common.cpp

//
// Copyright (C) 2023-2025 The llama.cpp authors
// Copyright (C) 2024-2025 Iwan Kawrakow
// MIT license
// SPDX-License-Identifier: MIT
//

#if defined(_MSC_VER)
#define _SILENCE_CXX17_CODECVT_HEADER_DEPRECATION_WARNING
#endif

#include "common.h"
// Change JSON_ASSERT from assert() to GGML_ASSERT:
#define JSON_ASSERT GGML_ASSERT
#include "llama-vocab.h"
#include "llama.h"
#include "chat.h"
#include "json-schema-to-grammar.h"
#include <algorithm>
#include <cinttypes>
#include <climits>
#include <cmath>
#include <codecvt>
#include <cstdlib>
#include <cstdarg>
#include <cstring>
#include <ctime>
#include <fstream>
#include <iostream>
#include <iterator>
#include <regex>
#include <sstream>
#include <string>
#include <unordered_map>
#include <unordered_set>
#include <vector>

#if defined(__APPLE__) && defined(__MACH__)
#include <sys/types.h>
#include <sys/sysctl.h>
#endif

#if defined(_WIN32)
#define WIN32_LEAN_AND_MEAN
#ifndef NOMINMAX
#   define NOMINMAX
#endif
#include <locale>
#include <windows.h>
#include <fcntl.h>
#include <io.h>
#else
#include <sys/ioctl.h>
#include <sys/stat.h>
#include <unistd.h>
#endif
#if defined(LLAMA_USE_CURL)
#include <curl/curl.h>
#include <curl/easy.h>
#include <thread>
#include <future>
#endif

#if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data
#endif

#if (defined(GGML_USE_CUDA) || defined(GGML_USE_SYCL))
#define GGML_USE_CUDA_SYCL
#endif

#if (defined(GGML_USE_CUDA) || defined(GGML_USE_SYCL)) || defined(GGML_USE_VULKAN)
#define GGML_USE_CUDA_SYCL_VULKAN
#endif

#if defined(LLAMA_USE_CURL)
#ifdef __linux__
#include <linux/limits.h>
#elif defined(_WIN32)
#define PATH_MAX MAX_PATH
#else
#include <sys/syslimits.h>
#endif
#define LLAMA_CURL_MAX_URL_LENGTH 2084 // Maximum URL Length in Chrome: 2083
#endif // LLAMA_USE_CURL
#ifdef GGML_USE_RPC
#  include "ggml-rpc.h"
#endif
using json = nlohmann::ordered_json;

common_time_meas::common_time_meas(int64_t & t_acc, bool disable) : t_start_us(disable ? -1 : ggml_time_us()), t_acc(t_acc) {}

common_time_meas::~common_time_meas() {
    if (t_start_us >= 0) {
        t_acc += ggml_time_us() - t_start_us;
    }
}

bool common_speculative_type_is_self_spec(enum common_speculative_type type) {
    switch (type) {
        case COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE:
        case COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K:
        case COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V:
        case COMMON_SPECULATIVE_TYPE_NGRAM_MOD:
        case COMMON_SPECULATIVE_TYPE_NGRAM_CACHE:
        case COMMON_SPECULATIVE_TYPE_SUFFIX:
            return true;
        default:
            return false;
    }
}

static int32_t common_speculative_stage_effective_n_max(
        const common_params_speculative & params,
        const common_speculative_stage_params & stage) {
    return stage.has_n_max_override() ? stage.n_max : params.n_max;
}

static int32_t common_speculative_stage_effective_n_min(
        const common_params_speculative & params,
        const common_speculative_stage_params & stage) {
    return stage.has_n_min_override() ? stage.n_min : params.n_min;
}

std::vector<common_speculative_stage_params> common_params_speculative::get_resolved_stages() const {
    if (!stages.empty()) {
        return stages;
    }

    if (type == COMMON_SPECULATIVE_TYPE_NONE) {
        return {};
    }

    return {{ .type = type }};
}

common_params_speculative common_params_speculative::with_stage_overrides(const common_speculative_stage_params & stage) const {
    common_params_speculative result = *this;

    result.type = stage.type;

    if (stage.has_n_max_override()) {
        result.n_max = stage.n_max;
    }
    if (stage.has_n_min_override()) {
        result.n_min = stage.n_min;
    }
    if (stage.has_p_min_override()) {
        result.p_min = stage.p_min;
    }
    if (stage.has_ngram_size_n_override()) {
        result.ngram_size_n = stage.ngram_size_n;
        result.ngram_mod.reset();
    }
    if (stage.has_ngram_size_m_override()) {
        result.ngram_size_m = stage.ngram_size_m;
    }
    if (stage.has_ngram_min_hits_override()) {
        result.ngram_min_hits = stage.ngram_min_hits;
    }
    if (stage.has_suffix_min_match_len_override()) {
        result.suffix_min_match_len = stage.suffix_min_match_len;
    }
    if (stage.has_suffix_max_depth_override()) {
        result.suffix_max_depth = stage.suffix_max_depth;
    }

    result.n_max = std::max(result.n_max, 0);
    result.n_min = std::max(0, std::min(result.n_min, result.n_max));
    result.stages.clear();

    return result;
}

bool common_params_speculative::has_stage_chain() const {
    return !get_resolved_stages().empty();
}

bool common_params_speculative::has_stage_type(common_speculative_type stage_type) const {
    const auto resolved = get_resolved_stages();
    return std::any_of(resolved.begin(), resolved.end(), [stage_type](const common_speculative_stage_params & stage) {
        return stage.type == stage_type;
    });
}

bool common_params_speculative::has_composite_stage_chain() const {
    return get_resolved_stages().size() > 1;
}

int32_t common_params_speculative::get_max_stage_n_max() const {
    const auto resolved = get_resolved_stages();
    if (resolved.empty()) {
        return std::max(n_max, 0);
    }

    int32_t max_n_max = 0;
    for (const auto & stage : resolved) {
        max_n_max = std::max(max_n_max, common_speculative_stage_effective_n_max(*this, stage));
    }

    return std::max(max_n_max, 0);
}

int32_t common_params_speculative::get_min_usable_stage_n_min() const {
    const auto resolved = get_resolved_stages();
    if (resolved.empty()) {
        return std::max(0, std::min(n_min, n_max));
    }

    int32_t min_n_min = INT_MAX;
    for (const auto & stage : resolved) {
        min_n_min = std::min(min_n_min, std::max(0, std::min(common_speculative_stage_effective_n_min(*this, stage), common_speculative_stage_effective_n_max(*this, stage))));
    }

    return min_n_min == INT_MAX ? 0 : min_n_min;
}

bool common_speculative_validate_chain(const common_params_speculative & params, std::string * error) {
    const auto fail = [error](const std::string & msg) {
        if (error != nullptr) {
            *error = msg;
        }
        return false;
    };

    const auto resolved = params.get_resolved_stages();
    if (resolved.empty()) {
        return true;
    }

    if (resolved.size() > 2) {
        return fail("at most two speculative stages are supported in this PR");
    }

    std::unordered_set<int> seen_types;
    for (const auto & stage : resolved) {
        if (stage.type == COMMON_SPECULATIVE_TYPE_NONE && resolved.size() > 1) {
            return fail("the 'none' speculative stage cannot be combined with other stages");
        }

        if (!seen_types.insert((int) stage.type).second) {
            return fail("duplicate speculative stage type in chain: " + common_speculative_type_to_str(stage.type));
        }

        const auto stage_params = params.with_stage_overrides(stage);
        if (stage_params.n_min > stage_params.n_max) {
            return fail("speculative stage has n_min greater than n_max");
        }

        if (stage.type == COMMON_SPECULATIVE_TYPE_DRAFT && !params.has_dft()) {
            return fail("draft speculative stage requires a draft model or draft params");
        }
    }

    if (resolved.size() == 2) {
        const auto first = resolved[0].type;
        const auto second = resolved[1].type;

        if (!common_speculative_type_is_self_spec(first)) {
            return fail("two-stage speculative mode currently requires a self-spec stage first");
        }

        if (second != COMMON_SPECULATIVE_TYPE_MTP && second != COMMON_SPECULATIVE_TYPE_DRAFT) {
            return fail("two-stage speculative mode currently supports only MTP or draft-model fallback after self-spec");
        }
    }

    return true;
}

std::string common_speculative_stage_chain_to_str(const common_params_speculative & params) {
    const auto resolved = params.get_resolved_stages();
    if (resolved.empty()) {
        return "none";
    }

    std::ostringstream oss;
    for (size_t i = 0; i < resolved.size(); ++i) {
        if (i > 0) {
            oss << " -> ";
        }
        oss << common_speculative_type_to_str(resolved[i].type);
    }

    return oss.str();
}
//
// Environment variable utils
//

template<typename T>
static typename std::enable_if<std::is_same<T, std::string>::value, void>::type
get_env(std::string name, T & target) {
    char * value = std::getenv(name.c_str());
    target = value ? std::string(value) : target;
}

template<typename T>
static typename std::enable_if<!std::is_same<T, bool>::value && std::is_integral<T>::value, void>::type
get_env(std::string name, T & target) {
    char * value = std::getenv(name.c_str());
    target = value ? std::stoi(value) : target;
}

template<typename T>
static typename std::enable_if<std::is_floating_point<T>::value, void>::type
get_env(std::string name, T & target) {
    char * value = std::getenv(name.c_str());
    target = value ? std::stof(value) : target;
}

template<typename T>
static typename std::enable_if<std::is_same<T, bool>::value, void>::type
get_env(std::string name, T & target) {
    char * value = std::getenv(name.c_str());
    if (value) {
        std::string val(value);
        target = val == "1" || val == "true";
    }
}

//
// CPU utils
//

int32_t cpu_get_num_physical_cores() {
#ifdef __linux__
    // enumerate the set of thread siblings, num entries is num cores
    std::unordered_set<std::string> siblings;
    for (uint32_t cpu=0; cpu < UINT32_MAX; ++cpu) {
        std::ifstream thread_siblings("/sys/devices/system/cpu/cpu"
            + std::to_string(cpu) + "/topology/thread_siblings");
        if (!thread_siblings.is_open()) {
            break; // no more cpus
        }
        std::string line;
        if (std::getline(thread_siblings, line)) {
            siblings.insert(line);
        }
    }
    if (!siblings.empty()) {
        return static_cast<int32_t>(siblings.size());
    }
#elif defined(__APPLE__) && defined(__MACH__)
    int32_t num_physical_cores;
    size_t len = sizeof(num_physical_cores);
    int result = sysctlbyname("hw.perflevel0.physicalcpu", &num_physical_cores, &len, NULL, 0);
    if (result == 0) {
        return num_physical_cores;
    }
    result = sysctlbyname("hw.physicalcpu", &num_physical_cores, &len, NULL, 0);
    if (result == 0) {
        return num_physical_cores;
    }
#elif defined(_WIN32)
    //TODO: Implement
#endif
    unsigned int n_threads = std::thread::hardware_concurrency();
    return n_threads > 0 ? (n_threads <= 4 ? n_threads : n_threads / 2) : 4;
}

#if defined(__x86_64__) && defined(__linux__) && !defined(__ANDROID__)
#include <pthread.h>

static void cpuid(unsigned leaf, unsigned subleaf,
                  unsigned *eax, unsigned *ebx, unsigned *ecx, unsigned *edx) {
    __asm__("movq\t%%rbx,%%rsi\n\t"
            "cpuid\n\t"
            "xchgq\t%%rbx,%%rsi"
            : "=a"(*eax), "=S"(*ebx), "=c"(*ecx), "=d"(*edx)
            : "0"(leaf), "2"(subleaf));
}

static int pin_cpu(int cpu) {
    cpu_set_t mask;
    CPU_ZERO(&mask);
    CPU_SET(cpu, &mask);
    return pthread_setaffinity_np(pthread_self(), sizeof(mask), &mask);
}

static bool is_hybrid_cpu(void) {
    unsigned eax, ebx, ecx, edx;
    cpuid(7, 0, &eax, &ebx, &ecx, &edx);
    return !!(edx & (1u << 15));
}

static bool is_running_on_efficiency_core(void) {
    unsigned eax, ebx, ecx, edx;
    cpuid(0x1a, 0, &eax, &ebx, &ecx, &edx);
    int intel_atom = 0x20;
    int core_type = (eax & 0xff000000u) >> 24;
    return core_type == intel_atom;
}

static int cpu_count_math_cpus(int n_cpu) {
    int result = 0;
    for (int cpu = 0; cpu < n_cpu; ++cpu) {
        if (pin_cpu(cpu)) {
            return -1;
        }
        if (is_running_on_efficiency_core()) {
            continue; // efficiency cores harm lockstep threading
        }
        ++cpu; // hyperthreading isn't useful for linear algebra
        ++result;
    }
    return result;
}

#endif // __x86_64__ && __linux__

/**
 * Returns number of CPUs on system that are useful for math.
 */
int32_t cpu_get_num_math() {
#if defined(__x86_64__) && defined(__linux__) && !defined(__ANDROID__)
    int n_cpu = sysconf(_SC_NPROCESSORS_ONLN);
    if (n_cpu < 1) {
        return cpu_get_num_physical_cores();
    }
    if (is_hybrid_cpu()) {
        cpu_set_t affinity;
        if (!pthread_getaffinity_np(pthread_self(), sizeof(affinity), &affinity)) {
            int result = cpu_count_math_cpus(n_cpu);
            pthread_setaffinity_np(pthread_self(), sizeof(affinity), &affinity);
            if (result > 0) {
                return result;
            }
        }
    }
#endif
    return cpu_get_num_physical_cores();
}

//
// Arg utils
//
common_webui common_webui_from_name(const std::string& format) {
    if (format == "none") {
        return COMMON_WEBUI_NONE;
    }
    else if (format == "auto") {
        return COMMON_WEBUI_AUTO;
    }
    else if (format == "llamacpp") {
        return COMMON_WEBUI_LLAMACPP;
    }
    else {
        return COMMON_WEBUI_AUTO;
    }
}

thinking_tokens thinking_tokens_from_string(const std::string& format) {
    thinking_tokens think_token;
    std::string token_string = string_strip(format);
    if (token_string == "none" || token_string == "None") {
        think_token.exclude = false;
        return think_token;
    }
    else if (token_string == "auto" || token_string == "Auto") {
        think_token.exclude = true;
        think_token.begin = "<think>";
        think_token.end = "</think>";
        return think_token;
    }
    // Use user provided think tokens
    auto start_end = string_split(format, ",");
    if (start_end.size() == 2) {
        think_token.exclude = true;
        think_token.begin = start_end[0];
        think_token.end = start_end[1];
    }
    return think_token;
}


static std::string read_file(const std::string& fname) {
    std::ifstream file(fname);
    if (!file) {
        throw std::runtime_error(string_format("error: failed to open file '%s'\n", fname.c_str()));
    }
    std::string content((std::istreambuf_iterator<char>(file)), std::istreambuf_iterator<char>());
    file.close();
    return content;
}

static std::string parse_device_list(const std::string& value) {
    if (value==" " || value.find("-")!= std::string::npos) {
        throw std::invalid_argument("no devices specified");
    }
    return value;
}

static std::string add_rpc_devices(std::string& servers) {
    std::string rpc_devices;
#ifdef GGML_USE_RPC
    std::vector<std::string> rpc_servers = string_split(servers, ",");
    if (rpc_servers.empty()) {
        throw std::invalid_argument("no RPC servers specified");
    }
    for (auto& server : rpc_servers) {
        uint32_t dev_count = ggml_backend_rpc_get_device_count(server.c_str());
        uint32_t device = 0;
        for (uint32_t i = 0; i < dev_count; ++i) {
            const auto buft = ggml_backend_rpc_buffer_type(server.c_str(), device);
            if (buft != nullptr) {
                rpc_devices = rpc_devices + server + "|" + std::to_string(device) + ",";
                ++device;
            }
        }
    }
    if (!rpc_devices.empty()) {
        rpc_devices = rpc_devices.substr(0, rpc_devices.size() - 1); // remove trailing comma
    }
#endif
    return rpc_devices;
}

std::pair<long, std::vector<char>> common_remote_get_content(const std::string& url, const common_remote_params&) {
    if (!url.empty()) {
        throw std::runtime_error("error: built without CURL, cannot download file from the internet");
    }
    return {};
}

//
// CLI argument parsing
//

std::pair<int, char**> parse_command_line(const std::string& commandLine) {
    std::vector<std::string> tokens;
    std::string current;
    bool inQuotes = false;

    for (size_t i = 0; i < commandLine.length(); i++) {
        char c = commandLine[i];

        if (c == '\"') {
            inQuotes = !inQuotes;
        }
        else if (c == ' ' && !inQuotes) {
            if (!current.empty()) {
                tokens.push_back(current);
                current.clear();
            }
        }
        else {
            current += c;
        }
    }

    if (!current.empty()) {
        tokens.push_back(current);
    }

    int argc = static_cast<int>(tokens.size());
    char** argv = new char* [static_cast<size_t>(argc) + 1];

    for (int i = 0; i < argc; i++) {
        argv[i] = new char[tokens[i].length() + 1];
        std::strcpy(argv[i], tokens[i].c_str());
    }
    argv[argc] = nullptr;
    return { argc, argv };
}

void free_command_line(int argc, char** argv) {
    if (argv == nullptr) return;

    for (int i = 0; i < argc; i++) {
        delete[] argv[i];
    }
    delete[] argv;
}


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;
    }
}

static bool is_truthy(const std::string & value) {
    return value == "on" || value == "enabled" || value == "true" || value == "1";
}

static bool is_falsey(const std::string & value) {
    return value == "off" || value == "disabled" || value == "false" || value == "0";
}

static bool is_autoy(const std::string & value) {
    return value == "auto" || value == "-1";
}

static void common_speculative_finalize_stages(gpt_params & params) {
    auto & spec = params.speculative;

    if (!spec.stages.empty()) {
        spec.type = spec.stages.front().type;
        params.has_mtp = spec.has_stage_type(COMMON_SPECULATIVE_TYPE_MTP);
        return;
    }

    const bool wants_mtp = params.has_mtp;
    const bool wants_draft = spec.has_dft();

    if (spec.type != COMMON_SPECULATIVE_TYPE_NONE) {
        spec.stages.push_back({ .type = spec.type });

        if (common_speculative_type_is_self_spec(spec.type)) {
            if (wants_mtp) {
                spec.stages.push_back({ .type = COMMON_SPECULATIVE_TYPE_MTP });
            } else if (wants_draft) {
                spec.stages.push_back({ .type = COMMON_SPECULATIVE_TYPE_DRAFT });
            }
        }
    } else if (wants_mtp) {
        spec.stages.push_back({ .type = COMMON_SPECULATIVE_TYPE_MTP });
    } else if (wants_draft) {
        spec.stages.push_back({ .type = COMMON_SPECULATIVE_TYPE_DRAFT });
    }

    spec.type = spec.stages.empty() ? COMMON_SPECULATIVE_TYPE_NONE : spec.stages.front().type;
    params.has_mtp = spec.has_stage_type(COMMON_SPECULATIVE_TYPE_MTP);
}

bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
    bool invalid_param = false;
    std::string arg;
    const std::string arg_prefix = "--";
    common_params_sampling & sparams = params.sparams;

    for (int i = 1; i < argc; i++) {
        arg = argv[i];
        if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) {
            std::replace(arg.begin(), arg.end(), '_', '-');
        }
        if (!gpt_params_find_arg(argc, argv, arg, params, i, invalid_param)) {
            throw std::invalid_argument("error: unknown argument: " + arg);
        }
        if (invalid_param) {
            throw std::invalid_argument("error: invalid parameter for argument: " + arg);
        }
    }

    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.hf_token.empty()) {
        get_env("HF_TOKEN", params.hf_token);
    }

    if (params.escape) {
        if (!params.prompt_is_binary) {
            string_process_escapes(params.prompt);
        }
        string_process_escapes(params.input_prefix);
        string_process_escapes(params.input_suffix);
        string_process_escapes(sparams.cfg_negative_prompt);
        for (auto & antiprompt : params.antiprompt) {
            string_process_escapes(antiprompt);
        }
    }

    for (auto & rep : params.speculative.replacements) {
        string_process_escapes(rep.first);
        string_process_escapes(rep.second);
    }

    if (!params.kv_overrides.empty()) {
        params.kv_overrides.emplace_back();
        params.kv_overrides.back().key[0] = 0;
    }
    if (!params.tensor_buft_overrides.empty()) {
        params.tensor_buft_overrides.push_back({nullptr, nullptr});
    }

    if (!params.chat_template.empty() && !common_chat_verify_template(params.chat_template, params.use_jinja)) {
        throw std::runtime_error(string_format(
            "error: the supplied chat template is not supported: %s%s\n",
            params.chat_template.c_str(),
            params.use_jinja ? "" : "\nnote: llama.cpp was started without --jinja, we only support commonly used templates"
        ));
    }

    common_speculative_finalize_stages(params);

    std::string spec_error;
    if (!common_speculative_validate_chain(params.speculative, &spec_error)) {
        throw std::invalid_argument("error: invalid speculative stage configuration: " + spec_error);
    }

    return true;
}

void gpt_params_parse_from_env(gpt_params & params) {
    // we only care about server-related params for now
    get_env("LLAMA_ARG_MODEL",            params.model);
    get_env("LLAMA_ARG_MODEL_URL",        params.model_url);
    get_env("LLAMA_ARG_MODEL_ALIAS",      params.model_alias);
    get_env("LLAMA_ARG_HF_REPO",          params.hf_repo);
    get_env("LLAMA_ARG_HF_FILE",          params.hf_file);
    get_env("LLAMA_ARG_THREADS",          params.n_threads);
    get_env("LLAMA_ARG_CTX_SIZE",         params.n_ctx);
    get_env("LLAMA_ARG_N_PARALLEL",       params.n_parallel);
    get_env("LLAMA_ARG_BATCH",            params.n_batch);
    get_env("LLAMA_ARG_UBATCH",           params.n_ubatch);
    get_env("LLAMA_ARG_N_GPU_LAYERS",     params.n_gpu_layers);
    get_env("LLAMA_ARG_THREADS_HTTP",     params.n_threads_http);
    get_env("LLAMA_ARG_CHAT_TEMPLATE",    params.chat_template);
    get_env("LLAMA_ARG_N_PREDICT",        params.n_predict);
    get_env("LLAMA_ARG_ENDPOINT_METRICS", params.endpoint_metrics);
    get_env("LLAMA_ARG_ENDPOINT_SLOTS",   params.endpoint_slots);
    get_env("LLAMA_ARG_EMBEDDINGS",       params.embedding);
    get_env("LLAMA_ARG_FLASH_ATTN",       params.flash_attn);
    get_env("LLAMA_ARG_DEFRAG_THOLD",     params.defrag_thold);
    get_env("LLAMA_ARG_CONT_BATCHING",    params.cont_batching);
    get_env("LLAMA_ARG_HOST",             params.hostname);
    get_env("LLAMA_ARG_PORT",             params.port);
    get_env("LLAMA_ARG_CACHE_TYPE_K",     params.cache_type_k);
    get_env("LLAMA_ARG_CACHE_TYPE_V",     params.cache_type_v);
    get_env("LLAMA_ARG_MLOCK",            params.use_mlock);
    get_env("LLAMA_ARG_K_CACHE_HADAMARD", params.k_cache_hadamard);
    get_env("LLAMA_ARG_V_CACHE_HADAMARD", params.v_cache_hadamard);

