` | how to split the model across multiple GPUs, one of:<br/>- none: use one GPU only<br/>- layer (default): split layers and KV across GPUs<br/>- row: split rows across GPUs |
 | `-ts, --tensor-split N0,N1,N2,...` | fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1 |
 | `-mg, --main-gpu INDEX` | the GPU to use for the model (with split-mode = none), or for intermediate results and KV (with split-mode = row) (default: 0) |
@@ -109,7 +104,7 @@ The project is under active development, and we are [looking for feedback and co
 | `--control-vector FNAME` | add a control vector<br/>note: this argument can be repeated to add multiple control vectors |
 | `--control-vector-scaled FNAME SCALE` | add a control vector with user defined scaling SCALE<br/>note: this argument can be repeated to add multiple scaled control vectors |
 | `--control-vector-layer-range START END` | layer range to apply the control vector(s) to, start and end inclusive |
-| `-a, --alias STRING` | set alias for model name (to be used by REST API)<br/>(env: LLAMA_ARG_MODEL) |
+| `-a, --alias STRING` | set alias for model name (to be used by REST API) |
 | `-m, --model FNAME` | model path (default: `models/$filename` with filename from `--hf-file` or `--model-url` if set, otherwise models/7B/ggml-model-f16.gguf)<br/>(env: LLAMA_ARG_MODEL) |
 | `-mu, --model-url MODEL_URL` | model download url (default: unused)<br/>(env: LLAMA_ARG_MODEL_URL) |
 | `-hfr, --hf-repo REPO` | Hugging Face model repository (default: unused)<br/>(env: LLAMA_ARG_HF_REPO) |
@@ -123,7 +118,7 @@ The project is under active development, and we are [looking for feedback and co
 | `--api-key-file FNAME` | path to file containing API keys (default: none) |
 | `--ssl-key-file FNAME` | path to file a PEM-encoded SSL private key |
 | `--ssl-cert-file FNAME` | path to file a PEM-encoded SSL certificate |
-| `--timeout N` | server read/write timeout in seconds (default: 600) |
+| `-to, --timeout N` | server read/write timeout in seconds (default: 600) |
 | `--threads-http N` | number of threads used to process HTTP requests (default: -1)<br/>(env: LLAMA_ARG_THREADS_HTTP) |
 | `-spf, --system-prompt-file FNAME` | set a file to load a system prompt (initial prompt of all slots), this is useful for chat applications |
 | `--log-format {text, json}` | log output format: json or text (default: json) |

examples/server/server.cpp

@@ -1,6 +1,8 @@
 #include "utils.hpp"
 
+#include "arg.h"
 #include "common.h"
+#include "sampling.h"
 #include "json-schema-to-grammar.h"
 #include "llama.h"
 
@@ -2423,8 +2425,7 @@ int main(int argc, char ** argv) {
     // own arguments required by this example
     gpt_params params;
 
-    auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_SERVER);
-    if (!gpt_params_parse(argc, argv, params, options)) {
+    if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_SERVER)) {
         return 1;
     }
 

examples/simple/simple.cpp

@@ -1,3 +1,4 @@
+#include "arg.h"
 #include "common.h"
 #include "llama.h"
 
@@ -18,8 +19,7 @@ int main(int argc, char ** argv) {
     params.prompt = "Hello my name is";
     params.n_predict = 32;
 
-    auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_COMMON, print_usage);
-    if (!gpt_params_parse(argc, argv, params, options)) {
+    if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON, print_usage)) {
         return 1;
     }
 

examples/speculative/speculative.cpp

@@ -1,11 +1,13 @@
+#include "arg.h"
 #include "common.h"
+#include "sampling.h"
 #include "llama.h"
 
-#include <cmath>
 #include <cstdio>
 #include <string>
 #include <vector>
 #include <set>
+#include <random>
 
 #define SPEC_VOCAB_MAX_SIZE_DIFFERENCE  100
 #define SPEC_VOCAB_CHECK_START_TOKEN_ID 5
@@ -27,8 +29,7 @@ struct seq_draft {
 int main(int argc, char ** argv) {
     gpt_params params;
 