}

bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
    gpt_params_parse_from_env(params);
    const auto params_org = params; // the example can modify the default params

    try {
        if (!gpt_params_parse_ex(argc, argv, params) || params.usage) {
            params = params_org;
            params.usage = true;
            return false;
        }
    } catch (const std::invalid_argument & ex) {
        fprintf(stderr, "%s\n", ex.what());
        params = params_org;
        return false;
    }

    return true;
}

namespace {
bool parse_buft_overrides(const std::string& value, std::vector<llama_model_tensor_buft_override>& overrides) {
    /* static */ std::map<std::string, ggml_backend_buffer_type_t> buft_list;
    if (buft_list.empty()) {
        // enumerate all the devices and add their buffer types to the list
        for (size_t i = 0; i < ggml_backend_reg_get_count(); ++i) {
            //auto * dev = ggml_backend_reg_get_name(i);
            auto * buft = ggml_backend_reg_get_default_buffer_type(i);
            if (buft) {
                buft_list[ggml_backend_buft_name(buft)] = buft;
            }
        }
    }
    for (const auto & override : string_split<std::string>(value, ',')) {
        std::string::size_type pos = override.find('=');
        if (pos == std::string::npos) {
            fprintf(stderr, "Invalid buft override argument %s\n", value.c_str());
            return false;
        }
        std::string tensor_name = override.substr(0, pos);
        std::string buffer_type = override.substr(pos + 1);
        if (buft_list.find(buffer_type) == buft_list.end()) {
            fprintf(stderr, "Available buffer types:\n");
            for (const auto & it : buft_list) {
                fprintf(stderr, "  %s\n", ggml_backend_buft_name(it.second));
            }
            return false;
        }
        overrides.push_back({strdup(tensor_name.c_str()), buft_list.at(buffer_type)});
    }
    return true;
}
template<class T1, class T2>
std::vector<std::pair<T1,T2>> string_split_pairs(const std::string & str, char delim) {
    std::vector<std::pair<T1,T2>> values;
    std::istringstream str_stream(str);
    std::string token;
    T1 first_value;
    int i = 0;
    while (std::getline(str_stream, token, delim)) {
        std::istringstream token_stream(token);
        if (i%2 == 0) {
            token_stream >> first_value;
        } else {
            T2 value;
            token_stream >> value;
            values.emplace_back(first_value, value);
        }
        i++;
    }
    return values;
}

static std::string common_normalize_spec_stage_key(std::string key) {
    while (!key.empty() && key.front() == '-') {
        key.erase(key.begin());
    }

    std::replace(key.begin(), key.end(), '-', '_');

    if (key.rfind("spec_", 0) == 0) {
        key.erase(0, 5);
    }

    return key;
}

static void common_speculative_remove_explicit_stage(common_params_speculative & params, common_speculative_type type) {
    params.stages.erase(std::remove_if(params.stages.begin(), params.stages.end(), [type](const common_speculative_stage_params & stage) {
        return stage.type == type;
    }), params.stages.end());

    if (params.stages.empty() && params.type == type) {
        params.type = COMMON_SPECULATIVE_TYPE_NONE;
    }
}

static void common_speculative_stage_apply_kv(
        common_speculative_stage_params & stage,
        const std::string & key_raw,
        const std::string & value_raw) {
    const std::string key = common_normalize_spec_stage_key(key_raw);

    if (key == "draft" || key == "draft_max" || key == "draft_n" || key == "n_max") {
        stage.n_max = std::stoi(value_raw);
        if (stage.n_max < 0) {
            throw std::invalid_argument("speculative stage n_max must be >= 0");
        }
        return;
    }
    if (key == "draft_min" || key == "draft_n_min" || key == "n_min") {
        stage.n_min = std::stoi(value_raw);
        if (stage.n_min < 0) {
            throw std::invalid_argument("speculative stage n_min must be >= 0");
        }
        return;
    }
    if (key == "draft_p_min" || key == "p_min") {
        stage.p_min = std::stof(value_raw);
        if (stage.p_min < 0.0f) {
            throw std::invalid_argument("speculative stage p_min must be >= 0");
        }
        return;
    }
    if (key == "ngram_size_n") {
        stage.ngram_size_n = std::stoi(value_raw);
        if (stage.ngram_size_n < 1 || stage.ngram_size_n > 1024) {
            throw std::invalid_argument("speculative stage ngram_size_n must be between 1 and 1024 inclusive");
        }
        return;
    }
    if (key == "ngram_size_m") {
        stage.ngram_size_m = std::stoi(value_raw);
        if (stage.ngram_size_m < 1 || stage.ngram_size_m > 1024) {
            throw std::invalid_argument("speculative stage ngram_size_m must be between 1 and 1024 inclusive");
        }
        return;
    }
    if (key == "ngram_min_hits") {
        stage.ngram_min_hits = std::stoi(value_raw);
        if (stage.ngram_min_hits < 1) {
            throw std::invalid_argument("speculative stage ngram_min_hits must be at least 1");
        }
        return;
    }
    if (key == "suffix_min_match_len" || key == "suffix_pattern_len") {
        stage.suffix_min_match_len = std::stoi(value_raw);
        if (stage.suffix_min_match_len < 1) {
            throw std::invalid_argument("speculative stage suffix_min_match_len must be at least 1");
        }
        return;
    }
    if (key == "suffix_max_depth") {
        stage.suffix_max_depth = std::stoi(value_raw);
        if (stage.suffix_max_depth < 1) {
            throw std::invalid_argument("speculative stage suffix_max_depth must be at least 1");
        }
        return;
    }

    throw std::invalid_argument("unknown speculative stage parameter: " + key_raw);
}

static common_speculative_stage_params common_speculative_stage_from_arg(const std::string & value) {
    const auto spec_pos = value.find(':');
    const std::string type_name = value.substr(0, spec_pos);

    common_speculative_stage_params stage;
    stage.type = common_speculative_type_from_name(type_name);
    if (stage.type == COMMON_SPECULATIVE_TYPE_COUNT) {
        throw std::invalid_argument("unknown speculative stage type: " + type_name);
    }

    if (spec_pos == std::string::npos) {
        return stage;
    }

    std::stringstream ss(value.substr(spec_pos + 1));
    std::string kv;
    while (std::getline(ss, kv, ',')) {
        const auto eq_pos = kv.find('=');
        if (eq_pos == std::string::npos) {
            throw std::invalid_argument("invalid speculative stage option: " + kv);
        }

        common_speculative_stage_apply_kv(stage, kv.substr(0, eq_pos), kv.substr(eq_pos + 1));
    }

    return stage;
}
}

#define CHECK_ARG if (++i >= argc) { invalid_param = true; return true; }

bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_params & params, int & i, bool & invalid_param) {
    const char split_delim = ',';

    common_params_sampling & sparams = params.sparams;

    if (arg == "-s" || arg == "--seed") {
        CHECK_ARG
        // TODO: this is temporary, in the future the sampling state will be moved fully to llama_sampling_context.
        params.seed = std::stoul(argv[i]);
        sparams.seed = std::stoul(argv[i]);
        return true;
    }
    if (arg == "-t" || arg == "--threads") {
        CHECK_ARG
        params.n_threads = std::stoi(argv[i]);
        if (params.n_threads <= 0) {
            params.n_threads = std::thread::hardware_concurrency();
        }
        return true;
    }
    if (arg == "-tb" || arg == "--threads-batch") {
        CHECK_ARG
        params.n_threads_batch = std::stoi(argv[i]);
        if (params.n_threads_batch <= 0) {
            params.n_threads_batch = std::thread::hardware_concurrency();
        }
        return true;
    }
    if (arg == "-tm" || arg == "--threads-mtmd") {
        CHECK_ARG
        params.n_threads_mtmd = std::stoi(argv[i]);
        if (params.n_threads_mtmd <= 0) {
            params.n_threads_mtmd = std::thread::hardware_concurrency();
        }
        return true;
    }
    if (arg == "-td" || arg == "--threads-draft") {
        CHECK_ARG
        params.speculative.n_threads = std::stoi(argv[i]);
        if (params.speculative.n_threads <= 0) {
            params.speculative.n_threads = std::thread::hardware_concurrency();
        }
        return true;
    }
    if (arg == "-tbd" || arg == "--threads-batch-draft") {
        CHECK_ARG
        params.speculative.n_threads_batch = std::stoi(argv[i]);
        if (params.speculative.n_threads_batch <= 0) {
            params.speculative.n_threads_batch = std::thread::hardware_concurrency();
        }
        return true;
    }
    if (arg == "-p" || arg == "--prompt") {
        CHECK_ARG
        params.prompt = argv[i];
        params.prompt_is_binary = false;
        return true;
    }
    if (arg == "-e" || arg == "--escape") {
        params.escape = true;
        return true;
    }
    if (arg == "--no-escape") {
        params.escape = false;
        return true;
    }
    if (arg == "--prompt-cache") {
        CHECK_ARG
        params.path_prompt_cache = argv[i];
        return true;
    }
    if (arg == "--prompt-cache-all") {
        params.prompt_cache_all = true;
        return true;
    }
    if (arg == "--prompt-cache-ro") {
        params.prompt_cache_ro = true;
        return true;
    }
    if (arg == "-bf" || arg == "--binary-file") {
        CHECK_ARG
        std::ifstream file(argv[i], std::ios::binary);
        if (!file) {
            fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
            invalid_param = true;
            return true;
        }
        // store the external file name in params
        params.prompt_file = argv[i];
        std::ostringstream ss;
        ss << file.rdbuf();
        params.prompt = ss.str();
        fprintf(stderr, "Read %zu bytes from binary file %s\n", params.prompt.size(), argv[i]);
        params.prompt_is_binary = true;
        return true;
    }
    if (arg == "-f" || arg == "--file") {
        CHECK_ARG
        std::ifstream file(argv[i]);
        if (!file) {
            fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
            invalid_param = true;
            return true;
        }
        // store the external file name in params
        params.prompt_file = argv[i];
        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();
        }
        params.prompt_is_binary = false;
        return true;
    }
    if (arg == "--in-file") {
        CHECK_ARG
        std::ifstream file(argv[i]);
        if (!file) {
            fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
            invalid_param = true;
            return true;
        }
        params.in_files.push_back(argv[i]);
        return true;
    }
    if (arg == "-n" || arg == "--predict" || arg == "--n-predict") {
        CHECK_ARG
        params.n_predict = std::stoi(argv[i]);
        return true;
    }
    if (arg == "--top-k") {
        CHECK_ARG
        sparams.top_k = std::stoi(argv[i]);
        return true;
    }
    if (arg == "-c" || arg == "--ctx-size") {
        CHECK_ARG
        params.n_ctx = std::stoi(argv[i]);
        return true;
    }
    if (arg == "-cd" || arg == "--ctx-size-draft") {
        CHECK_ARG
        params.speculative.n_ctx = std::stoi(argv[i]);
        return true;
    }
    if (arg == "--grp-attn-n" || arg == "-gan") {
        CHECK_ARG
        params.grp_attn_n = std::stoi(argv[i]);
        return true;
    }
    if (arg == "--grp-attn-w" || arg == "-gaw") {
        CHECK_ARG
        params.grp_attn_w = std::stoi(argv[i]);
        return true;
    }
    if (arg == "--rope-freq-base") {
        CHECK_ARG
        params.rope_freq_base = std::stof(argv[i]);
        return true;
    }
    if (arg == "--rope-freq-scale") {
        CHECK_ARG
        params.rope_freq_scale = std::stof(argv[i]);
        return true;
    }
    if (arg == "--rope-scaling") {
        CHECK_ARG
        std::string value(argv[i]);
        /**/ 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 { invalid_param = true; }
        return true;
    }
    if (arg == "--rope-scale") {
        CHECK_ARG
        params.rope_freq_scale = 1.0f / std::stof(argv[i]);
        return true;
    }
    if (arg == "--yarn-orig-ctx") {
        CHECK_ARG
        params.yarn_orig_ctx = std::stoi(argv[i]);
        return true;
    }
    if (arg == "--yarn-ext-factor") {
        CHECK_ARG
        params.yarn_ext_factor = std::stof(argv[i]);
        return true;
    }
    if (arg == "--yarn-attn-factor") {
        CHECK_ARG
        params.yarn_attn_factor = std::stof(argv[i]);
        return true;
    }
    if (arg == "--yarn-beta-fast") {
        CHECK_ARG
        params.yarn_beta_fast = std::stof(argv[i]);
        return true;
    }
    if (arg == "--yarn-beta-slow") {
        CHECK_ARG
        params.yarn_beta_slow = std::stof(argv[i]);
        return true;
    }
    if (arg == "--pooling") {
        CHECK_ARG
        std::string value(argv[i]);
        /**/ 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 { invalid_param = true; }
        return true;
    }
    if (arg == "--attention") {
        CHECK_ARG
        std::string value(argv[i]);
        /**/ if (value == "causal") { params.attention_type = LLAMA_ATTENTION_TYPE_CAUSAL; }
        else if (value == "non-causal") { params.attention_type = LLAMA_ATTENTION_TYPE_NON_CAUSAL; }
        else { invalid_param = true; }
        return true;
    }
    if (arg == "--defrag-thold" || arg == "-dt") {
        CHECK_ARG
        params.defrag_thold = std::stof(argv[i]);
        return true;
    }
    if (arg == "--max-extra-alloc" || arg == "-mea") {
        CHECK_ARG
        params.max_extra_alloc_MiB = std::stoi(argv[i]);
        return true;
    }
    if (arg == "-nrep" || arg == "--n-repetitions") {
        CHECK_ARG
        params.nrep = std::stoi(argv[i]);
        return true;
    }
    if (arg == "--samplers") {
        CHECK_ARG
        const auto sampler_names = string_split(argv[i], ";");
        sparams.samplers_sequence = llama_sampling_types_from_names(sampler_names, true);
        return true;
    }
    if (arg == "--sampling-seq") {
        CHECK_ARG
        sparams.samplers_sequence = llama_sampling_types_from_chars(argv[i]);
        return true;
    }
    if (arg == "--top-p") {
        CHECK_ARG
        sparams.top_p = std::stof(argv[i]);
        return true;
    }
    if (arg == "--min-p") {
        CHECK_ARG
        sparams.min_p = std::stof(argv[i]);
        return true;
    }
    if (arg == "--temp") {
        CHECK_ARG
        sparams.temp = std::stof(argv[i]);
        sparams.temp = std::max(sparams.temp, 0.0f);
        return true;
    }
    if (arg == "--tfs") {
        CHECK_ARG
        sparams.tfs_z = std::stof(argv[i]);
        return true;
    }
    if (arg == "--typical") {
        CHECK_ARG
        sparams.typical_p = std::stof(argv[i]);
        return true;
    }
    if (arg == "--repeat-last-n") {
        CHECK_ARG
        sparams.penalty_last_n = std::stoi(argv[i]);
        sparams.n_prev = std::max(sparams.n_prev, sparams.penalty_last_n);
        return true;
    }
    if (arg == "--repeat-penalty") {
        CHECK_ARG
        sparams.penalty_repeat = std::stof(argv[i]);
        return true;
    }
    if (arg == "--frequency-penalty") {
        CHECK_ARG
        sparams.penalty_freq = std::stof(argv[i]);
        return true;
    }
    if (arg == "--presence-penalty") {
        CHECK_ARG
        sparams.penalty_present = std::stof(argv[i]);
        return true;
    }
    if (arg == "--dynatemp-range") {
        CHECK_ARG
        sparams.dynatemp_range = std::stof(argv[i]);
        return true;
    }
    if (arg == "--dynatemp-exp") {
        CHECK_ARG
        sparams.dynatemp_exponent = std::stof(argv[i]);
        return true;
    }
    if (arg == "--mirostat") {
        CHECK_ARG
        sparams.mirostat = std::stoi(argv[i]);
        return true;
    }
    if (arg == "--mirostat-lr") {
        CHECK_ARG
        sparams.mirostat_eta = std::stof(argv[i]);
        return true;
    }
    if (arg == "--mirostat-ent") {
        CHECK_ARG
        sparams.mirostat_tau = std::stof(argv[i]);
        return true;
    }
    if (arg == "--xtc-probability") {
        CHECK_ARG
        sparams.xtc_probability = std::stof(argv[i]);
        return true;
    }
    if (arg == "--xtc-threshold") {
        CHECK_ARG
        sparams.xtc_threshold = std::stof(argv[i]);
        return true;
    }
    if (arg == "--top-n-sigma") {
        CHECK_ARG
        sparams.top_n_sigma = std::stof(argv[i]);
        return true;
    }

    if (arg == "--dry-multiplier") {
        CHECK_ARG
            sparams.dry_multiplier = std::stof(argv[i]);
        return true;
    }
    if (arg == "--dry-base") {
        CHECK_ARG
            sparams.dry_base = std::stof(argv[i]);
        return true;
    }
    if (arg == "--dry-allowed-length") {
        CHECK_ARG
            sparams.dry_allowed_length = std::stof(argv[i]);
        return true;
    }
    if (arg == "--dry-penalty-last-n") {
        CHECK_ARG
            sparams.dry_penalty_last_n = std::stof(argv[i]);
        return true;
    }
    if (arg == "--dry-sequence-breaker") {
        CHECK_ARG
        static bool defaults_cleared = false;

        if (!defaults_cleared) {
            params.sparams.dry_sequence_breakers.clear();
            defaults_cleared = true;
        }
       std::string value= std::string(argv[i]);
        if (value == "none") {
            params.sparams.dry_sequence_breakers.clear();
        }
        else {
            for (size_t i = 0; i < value.size(); i++)
            {
                params.sparams.dry_sequence_breakers.emplace_back(std::string{}+value[i]);
            }
        }
        return true;
    }
    if (arg == "--adaptive-target") {
        CHECK_ARG
        sparams.adaptive_target = std::stof(argv[i]);
        return true;
    }
    if (arg == "--adaptive-decay") {
        CHECK_ARG
        sparams.adaptive_decay = std::stof(argv[i]);
        return true;
    }
    if (arg == "--adaptive-updt-w-cur") {
        sparams.adaptive_updt_w_cur = true;
        return true;
    }
    if (arg == "--spec-replace") {
        CHECK_ARG
        std::string target = argv[i];
        CHECK_ARG
        std::string draft = argv[i];
        params.speculative.replacements.emplace_back(std::move(target), std::move(draft));
        return true;
    }
    if (arg == "--cfg-negative-prompt") {
        CHECK_ARG
        sparams.cfg_negative_prompt = argv[i];
        return true;
    }
    if (arg == "--cfg-negative-prompt-file") {
        CHECK_ARG
        std::ifstream file(argv[i]);
        if (!file) {
            fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
            invalid_param = true;
            return true;
        }
        std::copy(std::istreambuf_iterator<char>(file), std::istreambuf_iterator<char>(), back_inserter(sparams.cfg_negative_prompt));
        if (!sparams.cfg_negative_prompt.empty() && sparams.cfg_negative_prompt.back() == '\n') {
            sparams.cfg_negative_prompt.pop_back();
        }
        return true;
    }
    if (arg == "--cfg-scale") {
        CHECK_ARG
        sparams.cfg_scale = std::stof(argv[i]);
        return true;
    }
    if (arg == "-b" || arg == "--batch-size") {
        CHECK_ARG
        params.n_batch = std::stoi(argv[i]);
        return true;
    }
    if (arg == "-ub" || arg == "--ubatch-size") {
        CHECK_ARG
        params.n_ubatch = std::stoi(argv[i]);
        return true;
    }
    if (arg == "--keep") {
        CHECK_ARG
        params.n_keep = std::stoi(argv[i]);
        return true;
    }
    if (arg == "--draft" || arg == "--draft-max" || arg == "--draft-n") {
        CHECK_ARG
        params.speculative.n_max = std::stoi(argv[i]);
        return true;
    }
    if (arg == "--draft-min" || arg == "--draft-n-min") {
        CHECK_ARG
        params.speculative.n_min = std::stoi(argv[i]);
        return true;
    }
    if (arg == "--draft-p-min") {
        CHECK_ARG
        params.speculative.p_min = std::stof(argv[i]);
        return true;
    }
    if (arg == "--recurrent-ckpt-mode") {
        CHECK_ARG
        const std::string val = argv[i];
        if (val == "auto" || val == "AUTO") {
            params.speculative.recurrent_ckpt_mode = LLAMA_SPEC_CKPT_AUTO;
        } else if (val == "per-step" || val == "PER_STEP") {
            params.speculative.recurrent_ckpt_mode = LLAMA_SPEC_CKPT_PER_STEP;
        } else if (val == "gpu-fallback" || val == "GPU_FALLBACK") {
            params.speculative.recurrent_ckpt_mode = LLAMA_SPEC_CKPT_GPU_FALLBACK;
        } else if (val == "cpu" || val == "CPU") {
            params.speculative.recurrent_ckpt_mode = LLAMA_SPEC_CKPT_CPU;
        } else {
            throw std::invalid_argument("unknown --recurrent-ckpt-mode value: " + val +
                "; expected auto, per-step, gpu-fallback, or cpu");
        }
        return true;
    }
    if (arg == "--spec-autotune") {
        params.speculative.autotune = true;
        return true;
    }
    if (arg == "--chunks") {
        CHECK_ARG
        params.n_chunks = std::stoi(argv[i]);
        return true;
    }
    if (arg == "-np" || arg == "--parallel") {
        CHECK_ARG
        params.n_parallel = std::stoi(argv[i]);
        return true;
    }
    if (arg == "-ns" || arg == "--sequences") {
        CHECK_ARG
        params.n_sequences = std::stoi(argv[i]);
        return true;
    }
    if (arg == "--p-split" || arg == "-ps") {
        CHECK_ARG
        params.p_split = std::stof(argv[i]);
        return true;
    }
    if (arg == "-m" || arg == "--model") {
        CHECK_ARG
        params.model = argv[i];
        return true;
    }
    if (arg == "-md" || arg == "--model-draft") {
        CHECK_ARG
        params.speculative.model = argv[i];
        return true;
    }
    if (arg == "--spec-stage") {
        CHECK_ARG

        if (params.speculative.stages.empty()) {
            if (params.speculative.type != COMMON_SPECULATIVE_TYPE_NONE) {
                throw std::invalid_argument("--spec-stage cannot be combined with --spec-type; use only --spec-stage for explicit stage chains");
            }
            if (params.has_mtp) {
                throw std::invalid_argument("--spec-stage cannot be combined with -mtp/--multi-token-prediction; add the mtp fallback explicitly with --spec-stage mtp[:k=v,...]");
            }
        }

        params.speculative.stages.push_back(common_speculative_stage_from_arg(argv[i]));
        if (params.speculative.stages.size() == 1) {
            params.speculative.type = params.speculative.stages.front().type;
        }
        params.has_mtp = params.speculative.has_stage_type(COMMON_SPECULATIVE_TYPE_MTP);
        return true;
    }
    if (arg == "--spec-type") {
        CHECK_ARG
        if (!params.speculative.stages.empty()) {
            throw std::invalid_argument("--spec-type cannot be combined with --spec-stage; use only --spec-stage for explicit stage chains");
        }

        const auto type = common_speculative_type_from_name(argv[i]);
        if (type == COMMON_SPECULATIVE_TYPE_NONE || type == COMMON_SPECULATIVE_TYPE_MTP || common_speculative_type_is_self_spec(type)) {
            params.speculative.type = type;
            if (type == COMMON_SPECULATIVE_TYPE_MTP) {
                params.has_mtp = true;
            }
        } else {
            throw std::invalid_argument("unknown speculative decoding type");
        }
        return true;
    }
    if (arg == "--spec-ngram-size-n") {
        CHECK_ARG
        int value = std::stoi(argv[i]);
        if (value < 1 || value > 1024) {
            throw std::invalid_argument("ngram size N must be between 1 and 1024 inclusive");
        }
        params.speculative.ngram_size_n = value;
        return true;
    }
    if (arg == "--spec-ngram-size-m") {
        CHECK_ARG
        int value = std::stoi(argv[i]);
        if (value < 1 || value > 1024) {
            throw std::invalid_argument("ngram size M must be between 1 and 1024 inclusive");
        }
        params.speculative.ngram_size_m = value;
        return true;
    }
    if (arg == "--spec-ngram-min-hits") {
        CHECK_ARG
        int value = std::stoi(argv[i]);
        if (value < 1) {
            throw std::invalid_argument("ngram min hits must be at least 1");
        }
        params.speculative.ngram_min_hits = value;
        return true;
    }
    if (arg == "--suffix-pattern-len") {
        CHECK_ARG
        int value = std::stoi(argv[i]);
        if (value < 1) {
            throw std::invalid_argument("suffix pattern length must be at least 1");
        }
        params.speculative.suffix_min_match_len = value;
        return true;
    }
    if (arg == "--suffix-max-depth") {
        CHECK_ARG
        int value = std::stoi(argv[i]);
        if (value < 1) {
            throw std::invalid_argument("suffix max depth must be at least 1");
        }
        params.speculative.suffix_max_depth = value;
        return true;
    }
    if (arg == "--suffix-corpus") {
        CHECK_ARG
        params.speculative.suffix_corpus = argv[i];
        return true;
    }
    if (arg == "-a" || arg == "--alias") {
        CHECK_ARG
        params.model_alias = argv[i];
        return true;
    }
    if (arg == "-mu" || arg == "--model-url") {
        CHECK_ARG
        params.model_url = argv[i];
        return true;
    }
    if (arg == "-hft" || arg == "--hf-token") {
        if (++i >= argc) {
          invalid_param = true;
          return true;
        }
        params.hf_token = argv[i];
        return true;
    }
    if (arg == "-hfr" || arg == "--hf-repo") {
        CHECK_ARG
        params.hf_repo = argv[i];
        return true;
    }
    if (arg == "-hff" || arg == "--hf-file") {
        CHECK_ARG
        params.hf_file = argv[i];
        return true;
    }
    if (arg == "--lora") {
        CHECK_ARG
        params.lora_adapters.push_back({
            std::string(argv[i]),
            1.0,
        });
        return true;
    }
    if (arg == "--lora-scaled") {
        CHECK_ARG
        std::string lora_adapter = argv[i];
        CHECK_ARG
        params.lora_adapters.push_back({
            lora_adapter,
            std::stof(argv[i]),
        });
        return true;
    }
    if (arg == "--lora-init-without-apply") {
        params.lora_init_without_apply = true;
        return true;
    }
    if (arg == "--control-vector") {
        CHECK_ARG
        params.control_vectors.push_back({ 1.0f, argv[i], });
        return true;
    }
    if (arg == "--control-vector-scaled") {
        CHECK_ARG
        const char* fname = argv[i];
        CHECK_ARG
        params.control_vectors.push_back({ std::stof(argv[i]), fname, });
        return true;
    }
    if (arg == "--control-vector-layer-range") {
        CHECK_ARG
        params.control_vector_layer_start = std::stoi(argv[i]);
        CHECK_ARG
        params.control_vector_layer_end = std::stoi(argv[i]);
        return true;
    }
    if (arg == "--mmproj") {
        CHECK_ARG
        params.mmproj.path = argv[i];
        return true;
    }
    if (arg == "--mmproj-url") {
        CHECK_ARG
        params.mmproj.url = argv[i];
        return true;
    }
    if (arg == "--no-mmproj-offload") {
        params.mmproj_use_gpu = false;
        return true;
    }
    if (arg == "--mtmd-kq-type") {
        CHECK_ARG
        params.mtmd_kq_type = argv[i];
        return true;
    }
    if (arg == "--image" || arg == "--audio") {
        CHECK_ARG
        params.image.emplace_back(argv[i]);
        return true;
    }
    if (arg == "--image-min-tokens") {
        CHECK_ARG
            params.image_min_tokens = std::stoi(argv[i]);
        return true;
    }
    if (arg == "--image-max-tokens") {
        CHECK_ARG
            params.image_max_tokens = std::stoi(argv[i]);
        return true;
    }
    if (arg == "-i" || arg == "--interactive") {
        params.interactive = true;
        return true;
    }
    if (arg == "-sp" || arg == "--special") {
        params.special = true;
        return true;
    }
    if (arg == "--embedding" || arg == "--embeddings") {
        params.embedding = true;
        return true;
    }
    if (arg == "--embd-normalize") {
        CHECK_ARG
        params.embd_normalize = std::stoi(argv[i]);
        return true;
    }
    if (arg == "--embd-output-format") {
        CHECK_ARG
        params.embd_out = argv[i];
        return true;
    }
    if (arg == "--embd-separator") {
        CHECK_ARG
        params.embd_sep = argv[i];
        return true;
    }
    if (arg == "-if" || arg == "--interactive-first") {
        params.interactive_first = true;
        return true;
    }
    if (arg == "-cnv" || arg == "--conversation") {
        params.conversation = true;
        return true;
    }
    if (arg == "--infill") {
        params.infill = true;
        return true;
    }
    if (arg == "-dkvc" || arg == "--dump-kv-cache") {
        params.dump_kv_cache = true;
        return true;
    }
    if (arg == "-nkvo" || arg == "--no-kv-offload") {
        params.no_kv_offload = true;
        return true;
    }
    if (arg == "-ctk" || arg == "--cache-type-k") {
        params.cache_type_k = argv[++i];
        return true;
    }
    if (arg == "-ctv" || arg == "--cache-type-v") {
        params.cache_type_v = argv[++i];
        return true;
    }
    if (arg == "-ctk-first" || arg == "--cache-type-k-first") {
        CHECK_ARG
        auto p = string_split(argv[i], ",");
        if (p.size() != 2) {
            invalid_param = true;
        } else {
            params.type_k_first = p[0];
            params.n_k_first = std::stoi(p[1].c_str());
        }
        return true;
    }
    if (arg == "-ctk-last" || arg == "--cache-type-k-last") {
        CHECK_ARG
        auto p = string_split(argv[i], ",");
        if (p.size() != 2) {
            invalid_param = true;
        } else {
            params.type_k_last = p[0];
            params.n_k_last = std::stoi(p[1].c_str());
        }
        return true;
    }
    if (arg == "-ctv-first" || arg == "--cache-type-v-first") {
        CHECK_ARG
        auto p = string_split(argv[i], ",");
        if (p.size() != 2) {
            invalid_param = true;
        } else {
            params.type_v_first = p[0];
            params.n_v_first = std::stoi(p[1].c_str());
        }
        return true;
    }
    if (arg == "-ctv-last" || arg == "--cache-type-v-last") {
        CHECK_ARG
        auto p = string_split(argv[i], ",");
        if (p.size() != 2) {
            invalid_param = true;
        } else {
            params.type_v_last = p[0];
            params.n_v_last = std::stoi(p[1].c_str());
        }
        return true;
    }
    if (arg == "--mtp-requantized-output-tensor" || arg == "-mtprot") {
        CHECK_ARG
        params.extra_output_type = argv[i];
        return true;
    }
    if (arg == "-ctkd" || arg == "--cache-type-k-draft") {
        params.speculative.cache_type_k = argv[++i];
        return true;
    }
    if (arg == "-ctvd" || arg == "--cache-type-v-draft") {
        params.speculative.cache_type_v = argv[++i];
        return true;
    }
    if (arg == "-mli" || arg == "--multiline-input") {
        params.multiline_input = true;
        return true;
    }
    if (arg == "--simple-io") {
        params.simple_io = true;
        return true;
    }
    if (arg == "-cb" || arg == "--cont-batching") {
        params.cont_batching = true;
        return true;
    }
    if (arg == "-nocb" || arg == "--no-cont-batching") {
        params.cont_batching = false;
        return true;
    }
    if (arg == "-no-fa" || arg == "--no-flash-attn") {
        params.flash_attn = false;
        return true;
    }

    if (arg == "-fa" || arg == "--flash-attn") {
        CHECK_ARG
        std::string next_arg{argv[i]};
        for (auto& c : next_arg) c = std::tolower(c);
        if (next_arg == "auto" || next_arg == "1" || next_arg == "on") {
            params.flash_attn = true;
        }
        else if (next_arg == "off" || next_arg == "0") {
            params.flash_attn = false;
        }
        else {
            invalid_param = true;
        }
        return true;
    }
    if (arg == "-mla" || arg == "--mla-use") {
        CHECK_ARG
        params.mla_attn = std::stoi(argv[i]);
        return true;
    }
    if (arg == "-amb" || arg == "--attention-max-batch") {
        CHECK_ARG
        params.attn_max_batch = std::stoi(argv[i]);
        return true;
    }
    if (arg == "-no-fmoe" || arg == "--no-fused-moe") {
        params.fused_moe_up_gate = false;
        return true;
    }
    if (arg == "-ger" || arg == "--grouped-expert-routing") {
        params.grouped_expert_routing = true;
        return true;
    }
    if (arg == "-no-fug" || arg == "--no-fused-up-gate") {
        params.fused_up_gate = false;
        return true;
    }
    if (arg == "-no-mmad" || arg == "--no-fused-mul-multiadd") {
        params.fused_mmad = false;
        return true;
    }
    if (arg == "-rcache" || arg == "--rope-cache") {
        fprintf(stderr, "=================================================================================\n");
        fprintf(stderr, "  -rcache, --rope-cache is no longer supported\n");
        fprintf(stderr, "=================================================================================\n");
        //params.rope_cache = true;
        return true;
    }
    if (arg == "-gr" || arg == "--graph-reuse") {
        params.graph_reuse = true;
        return true;
    }
    if (arg == "-no-gr" || arg == "--no-graph-reuse") {
        params.graph_reuse = false;
        return true;
    }
    if (arg == "-ser" || arg == "--smart-expert-reduction") {
        CHECK_ARG
        auto values = string_split_pairs<int,float>(argv[i], ',');
        if (values.size() == 1) {
            params.min_experts    = values.front().first;
            params.thresh_experts = values.front().second;
        } else {
            invalid_param = true;
        }
        return true;
    }
    if (arg == "-co" || arg == "--color") {
        params.use_color = true;
        return true;
    }
    if (arg == "--mlock") {
        params.use_mlock = true;
        return true;
    }
    if (arg == "-ngl" || arg == "--gpu-layers" || arg == "--n-gpu-layers") {
        CHECK_ARG
        params.n_gpu_layers = std::stoi(argv[i]);
        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");
        }
        return true;
    }
    if (arg == "-ngld" || arg == "--gpu-layers-draft" || arg == "--n-gpu-layers-draft") {
        CHECK_ARG
        params.speculative.n_gpu_layers = std::stoi(argv[i]);
        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");
        }
        return true;
    }
    if (arg == "--main-gpu" || arg == "-mg") {
        CHECK_ARG
        params.main_gpu = std::stoi(argv[i]);
#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
        return true;
    }
    else if (arg == "--max-gpu") {
        CHECK_ARG
        params.max_gpu = std::stoi(argv[i]);
        return true;
    }
    if (arg == "--split-mode" || arg == "-sm") {
        CHECK_ARG
        std::string arg_next = argv[i];
        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 == "attn") {
            params.split_mode = LLAMA_SPLIT_MODE_ATTN;
        }
        else if (arg_next == "graph") {
            params.split_mode = LLAMA_SPLIT_MODE_GRAPH;
        }
        else {
            invalid_param = true;
            return true;
        }
#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
        return true;
    }
    if (arg == "--tensor-split" || arg == "-ts") {
        CHECK_ARG
        std::string arg_next = argv[i];

        // 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()) {
            invalid_param = true;
            return true;
        }
        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
        return true;
    }
    if (arg == "--rpc") {
        CHECK_ARG
#ifdef GGML_USE_RPC
        std::string servers(argv[i]);
        servers = add_rpc_devices(servers);
        if (servers.empty()) {
            return false;
        }
        params.rpc_servers = servers;
#endif
        return true;
    }
    if (arg == "--override-kv") {
        CHECK_ARG
            if (!string_parse_kv_override(argv[i], params.kv_overrides)) {
                fprintf(stderr, "error: Invalid type for KV override: %s\n", argv[i]);
                invalid_param = true;
                return true;
            }
        return true;
    }
    if (arg == "--override-tensor" || arg == "-ot") {
        CHECK_ARG
        if (!parse_buft_overrides(std::string{ argv[i] }, params.tensor_buft_overrides)) {
            fprintf(stderr, "error: Invalid tensor buffer type override: %s\n", argv[i]);
            invalid_param = true;
        }
        return true;
    }
    if (arg == "-cuda" || arg == "--cuda-params") {
        CHECK_ARG
        params.cuda_params = argv[i];
        return true;
    }
    if (arg == "-mtp" || arg == "--multi-token-prediction") {
        if (!params.speculative.stages.empty()) {
            throw std::invalid_argument("-mtp/--multi-token-prediction cannot be combined with --spec-stage; add the mtp fallback explicitly with --spec-stage mtp[:k=v,...]");
        }

        params.has_mtp = true;
        return true;
    }
    if (arg == "-no-mtp" || arg == "--no-multi-token-prediction") {
        params.has_mtp = false;
        common_speculative_remove_explicit_stage(params.speculative, COMMON_SPECULATIVE_TYPE_MTP);
        return true;
    }
    if (arg == "-draft" || arg == "--draft-params") {
        CHECK_ARG
        params.speculative.params = argv[i];
        return true;
    }
    if (arg == "--cpu-moe" || arg == "-cmoe") {
        params.ncmoe = 999;
        //params.tensor_buft_overrides.push_back({strdup("\\.ffn_(up|down|gate|gate_up)_exps\\.weight"), ggml_backend_cpu_buffer_type()});
        return true;
    }
    if (arg == "--n-cpu-moe" || arg == "-ncmoe") {
        CHECK_ARG
        int32_t n_layers = std::stoi(argv[i]);
        if (n_layers < 0) {
            fprintf(stderr, "error: Invalid value for --n-cpu-moe: %d (must be >= 0)\n", n_layers);
            invalid_param = true;
            return true;
        }
        params.ncmoe = n_layers;
        //for (int32_t l = 0; l < n_layers; ++l) {
        //    std::string pattern = "blk\\." + std::to_string(l) + "\\.(ffn_(up|down|gate|gate_up)_exps\\.weight)";
        //    params.tensor_buft_overrides.push_back({strdup(pattern.c_str()), ggml_backend_cpu_buffer_type()});
        //}
        return true;
    }
    if (arg == "--fit") {
        params.fit = true;
        return true;
    }
    if (arg == "--defer-experts") {
        params.defer_experts = true;
        params.warmup = false;
        return true;
    }
    if (arg == "--fit-margin") {
        CHECK_ARG;
        int32_t margin = std::stoi(argv[i]);
        if (margin < 0) {
            fprintf(stderr, "error: Invalid value for --fit-margin: %d (must be >= 0)\n", margin);
            invalid_param = true;
        } else {
            params.fit_margin = margin;
        }
        return true;
    }
    if (arg == "-wgt" || arg == "--worst-graph-tokens") {
        CHECK_ARG;
        params.worst_graph_tokens = std::stoi(argv[i]);
        return true;
    }
    if (arg == "--no-mmap") {
        params.use_mmap = false;
        return true;
    }
    if (arg == "-rtr" || arg == "--run-time-repack") {
        params.repack_tensors = true;
        params.use_mmap = false;
        return true;
    }
    if (arg == "-thp" || arg == "--transparent-huge-pages") {
        params.use_thp = true;
        return true;
    }
    if (arg == "-vq" || arg == "--validate-quants") {
        params.validate_quants = true;
        return true;
    }
    if (arg == "-mqkv" || arg == "--merge-qkv") {
        params.merge_qkv = true;
        return true;
    }
    if (arg == "-muge" || arg == "--merge-up-gate-experts") {
        params.merge_up_gate_exps = true;
        return true;
    }
    if (arg == "-khad" || arg == "--k-cache-hadamard") {
        params.k_cache_hadamard = true;
        return true;
    }
    if (arg == "-vhad" || arg == "--v-cache-hadamard") {
        params.v_cache_hadamard = true;
        return true;
    }
    if (arg == "-smgs" || arg == "--split-mode-graph-scheduling") {
        params.split_mode_graph_scheduling = true;
        return true;
    }
    if (arg == "-sas" || arg == "--scheduler-async") {
        params.scheduler_async = true;
        return true;
    }
    if (arg == "-fdn" || arg == "--fused-delta-net") {
        CHECK_ARG
        fprintf(stderr, "=================== %s has been deprecated\n", arg.c_str());
        return true;
    }
    if (arg == "-smf16" || arg == "--split-mode-f16") {
        params.reduce_type = "f16";
        //params.split_mode_f16 = true;
        return true;
    }
    if (arg == "-smf32" || arg == "--split-mode-f32") {
        params.reduce_type = "f32";
        //params.split_mode_f16 = false;
        return true;
    }
    if (arg == "-grt" || arg == "--graph-reduce-type") {
        CHECK_ARG
        params.reduce_type = argv[i];
        return true;
    }
    if (arg == "--numa") {
        CHECK_ARG
        std::string value(argv[i]);
        /**/ 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 { invalid_param = true; }
        return true;
    }
    if (arg == "-dev" || arg == "--device") {
        CHECK_ARG
        std::string value(argv[i]);
        params.devices = parse_device_list(value);
        return true;
    }
    if (arg == "-devd" || arg == "--device-draft") {
        CHECK_ARG
        std::string value(argv[i]);
        params.speculative.devices = parse_device_list(value);
        return true;
    }
    if (arg == "-v" || arg == "--verbose") {
        params.verbosity = 1;
        return true;
    }
    if (arg == "--verbosity") {
        CHECK_ARG
        params.verbosity = std::stoi(argv[i]);
        return true;
    }
    if (arg == "--verbose-prompt") {
        params.verbose_prompt = true;
        return true;
    }
    if (arg == "--no-display-prompt") {
        params.display_prompt = false;
        return true;
    }
    if (arg == "-r" || arg == "--reverse-prompt") {
        CHECK_ARG
        params.antiprompt.emplace_back(argv[i]);
        return true;
    }
    if (arg == "--banned-string-file") {
        CHECK_ARG
        std::string files = read_file(std::string(argv[i]));
        std::vector<std::string> ban_strings=string_split(files, "\n");
        std::vector<std::string> ban_phrases;
        for (auto& str : ban_strings) {
            std::erase(str, '"');
            if (!str.empty()) {
                ban_phrases.push_back(str);
            }
        }
        params.ban_phrases = ban_phrases;
        return true;
    }
    if (arg == "--banned-n") {
        CHECK_ARG
        params.banned_n = std::stoi(argv[i]);
        return true;
    }
    if (arg == "--allowlist-unicode-rule") {
        CHECK_ARG
        if (params.allow_ruless.size() == 0) {
            params.allow_ruless.push_back({});
        }
        params.allow_ruless.back().push_back(argparse_allowlist_unicode_rule(argv[i]));
        return true;
    }
    if (arg == "--allowlist-pieces") {
        CHECK_ARG
        params.allow_pieces.push_back(argv[i]);
        return true;
    }
    if (arg == "--allowlist-keyword") {
        CHECK_ARG
        params.allow_kws.push_back(argv[i]);
        params.allow_ruless.push_back({});
        return true;
    }
    if (arg == "--allowlist-keyword-delay") {
        CHECK_ARG
        params.allow_kw_delay = std::stoul(argv[i]);
        return true;
    }
    if (arg == "--expiring-logit-bias-file") {
        CHECK_ARG
        std::string content = read_file(argv[i]);
        argparse_expiring_logit_bias(content, sparams);
        return true;
    }
    if (arg == "-ld" || arg == "--logdir") {
        CHECK_ARG
        params.logdir = argv[i];

        if (params.logdir.back() != DIRECTORY_SEPARATOR) {
            params.logdir += DIRECTORY_SEPARATOR;
        }
        return true;
    }
    if (arg == "-lcs" || arg == "--lookup-cache-static") {
        CHECK_ARG
        params.lookup_cache_static = argv[i];
        return true;
    }
    if (arg == "-lcd" || arg == "--lookup-cache-dynamic") {
        CHECK_ARG
        params.lookup_cache_dynamic = argv[i];
        return true;
    }
    if (arg == "--save-all-logits" || arg == "--kl-divergence-base") {
        CHECK_ARG
        params.logits_file = argv[i];
        return true;
    }
    if (arg == "--perplexity" || arg == "--all-logits") {
        params.logits_all = true;
        return true;
    }
    if (arg == "--ppl-stride") {
        CHECK_ARG
        params.ppl_stride = std::stoi(argv[i]);
        return true;
    }
    if (arg == "--ppl-output-type") {
        CHECK_ARG
        params.ppl_output_type = std::stoi(argv[i]);
        return true;
    }
    if (arg == "-ptc" || arg == "--print-token-count") {
        CHECK_ARG
        params.n_print = std::stoi(argv[i]);
        return true;
    }
    if (arg == "--check-tensors") {
        params.check_tensors = true;
        return true;
    }
    if (arg == "--hellaswag") {
        params.hellaswag = true;
        return true;
    }
    if (arg == "--hellaswag-tasks") {
        CHECK_ARG
        params.hellaswag_tasks = std::stoi(argv[i]);
        return true;
    }
    if (arg == "--winogrande") {
        params.winogrande = true;
        return true;
    }
    if (arg == "--winogrande-tasks") {
        CHECK_ARG
        params.winogrande_tasks = std::stoi(argv[i]);
        return true;
    }
    if (arg == "--multiple-choice") {
        params.multiple_choice = true;
        return true;
    }
    if (arg == "--multiple-choice-tasks") {
        CHECK_ARG
        params.multiple_choice_tasks = std::stoi(argv[i]);
        return true;
    }
    if (arg == "--kl-divergence") {
        params.kl_divergence = true;
        return true;
    }
    if (arg == "--ignore-eos") {
        params.ignore_eos = true;
        return true;
    }
    if (arg == "--penalize-nl") {
        sparams.penalize_nl = true;
        return true;
    }
    if (arg == "-l" || arg == "--logit-bias") {
        CHECK_ARG
        std::stringstream ss(argv[i]);
        llama_token key;
        char sign;
        std::string value_str;
        try {
            if (ss >> key && ss >> sign && std::getline(ss, value_str) && (sign == '+' || sign == '-')) {
                sparams.logit_bias[key] = std::stof(value_str) * ((sign == '-') ? -1.0f : 1.0f);
            }
            else {
                throw std::exception();
            }
        }
        catch (const std::exception&) {
            invalid_param = true;
            return true;
        }
        return true;
    }
    if (arg == "-h" || arg == "--help" || arg == "--usage"  ) {
        params.usage = true;
        return true;
    }
    if (arg == "--version") {
        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);
    }
    if (arg == "--dry-run" || arg == "-dr") {
        params.dry_run = true;
        return true;
    }
    if (arg == "--in-prefix-bos") {
        params.input_prefix_bos = true;
        params.enable_chat_template = false;
        return true;
    }
    if (arg == "--in-prefix") {
        CHECK_ARG
        params.input_prefix = argv[i];
        params.enable_chat_template = false;
        return true;
    }
    if (arg == "--in-suffix") {
        CHECK_ARG
        params.input_suffix = argv[i];
        params.enable_chat_template = false;
        return true;
    }
    if (arg == "--spm-infill") {
        params.spm_infill = true;
        return true;
    }
    if (arg == "--grammar") {
        CHECK_ARG
            sparams.grammar = { COMMON_GRAMMAR_TYPE_USER, argv[i] };
        return true;
    }
    if (arg == "--grammar-file") {
        CHECK_ARG
        std::ifstream file(argv[i]);
        if (!file) {
            fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
            invalid_param = true;
            return true;
        }
        sparams.grammar = {COMMON_GRAMMAR_TYPE_USER, read_file(argv[i])};
        return true;
    }
    if (arg == "-j" || arg == "--json-schema") {
        CHECK_ARG
        sparams.grammar = { COMMON_GRAMMAR_TYPE_OUTPUT_FORMAT,  json_schema_to_grammar(json::parse(argv[i]))};
        return true;
    }