-    auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_SPECULATIVE);
-    if (!gpt_params_parse(argc, argv, params, options)) {
+    if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_SPECULATIVE)) {
         return 1;
     }
 

tests/test-arg-parser.cpp

@@ -1,3 +1,6 @@
+#include "arg.h"
+#include "common.h"
+
 #include <string>
 #include <vector>
 #include <sstream>
@@ -6,18 +9,16 @@
 #undef NDEBUG
 #include <cassert>
 
-#include "common.h"
-
 int main(void) {
     gpt_params params;
 
     printf("test-arg-parser: make sure there is no duplicated arguments in any examples\n\n");
     for (int ex = 0; ex < LLAMA_EXAMPLE_COUNT; ex++) {
         try {
-            auto options = gpt_params_parser_init(params, (enum llama_example)ex);
+            auto ctx_arg = gpt_params_parser_init(params, (enum llama_example)ex);
             std::unordered_set<std::string> seen_args;
             std::unordered_set<std::string> seen_env_vars;
-            for (const auto & opt : options) {
+            for (const auto & opt : ctx_arg.options) {
                 // check for args duplications
                 for (const auto & arg : opt.args) {
                     if (seen_args.find(arg) == seen_args.end()) {
@@ -52,40 +53,51 @@ int main(void) {
     };
 
     std::vector<std::string> argv;
-    auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_COMMON);
 
     printf("test-arg-parser: test invalid usage\n\n");
 
+    // missing value
     argv = {"binary_name", "-m"};
-    assert(false == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, options));
+    assert(false == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
 
+    // wrong value (int)
     argv = {"binary_name", "-ngl", "hello"};
-    assert(false == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, options));
+    assert(false == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
 
+    // wrong value (enum)
     argv = {"binary_name", "-sm", "hello"};
-    assert(false == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, options));
+    assert(false == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
+
+    // non-existence arg in specific example (--draft cannot be used outside llama-speculative)
+    argv = {"binary_name", "--draft", "123"};
+    assert(false == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_SERVER));
 
 
     printf("test-arg-parser: test valid usage\n\n");
 
     argv = {"binary_name", "-m", "model_file.gguf"};
-    assert(true == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, options));
+    assert(true == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
     assert(params.model == "model_file.gguf");
 
     argv = {"binary_name", "-t", "1234"};
-    assert(true == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, options));
+    assert(true == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
     assert(params.cpuparams.n_threads == 1234);
 
     argv = {"binary_name", "--verbose"};
-    assert(true == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, options));
+    assert(true == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
     assert(params.verbosity == 1);
 
     argv = {"binary_name", "-m", "abc.gguf", "--predict", "6789", "--batch-size", "9090"};
-    assert(true == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, options));
+    assert(true == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
     assert(params.model == "abc.gguf");
     assert(params.n_predict == 6789);
     assert(params.n_batch == 9090);
 
+    // --draft cannot be used outside llama-speculative
+    argv = {"binary_name", "--draft", "123"};
+    assert(true == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_SPECULATIVE));
+    assert(params.n_draft == 123);
+
 // skip this part on windows, because setenv is not supported
 #ifdef _WIN32
     printf("test-arg-parser: skip on windows build\n");
@@ -94,12 +106,12 @@ int main(void) {
 
     setenv("LLAMA_ARG_THREADS", "blah", true);
     argv = {"binary_name"};
-    assert(false == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, options));
+    assert(false == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
 
     setenv("LLAMA_ARG_MODEL", "blah.gguf", true);
     setenv("LLAMA_ARG_THREADS", "1010", true);
     argv = {"binary_name"};
-    assert(true == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, options));
+    assert(true == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
     assert(params.model == "blah.gguf");
     assert(params.cpuparams.n_threads == 1010);
 
@@ -109,7 +121,7 @@ int main(void) {
     setenv("LLAMA_ARG_MODEL", "blah.gguf", true);
     setenv("LLAMA_ARG_THREADS", "1010", true);
     argv = {"binary_name", "-m", "overwritten.gguf"};
-    assert(true == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, options));
+    assert(true == gpt_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
     assert(params.model == "overwritten.gguf");
     assert(params.cpuparams.n_threads == 1010);
 #endif // _WIN32