    if (arg == "--offload-policy" || arg == "-op") {
        CHECK_ARG
        auto p = string_split_pairs<int,int>(argv[i], ',');
        if (p.empty()) {
            fprintf(stderr, "error: Invalid offload policy argument: %s\n", argv[i]);
            invalid_param = true;
        } else {
            params.offload_policy.insert(params.offload_policy.end(), p.begin(), p.end());
        }
        return true;
    }
    if (arg == "--no-offload-only-active-experts" || arg == "-no-ooae") {
        params.only_active_exps = false;
        return true;
    }
    if (arg == "--host") {
        CHECK_ARG
        params.hostname = argv[i];
        return true;
    }
    if (arg == "--port") {
        CHECK_ARG
        params.port = std::stoi(argv[i]);
        return true;
    }
    if (arg == "--send-done") {
        params.send_done = true;
        return true;
    }
    if (arg == "--path") {
        CHECK_ARG
        params.public_path = argv[i];
        return true;
    }
    if (arg == "--webui") {
        CHECK_ARG
        params.webui = common_webui_from_name(std::string(argv[i]));
        return true;
    }
    if (arg == "--api-key") {
        CHECK_ARG
        params.api_keys.push_back(argv[i]);
        return true;
    }
    if (arg == "--api-key-file") {
        CHECK_ARG
        std::ifstream key_file(argv[i]);
        if (!key_file) {
            fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
            invalid_param = true;
            return true;
        }
        std::string key;
        while (std::getline(key_file, key)) {
            if (!key.empty()) {
                params.api_keys.push_back(key);
            }
        }
        key_file.close();
        return true;
    }
    if (arg == "--ssl-key-file") {
        CHECK_ARG
        params.ssl_file_key = argv[i];
        return true;
    }
    if (arg == "--ssl-cert-file") {
        CHECK_ARG
        params.ssl_file_cert = argv[i];
        return true;
    }
    if (arg == "--timeout" || arg == "-to") {
        CHECK_ARG
        params.timeout_read  = std::stoi(argv[i]);
        params.timeout_write = std::stoi(argv[i]);
        return true;
    }
    if (arg == "--threads-http") {
        CHECK_ARG
        params.n_threads_http = std::stoi(argv[i]);
        return true;
    }
    if (arg == "-spf" || arg == "--system-prompt-file") {
        CHECK_ARG
        std::ifstream file(argv[i]);
        if (!file) {
            fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
            invalid_param = true;
            return true;
        }
        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;
        return true;
    }
    if (arg == "--log-format") {
        CHECK_ARG
        if (std::strcmp(argv[i], "json") == 0) {
            params.log_json = true;
        } else if (std::strcmp(argv[i], "text") == 0) {
            params.log_json = false;
        } else {
            invalid_param = true;
            return true;
        }
        return true;
    }
    if (arg == "--no-slots") {
        params.endpoint_slots = false;
        return true;
    }
    if (arg == "--metrics") {
        params.endpoint_metrics = true;
        return true;
    }
    if (arg == "--slot-save-path") {
        CHECK_ARG
        params.slot_save_path = argv[i];
        // 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;
        }
        return true;
    }
    if (arg == "--reasoning-tokens") {
        CHECK_ARG
        params.think_tokens = thinking_tokens_from_string(std::string(argv[i]));
        return true;
    }
    if (arg == "--reasoning-budget") {
        CHECK_ARG
        params.reasoning_budget = std::stoi(argv[i]);
        return true;
    }
    if (arg == "--sql-save-file") {
        CHECK_ARG
        params.sql_save_file = argv[i];
        return true;
    }
    if (arg == "--sqlite-zstd-ext-file") {
        CHECK_ARG
        params.sqlite_zstd_ext_file = argv[i];
        return true;
    }
    if (arg == "--chat-template") {
        CHECK_ARG
        if (!common_chat_verify_template(argv[i], true)) {
            fprintf(stderr, "error: the supplied chat template is not supported: %s\n", argv[i]);
            fprintf(stderr, "note: llama.cpp does not use jinja parser, we only support commonly used templates\n");
            invalid_param = true;
            return true;
        }
        params.chat_template = argv[i];
        return true;
    }
    if (arg == "--chat-template-file") {
        CHECK_ARG
        std::string chat_template = read_file(std::string(argv[i]));
        if (!common_chat_verify_template(chat_template, true)) {
            fprintf(stderr, "error: the supplied chat template is not supported: %s\n", argv[i]);
            invalid_param = true;
            return true;
        }
        params.chat_template = chat_template;
        return true;
    }
    if (arg == "--jinja") {
        params.use_jinja = true;
        return true;
    }
    if (arg == "--peg") {
        return true;
    }
    if (arg == "--chat-template-kwargs") {
        CHECK_ARG
        std::string value = argv[i];
        auto parsed = json::parse(value);
        for (const auto& item : parsed.items()) {
            if (item.key() == "enable_thinking") {
                LOG_WRN("Setting 'enable_thinking' via --chat-template-kwargs is deprecated. "
                    "Use --reasoning on / --reasoning off instead.\n");
            }
            params.default_template_kwargs[item.key()] = item.value().dump();
        }
        return true;
    }
    if (arg == "--reasoning-format") {
        CHECK_ARG
        std::string value = argv[i];
        params.reasoning_format = common_reasoning_format_from_name(value);
        return true;
    }
    if (arg == "-rea" || arg == "--reasoning") {
        CHECK_ARG
        std::string value = argv[i];
        if (is_truthy(value)) {
            params.enable_reasoning = 1;
            params.default_template_kwargs["enable_thinking"] = "true";
        } else if (is_falsey(value)) {
            params.enable_reasoning = 0;
            params.default_template_kwargs["enable_thinking"] = "false";
        } else if (is_autoy(value)) {
            params.enable_reasoning = -1;
        } else {
            throw std::invalid_argument(
                string_format("error: unknown value for --reasoning: '%s'\n", value.c_str()));
        }
        return true;
    }
    if (arg == "--reasoning-budget-message") {
        CHECK_ARG
        std::string value = argv[i];
        params.reasoning_budget_message = value;
        return true;
    }
    if (arg == "--skip-chat-parsing") {
        CHECK_ARG
        params.force_pure_content_parser = true;
        return true;
    }
    if (arg == "--no-prefill-assistant") {
        CHECK_ARG
        params.prefill_assistant = false;
        return true;
    }
    if (arg == "--parallel-tool-calls") {
        params.parallel_tool_calls = true;
        return true;
    }
    if (arg == "--slot-prompt-similarity" || arg == "-sps") {
        CHECK_ARG
        params.slot_prompt_similarity = std::stof(argv[i]);
        return true;
    }
    if (arg == "-pps") {
        params.is_pp_shared = true;
        return true;
    }
    if (arg == "-npp") {
        CHECK_ARG
        auto p = string_split<int>(argv[i], split_delim);
        params.n_pp.insert(params.n_pp.end(), p.begin(), p.end());
        return true;
    }
    if (arg == "-ntg") {
        CHECK_ARG
        auto p = string_split<int>(argv[i], split_delim);
        params.n_tg.insert(params.n_tg.end(), p.begin(), p.end());
        return true;
    }
    if (arg == "-npl") {
        CHECK_ARG
        auto p = string_split<int>(argv[i], split_delim);
        params.n_pl.insert(params.n_pl.end(), p.begin(), p.end());
        return true;
    }
    if (arg == "--context-file") {
        CHECK_ARG
        std::ifstream file(argv[i], std::ios::binary);
        if (!file) {
            fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
            invalid_param = true;
            return true;
        }
        params.context_files.push_back(argv[i]);
        return true;
    }
    if (arg == "--chunk-size") {
        CHECK_ARG
        params.chunk_size = std::stoi(argv[i]);
        return true;
    }
    if (arg == "--chunk-separator") {
        CHECK_ARG
        params.chunk_separator = argv[i];
        return true;
    }
    if (arg == "--junk") {
        CHECK_ARG
        params.n_junk = std::stoi(argv[i]);
        return true;
    }
    if (arg == "--no-context-shift") {
        params.ctx_shift = false;
        return true;
    }
    if (arg == "--context-shift") {
        CHECK_ARG
        std::string next_arg{ argv[i] };
        for (auto& c : next_arg) c = std::tolower(c);
        if (next_arg == "auto" || next_arg == "1" || next_arg == "on") {
            params.ctx_shift = true;
        }
        else if (next_arg == "off" || next_arg == "0") {
            params.ctx_shift = false;
        }
        else {
            invalid_param = true;
        }
        return true;
    }
    if (arg == "--ctx-checkpoints") {
        CHECK_ARG
        params.ctx_checkpoints_n = std::stoi(argv[i]);
        return true;
    }
    if (arg == "--ctx-checkpoints-interval") {
        CHECK_ARG
        params.ctx_checkpoints_interval = std::stoi(argv[i]);
        return true;
    }
    if (arg == "--ctx-checkpoints-tolerance") {
        CHECK_ARG
        params.ctx_checkpoints_tolerance = std::stoi(argv[i]);
        return true;
    }
    if (arg == "-cram" || arg == "--cache-ram") {
        CHECK_ARG
        params.cache_ram_mib = std::stoi(argv[i]);
        return true;
    }
    if (arg == "-crs" || arg == "--cache-ram-similarity") {
        CHECK_ARG
        params.cache_ram_similarity = std::stof(argv[i]);
        return true;
    }
    if (arg == "-cram-n-min" || arg == "--cache-ram-n-min") {
        CHECK_ARG
        params.cache_ram_n_min = std::stoi(argv[i]);
        return true;
    }
    if (arg == "--pos") {
        CHECK_ARG
        params.i_pos = std::stoi(argv[i]);
        return true;
    }
    if (arg == "-o" || arg == "--output" || arg == "--output-file") {
        CHECK_ARG
        params.out_file = argv[i];
        params.cvector_outfile = argv[i];
        params.lora_outfile = argv[i];
        return true;
    }
    if (arg == "--output-draft" || arg == "--draft-output" || arg == "--draft-output-file") {
        CHECK_ARG
        params.out_file_draft = argv[i];
        return true;
    }
    if (arg == "-ofreq" || arg == "--output-frequency") {
        CHECK_ARG
        params.n_out_freq = std::stoi(argv[i]);
        return true;
    }
    if (arg == "--save-frequency") {
        CHECK_ARG
        params.n_save_freq = std::stoi(argv[i]);
        return true;
    }
    if (arg == "--process-output") {
        params.process_output = true;
        return true;
    }
    if (arg == "--output-tensor-name") {
        if (++i >= argc) {
            invalid_param = true;
            return true;
        }
        params.output_tensor_name = argv[i];
        return true;
    }
    if (arg == "--no-ppl") {
        params.compute_ppl = false;
        return true;
    }
    if (arg == "--chunk" || arg == "--from-chunk") {
        CHECK_ARG
        params.i_chunk = std::stoi(argv[i]);
        return true;
    }
    // cvector params
    if (arg == "--positive-file") {
        CHECK_ARG
        params.cvector_positive_file = argv[i];
        return true;
    }
    if (arg == "--negative-file") {
        CHECK_ARG
        params.cvector_negative_file = argv[i];
        return true;
    }
    if (arg == "--pca-batch") {
        CHECK_ARG
        params.n_pca_batch = std::stoi(argv[i]);
        return true;
    }
    if (arg == "--pca-iter") {
        CHECK_ARG
        params.n_pca_iterations = std::stoi(argv[i]);
        return true;
    }
    if (arg == "--method") {
        CHECK_ARG
        std::string value(argv[i]);
        /**/ if (value == "pca") { params.cvector_dimre_method = DIMRE_METHOD_PCA; }
        else if (value == "mean") { params.cvector_dimre_method = DIMRE_METHOD_MEAN; }
        else { invalid_param = true; }
        return true;
    }
    if (arg == "--no-warmup") {
        params.warmup = false;
        return true;
    }
    if (arg == "--warmup-batch" || arg == "-wb") {
        params.batch_warmup = true;
        return true;
    }
    if (arg == "--output-format") {
        CHECK_ARG
        std::string value(argv[i]);
        /**/ if (value == "jsonl") { params.sweep_bench_output_jsonl = true; }
        else if (value == "md") { params.sweep_bench_output_jsonl = false; }
        else { invalid_param = true; }
        return true;
    }
    if (arg == "--minilog") {
        params.minilog = true;
        return true;
    }

#ifndef LOG_DISABLE_LOGS
    // Parse args for logging parameters
    if (log_param_single_parse(argv[i])) {
        // Do nothing, log_param_single_parse automatically does it's thing
        //  and returns if a match was found and parsed.
        return true;
    }
    if (log_param_pair_parse( /*check_but_dont_parse*/ true, argv[i])) {
        // We have a matching known parameter requiring an argument,
        //  now we need to check if there is anything after this argv
        //  and flag invalid_param or parse it.
        CHECK_ARG
        if (!log_param_pair_parse( /*check_but_dont_parse*/ false, argv[i - 1], argv[i])) {
            invalid_param = true;
            return true;
        }
        return true;
    }
    // End of Parse args for logging parameters
#endif // LOG_DISABLE_LOGS

    return false;
}

#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

void gpt_params_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
    const common_params_sampling & sparams = params.sparams;

    std::string sampler_type_chars;
    std::string sampler_type_names;
    for (const auto sampler_type : sparams.samplers_sequence) {
        sampler_type_chars += static_cast<char>(sampler_type);
        sampler_type_names += llama_sampling_type_to_str(sampler_type) + ";";
    }
    sampler_type_names.pop_back();

    struct option_info {
        LLAMA_COMMON_ATTRIBUTE_FORMAT(4, 5)
        option_info(const std::string & tags, const char * args, const char * desc, ...) : tags(tags), args(args), desc(desc) {
            va_list args_list;
            va_start(args_list, desc);
            char buffer[1024];
            vsnprintf(buffer, sizeof(buffer), desc, args_list);
            va_end(args_list);
            this->desc = buffer;
        }

        option_info(const std::string & grp) : grp(grp) {}

        std::string tags;
        std::string args;
        std::string desc;
        std::string grp;
    };

    std::vector<option_info> options;

    // TODO: filter by tags

    options.push_back({ "general" });
    options.push_back({ "*",           "-h,    --help, --usage",        "print usage and exit" });
    options.push_back({ "*",           "       --version",              "show version and build info" });
    options.push_back({ "*",           "-v,    --verbose",              "print verbose information" });
    options.push_back({ "*",           "       --minilog",              "print important information" });
    options.push_back({ "*",           "       --verbosity N",          "set specific verbosity level (default: %d)", params.verbosity });
    options.push_back({ "*",           "       --verbose-prompt",       "print a verbose prompt before generation (default: %s)", params.verbose_prompt ? "true" : "false" });
    options.push_back({ "*",           "-dr,   --dry-run",       "skip loading tensors in the files"});
    options.push_back({ "*",           "       --no-display-prompt",    "don't print prompt at generation (default: %s)", !params.display_prompt ? "true" : "false" });
    options.push_back({ "*",           "-co,   --color",                "colorise output to distinguish prompt and user input from generations (default: %s)", params.use_color ? "true" : "false" });
    options.push_back({ "*",           "-s,    --seed SEED",            "RNG seed (default: %d, use random seed for < 0)", params.seed });
    options.push_back({ "*",           "-t,    --threads N",            "number of threads to use during generation (default: %d)", params.n_threads });
    options.push_back({ "*",           "-tb,   --threads-batch N",      "number of threads to use during batch and prompt processing (default: same as --threads)" });
    options.push_back({ "multi-modality", "-tm,   --threads-mtmd N",    "number of threads to use during multimodal image processing (default: same as --threads-batch)" });
    options.push_back({ "speculative", "-td,   --threads-draft N",      "number of threads to use during generation (default: same as --threads)" });
    options.push_back({ "speculative", "-tbd,  --threads-batch-draft N",
                                                                        "number of threads to use during batch and prompt processing (default: same as --threads-draft)" });
    options.push_back({ "speculative", "-ps,   --p-split N",            "speculative decoding split probability (default: %.1f)", (double)params.p_split });
    options.push_back({ "*",           "-lcs,  --lookup-cache-static FNAME",
                                                                        "path to static lookup cache to use for lookup decoding (not updated by generation)" });
    options.push_back({ "*",           "-lcd,  --lookup-cache-dynamic FNAME",
                                                                        "path to dynamic lookup cache to use for lookup decoding (updated by generation)" });

    options.push_back({ "*",           "-c,    --ctx-size N",           "size of the prompt context (default: %d, 0 = loaded from model)", params.n_ctx });
    options.push_back({ "*",           "-cd,   --ctx-size-draft N",     "size of the prompt context for the draft model (default: %d, 0 = loaded from model)", params.speculative.n_ctx });

    options.push_back({ "*",           "--ctx-checkpoints N",           "max number of context checkpoints to create per slot (default: %d)",params.ctx_checkpoints_n});
    options.push_back({ "*",           "--ctx-checkpoints-interval N",  "minimum number of tokens between each context checkpoint.  (default: %d, <=0 disable)",params.ctx_checkpoints_interval});
    options.push_back({ "*",           "--ctx-checkpoints-tolerance N", "the number of tokens before the full prompt to create the checkpoint.  (default: %d, <=0 disable)",params.ctx_checkpoints_tolerance});
    options.push_back({ "*",           "-cram, --cache-ram N",          "set the maximum cache size in MiB (default: %d, -1 - no limit, 0 - disable)",params.cache_ram_mib });
    options.push_back({ "*",           "-crs,  --cache-ram-similarity N",           "max of similarity of prompt tokens to cache tokens that triggers prompt cache (default: %.2f).",params.cache_ram_similarity });
    options.push_back({ "*",           "-cram-n-min --cache-ram-n-min N",           "minimum number of the cached tokens that triggers prompt cache (default: %d).", params.cache_ram_n_min });
    options.push_back({ "*",           "-n,    --predict N",            "number of tokens to predict (default: %d, -1 = infinity, -2 = until context filled)", params.n_predict });
    options.push_back({ "*",           "-b,    --batch-size N",         "logical maximum batch size (default: %d)", params.n_batch });
    options.push_back({ "*",           "-ub,   --ubatch-size N",        "physical maximum batch size (default: %d)", params.n_ubatch });
    options.push_back({ "*",           "       --keep N",               "number of tokens to keep from the initial prompt (default: %d, -1 = all)", params.n_keep });
    options.push_back({ "*",           "       --chunks N",             "max number of chunks to process (default: %d, -1 = all)", params.n_chunks });
    options.push_back({ "*",           "-no-fa, --no-flash-attn",       "disable Flash Attention (default: %s)", params.flash_attn ? "enabled" : "disabled" });
    options.push_back({ "*",           "-fa, --flash-attn (auto|on|off|0|1)", "set Flash Attention (default: %s)", params.flash_attn ? "on" : "off" });
    options.push_back({ "*",           "-mla,  --mla-use",              "enable MLA (default: %d)", params.mla_attn });
    options.push_back({ "*",           "-amb,  --attention-max-batch",  "max batch size for attention computations (default: %d)", params.attn_max_batch});
    options.push_back({ "*",           "-no-fmoe, --no-fused-moe",      "disable fused MoE (default: %s)", params.fused_moe_up_gate ? "enabled" : "disabled" });
    options.push_back({ "*",           "-ger,  --grouped-expert-routing", "enable grouped expert routing (default: %s)", params.grouped_expert_routing ? "enabled" : "disabled" });
    options.push_back({ "*",           "-no-fug, --no-fused-up-gate",   "disable fused up-gate (default: %s)", params.fused_up_gate ? "enabled" : "disabled" });
    options.push_back({ "*",           "-no-mmad, --no-fused-mul-multiadd", "disable fused mul-multi_add (default: %s)", params.fused_mmad? "enabled" : "disabled" });
    //options.push_back({ "*",           "-rcache, --rope-cache",         "enable RoPE cache (default: %s)", params.rope_cache ? "enabled" : "disabled" });
    options.push_back({ "*",           "-gr, --graph-reuse",            "enable graph reuse (default: %s)", params.graph_reuse ? "enabled" : "disabled" });
    options.push_back({ "*",           "-no-gr, --no-graph-reuse",      "disable graph reuse (default: %s)", !params.graph_reuse ? "enabled" : "disabled" });
    options.push_back({ "*",         "-ser,  --smart-expert-reduction", "experts reduction (default: %d,%g)", params.min_experts, params.thresh_experts});
    options.push_back({ "*",         "-mqkv,  --merge-qkv,",            "merge Q,K,V (default: %d)", params.merge_qkv});
    options.push_back({ "*",         "-muge,  --merge-up-gate-experts,","merge ffn_up/gate_exps (default: %d)", params.merge_up_gate_exps});
    options.push_back({ "*",         "-khad,  --k-cache-hadamard,",     "Use Hadamard transform for K-cache (default: %d)", params.k_cache_hadamard});
    options.push_back({ "*",         "-vhad,  --v-cache-hadamard,",     "Use Hadamard transform for V-cache (default: %d)", params.v_cache_hadamard});
    options.push_back({ "*",         "-smf16, --split-mode-f16,",       "Use f16 for data exchange between GPUs (default: %d)", true});
    options.push_back({ "*",         "-smf32, --split-mode-f32,",       "Use f32 for data exchange between GPUs (default: %d)", false});
    options.push_back({ "*",         "-grt, --graph-reduce-type",       "Type for data exchange between GPUs (default: %s)", "f32"});
    options.push_back({ "*",         "-smgs, --split-mode-graph-scheduling,", "Force Split Mode Graph Scheduling (default: %d)", params.split_mode_graph_scheduling});
    options.push_back({ "*",         "-sas,  --scheduler_async,",       "Async evaluation of compute graphs: %d)", params.scheduler_async});
    options.push_back({ "*",         "-vq, --validate-quants",          "validate quantized data while loading the model (default: %d)", params.validate_quants});
    options.push_back({ "*",           "-p,    --prompt PROMPT",        "prompt to start generation with\n"
                                                                        "in conversation mode, this will be used as system prompt\n"
                                                                        "(default: '%s')", params.prompt.c_str() });
    options.push_back({ "*",           "-f,    --file FNAME",           "a file containing the prompt (default: none)" });
    options.push_back({ "*",           "       --in-file FNAME",        "an input file (repeat to specify multiple files)" });
    options.push_back({ "*",           "-bf,   --binary-file FNAME",    "binary file containing the prompt (default: none)" });
    options.push_back({ "*",           "-e,    --escape",               "process escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\) (default: %s)", params.escape ? "true" : "false" });
    options.push_back({ "*",           "       --no-escape",            "do not process escape sequences" });
    options.push_back({ "main",        "-ptc,  --print-token-count N",  "print token count every N tokens (default: %d)", params.n_print });
    options.push_back({ "main",        "       --prompt-cache FNAME",   "file to cache prompt state for faster startup (default: none)" });
    options.push_back({ "main",        "       --prompt-cache-all",     "if specified, saves user input and generations to cache as well\n"
                                                                        "not supported with --interactive or other interactive options" });
    options.push_back({ "main",        "       --prompt-cache-ro",      "if specified, uses the prompt cache but does not update it" });
    options.push_back({ "main",        "-r,    --reverse-prompt PROMPT",
                                                                        "halt generation at PROMPT, return control in interactive mode\n"
                                                                        "can be specified more than once for multiple prompts" });
    options.push_back({ "main",        "-sp,   --special",              "special tokens output enabled (default: %s)", params.special ? "true" : "false" });
    options.push_back({ "main",        "-cnv,  --conversation",         "run in conversation mode, does not print special tokens and suffix/prefix\n"
                                                                        "if suffix/prefix are not specified, default chat template will be used\n"
                                                                        "(default: %s)", params.conversation ? "true" : "false" });
    options.push_back({ "main infill", "-i,    --interactive",          "run in interactive mode (default: %s)", params.interactive ? "true" : "false" });
    options.push_back({ "main infill", "-if,   --interactive-first",    "run in interactive mode and wait for input right away (default: %s)", params.interactive_first ? "true" : "false" });
    options.push_back({ "main infill", "-mli,  --multiline-input",      "allows you to write or paste multiple lines without ending each in '\\'" });
    options.push_back({ "main infill", "       --in-prefix-bos",        "prefix BOS to user inputs, preceding the `--in-prefix` string" });
    options.push_back({ "main infill", "       --in-prefix STRING",     "string to prefix user inputs with (default: empty)" });
    options.push_back({ "main infill", "       --in-suffix STRING",     "string to suffix after user inputs with (default: empty)" });
    options.push_back({ "main",        "       --no-warmup",            "skip warming up the model with an empty run" });
    options.push_back({ "server infill",
                                       "       --spm-infill",           "use Suffix/Prefix/Middle pattern for infill (instead of Prefix/Suffix/Middle) as some models prefer this. (default: %s)", params.spm_infill ? "enabled" : "disabled" });

    options.push_back({ "sampling" });
    options.push_back({ "*",           "       --samplers SAMPLERS",    "samplers that will be used for generation in the order, separated by \';\'\n"
                                                                        "(default: %s)", sampler_type_names.c_str() });
    options.push_back({ "*",           "       --sampling-seq SEQUENCE",
                                                                        "simplified sequence for samplers that will be used (default: %s)", sampler_type_chars.c_str() });
    options.push_back({ "*",           "       --ignore-eos",           "ignore end of stream token and continue generating (implies --logit-bias EOS-inf)" });
    options.push_back({ "*",           "       --penalize-nl",          "penalize newline tokens (default: %s)", sparams.penalize_nl ? "true" : "false" });
    options.push_back({ "*",           "       --temp N",               "temperature (default: %.1f)", (double)sparams.temp });
    options.push_back({ "*",           "       --top-k N",              "top-k sampling (default: %d, 0 = disabled)", sparams.top_k });
    options.push_back({ "*",           "       --top-p N",              "top-p sampling (default: %.1f, 1.0 = disabled)", (double)sparams.top_p });
    options.push_back({ "*",           "       --min-p N",              "min-p sampling (default: %.1f, 0.0 = disabled)", (double)sparams.min_p });
    options.push_back({ "*",           "       --tfs N",                "tail free sampling, parameter z (default: %.1f, 1.0 = disabled)", (double)sparams.tfs_z });
    options.push_back({ "*",           "       --typical N",            "locally typical sampling, parameter p (default: %.1f, 1.0 = disabled)", (double)sparams.typical_p });
    options.push_back({ "*",           "       --repeat-last-n N",      "last n tokens to consider for penalize (default: %d, 0 = disabled, -1 = ctx_size)", sparams.penalty_last_n });
    options.push_back({ "*",           "       --repeat-penalty N",     "penalize repeat sequence of tokens (default: %.1f, 1.0 = disabled)", (double)sparams.penalty_repeat });
    options.push_back({ "*",           "       --presence-penalty N",   "repeat alpha presence penalty (default: %.1f, 0.0 = disabled)", (double)sparams.penalty_present });
    options.push_back({ "*",           "       --frequency-penalty N",  "repeat alpha frequency penalty (default: %.1f, 0.0 = disabled)", (double)sparams.penalty_freq });
    options.push_back({ "*",           "       --dynatemp-range N",     "dynamic temperature range (default: %.1f, 0.0 = disabled)", (double)sparams.dynatemp_range });
    options.push_back({ "*",           "       --dynatemp-exp N",       "dynamic temperature exponent (default: %.1f)", (double)sparams.dynatemp_exponent });
    options.push_back({ "*",           "       --mirostat N",           "use Mirostat sampling.\n"
                                                                        "Top K, Nucleus, Tail Free and Locally Typical samplers are ignored if used.\n"
                                                                        "(default: %d, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)", sparams.mirostat });
    options.push_back({ "*",           "       --mirostat-lr N",        "Mirostat learning rate, parameter eta (default: %.1f)", (double)sparams.mirostat_eta });
    options.push_back({ "*",           "       --mirostat-ent N",       "Mirostat target entropy, parameter tau (default: %.1f)", (double)sparams.mirostat_tau });
    options.push_back({ "*",           "       --xtc-probability p",    "xtc probability (default: %.1f, 0.0 = disabled)", (double)sparams.xtc_probability });
    options.push_back({ "*",           "       --xtc-threshold t",      "xtc threshold (default: %.1f, >0.5 = disabled)", (double)sparams.xtc_threshold});
    options.push_back({ "*",           "       --top-n-sigma t",        "top-n-sigma parmeter (default: %.1f, 0.0 = disabled)", (double)sparams.top_n_sigma});
    options.push_back({ "*",           "       --adaptive-target",      "adaptive-p sampling: (default: %.2f, <0.0 = disabled)", (double)sparams.adaptive_target});
    options.push_back({ "*",           "       --adaptive-decay",       "adaptive-p sampling: (default: %.2f)", (double)sparams.adaptive_decay});
    options.push_back({ "*",           "       --adaptive-updt-w-cur",  "adaptive-p sampling: (default: %s)", sparams.adaptive_updt_w_cur ? "true" : "false"});
    options.push_back({ "*",           "       --banned-string-file",   "file path of the list of banned strings on each line" });
    options.push_back({ "*",           "       --banned-n",             "number of tokens banned in the phrase during rewind. -1 means all tokens: (default: %d)",params.banned_n });
    options.push_back({ "*",           "       --allowlist-unicode-rule",
                                                                        "rule for allowlisting unicode script and/or codepoints. disabled without any rule. format: `LOWER..UPPER,SCRIPT:BIAS`\n"
                                                                        "if unspecified: LOWER = 0, UPPER = -1(=max), SCRIPT=\"\", BIAS = 0. at least one of LOWER, UPPER, or SCRIPT is required\n" });
    options.push_back({ "*",           "       --allowlist-pieces",     "allowlist each token in argument. inherits max BIAS in --allowlist-unicode-rule. overrides --allowlist-unicode-rule" });
    options.push_back({ "*",           "       --allowlist-keyword",    "keyword to expire earlier allowlist rules if matched during generation. does not affect later rules" });
    options.push_back({ "*",           "       --allowlist-keyword-delay",
                                                                        "# tokens to delay matching for the first keyword (default: %zu)", params.allow_kw_delay });
    options.push_back({ "*",           "       -l 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'" });
    options.push_back({ "main",        "       --cfg-negative-prompt PROMPT",
                                                                        "negative prompt to use for guidance (default: '%s')", sparams.cfg_negative_prompt.c_str() });
    options.push_back({ "main",        "       --cfg-negative-prompt-file FNAME",
                                                                        "negative prompt file to use for guidance" });
    options.push_back({ "main",        "       --cfg-scale N",          "strength of guidance (default: %.1f, 1.0 = disable)", (double)sparams.cfg_scale });
    options.push_back({ "template" });
    options.push_back({ "main",        "       --jinja",
                                                                        "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:\n"
                                                                        "https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template" });
    options.push_back({ "main",        "       --parallel-tool-calls",  "enable parallel tool calls\n" });
    options.push_back({ "main",        "       --chat-template JINJA_TEMPLATE",
                                                                        "use jinja template for chat (default: disabled)\n" });
    options.push_back({ "main",        "       --chat-template-file file_with_JINJA_TEMPLATE",
                                                                        "load jinja template for chat from the file\n" });
    options.push_back({ "main",        "       --reasoning-format FORMAT",
                                                                 "controls whether thought tags are allowed and/or extracted from the response, and in which format they're returned; one of:\n"
                        "- none: leaves thoughts unparsed in `message.content`\n"
                        "- deepseek: puts thoughts in `message.reasoning_content` (except in streaming mode, which behaves as `none`)\n"
                        "- deepseek-legacy: keeps `<think>` tags in `message.content` while also populating `message.reasoning_content`\n"
                        "(default: none)", });
    options.push_back({ "main",        "-rea,  --reasoning",  "[on|off|auto]"
                                                                 "Use reasoning/thinking in the chat ('on', 'off', or 'auto', default: 'auto' (detect from template))" });
    options.push_back({ "main",        "       --chat-template-kwargs JSON",  "sets additional params for the json template parser"});

    options.push_back({ "main",        "       --reasoning-budget N",  "token budget for thinking: -1 for unrestricted, 0 for immediate end, N>0 for token budget (default: -1)" });
    options.push_back({ "main",        "       --reasoning-tokens FORMAT",     "exclude reasoning tokens to select the slot more accurately.\n"
						                                                                                            "none: include all tokens\n"
                                                                                                                    "auto: exclude all tokens between <think> and </think>\n"
						                                                                                            "Or comma separated start and end tokens such as [THINK],[/THINK]\n"
						                                                                                            "(default: auto)" });
    options.push_back({ "main",        "       --reasoning-budget-message",  "message injected before the end-of-thinking tag when reasoning budget is exhausted (default: none)" });
    options.push_back({ "main",        "       --skip-chat-parsing",  "force a pure content parser, even if a Jinja template is specified; model will output everything "
            "in the content section, including any reasoning and/or tool calls (default: disabled)" });
    options.push_back({ "main",        "       --reasoning-budget N",  "token budget for thinking: -1 for unrestricted, 0 for immediate end, N>0 for token budget (default: -1)" });
    options.push_back({ "main",        "       --no-prefill-assistant",  "whether to prefill the assistant's response if the last message is an assistant message (default: prefill enabled)\n"
            "when this flag is set, if the last message is an assistant message then it will be treated as a full message and not prefilled\n" });
    options.push_back({ "main",        "       -ptc, --parallel-tool-calls", "enable parallel tool calls\n" });
    options.push_back({ "grammar" });
    options.push_back({ "*",           "       --grammar GRAMMAR",      "BNF-like grammar to constrain generations (see samples in grammars/ dir) (default: '%s')", sparams.grammar.grammar.c_str() });
    options.push_back({ "*",           "       --grammar-file FNAME",   "file to read grammar from" });
    options.push_back({ "*",           "-j,    --json-schema SCHEMA",
                                                                        "JSON schema to constrain generations (https://json-schema.org/), e.g. `{}` for any JSON object\n"
                                                                        "For schemas w/ external $refs, use --grammar + example/json_schema_to_grammar.py instead" });

    options.push_back({ "embedding" });
    options.push_back({ "embedding",   "       --pooling {none,mean,cls,last}",
                                                                        "pooling type for embeddings, use model default if unspecified" });
    options.push_back({ "embedding",   "       --attention {causal,non-causal}",
                                                                        "attention type for embeddings, use model default if unspecified" });

    options.push_back({ "context hacking" });
    options.push_back({ "*",           "       --rope-scaling {none,linear,yarn}",
                                                                        "RoPE frequency scaling method, defaults to linear unless specified by the model" });
    options.push_back({ "*",           "       --rope-scale N",         "RoPE context scaling factor, expands context by a factor of N" });
    options.push_back({ "*",           "       --rope-freq-base N",     "RoPE base frequency, used by NTK-aware scaling (default: loaded from model)" });
    options.push_back({ "*",           "       --rope-freq-scale N",    "RoPE frequency scaling factor, expands context by a factor of 1/N" });
    options.push_back({ "*",           "       --yarn-orig-ctx N",      "YaRN: original context size of model (default: %d = model training context size)", params.yarn_orig_ctx });
    options.push_back({ "*",           "       --yarn-ext-factor N",    "YaRN: extrapolation mix factor (default: %.1f, 0.0 = full interpolation)", (double)params.yarn_ext_factor });
    options.push_back({ "*",           "       --yarn-attn-factor N",   "YaRN: scale sqrt(t) or attention magnitude (default: %.1f)", (double)params.yarn_attn_factor });
    options.push_back({ "*",           "       --yarn-beta-slow N",     "YaRN: high correction dim or alpha (default: %.1f)", (double)params.yarn_beta_slow });
    options.push_back({ "*",           "       --yarn-beta-fast N",     "YaRN: low correction dim or beta (default: %.1f)", (double)params.yarn_beta_fast });
    options.push_back({ "*",           "-gan,  --grp-attn-n N",         "group-attention factor (default: %d)", params.grp_attn_n });
    options.push_back({ "*",           "-gaw,  --grp-attn-w N",         "group-attention width (default: %.1f)", (double)params.grp_attn_w });
    options.push_back({ "*",           "-dkvc, --dump-kv-cache",        "verbose print of the KV cache" });
    options.push_back({ "*",           "-nkvo, --no-kv-offload",        "disable KV offload" });
    options.push_back({ "*",           "-ctk,  --cache-type-k TYPE",    "KV cache data type for K (default: %s)", params.cache_type_k.c_str() });
    options.push_back({ "*",           "-ctv,  --cache-type-v TYPE",    "KV cache data type for V (default: %s)", params.cache_type_v.c_str() });
    options.push_back({ "*",           "-ctk-first, --cache-type-k-first TYPE,N", "KV cache data type for the first N layers of K (default: %s,-1)", params.type_k_first.c_str() });
    options.push_back({ "*",           "-ctv-last,  --cache-type-k-last  TYPE,N", "KV cache data type for the last N layers of K  (default: %s,-1)", params.type_k_last.c_str() });
    options.push_back({ "*",           "-ctv-first, --cache-type-v-first TYPE,N", "KV cache data type for the first N layers of V (default: %s,-1)", params.type_v_first.c_str() });
    options.push_back({ "*",           "-ctk-last,  --cache-type-v-last  TYPE,N", "KV cache data type for the last N layers of V  (default: %s,-1)", params.type_v_last.c_str() });
    options.push_back({ "*",           "-mtprot, --mtp-requantize-output-tensor type", "Use output requantized to type for MTP (default: %s)", params.extra_output_type.c_str() });
    options.push_back({ "*",           "-ctkd, --cache-type-k-draft TYPE", "KV cache data type for K for the draft model" });
    options.push_back({ "*",           "-ctvd, --cache-type-v-draft TYPE", "KV cache data type for V for the draft model" });

    options.push_back({ "perplexity" });
    options.push_back({ "perplexity",  "       --all-logits",           "return logits for all tokens in the batch (default: %s)", params.logits_all ? "true" : "false" });
    options.push_back({ "perplexity",  "       --hellaswag",            "compute HellaSwag score over random tasks from datafile supplied with -f" });
    options.push_back({ "perplexity",  "       --hellaswag-tasks N",    "number of tasks to use when computing the HellaSwag score (default: %zu)", params.hellaswag_tasks });
    options.push_back({ "perplexity",  "       --winogrande",           "compute Winogrande score over random tasks from datafile supplied with -f" });
    options.push_back({ "perplexity",  "       --winogrande-tasks N",   "number of tasks to use when computing the Winogrande score (default: %zu)", params.winogrande_tasks });
    options.push_back({ "perplexity",  "       --multiple-choice",      "compute multiple choice score over random tasks from datafile supplied with -f" });
    options.push_back({ "perplexity",  "       --multiple-choice-tasks N",
                                                                        "number of tasks to use when computing the multiple choice score (default: %zu)", params.multiple_choice_tasks });
    options.push_back({ "perplexity",  "       --kl-divergence",        "computes KL-divergence to logits provided via --kl-divergence-base" });
    options.push_back({ "perplexity",  "       --ppl-stride N",         "stride for perplexity calculation (default: %d)", params.ppl_stride });
    options.push_back({ "perplexity",  "       --ppl-output-type {0,1}",
                                                                        "output type for perplexity calculation (default: %d)", params.ppl_output_type });

    options.push_back({ "parallel" });
    options.push_back({ "*",           "-dt,   --defrag-thold N",       "KV cache defragmentation threshold (default: %.1f, < 0 - disabled)", (double)params.defrag_thold });
    options.push_back({ "*",           "-mea,  --max-extra-alloc",      "Max extra VRAM allocation per GPU (default: %d)", params.max_extra_alloc_MiB});
    options.push_back({ "*",           "-np,   --parallel N",           "number of parallel sequences to decode (default: %d)", params.n_parallel });
    options.push_back({ "*",           "-ns,   --sequences N",          "number of sequences to decode (default: %d)", params.n_sequences });
    options.push_back({ "*",           "-cb,   --cont-batching",        "enable continuous batching (a.k.a dynamic batching) (default: %s)", params.cont_batching ? "enabled" : "disabled" });
    options.push_back({ "*",           "-nocb, --no-cont-batching",     "disable continuous batching" });

    options.push_back({ "multi-modality" });
    options.push_back({ "*",           "       --mmproj FILE",          "path to a multimodal projector file for LLaVA. see examples/llava/README.md" });
    options.push_back({ "*",           "       --image FILE",           "path to an image file. use with multimodal models. Specify multiple times for batching" });
    options.push_back({ "*",           "       --image-min-tokens N",   "minimum number of tokens each image can take, only used by vision models with dynamic resolution (default: read from model)"});
    options.push_back({ "*",           "       --image-max-tokens N",   "maximum number of tokens each image can take, only used by vision models with dynamic resolution (default: read from model)" });
    options.push_back({ "*",           "       --mtmd-kq-type TYPE",    "data type for multimodality K*Q (default: %s)", params.mtmd_kq_type.c_str() });
    options.push_back({ "*",           "       --no-context-shift",     "disable context-shift." });
    options.push_back({ "*",           "--context-shift (auto|on|off|0|1)", "set context-shift (default: %s)", params.ctx_shift ? "on" : "off" });
    options.push_back({ "backend" });
    options.push_back({ "*",           "       --rpc SERVERS",          "comma separated list of RPC servers" });
    options.push_back({ "*",           "-cuda, --cuda-params",          "comma separate list of cuda parameters" });
    options.push_back({ "*",           "-draft, --draft-params",        "comma separate list of draft model parameters" });
    if (llama_supports_mlock()) {
        options.push_back({ "*",           "       --mlock",                "force system to keep model in RAM rather than swapping or compressing" });
    }
    if (llama_supports_mmap()) {
        options.push_back({ "*",           "       --no-mmap",              "do not memory-map model (slower load but may reduce pageouts if not using mlock)" });
    }
    options.push_back({ "*",           "       --run-time-repack",      "repack tensors if interleaved variant is available"});
    options.push_back({ "*",           "       --cpu-moe",              "keep all MoE weights in CPU memory"});
    options.push_back({ "*",           "       --n-cpu-moe N",          "keep MoE weights of the first N layers in CPU memory"});
    options.push_back({ "*",           "       --defer-experts",        "defer expert mmap residency on Linux to reduce model load time"});
    options.push_back({ "*",           "       --fit-margin N",         "safety margin in MiB when auto-fitting model offloading"});
    options.push_back({ "*",           "-wgt, --worst-graph-tokens N",  "number of tokens to use for worst-case graph"});
    options.push_back({ "*",           "       --fit",                  "automatically determine which tensors to offload to the GPU(s)"});
    options.push_back({ "*",           "       --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" });

    if (llama_supports_gpu_offload()) {
        options.push_back({ "*",           "-ngl,  --gpu-layers N",
                                                                        "number of layers to store in VRAM" });
        options.push_back({ "*",           "-ngld, --gpu-layers-draft N",
                                                                        "number of layers to store in VRAM for the draft model" });
        options.push_back({ "*",           "-sm,   --split-mode SPLIT_MODE",
                                                                        "how to split the model across multiple GPUs, one of:\n"
                                                                        "  - none: use one GPU only\n"
                                                                        "  - graph: split model tensors and computation graph across GPUs\n"
                                                                        "  - layer (default): split layers and KV across GPUs\n" });
        options.push_back({ "*",           "-ts,   --tensor-split SPLIT",
                                                                        "fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1" });
        options.push_back({ "*",           "-dev,   --device dev1,dev2",
                                                                         "comma-separated list of devices to use for offloading (none = don't offload)\n"
                                                                         "Example: CUDA0,CUDA1,RPC[192.168.0.1:8080]\n" });
        options.push_back({ "*",           "-devd,   --device-draft dev1,dev2",
                                                                         "comma-separated list of devices to use for offloading for the draft model (none = don't offload)\n"
                                                                         "Example: CUDA0,CUDA1,RPC[192.168.0.1:8080]\n" });
        options.push_back({ "*",           "-mg,   --main-gpu i",       "the GPU to use for the model (with split-mode = none),\n"
                                                                        "or for intermediate results and KV (with split-mode = row) (default: %d)", params.main_gpu });
        options.push_back({ "*",           "--max-gpu i",               "max. number of GPUs to use at a time with split mode 'graph', (default: %d)", params.max_gpu });
    }

    options.push_back({ "model" });
    options.push_back({ "*",           "       --check-tensors",        "check model tensor data for invalid values (default: %s)", params.check_tensors ? "true" : "false" });
    options.push_back({ "*",           "       --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" });
    options.push_back({ "*",           "       --lora FNAME",           "apply LoRA adapter (can be repeated to use multiple adapters)" });
    options.push_back({ "*",           "       --lora-scaled FNAME S",  "apply LoRA adapter with user defined scaling S (can be repeated to use multiple adapters)" });
    options.push_back({ "*",           "       --control-vector FNAME", "add a control vector\n"
                                                                        "note: this argument can be repeated to add multiple control vectors" });
    options.push_back({ "*",           "       --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" });
    options.push_back({ "*",           "       --control-vector-layer-range START END",
                                                                        "layer range to apply the control vector(s) to, start and end inclusive" });
    options.push_back({ "*",           "-m,    --model FNAME",          "model path (default: models/$filename with filename from --hf-file\n"
                                                                        "or --model-url if set, otherwise %s)", DEFAULT_MODEL_PATH });
    options.push_back({ "*",           "-md,   --model-draft FNAME",    "draft model for speculative decoding (default: unused)" });
    options.push_back({ "*",           "-mu,   --model-url MODEL_URL",  "model download url (default: unused)" });
    options.push_back({ "*",           "-hfr,  --hf-repo REPO",         "Hugging Face model repository (default: unused)" });
    options.push_back({ "*",           "-hff,  --hf-file FILE",         "Hugging Face model file (default: unused)" });
    options.push_back({ "*",           "-hft,  --hf-token TOKEN",       "Hugging Face access token (default: value from HF_TOKEN environment variable)" });
    options.push_back({ "*", "-mtp, --multi-token-prediction",          "legacy shortcut for enabling MTP when --spec-stage is not used (default: %s)", params.has_mtp ? "true" : "false" });
    options.push_back({ "*", "-no-mtp, --no-multi-token-prediction",    "disable the legacy MTP shortcut or remove an explicit MTP stage (default: %s)", !params.has_mtp ? "true" : "false" });
    options.push_back({ "*", "--draft-max, --draft, --draft-n N",
                                                                        "global default number of tokens to draft for speculative decoding or for stages without an explicit n_max override (default: %d)", params.speculative.n_max });
    options.push_back({ "*", "--draft-min, --draft-n-min N",   "global default minimum draft threshold or fallback threshold for stages without an explicit n_min override" });
    options.push_back({ "*", "--draft-p-min P",                "global default minimum speculative decoding probability (greedy) for stages without an explicit p_min override (default: %.1f)", (double)params.speculative.p_min });
    options.push_back({ "*", "--recurrent-ckpt-mode MODE",    "checkpoint strategy for recurrent/hybrid speculative decoding\n"
                                                              "  auto         auto-select: per-step if CUDA full-GPU, gpu-fallback otherwise (default)\n"
                                                              "  per-step     save SSM state per draft step in VRAM; no re-decode on rejection\n"
                                                              "  gpu-fallback copy state to GPU buffer; re-decode on rejection\n"
                                                              "  cpu          serialise state via llama_state_seq; re-decode on rejection" });
    options.push_back({ "*", "--spec-stage SPEC[:k=v,...]",    "explicit speculative stage. repeat once for a supported two-stage chain.\n"
                                                              "examples: --spec-stage ngram-mod:n_max=64,n_min=2 --spec-stage mtp:n_max=1\n"
                                                              "supported two-stage shape in this PR: self-spec first, then mtp or draft fallback" });
    options.push_back({ "*", "--spec-type Name [none | mtp | ngram-cache | ngram-simple | ngram-map-k | ngram-map-k4v | ngram-mod | suffix]", "single-stage speculative selection when --spec-stage is not used (default: %d)\n", (int)params.speculative.type});
    options.push_back({ "*", "--spec-ngram-size-n N", "ngram size N for ngram-simple/ngram-map speculative decoding, length of lookup n-gram (default: %d)\n",params.speculative.ngram_size_n });

    options.push_back({ "*", "--spec-ngram-size-m N", "ngram size M for ngram-simple/ngram-map speculative decoding, length of draft m-gram (default: %d)\n", params.speculative.ngram_size_m });

    options.push_back({ "*", "--spec-ngram-min-hits N", "minimum hits for ngram-map speculative decoding (default: %d)\n", params.speculative.ngram_min_hits });
    options.push_back({ "*", "--suffix-pattern-len N",   "minimum context match length for suffix decoding (default: %d)", params.speculative.suffix_min_match_len });
    options.push_back({ "*", "--suffix-max-depth N",     "suffix tree maximum depth for suffix decoding (default: %d)",    params.speculative.suffix_max_depth });
    options.push_back({ "*", "--suffix-corpus PATH",     "corpus file to pre-warm the suffix tree: .json (array of strings or conversation messages) or .bin (raw int32 token IDs)" });
    options.push_back({ "*", "--spec-autotune",          "automatically tune speculative params to maximize tokens/sec" });

    options.push_back({ "retrieval" });
    options.push_back({ "retrieval",   "       --context-file FNAME",   "file to load context from (repeat to specify multiple files)" });
    options.push_back({ "retrieval",   "       --chunk-size N",         "minimum length of embedded text chunks (default: %d)", params.chunk_size });
    options.push_back({ "retrieval",   "       --chunk-separator STRING",
                                                                        "separator between chunks (default: '%s')", params.chunk_separator.c_str() });

    options.push_back({ "passkey" });
    options.push_back({ "passkey",     "       --junk N",               "number of times to repeat the junk text (default: %d)", params.n_junk });
    options.push_back({ "passkey",     "       --pos N",                "position of the passkey in the junk text (default: %d)", params.i_pos });

    options.push_back({ "imatrix" });
    options.push_back({ "imatrix",     "-o,    --output FNAME",         "output file (default: '%s')", params.out_file.c_str() });
    options.push_back({ "imatrix",     "       --output-draft FNAME",   "paired draft output file (default: derived from --output)" });
    options.push_back({ "imatrix",     "       --output-frequency N",   "output the imatrix every N iterations (default: %d)", params.n_out_freq });
    options.push_back({ "imatrix",     "       --save-frequency N",     "save an imatrix copy every N iterations (default: %d)", params.n_save_freq });
    options.push_back({ "imatrix",     "       --process-output",       "collect data for the output tensor (default: %s)", params.process_output ? "true" : "false" });
    options.push_back({ "imatrix",     "       --no-ppl",               "do not compute perplexity (default: %s)", params.compute_ppl ? "true" : "false" });
    options.push_back({ "imatrix",     "       --chunk N",              "start processing the input from chunk N (default: %d)", params.i_chunk });

    options.push_back({ "bench" });
    options.push_back({ "bench",       "-pps",                          "is the prompt shared across parallel sequences (default: %s)", params.is_pp_shared ? "true" : "false" });
    options.push_back({ "bench",       "-npp n0,n1,...",                "number of prompt tokens" });
    options.push_back({ "bench",       "-ntg n0,n1,...",                "number of text generation tokens" });
    options.push_back({ "bench",       "-npl n0,n1,...",                "number of parallel prompts" });

    options.push_back({ "embedding" });
    options.push_back({ "embedding",   "       --embd-normalize",       "normalisation for embendings (default: %d) (-1=none, 0=max absolute int16, 1=taxicab, 2=euclidean, >2=p-norm)", params.embd_normalize });
    options.push_back({ "embedding",   "       --embd-output-format",   "empty = default, \"array\" = [[],[]...], \"json\" = openai style, \"json+\" = same \"json\" + cosine similarity matrix" });
    options.push_back({ "embedding",   "       --embd-separator",       "separator of embendings (default \\n) for example \"<#sep#>\"" });

    options.push_back({ "server" });
    options.push_back({ "server",      "       --host HOST",            "ip address to listen (default: %s)", params.hostname.c_str() });
    options.push_back({ "server",      "       --port PORT",            "port to listen (default: %d)", params.port });
    options.push_back({ "server",      "       --path PATH",            "path to serve static files from (default: %s)", params.public_path.c_str() });
    options.push_back({ "server",      "       --embedding(s)",         "restrict to only support embedding use case; use only with dedicated embedding models (default: %s)", params.embedding ? "enabled" : "disabled" });
    options.push_back({ "server",        "       --webui NAME",
                                                             "controls which webui to server:\n"
                                                            "- none: disable webui\n"
                                                            "- auto: default webui \n"
                                                            "- llamacpp: llamacpp webui \n"
                                                            "(default: auto)", });
    options.push_back({ "server",      "       --api-key KEY",          "API key to use for authentication (default: none)" });
    options.push_back({ "server",      "       --api-key-file FNAME",   "path to file containing API keys (default: none)" });
    options.push_back({ "server",      "       --ssl-key-file FNAME",   "path to file a PEM-encoded SSL private key" });
    options.push_back({ "server",      "       --ssl-cert-file FNAME",  "path to file a PEM-encoded SSL certificate" });
    options.push_back({ "server",      "       --timeout N",            "server read/write timeout in seconds (default: %d)", params.timeout_read });
    options.push_back({ "server",      "       --threads-http N",       "number of threads used to process HTTP requests (default: %d)", params.n_threads_http });
    options.push_back({ "server",      "       --system-prompt-file FNAME",
                                                                        "set a file to load a system prompt (initial prompt of all slots), this is useful for chat applications" });
    options.push_back({ "server",      "       --log-format {text,json}",
                                                                        "log output format: json or text (default: json)" });
    options.push_back({ "server",      "       --metrics",              "enable prometheus compatible metrics endpoint (default: %s)", params.endpoint_metrics ? "enabled" : "disabled" });
    options.push_back({ "server",      "       --no-slots",             "disables slots monitoring endpoint (default: %s)", params.endpoint_slots ? "enabled" : "disabled" });
    options.push_back({ "server",      "       --slot-save-path PATH",  "path to save slot kv cache (default: disabled)" });
    options.push_back({ "server",      "       --chat-template JINJA_TEMPLATE",
                                                                        "set custom jinja chat template (default: template taken from model's metadata)\n"
                                                                        "only commonly used templates are accepted:\n"
                                                                        "https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template" });
    options.push_back({ "server",      "-sps,  --slot-prompt-similarity SIMILARITY",
                                                                        "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 });
    options.push_back({ "server",      "       --lora-init-without-apply",     "load LoRA adapters without applying them (apply later via POST /lora-adapters) (default: %s)", params.lora_init_without_apply ? "enabled" : "disabled"});

#ifndef LOG_DISABLE_LOGS
    options.push_back({ "logging" });
    options.push_back({ "*",           "       --simple-io",            "use basic IO for better compatibility in subprocesses and limited consoles" });
    options.push_back({ "*",           "-ld,   --logdir LOGDIR",        "path under which to save YAML logs (no logging if unset)" });
    options.push_back({ "logging",     "       --log-test",             "Run simple logging test" });
    options.push_back({ "logging",     "       --log-disable",          "Disable trace logs" });
    options.push_back({ "logging",     "       --log-enable",           "Enable trace logs" });
    options.push_back({ "logging",     "       --log-file FNAME",       "Specify a log filename (without extension)" });
    options.push_back({ "logging",     "       --log-new",              "Create a separate new log file on start. "
                                                                        "Each log file will have unique name: \"<name>.<ID>.log\"" });
    options.push_back({ "logging",     "       --log-append",           "Don't truncate the old log file." });
#endif // LOG_DISABLE_LOGS

    options.push_back({ "cvector" });
    options.push_back({ "cvector",     "-o,    --output FNAME",         "output file (default: '%s')", params.cvector_outfile.c_str() });
    options.push_back({ "cvector",     "       --positive-file FNAME",  "positive prompts file, one prompt per line (default: '%s')", params.cvector_positive_file.c_str() });
    options.push_back({ "cvector",     "       --negative-file FNAME",  "negative prompts file, one prompt per line (default: '%s')", params.cvector_negative_file.c_str() });
    options.push_back({ "cvector",     "       --pca-batch N",          "batch size used for PCA. Larger batch runs faster, but uses more memory (default: %d)", params.n_pca_batch });
    options.push_back({ "cvector",     "       --pca-iter N",           "number of iterations used for PCA (default: %d)", params.n_pca_iterations });
    options.push_back({ "cvector",     "       --method {pca,mean}",    "dimensionality reduction method to be used (default: pca)" });

    options.push_back({ "export-lora" });
    options.push_back({ "export-lora", "-m,    --model",                "model path from which to load base model (default '%s')", params.model.c_str() });
    options.push_back({ "export-lora", "       --lora FNAME",           "path to LoRA adapter  (can be repeated to use multiple adapters)" });
    options.push_back({ "export-lora", "       --lora-scaled FNAME S",  "path to LoRA adapter with user defined scaling S  (can be repeated to use multiple adapters)" });
    options.push_back({ "*",           "-t,    --threads N",            "number of threads to use during computation (default: %d)", params.n_threads });
    options.push_back({ "export-lora", "-o,    --output FNAME",         "output file (default: '%s')", params.lora_outfile.c_str() });

    printf("usage: %s [options]\n", argv[0]);

    for (const auto & o : options) {
        if (!o.grp.empty()) {
            printf("\n%s:\n\n", o.grp.c_str());
            continue;
        }
        printf("  %-32s", o.args.c_str());
        if (o.args.length() > 30) {
            printf("\n%34s", "");
        }

        const auto desc = o.desc;
        size_t start = 0;
        size_t end = desc.find('\n');
        while (end != std::string::npos) {
            printf("%s\n%34s", desc.substr(start, end - start).c_str(), "");
            start = end + 1;
            end = desc.find('\n', start);
        }

        printf("%s\n", desc.substr(start).c_str());
    }
    printf("\n");
}

std::string gpt_params_get_system_info(const gpt_params & params) {
    std::ostringstream os;

    os << "system_info: n_threads = " << params.n_threads;
    if (params.n_threads_batch != -1) {
        os << " (n_threads_batch = " << params.n_threads_batch << ")";
    }
    if (params.n_threads_mtmd != -1) {
        os << " (n_threads_mtmd = " << params.n_threads_mtmd << ")";
    }
    os << " / " << std::thread::hardware_concurrency() << " | " << llama_print_system_info();

    return os.str();
}

//
// String utils
//

std::string string_format(const char* fmt, ...) {
    va_list ap;
    va_list ap2;
    va_start(ap, fmt);
    va_copy(ap2, ap);
    int size = vsnprintf(NULL, 0, fmt, ap);
    GGML_ASSERT(size >= 0 && size < INT_MAX); // NOLINT
    std::vector<char> buf(size + 1);
    int size2 = vsnprintf(buf.data(), size + 1, fmt, ap2);
    GGML_ASSERT(size2 == size);
    va_end(ap2);
    va_end(ap);
    return std::string(buf.data(), size);
}

std::string regex_escape(const std::string& s) {
    static const std::regex special_chars("[.^$|()*+?\\[\\]{}\\\\]");
    return std::regex_replace(s, special_chars, "\\$0");
}

std::string string_join(const std::vector<std::string>& values, const std::string& separator) {
    std::ostringstream result;
    for (size_t i = 0; i < values.size(); ++i) {
        if (i > 0) {
            result << separator;
        }
        result << values[i];
    }
    return result.str();
}


std::vector<std::string> string_split(const std::string& str, const std::string& delimiter) {
    std::vector<std::string> parts;
    size_t start = 0;
    size_t end = str.find(delimiter);

    while (end != std::string::npos) {
        parts.push_back(str.substr(start, end - start));
        start = end + delimiter.length();
        end = str.find(delimiter, start);
    }

    parts.push_back(str.substr(start));

    return parts;
}

std::vector<std::string> string_split(const std::string& str, char delim) {
    std::vector<std::string> values;
    std::istringstream str_stream(str);
    std::string token;
    while (std::getline(str_stream, token, delim)) {
        std::string value;
        std::istringstream token_stream(token);
        token_stream >> value;
        values.push_back(value);
    }
    return values;
}

std::string string_repeat(const std::string & str, size_t n) {
    if (n == 0) {
        return "";
    }

    std::string result;
    result.reserve(str.length() * n);

    for (size_t i = 0; i < n; ++i) {
        result += str;
    }

    return result;
}

static bool is_utf8_whitespace(uint8_t c) {
    // Basic ASCII whitespace
    if (c <= 0x7F) return isspace(c);
    // Else: Not whitespace (or you'd need a full Unicode table)
    return false;
}

std::string string_strip(const std::string & str) {
    size_t start = 0;
    size_t end = str.size();
    while (start < end && is_utf8_whitespace(str[start])) {
        start++;
    }
    while (end > start && is_utf8_whitespace(str[end - 1])) {
        end--;
    }
    return str.substr(start, end - start);
}

std::string string_get_sortable_timestamp() {
    using clock = std::chrono::system_clock;

    const clock::time_point current_time = clock::now();
    const time_t as_time_t = clock::to_time_t(current_time);
    char timestamp_no_ns[100];
    std::strftime(timestamp_no_ns, 100, "%Y_%m_%d-%H_%M_%S", std::localtime(&as_time_t));

    const int64_t ns = std::chrono::duration_cast<std::chrono::nanoseconds>(
        current_time.time_since_epoch() % 1000000000).count();
    char timestamp_ns[11];
    snprintf(timestamp_ns, 11, "%09" PRId64, ns);

    return std::string(timestamp_no_ns) + "." + std::string(timestamp_ns);
}

// could be improved to support more languages
std::string string_lower(const std::string& str) {
    std::string result = str;
    for (char& c : result) {
        if (c >= 'A' && c <= 'Z') {
            c = static_cast<char>(c + ('a' - 'A'));
        }
    }
    return result;
}


void string_replace_all(std::string & s, const std::string & search, const std::string & replace) {
    if (search.empty()) {
        return; // Avoid infinite loop if 'search' is an empty string
    }
    size_t pos = 0;
    while ((pos = s.find(search, pos)) != std::string::npos) {
        s.replace(pos, search.length(), replace);
        pos += replace.length();
    }
}

bool string_ends_with(const std::string_view& str, const std::string_view& suffix) {
    return str.size() >= suffix.size() && str.compare(str.size() - suffix.size(), suffix.size(), suffix) == 0;
}
size_t string_find_partial_stop(const std::string_view& str, const std::string_view& stop) {
    if (!str.empty() && !stop.empty()) {
        const char text_last_char = str.back();
        for (int64_t char_index = stop.size() - 1; char_index >= 0; char_index--) {
            if (stop[char_index] == text_last_char) {
                const auto current_partial = stop.substr(0, char_index + 1);
                if (string_ends_with(str, current_partial)) {
                    return str.size() - char_index - 1;
                }
            }
        }
    }

    return std::string::npos;
}

void string_process_escapes(std::string & input) {
    std::size_t input_len = input.length();
    std::size_t output_idx = 0;

    for (std::size_t input_idx = 0; input_idx < input_len; ++input_idx) {
        if (input[input_idx] == '\\' && input_idx + 1 < input_len) {
            switch (input[++input_idx]) {
                case 'n':  input[output_idx++] = '\n'; break;
                case 'r':  input[output_idx++] = '\r'; break;
                case 't':  input[output_idx++] = '\t'; break;
                case '\'': input[output_idx++] = '\''; break;
                case '\"': input[output_idx++] = '\"'; break;
                case '\\': input[output_idx++] = '\\'; break;
                case 'x':
                    // Handle \x12, etc
                    if (input_idx + 2 < input_len) {
                        const char x[3] = { input[input_idx + 1], input[input_idx + 2], 0 };
                        char *err_p = nullptr;
                        const long val = std::strtol(x, &err_p, 16);
                        if (err_p == x + 2) {
                            input_idx += 2;
                            input[output_idx++] = char(val);
                            break;
                        }
                    }
                    // fall through
                default:   input[output_idx++] = '\\';
                           input[output_idx++] = input[input_idx]; break;
            }
        } else {
            input[output_idx++] = input[input_idx];
        }
    }

    input.resize(output_idx);
}

bool string_parse_kv_override(const char * data, std::vector<llama_model_kv_override> & overrides) {
    const char * sep = strchr(data, '=');
    if (sep == nullptr || sep - data >= 128) {
        fprintf(stderr, "%s: malformed KV override '%s'\n", __func__, data);
        return false;
    }
    llama_model_kv_override kvo;
    std::strncpy(kvo.key, data, sep - data);
    kvo.key[sep - data] = 0;
    sep++;
    if (strncmp(sep, "int:", 4) == 0) {
        sep += 4;
        kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT;
        kvo.val_i64 = std::atol(sep);
    } else if (strncmp(sep, "float:", 6) == 0) {
        sep += 6;
        kvo.tag = LLAMA_KV_OVERRIDE_TYPE_FLOAT;
        kvo.val_f64 = std::atof(sep);
    } else if (strncmp(sep, "bool:", 5) == 0) {
        sep += 5;
        kvo.tag = LLAMA_KV_OVERRIDE_TYPE_BOOL;
        if (std::strcmp(sep, "true") == 0) {
            kvo.val_bool = true;
        } else if (std::strcmp(sep, "false") == 0) {
            kvo.val_bool = false;
        } else {
            fprintf(stderr, "%s: invalid boolean value for KV override '%s'\n", __func__, data);
            return false;
        }
    } else if (strncmp(sep, "str:", 4) == 0) {
        sep += 4;
        kvo.tag = LLAMA_KV_OVERRIDE_TYPE_STR;
        if (strlen(sep) > 127) {
            fprintf(stderr, "%s: malformed KV override '%s', value cannot exceed 127 chars\n", __func__, data);
            return false;
        }
        strncpy(kvo.val_str, sep, 127);
        kvo.val_str[127] = '\0';
    } else {
        fprintf(stderr, "%s: invalid type for KV override '%s'\n", __func__, data);
        return false;
    }
    overrides.emplace_back(std::move(kvo));
    return true;
}

//
// Filesystem utils
//

// Validate if a filename is safe to use
// To validate a full path, split the path by the OS-specific path separator, and validate each part with this function
bool fs_validate_filename(const std::string & filename) {
    if (!filename.length()) {
        // Empty filename invalid
        return false;
    }
    if (filename.length() > 255) {
        // Limit at common largest possible filename on Linux filesystems
        // to avoid unnecessary further validation
        // (On systems with smaller limits it will be caught by the OS)
        return false;
    }

    std::u32string filename_utf32;
    try {
#if defined(__clang__)
#    pragma clang diagnostic push
#    pragma clang diagnostic ignored "-Wdeprecated-declarations"
#elif defined(__GNUC__)
#    pragma GCC diagnostic push
#    pragma GCC diagnostic ignored "-Wdeprecated-declarations"
#endif
        std::wstring_convert<std::codecvt_utf8<char32_t>, char32_t> converter;
#if defined(__clang__)
#    pragma clang diagnostic pop
#elif defined(__GNUC__)
#    pragma GCC diagnostic pop
#endif
        filename_utf32 = converter.from_bytes(filename);

        // If the reverse conversion mismatches, it means overlong UTF-8 sequences were used,
        // or invalid encodings were encountered. Reject such attempts
        std::string filename_reencoded = converter.to_bytes(filename_utf32);
        if (filename_reencoded != filename) {
            return false;
        }
    } catch (const std::exception &) {
        return false;
    }

    // Check for forbidden codepoints:
    // - Control characters
    // - Unicode equivalents of illegal characters
    // - UTF-16 surrogate pairs
    // - UTF-8 replacement character
    // - Byte order mark (BOM)
    // - Illegal characters: / \ : * ? " < > |
    for (char32_t c : filename_utf32) {
        if (c <= 0x1F // Control characters (C0)
            || c == 0x7F // Control characters (DEL)
            || (c >= 0x80 && c <= 0x9F) // Control characters (C1)
            || c == 0xFF0E // Fullwidth Full Stop (period equivalent)
            || c == 0x2215 // Division Slash (forward slash equivalent)
            || c == 0x2216 // Set Minus (backslash equivalent)
            || (c >= 0xD800 && c <= 0xDFFF) // UTF-16 surrogate pairs
            || c == 0xFFFD // Replacement Character (UTF-8)
            || c == 0xFEFF // Byte Order Mark (BOM)
            || c == '/' || c == '\\' || c == ':' || c == '*' // Illegal characters
            || c == '?' || c == '"' || c == '<' || c == '>' || c == '|') {
            return false;
        }
    }

    // Reject any leading or trailing ' ', or any trailing '.', these are stripped on Windows and will cause a different filename
    // Unicode and other whitespace is not affected, only 0x20 space
    if (filename.front() == ' ' || filename.back() == ' ' || filename.back() == '.') {
        return false;
    }

    // Reject any ".." (currently stricter than necessary, it should be fine to just check for == ".." instead)
    if (filename.find("..") != std::string::npos) {
        return false;
    }

    // Reject "."
    if (filename == ".") {
        return false;
    }

    return true;
}

#ifdef _WIN32
static std::wstring utf8_to_wstring(const std::string& str) {
    if (str.empty()) {
        return std::wstring();
    }

    int size = MultiByteToWideChar(CP_UTF8, 0, str.c_str(), (int)str.size(), NULL, 0);

    if (size <= 0) {
        return std::wstring();
    }

    std::wstring wstr(size, 0);
    MultiByteToWideChar(CP_UTF8, 0, str.c_str(), (int)str.size(), &wstr[0], size);

    return wstr;
}
#endif

// returns true if successful, false otherwise
bool fs_create_directory_with_parents(const std::string & path) {
#ifdef _WIN32
    std::wstring wpath = utf8_to_wstring(path);

    // if the path already exists, check whether it's a directory
    const DWORD attributes = GetFileAttributesW(wpath.c_str());
    if ((attributes != INVALID_FILE_ATTRIBUTES) && (attributes & FILE_ATTRIBUTE_DIRECTORY)) {
        return true;
    }

    size_t pos_slash = 0;

    // process path from front to back, procedurally creating directories
    while ((pos_slash = path.find('\\', pos_slash)) != std::string::npos) {
        const std::wstring subpath = wpath.substr(0, pos_slash);
        const wchar_t * test = subpath.c_str();

        const bool success = CreateDirectoryW(test, NULL);
        if (!success) {
            const DWORD error = GetLastError();

            // if the path already exists, ensure that it's a directory
            if (error == ERROR_ALREADY_EXISTS) {
                const DWORD attributes = GetFileAttributesW(subpath.c_str());
                if (attributes == INVALID_FILE_ATTRIBUTES || !(attributes & FILE_ATTRIBUTE_DIRECTORY)) {
                    return false;
                }
            } else {
                return false;
            }
        }

        pos_slash += 1;
    }

    return true;
#else
    // if the path already exists, check whether it's a directory
    struct stat info;
    if (stat(path.c_str(), &info) == 0) {
        return S_ISDIR(info.st_mode);
    }

    size_t pos_slash = 1; // skip leading slashes for directory creation

    // process path from front to back, procedurally creating directories
    while ((pos_slash = path.find('/', pos_slash)) != std::string::npos) {
        const std::string subpath = path.substr(0, pos_slash);
        struct stat info;

        // if the path already exists, ensure that it's a directory
        if (stat(subpath.c_str(), &info) == 0) {
            if (!S_ISDIR(info.st_mode)) {
                return false;
            }
        } else {
            // create parent directories
            const int ret = mkdir(subpath.c_str(), 0755);
            if (ret != 0) {
                return false;
            }
        }

        pos_slash += 1;
    }

    return true;
#endif // _WIN32
}

std::string fs_get_cache_directory() {
    std::string cache_directory = "";
    auto ensure_trailing_slash = [](std::string p) {
        // Make sure to add trailing slash
        if (p.back() != DIRECTORY_SEPARATOR) {
            p += DIRECTORY_SEPARATOR;
        }
        return p;
    };
    if (getenv("LLAMA_CACHE")) {
        cache_directory = std::getenv("LLAMA_CACHE");
    } else {
#ifdef __linux__
        if (std::getenv("XDG_CACHE_HOME")) {
            cache_directory = std::getenv("XDG_CACHE_HOME");
        } else {
            cache_directory = std::getenv("HOME") + std::string("/.cache/");
        }
#elif defined(__APPLE__)
        cache_directory = std::getenv("HOME") + std::string("/Library/Caches/");
#elif defined(_WIN32)
        cache_directory = std::getenv("LOCALAPPDATA");
#endif // __linux__
        cache_directory = ensure_trailing_slash(cache_directory);
        cache_directory += "llama.cpp";
    }
    return ensure_trailing_slash(cache_directory);
}

std::string fs_get_cache_file(const std::string & filename) {
    GGML_ASSERT(filename.find(DIRECTORY_SEPARATOR) == std::string::npos);
    std::string cache_directory = fs_get_cache_directory();
    const bool success = fs_create_directory_with_parents(cache_directory);
    if (!success) {
        throw std::runtime_error("failed to create cache directory: " + cache_directory);
    }
    return cache_directory + filename;
}


struct llama_init_result llama_init_from_gpt_params(gpt_params & params) {
    llama_init_result iparams;

    auto mparams = common_model_params_to_llama(params);

    llama_model * model = nullptr;

    if (!params.hf_repo.empty() && !params.hf_file.empty()) {
        model = llama_load_model_from_hf(params.hf_repo.c_str(), params.hf_file.c_str(), params.model.c_str(), params.hf_token.c_str(), mparams);
    } else if (!params.model_url.empty()) {
        model = llama_load_model_from_url(params.model_url.c_str(), params.model.c_str(), params.hf_token.c_str(), mparams);
    } else {
        model = llama_model_load_from_file(params.model.c_str(), mparams);
    }

    if (model == NULL) {
        fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, params.model.c_str());
        return iparams;
    }

    auto cparams = common_context_params_to_llama(params);

    llama_context * lctx = llama_init_from_model(model, cparams);
    if (lctx == NULL) {
        fprintf(stderr, "%s: error: failed to create context with model '%s'\n", __func__, params.model.c_str());
        llama_free_model(model);
        return iparams;
    }

    for (auto [op, on_off] : params.offload_policy) {
        llama_set_offload_policy(lctx, op, on_off);
    }

    if (!params.control_vectors.empty()) {
        if (params.control_vector_layer_start <= 0) params.control_vector_layer_start = 1;
        if (params.control_vector_layer_end   <= 0) params.control_vector_layer_end   = llama_n_layer(model);

        const auto cvec = llama_control_vector_load(params.control_vectors);
        if (cvec.n_embd == -1) {
            llama_free(lctx);
            llama_free_model(model);
            return iparams;
        }

        int err = llama_control_vector_apply(lctx,
                                             cvec.data.data(),
                                             cvec.data.size(),
                                             cvec.n_embd,
                                             params.control_vector_layer_start,
                                             params.control_vector_layer_end);
        if (err) {
            llama_free(lctx);
            llama_free_model(model);
            return iparams;
        }
    }

    // load and optionally apply lora adapters
    for (auto & la : params.lora_adapters) {
        llama_lora_adapter_container loaded_la;
        loaded_la.path = la.path;
        loaded_la.scale = la.scale;
        loaded_la.adapter = llama_lora_adapter_init(model, la.path.c_str());
        if (loaded_la.adapter == nullptr) {
            fprintf(stderr, "%s: error: failed to apply lora adapter '%s'\n", __func__, la.path.c_str());
            llama_free(lctx);
            llama_free_model(model);
            return iparams;
        }
        iparams.lora_adapters.push_back(loaded_la); // copy to list of loaded adapters
    }
    if (!params.lora_init_without_apply) {
        llama_lora_adapters_apply(lctx, iparams.lora_adapters);
    }

    if (params.ignore_eos) {
        params.sparams.logit_bias[llama_token_eos(model)] = -INFINITY;
    }

    if (params.sparams.dry_penalty_last_n == -1) {
        LOG("%s: setting dry_penalty_last_n to ctx_size = %d\n", __func__, llama_n_ctx(lctx));
        params.sparams.dry_penalty_last_n = llama_n_ctx(lctx);
    }

    if (params.warmup) {
        LOG("warming up the model with an empty run\n");

        std::vector<llama_token> tmp;
        llama_token bos = llama_token_bos(model);
        llama_token eos = llama_token_eos(model);
        // some models (e.g. T5) don't have a BOS token
        if (bos != -1) {
            tmp.push_back(bos);
        }
	else
	{
	    tmp.push_back(eos);
	}
        if (llama_model_has_encoder(model)) {
            llama_encode(lctx, llama_batch_get_one(tmp.data(), tmp.size(), 0, 0));
            llama_token decoder_start_token_id = llama_model_decoder_start_token(model);
            if (decoder_start_token_id == LLAMA_TOKEN_NULL) {
                decoder_start_token_id = bos;
            }
            tmp.clear();
            tmp.push_back(decoder_start_token_id);
        }
        if (llama_model_has_decoder(model)) {
            llama_decode(lctx, llama_batch_get_one(tmp.data(), std::min(tmp.size(), (size_t) params.n_batch), 0, 0));
        }
        llama_kv_cache_clear(lctx);
        llama_synchronize(lctx);
        llama_reset_timings(lctx);
    }

    iparams.model   = model;
    iparams.context = lctx;
    return iparams;
}

void llama_lora_adapters_apply(struct llama_context * ctx, std::vector<llama_lora_adapter_container> & lora_adapters) {
    llama_lora_adapter_clear(ctx);
    for (auto & la : lora_adapters) {
        if (la.scale != 0.0f) {
            llama_lora_adapter_set(ctx, la.adapter, la.scale);
        }
    }
}

static ggml_type kv_cache_type_from_str(const std::string & s) {
    if (s == "f32") {
        return GGML_TYPE_F32;
    }
    if (s == "f16") {
        return GGML_TYPE_F16;
    }
    if (s == "bf16") {
        return GGML_TYPE_BF16;
    }
    if (s == "q8_0") {
        return GGML_TYPE_Q8_0;
    }
    if (s == "q4_0") {
        return GGML_TYPE_Q4_0;
    }
    if (s == "q4_1") {
        return GGML_TYPE_Q4_1;
    }
    if (s == "iq4_nl") {
        return GGML_TYPE_IQ4_NL;
    }
    if (s == "q5_0") {
        return GGML_TYPE_Q5_0;
    }
    if (s == "q5_1") {
        return GGML_TYPE_Q5_1;
    }
    if (s == "q6_0") {
        return GGML_TYPE_Q6_0;
    }
    if (s == "q8_KV") {
        return GGML_TYPE_Q8_KV;
    }

    throw std::runtime_error("Invalid cache type: " + s);
}

static std::pair<int, int> get_batch_ubatch(const gpt_params & params) {
    int n_batch = params.n_batch;
    int n_ubatch = params.n_ubatch;
    if (params.n_ctx > 0) {
        n_batch = std::min(n_batch, params.n_ctx);
    }
    if (!params.mmproj.path.empty() && params.mmproj_use_gpu) {
        // temporary fix for qwen mtmd (only when mmproj is on GPU)
        n_batch = std::max(n_batch, n_ubatch);
        n_ubatch = n_batch;
        fprintf(stdout, "Adjust batch size for mtmd: u_batch = %d, batch = %d\n", n_ubatch, n_batch);
    } else {
        n_ubatch = std::min(n_batch, n_ubatch);
    }
    return {n_batch, n_ubatch};
}

static ggml_type parse_ggml_type(const char * arg) {
    for (int j = 0; j < GGML_TYPE_COUNT; ++j) {
        auto type = ggml_type(j);
        const auto * name = ggml_type_name(type);
        if (name && strcmp(arg, name) == 0) {
            return type;
        }
    }
    return GGML_TYPE_COUNT;
}

struct llama_model_params common_model_params_to_llama(const gpt_params & params) {
    auto mparams = llama_model_default_params();
    mparams.devices = params.devices.c_str();

    if (params.n_gpu_layers != -1) {
        mparams.n_gpu_layers = params.n_gpu_layers;
    }
    mparams.mla             = params.mla_attn;
    mparams.dry_run         = params.dry_run;
    mparams.rpc_servers     = params.rpc_servers.c_str();
    mparams.main_gpu        = params.main_gpu;
    mparams.max_gpu         = params.max_gpu;
    mparams.ncmoe           = params.ncmoe;
    mparams.fit             = params.fit;
    mparams.fit_margin      = params.fit_margin;
    mparams.worst_graph_tokens = params.worst_graph_tokens;
    mparams.type_k          = kv_cache_type_from_str(params.cache_type_k);
    mparams.type_v          = kv_cache_type_from_str(params.cache_type_v);
    mparams.type_k_first    = kv_cache_type_from_str(params.type_k_first);
    mparams.type_k_last     = kv_cache_type_from_str(params.type_k_last );
    mparams.type_v_first    = kv_cache_type_from_str(params.type_v_first);
    mparams.type_v_last     = kv_cache_type_from_str(params.type_v_last );
    if (!params.extra_output_type.empty()) {
        mparams.extra_output_type = parse_ggml_type(params.extra_output_type.c_str());
    }
    mparams.n_k_first       = params.n_k_first;
    mparams.n_k_last        = params.n_k_last;
    mparams.n_v_first       = params.n_v_first;
    mparams.n_v_last        = params.n_v_last;
    mparams.max_ctx_size    = params.n_ctx;
    mparams.n_seq_max       = params.n_parallel;
    mparams.n_ubatch        = get_batch_ubatch(params).second;
    mparams.amb             = params.attn_max_batch;
    mparams.split_mode      = params.split_mode;
    mparams.tensor_split    = params.tensor_split;
    mparams.use_mmap        = params.use_mmap;
    mparams.use_mlock       = params.use_mlock;
    mparams.check_tensors   = params.check_tensors;
    mparams.repack_tensors  = params.repack_tensors;
    mparams.use_thp         = params.use_thp;
    mparams.validate_quants = params.validate_quants;
    mparams.merge_qkv       = params.merge_qkv;
    mparams.merge_up_gate_exps = params.merge_up_gate_exps;
    mparams.mtp             = params.speculative.has_stage_type(COMMON_SPECULATIVE_TYPE_MTP);
    mparams.flash_attn      = params.flash_attn;
    mparams.defer_experts   = params.defer_experts;
    if (params.kv_overrides.empty()) {
        mparams.kv_overrides = NULL;
    } else {
        GGML_ASSERT(params.kv_overrides.back().key[0] == 0 && "KV overrides not terminated with empty key");
        mparams.kv_overrides = params.kv_overrides.data();
    }
    if (params.tensor_buft_overrides.empty()) {
        mparams.tensor_buft_overrides = NULL;
    } else {
        GGML_ASSERT(params.tensor_buft_overrides.back().pattern == nullptr && "Tensor buffer overrides not terminated with empty pattern");
        mparams.tensor_buft_overrides = params.tensor_buft_overrides.data();
    }
    if (!mparams.flash_attn && ggml_is_quantized(mparams.type_v)) {
        throw std::runtime_error("Quantized V cache cannot be used without flash attention");
    }

    return mparams;
}

static ggml_type ggml_type_from_str(const std::string & s) {
    if (s == "f32") {
        return GGML_TYPE_F32;
    }
    if (s == "f16") {
        return GGML_TYPE_F16;
    }
    if (s == "bf16") {
        return GGML_TYPE_BF16;
    }
    if (s == "q8_0") {
        return GGML_TYPE_Q8_0;
    }
    throw std::runtime_error("Invalid graph reduce type: " + s);
}

struct llama_context_params common_context_params_to_llama(const gpt_params & params) {
    auto cparams = llama_context_default_params();

    auto [n_batch, n_ubatch] = get_batch_ubatch(params);

    cparams.n_ctx             = params.n_ctx;
    cparams.n_seq_max         = params.n_parallel;
    cparams.n_batch           = n_batch;
    cparams.n_ubatch          = n_ubatch;
    cparams.n_threads         = params.n_threads;
    cparams.n_threads_batch   = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch;
    cparams.seed              = params.seed;
    cparams.logits_all        = params.logits_all;
    cparams.embeddings        = params.embedding;
    cparams.worst_case_tokens = params.worst_graph_tokens;
    cparams.rope_scaling_type = params.rope_scaling_type;
    cparams.rope_freq_base    = params.rope_freq_base;
    cparams.rope_freq_scale   = params.rope_freq_scale;
    cparams.yarn_ext_factor   = params.yarn_ext_factor;
    cparams.yarn_attn_factor  = params.yarn_attn_factor;
    cparams.yarn_beta_fast    = params.yarn_beta_fast;
    cparams.yarn_beta_slow    = params.yarn_beta_slow;
    cparams.yarn_orig_ctx     = params.yarn_orig_ctx;
    cparams.pooling_type      = params.pooling_type;
    cparams.attention_type    = params.attention_type;
    cparams.defrag_thold      = params.defrag_thold;
    cparams.cb_eval           = params.cb_eval;
    cparams.cb_eval_user_data = params.cb_eval_user_data;
    cparams.offload_kqv       = !params.no_kv_offload;
    cparams.flash_attn        = params.flash_attn;
    cparams.mla_attn          = params.mla_attn;
    cparams.attn_max_batch    = params.attn_max_batch;
    cparams.fused_moe_up_gate = params.fused_moe_up_gate;
    cparams.grouped_expert_routing = params.grouped_expert_routing;
    cparams.fused_up_gate     = params.fused_up_gate;
    cparams.fused_mmad        = params.fused_mmad;
    cparams.rope_cache        = params.rope_cache;
    cparams.graph_reuse       = params.graph_reuse;
    cparams.k_cache_hadamard  = params.k_cache_hadamard;
    cparams.v_cache_hadamard  = params.v_cache_hadamard;
    cparams.split_mode_graph_scheduling = params.split_mode_graph_scheduling;
    //cparams.split_mode_f16    = params.split_mode_f16;
    cparams.scheduler_async   = params.scheduler_async;
    cparams.min_experts       = params.min_experts;
    cparams.thresh_experts    = params.thresh_experts;
    cparams.only_active_experts = params.only_active_exps;
    cparams.max_extra_alloc   = params.max_extra_alloc_MiB;
    cparams.mtp               = params.speculative.has_stage_type(COMMON_SPECULATIVE_TYPE_MTP);
    cparams.mtp_op_type      = MTP_OP_NONE;

    cparams.type_k = kv_cache_type_from_str(params.cache_type_k);
    cparams.type_v = kv_cache_type_from_str(params.cache_type_v);
    cparams.type_reduce = ggml_type_from_str(params.reduce_type);
    if (!cparams.flash_attn && ggml_is_quantized(cparams.type_v)) {
        throw std::runtime_error("Quantized V cache cannot be used without flash attention");
    }
    cparams.type_k_first    = kv_cache_type_from_str(params.type_k_first);
    cparams.type_k_last     = kv_cache_type_from_str(params.type_k_last );
    cparams.type_v_first    = kv_cache_type_from_str(params.type_v_first);
    cparams.type_v_last     = kv_cache_type_from_str(params.type_v_last );
    cparams.n_k_first       = params.n_k_first;
    cparams.n_k_last        = params.n_k_last;
    cparams.n_v_first       = params.n_v_first;
    cparams.n_v_last        = params.n_v_last;
    if (!cparams.flash_attn && ggml_is_quantized(cparams.type_v_first) && cparams.n_v_first > 0) {
        throw std::runtime_error("Quantized V cache cannot be used without flash attention");
    }
    if (!cparams.flash_attn && ggml_is_quantized(cparams.type_v_last) && cparams.n_v_last > 0) {
        throw std::runtime_error("Quantized V cache cannot be used without flash attention");
    }

    if (!params.offload_policy.empty()) cparams.offload_policy = (void *)&params.offload_policy;
    if (!params.cuda_params.empty()) cparams.cuda_params = (void *)params.cuda_params.data();

    return cparams;
}

#ifdef LLAMA_USE_CURL

static bool starts_with(const std::string & str, const std::string & prefix) {
    // While we wait for C++20's std::string::starts_with...
    return str.rfind(prefix, 0) == 0;
}

static bool llama_download_file(const std::string & url, const std::string & path, const std::string & hf_token) {

    // Initialize libcurl
    std::unique_ptr<CURL, decltype(&curl_easy_cleanup)> curl(curl_easy_init(), &curl_easy_cleanup);
    if (!curl) {
        fprintf(stderr, "%s: error initializing libcurl\n", __func__);
        return false;
    }

    bool force_download = false;

    // Set the URL, allow to follow http redirection
    curl_easy_setopt(curl.get(), CURLOPT_URL, url.c_str());
    curl_easy_setopt(curl.get(), CURLOPT_FOLLOWLOCATION, 1L);

    // Check if hf-token or bearer-token was specified
    if (!hf_token.empty()) {
      std::string auth_header = "Authorization: Bearer ";
      auth_header += hf_token.c_str();
      struct curl_slist *http_headers = NULL;
      http_headers = curl_slist_append(http_headers, auth_header.c_str());
      curl_easy_setopt(curl.get(), CURLOPT_HTTPHEADER, http_headers);
    }

#if defined(_WIN32)
    // CURLSSLOPT_NATIVE_CA tells libcurl to use standard certificate store of
    //   operating system. Currently implemented under MS-Windows.
    curl_easy_setopt(curl.get(), CURLOPT_SSL_OPTIONS, CURLSSLOPT_NATIVE_CA);
#endif

    // Check if the file already exists locally
    struct stat model_file_info;
    auto file_exists = (stat(path.c_str(), &model_file_info) == 0);

    // If the file exists, check its JSON metadata companion file.
    std::string metadata_path = path + ".json";
    nlohmann::json metadata;
    std::string etag;
    std::string last_modified;

    if (file_exists) {
        // Try and read the JSON metadata file (note: stream autoclosed upon exiting this block).
        std::ifstream metadata_in(metadata_path);
        if (metadata_in.good()) {
            try {
                metadata_in >> metadata;
                fprintf(stderr, "%s: previous metadata file found %s: %s\n", __func__, metadata_path.c_str(), metadata.dump().c_str());
                if (metadata.contains("url") && metadata.at("url").is_string()) {
                    auto previous_url = metadata.at("url").get<std::string>();
                    if (previous_url != url) {
                        fprintf(stderr, "%s: Model URL mismatch: %s != %s\n", __func__, url.c_str(), previous_url.c_str());
                        return false;
                    }
                }
                if (metadata.contains("etag") && metadata.at("etag").is_string()) {
                    etag = metadata.at("etag");
                }
                if (metadata.contains("lastModified") && metadata.at("lastModified").is_string()) {
                    last_modified = metadata.at("lastModified");
                }
            } catch (const nlohmann::json::exception & e) {
                fprintf(stderr, "%s: error reading metadata file %s: %s\n", __func__, metadata_path.c_str(), e.what());
                return false;
            }
        }
    } else {
        fprintf(stderr, "%s: no previous model file found %s\n", __func__, path.c_str());
    }

    // Send a HEAD request to retrieve the etag and last-modified headers
    struct llama_load_model_from_url_headers {
        std::string etag;
        std::string last_modified;
    };
    llama_load_model_from_url_headers headers;
    {
        typedef size_t(*CURLOPT_HEADERFUNCTION_PTR)(char *, size_t, size_t, void *);
        auto header_callback = [](char * buffer, size_t /*size*/, size_t n_items, void * userdata) -> size_t {
            llama_load_model_from_url_headers *headers = (llama_load_model_from_url_headers *) userdata;

            static std::regex header_regex("([^:]+): (.*)\r\n");
            static std::regex etag_regex("ETag", std::regex_constants::icase);
            static std::regex last_modified_regex("Last-Modified", std::regex_constants::icase);

            std::string header(buffer, n_items);
            std::smatch match;
            if (std::regex_match(header, match, header_regex)) {
                const std::string & key = match[1];
                const std::string & value = match[2];
                if (std::regex_match(key, match, etag_regex)) {
                    headers->etag = value;
                } else if (std::regex_match(key, match, last_modified_regex)) {
                    headers->last_modified = value;
                }
            }
            return n_items;
        };

        curl_easy_setopt(curl.get(), CURLOPT_NOBODY, 1L); // will trigger the HEAD verb
        curl_easy_setopt(curl.get(), CURLOPT_NOPROGRESS, 1L); // hide head request progress
        curl_easy_setopt(curl.get(), CURLOPT_HEADERFUNCTION, static_cast<CURLOPT_HEADERFUNCTION_PTR>(header_callback));
        curl_easy_setopt(curl.get(), CURLOPT_HEADERDATA, &headers);

        CURLcode res = curl_easy_perform(curl.get());
        if (res != CURLE_OK) {
            fprintf(stderr, "%s: curl_easy_perform() failed: %s\n", __func__, curl_easy_strerror(res));
            return false;
        }

        long http_code = 0;
        curl_easy_getinfo(curl.get(), CURLINFO_RESPONSE_CODE, &http_code);
        if (http_code != 200) {
            // HEAD not supported, we don't know if the file has changed
            // force trigger downloading
            force_download = true;
            fprintf(stderr, "%s: HEAD invalid http status code received: %ld\n", __func__, http_code);
        }
    }

    bool should_download = !file_exists || force_download;
    if (!should_download) {
        if (!etag.empty() && etag != headers.etag) {
            fprintf(stderr, "%s: ETag header is different (%s != %s): triggering a new download\n", __func__, etag.c_str(), headers.etag.c_str());
            should_download = true;
        } else if (!last_modified.empty() && last_modified != headers.last_modified) {
            fprintf(stderr, "%s: Last-Modified header is different (%s != %s): triggering a new download\n", __func__, last_modified.c_str(), headers.last_modified.c_str());
            should_download = true;
        }
    }
    if (should_download) {
        std::string path_temporary = path + ".downloadInProgress";
        if (file_exists) {
            fprintf(stderr, "%s: deleting previous downloaded file: %s\n", __func__, path.c_str());
            if (remove(path.c_str()) != 0) {
                fprintf(stderr, "%s: unable to delete file: %s\n", __func__, path.c_str());
                return false;
            }
        }

        // Set the output file

        struct FILE_deleter {
            void operator()(FILE * f) const {
                fclose(f);
            }
        };

        std::unique_ptr<FILE, FILE_deleter> outfile(fopen(path_temporary.c_str(), "wb"));
        if (!outfile) {
            fprintf(stderr, "%s: error opening local file for writing: %s\n", __func__, path.c_str());
            return false;
        }

        typedef size_t(*CURLOPT_WRITEFUNCTION_PTR)(void * data, size_t size, size_t nmemb, void * fd);
        auto write_callback = [](void * data, size_t size, size_t nmemb, void * fd) -> size_t {
            return fwrite(data, size, nmemb, (FILE *)fd);
        };
        curl_easy_setopt(curl.get(), CURLOPT_NOBODY, 0L);
        curl_easy_setopt(curl.get(), CURLOPT_WRITEFUNCTION, static_cast<CURLOPT_WRITEFUNCTION_PTR>(write_callback));
        curl_easy_setopt(curl.get(), CURLOPT_WRITEDATA, outfile.get());

        //  display download progress
        curl_easy_setopt(curl.get(), CURLOPT_NOPROGRESS, 0L);

        // helper function to hide password in URL
        auto llama_download_hide_password_in_url = [](const std::string & url) -> std::string {
            std::size_t protocol_pos = url.find("://");
            if (protocol_pos == std::string::npos) {
                return url;  // Malformed URL
            }

            std::size_t at_pos = url.find('@', protocol_pos + 3);
            if (at_pos == std::string::npos) {
                return url;  // No password in URL
            }

            return url.substr(0, protocol_pos + 3) + "********" + url.substr(at_pos);
        };

        // start the download
        fprintf(stderr, "%s: downloading from %s to %s (server_etag:%s, server_last_modified:%s)...\n", __func__,
                llama_download_hide_password_in_url(url).c_str(), path.c_str(), headers.etag.c_str(), headers.last_modified.c_str());
        auto res = curl_easy_perform(curl.get());
        if (res != CURLE_OK) {
            fprintf(stderr, "%s: curl_easy_perform() failed: %s\n", __func__, curl_easy_strerror(res));
            return false;
        }

        long http_code = 0;
        curl_easy_getinfo (curl.get(), CURLINFO_RESPONSE_CODE, &http_code);
        if (http_code < 200 || http_code >= 400) {
            fprintf(stderr, "%s: invalid http status code received: %ld\n", __func__, http_code);
            return false;
        }

        // Causes file to be closed explicitly here before we rename it.
        outfile.reset();

        // Write the updated JSON metadata file.
        metadata.update({
            {"url", url},
            {"etag", headers.etag},
            {"lastModified", headers.last_modified}
        });
        std::ofstream(metadata_path) << metadata.dump(4);
        fprintf(stderr, "%s: file metadata saved: %s\n", __func__, metadata_path.c_str());

        if (rename(path_temporary.c_str(), path.c_str()) != 0) {
            fprintf(stderr, "%s: unable to rename file: %s to %s\n", __func__, path_temporary.c_str(), path.c_str());
            return false;
        }
    }

    return true;
}

struct llama_model * llama_load_model_from_url(
        const char * model_url,
        const char * path_model,
        const char * hf_token,
        const struct llama_model_params & params) {
    // Basic validation of the model_url
    if (!model_url || strlen(model_url) == 0) {
        fprintf(stderr, "%s: invalid model_url\n", __func__);
        return NULL;
    }

    if (!llama_download_file(model_url, path_model, hf_token)) {
        return NULL;
    }

    // check for additional GGUFs split to download
    int n_split = 0;
    {
        struct gguf_init_params gguf_params = {
            /*.no_alloc = */ true,
            /*.ctx      = */ NULL,
        };
        auto * ctx_gguf = gguf_init_from_file(path_model, gguf_params);
        if (!ctx_gguf) {
            fprintf(stderr, "\n%s:  failed to load input GGUF from %s\n", __func__, path_model);
            return NULL;
        }

        auto key_n_split = gguf_find_key(ctx_gguf, LLM_KV_SPLIT_COUNT);
        if (key_n_split >= 0) {
            n_split = gguf_get_val_u16(ctx_gguf, key_n_split);
        }

        gguf_free(ctx_gguf);
    }

    if (n_split > 1) {
        char split_prefix[PATH_MAX] = {0};
        char split_url_prefix[LLAMA_CURL_MAX_URL_LENGTH] = {0};

        // Verify the first split file format
        // and extract split URL and PATH prefixes
        {
            if (!llama_split_prefix(split_prefix, sizeof(split_prefix), path_model, 0, n_split)) {
                fprintf(stderr, "\n%s: unexpected model file name: %s"
                                " n_split=%d\n", __func__, path_model, n_split);
                return NULL;
            }

            if (!llama_split_prefix(split_url_prefix, sizeof(split_url_prefix), model_url, 0, n_split)) {
                fprintf(stderr, "\n%s: unexpected model url: %s"
                                " n_split=%d\n", __func__, model_url, n_split);
                return NULL;
            }
        }

        // Prepare download in parallel
        std::vector<std::future<bool>> futures_download;
        for (int idx = 1; idx < n_split; idx++) {
            futures_download.push_back(std::async(std::launch::async, [&split_prefix, &split_url_prefix, &n_split, hf_token](int download_idx) -> bool {
                char split_path[PATH_MAX] = {0};
                llama_split_path(split_path, sizeof(split_path), split_prefix, download_idx, n_split);

                char split_url[LLAMA_CURL_MAX_URL_LENGTH] = {0};
                llama_split_path(split_url, sizeof(split_url), split_url_prefix, download_idx, n_split);

                return llama_download_file(split_url, split_path, hf_token);
            }, idx));
        }

        // Wait for all downloads to complete
        for (auto & f : futures_download) {
            if (!f.get()) {
                return NULL;
            }
        }
    }

    return llama_model_load_from_file(path_model, params);
}

struct llama_model * llama_load_model_from_hf(
        const char * repo,
        const char * model,
        const char * path_model,
        const char * hf_token,
        const struct llama_model_params & params) {
    // construct hugging face model url:
    //
    //  --repo ggml-org/models --file tinyllama-1.1b/ggml-model-f16.gguf
    //    https://huggingface.co/ggml-org/models/resolve/main/tinyllama-1.1b/ggml-model-f16.gguf
    //
    //  --repo TheBloke/Mixtral-8x7B-v0.1-GGUF --file mixtral-8x7b-v0.1.Q4_K_M.gguf
    //    https://huggingface.co/TheBloke/Mixtral-8x7B-v0.1-GGUF/resolve/main/mixtral-8x7b-v0.1.Q4_K_M.gguf
    //

    std::string model_url = "https://huggingface.co/";
    model_url += repo;
    model_url += "/resolve/main/";
    model_url += model;

    return llama_load_model_from_url(model_url.c_str(), path_model, hf_token, params);
}

#else

struct llama_model * llama_load_model_from_url(
        const char * /*model_url*/,
        const char * /*path_model*/,
        const char * /*hf_token*/,
        const struct llama_model_params & /*params*/) {
    fprintf(stderr, "%s: llama.cpp built without libcurl, downloading from an url not supported.\n", __func__);
    return nullptr;
}

struct llama_model * llama_load_model_from_hf(
        const char * /*repo*/,
        const char * /*model*/,
        const char * /*path_model*/,
        const char * /*hf_token*/,
        const struct llama_model_params & /*params*/) {
    fprintf(stderr, "%s: llama.cpp built without libcurl, downloading from Hugging Face not supported.\n", __func__);
    return nullptr;
}

#endif // LLAMA_USE_CURL

//
// Batch utils
//

void common_batch_clear(struct llama_batch & batch) {
    batch.n_tokens = 0;
}

void common_batch_add(
                 struct llama_batch & batch,
                        llama_token   id,
                          llama_pos   pos,
    const std::vector<llama_seq_id> & seq_ids,
                               bool   logits) {
    GGML_ASSERT(batch.seq_id[batch.n_tokens] && "llama_batch size exceeded");
    batch.token   [batch.n_tokens] = id;
    batch.pos     [batch.n_tokens] = pos;
    batch.n_seq_id[batch.n_tokens] = seq_ids.size();
    for (size_t i = 0; i < seq_ids.size(); ++i) {
        batch.seq_id[batch.n_tokens][i] = seq_ids[i];
    }
    batch.logits  [batch.n_tokens] = logits;

    batch.n_tokens++;
}

//
// Vocab utils
//

std::vector<llama_token> common_tokenize(
  const struct llama_context * ctx,
           const std::string & text,
                        bool   add_special,
                        bool   parse_special) {
    return common_tokenize(llama_get_model(ctx), text, add_special, parse_special);
}

std::vector<llama_token> common_tokenize(
    const struct llama_model * model,
           const std::string & text,
                        bool   add_special,
                        bool   parse_special) {
    // upper limit for the number of tokens
    int n_tokens = text.length() + 2 * add_special;
    std::vector<llama_token> result(n_tokens);
    n_tokens = llama_tokenize(model, text.data(), text.length(), result.data(), result.size(), add_special, parse_special);
    if (n_tokens < 0) {
        result.resize(-n_tokens);
        int check = llama_tokenize(model, text.data(), text.length(), result.data(), result.size(), add_special, parse_special);
        GGML_ASSERT(check == -n_tokens);
    } else {
        result.resize(n_tokens);
    }
    return result;
}

std::vector<llama_token> llama_tokenize(
    const struct llama_vocab* vocab,
    const std::string& text,
    bool   add_special,
    bool   parse_special) {
    // upper limit for the number of tokens
    int n_tokens = text.length() + 2 * add_special;
    std::vector<llama_token> result(n_tokens);
    n_tokens = llama_vocab_tokenize(vocab, text.data(), text.length(), result.data(), result.size(), add_special, parse_special);
    if (n_tokens == std::numeric_limits<int32_t>::min()) {
        throw std::runtime_error("Tokenization failed: input text too large, tokenization result exceeds int32_t limit");
    }
    if (n_tokens < 0) {
        result.resize(-n_tokens);
        int check = llama_vocab_tokenize(vocab, text.data(), text.length(), result.data(), result.size(), add_special, parse_special);
        GGML_ASSERT(check == -n_tokens);
    }
    else {
        result.resize(n_tokens);
    }
    return result;
}

std::vector<llama_token> common_tokenize(
    const struct llama_vocab * vocab,
    const std::string & text,
    bool   add_special,
    bool   parse_special){

    return llama_tokenize(vocab, text, add_special, parse_special);
}

std::string common_token_to_piece(const struct llama_context * ctx, llama_token token, bool special) {
    std::string piece;
    piece.resize(piece.capacity());  // using string internal cache, 15 bytes + '\n'
    const int n_chars = llama_token_to_piece(llama_get_model(ctx), token, &piece[0], piece.size(), 0, special);
    if (n_chars < 0) {
        piece.resize(-n_chars);
        int check = llama_token_to_piece(llama_get_model(ctx), token, &piece[0], piece.size(), 0, special);
        GGML_ASSERT(check == -n_chars);
    }
    else {
        piece.resize(n_chars);
    }

    return piece;
}

std::string llama_token_to_piece(const struct llama_model* model, llama_token token, bool special) {
    std::string piece;
    piece.resize(piece.capacity());  // using string internal cache, 15 bytes + '\n'
    const int n_chars = llama_token_to_piece(model, token, &piece[0], piece.size(), 0, special);
    if (n_chars < 0) {
        piece.resize(-n_chars);
        int check = llama_token_to_piece(model, token, &piece[0], piece.size(), 0, special);
        GGML_ASSERT(check == -n_chars);
    }
    else {
        piece.resize(n_chars);
    }

    return piece;
}

std::string common_detokenize(const struct llama_context * ctx, const std::vector<llama_token> & tokens, bool special) {
    const llama_model * model = llama_get_model(ctx);
    const llama_vocab * vocab = llama_model_get_vocab(model);
    return common_detokenize(vocab, tokens, special);
}

std::string common_detokenize(const struct llama_vocab * vocab, const std::vector<llama_token> & tokens, bool special) {
    std::string text;
    text.resize(std::max(text.capacity(), tokens.size()));
    int32_t n_chars = llama_detokenize(vocab, tokens.data(), (int32_t)tokens.size(), &text[0], (int32_t)text.size(), false, special);
    if (n_chars < 0) {
        text.resize(-n_chars);
        n_chars = llama_detokenize(vocab, tokens.data(), (int32_t)tokens.size(), &text[0], (int32_t)text.size(), false, special);
        GGML_ASSERT(n_chars <= (int32_t)text.size());  // whitespace trimming is performed after per-token detokenization
    }

    text.resize(n_chars);

    // NOTE: the original tokenizer decodes bytes after collecting the pieces.
    return text;
}

std::string common_token_to_piece(const struct llama_vocab * vocab, llama_token token, bool special) {
    std::string piece;
    piece.resize(piece.capacity());  // using string internal cache, 15 bytes + '\n'
    const int n_chars = llama_token_to_piece_vocab(vocab, token, &piece[0], piece.size(), 0, special);
    if (n_chars < 0) {
        piece.resize(-n_chars);
        int check = llama_token_to_piece_vocab(vocab, token, &piece[0], piece.size(), 0, special);
        GGML_ASSERT(check == -n_chars);
    } else {
        piece.resize(n_chars);
    }

    return piece;
}

bool llama_should_add_bos_token(const llama_model * model) {
    const int add_bos = llama_add_bos_token(model);
    const llama_vocab * vocab = llama_get_model_vocab(model);
    return add_bos != -1 ? bool(add_bos) : (llama_vocab_type(vocab) == LLAMA_VOCAB_TYPE_SPM);
}


//
// KV cache utils
//

void llama_kv_cache_dump_view(const llama_kv_cache_view & view, int row_size) {
    static const char slot_chars[] = ".123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz+";

    printf("=== Dumping KV cache. total cells %d, max sequences per cell %d, populated cells %d, total tokens in cache %d, largest empty slot=%d @ %d",
        view.n_cells, view.n_seq_max, view.used_cells, view.token_count, view.max_contiguous, view.max_contiguous_idx);

    llama_kv_cache_view_cell * c_curr = view.cells;
    llama_seq_id * cs_curr = view.cells_sequences;

    for (int i = 0; i < view.n_cells; i++, c_curr++, cs_curr += view.n_seq_max) {
        if (i % row_size == 0) {
            printf("\n%5d: ", i);
        }
        int seq_count = 0;
        for (int j = 0; j < view.n_seq_max; j++) {
            if (cs_curr[j] >= 0) { seq_count++; }
        }
        putchar(slot_chars[std::min(sizeof(slot_chars) - 2, size_t(seq_count))]);
    }

    printf("\n=== Done dumping\n");
}

void llama_kv_cache_dump_view_seqs(const llama_kv_cache_view & view, int row_size) {
    static const char slot_chars[] = "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz";

    printf("=== Dumping KV cache. total cells %d, max sequences per cell %d, populated cells %d, total tokens in cache %d, largest empty slot=%d @ %d\n",
        view.n_cells, view.n_seq_max, view.used_cells, view.token_count, view.max_contiguous, view.max_contiguous_idx);

    std::unordered_map<llama_seq_id, size_t> seqs;
    llama_kv_cache_view_cell * c_curr = view.cells;
    llama_seq_id * cs_curr = view.cells_sequences;

    for (int i = 0; i < view.n_cells; i++, c_curr++, cs_curr += view.n_seq_max) {
        for (int j = 0; j < view.n_seq_max; j++) {
            if (cs_curr[j] < 0) { continue; }
            if (seqs.find(cs_curr[j]) == seqs.end()) {
                if (seqs.size() + 1 >= sizeof(slot_chars)) { break; }
                const size_t sz = seqs.size();
                seqs[cs_curr[j]] = sz;
            }
        }
        if (seqs.size() + 1 >= sizeof(slot_chars)) { break; }
    }

    printf("=== Sequence legend: ");
    for (const auto & it : seqs) {
        printf("%zu=%d, ", it.second, it.first);
    }
    printf("'+'=other sequence ids");

    c_curr = view.cells;
    cs_curr = view.cells_sequences;
    for (int i = 0; i < view.n_cells; i++, c_curr++, cs_curr += view.n_seq_max) {
        if (i % row_size == 0) {
            printf("\n%5d: ", i);
        }
        for (int j = 0; j < view.n_seq_max; j++) {
            if (cs_curr[j] >= 0) {
                const auto & it = seqs.find(cs_curr[j]);
                putchar(it != seqs.end() ? int(slot_chars[it->second]) : '+');
            } else {
                putchar('.');
            }
        }
        putchar(' ');
    }

    printf("\n=== Done dumping\n");
}

//
// Embedding utils
//

void common_embd_normalize(const float * inp, float * out, int n, int embd_norm) {
    double sum = 0.0;

    switch (embd_norm) {
        case -1: // no normalisation
            sum = 1.0;
            break;
        case 0: // max absolute
            for (int i = 0; i < n; i++) {
                if (sum < std::abs(inp[i])) sum = std::abs(inp[i]);
            }
            sum /= 32760.0; // make an int16 range
            break;
        case 2: // euclidean
            for (int i = 0; i < n; i++) {
                sum += inp[i] * inp[i];
            }
            sum = std::sqrt(sum);
            break;
        default: // p-norm (euclidean is p-norm p=2)
            for (int i = 0; i < n; i++) {
                sum += std::pow(std::abs(inp[i]), embd_norm);
            }
            sum = std::pow(sum, 1.0 / embd_norm);
            break;
    }

    const float norm = sum > 0.0 ? 1.0 / sum : 0.0f;

    for (int i = 0; i < n; i++) {
        out[i] = inp[i] * norm;
    }
}

float common_embd_similarity_cos(const float * embd1, const float * embd2, int n){
    double sum  = 0.0;
    double sum1 = 0.0;
    double sum2 = 0.0;

    for (int i = 0; i < n; i++) {
        sum  += embd1[i] * embd2[i];
        sum1 += embd1[i] * embd1[i];
        sum2 += embd2[i] * embd2[i];
    }

    // Handle the case where one or both vectors are zero vectors
    if (sum1 == 0.0 || sum2 == 0.0) {
        if (sum1 == 0.0 && sum2 == 0.0) {
            return 1.0f; // two zero vectors are similar
        }
        return 0.0f;
    }

    return sum / (sqrt(sum1) * sqrt(sum2));
}

//
// Control vector utils
//

static llama_control_vector_data llama_control_vector_load_one(const llama_control_vector_load_info & load_info) {
    llama_control_vector_data result = { -1, {} };

    ggml_context * ctx = nullptr;
    struct gguf_init_params meta_gguf_params = {
        /* .no_alloc = */ false,
        /* .ctx      = */ &ctx,
    };
    struct gguf_context * ctx_gguf = gguf_init_from_file(load_info.fname.c_str(), meta_gguf_params);
    if (!ctx_gguf) {
        fprintf(stderr, "%s: failed to load control vector file from %s\n", __func__, load_info.fname.c_str());
        return result;
    }

    int32_t n_tensors = gguf_get_n_tensors(ctx_gguf);
    if (n_tensors == 0) {
        fprintf(stderr, "%s: no direction tensors found in %s\n", __func__, load_info.fname.c_str());
    }

    for (int i = 0; i < n_tensors; i++) {
        std::string name = gguf_get_tensor_name(ctx_gguf, i);

        int layer_idx = -1;

        // split on '.'
        size_t dotpos = name.find('.');
        if (dotpos != std::string::npos && name.substr(0, dotpos) == "direction") {
            try {
                layer_idx = std::stoi(name.substr(dotpos + 1));
            } catch (...) {
                layer_idx = -1;
            }
        }
        if (layer_idx < 0) {
            fprintf(stderr, "%s: invalid/unparsable direction tensor layer index in %s\n", __func__, load_info.fname.c_str());
            result.n_embd = -1;
            break;
        } else if (layer_idx == 0) {
            fprintf(stderr, "%s: invalid (zero) direction tensor layer index in %s\n", __func__, load_info.fname.c_str());
            result.n_embd = -1;
            break;
        }

        struct ggml_tensor * tensor = ggml_get_tensor(ctx, name.c_str());
        if (tensor->type != GGML_TYPE_F32) {
            fprintf(stderr, "%s: invalid (non-F32) direction tensor type in %s\n", __func__, load_info.fname.c_str());
            result.n_embd = -1;
            break;
        }
        if (ggml_n_dims(tensor) != 1) {
            fprintf(stderr, "%s: invalid (non-1D) direction tensor shape in %s\n", __func__, load_info.fname.c_str());
            result.n_embd = -1;
            break;
        }

        if (result.n_embd == -1) {
            result.n_embd = ggml_nelements(tensor);
        } else if (ggml_nelements(tensor) != result.n_embd) {
            fprintf(stderr, "%s: direction tensor in %s does not match previous dimensions\n", __func__, load_info.fname.c_str());
            result.n_embd = -1;
            break;
        }

        // extend if necessary - do not store data for layer 0 (it's not used)
        result.data.resize(std::max(result.data.size(), static_cast<size_t>(result.n_embd * layer_idx)), 0.0f);

        const float * src = (const float *) tensor->data;
        float * dst = result.data.data() + result.n_embd * (layer_idx - 1);  // layer 1 at [0]
        for (int j = 0; j < result.n_embd; j++) {
            dst[j] += src[j] * load_info.strength;  // allows multiple directions for same layer in same file
        }

    }

    if (result.n_embd == -1) {
        fprintf(stderr, "%s: skipping %s due to invalid direction tensors\n", __func__, load_info.fname.c_str());
        result.data.clear();
    }

    gguf_free(ctx_gguf);
    ggml_free(ctx);

    return result;
}

llama_control_vector_data llama_control_vector_load(const std::vector<llama_control_vector_load_info> & load_infos) {
    llama_control_vector_data result = { -1, {} };

    for (const auto & info : load_infos) {
        auto cur = llama_control_vector_load_one(info);

        if (cur.n_embd == -1) {
            result.n_embd = -1;
            break;
        }
        if (result.n_embd != -1 && result.n_embd != cur.n_embd) {
            fprintf(stderr, "%s: control vectors in %s does not match previous dimensions\n", __func__, info.fname.c_str());
            result.n_embd = -1;
            break;
        }

        if (result.n_embd == -1) {
            result = std::move(cur);
        } else {
            result.data.resize(std::max(result.data.size(), cur.data.size()), 0.0f);  // extend if necessary
            for (size_t i = 0; i < cur.data.size(); i++) {
                result.data[i] += cur.data[i];
            }
        }
    }

    if (result.n_embd == -1) {
        fprintf(stderr, "%s: no valid control vector files passed\n", __func__);
        result.data.clear();
    }

    return result;
}

//
// YAML utils
//

void yaml_dump_vector_float(FILE * stream, const char * prop_name, const std::vector<float> & data) {
    if (data.empty()) {
        fprintf(stream, "%s:\n", prop_name);
        return;
    }

    fprintf(stream, "%s: [", prop_name);
    for (size_t i = 0; i < data.size() - 1; ++i) {
        fprintf(stream, "%e, ", data[i]);
    }
    fprintf(stream, "%e]\n", data.back());
}

void yaml_dump_vector_int(FILE * stream, const char * prop_name, const std::vector<int> & data) {
    if (data.empty()) {
        fprintf(stream, "%s:\n", prop_name);
        return;
    }

    fprintf(stream, "%s: [", prop_name);
    for (size_t i = 0; i < data.size() - 1; ++i) {
        fprintf(stream, "%d, ", data[i]);
    }
    fprintf(stream, "%d]\n", data.back());
}

void yaml_dump_string_multiline(FILE * stream, const char * prop_name, const char * data) {
    std::string data_str(data == NULL ? "" : data);

    if (data_str.empty()) {
        fprintf(stream, "%s:\n", prop_name);
        return;
    }

    size_t pos_start = 0;
    size_t pos_found = 0;

    if (std::isspace(data_str[0]) || std::isspace(data_str.back())) {
        data_str = std::regex_replace(data_str, std::regex("\n"), "\\n");
        data_str = std::regex_replace(data_str, std::regex("\""), "\\\"");
        data_str = std::regex_replace(data_str, std::regex(R"(\\[^n"])"), R"(\$&)");
        data_str = "\"" + data_str + "\"";
        fprintf(stream, "%s: %s\n", prop_name, data_str.c_str());
        return;
    }

    if (data_str.find('\n') == std::string::npos) {
        fprintf(stream, "%s: %s\n", prop_name, data_str.c_str());
        return;
    }

    fprintf(stream, "%s: |\n", prop_name);
    while ((pos_found = data_str.find('\n', pos_start)) != std::string::npos) {
        fprintf(stream, "  %s\n", data_str.substr(pos_start, pos_found-pos_start).c_str());
        pos_start = pos_found + 1;
    }
}

void yaml_dump_non_result_info(FILE * stream, const gpt_params & params, const llama_context * lctx,
                               const std::string & timestamp, const std::vector<int> & prompt_tokens, const char * model_desc) {
    const common_params_sampling & sparams = params.sparams;

    fprintf(stream, "build_commit: %s\n",        LLAMA_COMMIT);
    fprintf(stream, "build_number: %d\n",        LLAMA_BUILD_NUMBER);
    fprintf(stream, "cpu_has_arm_fma: %s\n",     ggml_cpu_has_arm_fma()     ? "true" : "false");
    fprintf(stream, "cpu_has_avx: %s\n",         ggml_cpu_has_avx()         ? "true" : "false");
    fprintf(stream, "cpu_has_avx_vnni: %s\n",    ggml_cpu_has_avx_vnni()    ? "true" : "false");
    fprintf(stream, "cpu_has_avx2: %s\n",        ggml_cpu_has_avx2()        ? "true" : "false");
    fprintf(stream, "cpu_has_avx512: %s\n",      ggml_cpu_has_avx512()      ? "true" : "false");
    fprintf(stream, "cpu_has_avx512_vbmi: %s\n", ggml_cpu_has_avx512_vbmi() ? "true" : "false");
    fprintf(stream, "cpu_has_avx512_vnni: %s\n", ggml_cpu_has_avx512_vnni() ? "true" : "false");
    fprintf(stream, "cpu_has_cuda: %s\n",        ggml_cpu_has_cuda()        ? "true" : "false");
    fprintf(stream, "cpu_has_vulkan: %s\n",      ggml_cpu_has_vulkan()      ? "true" : "false");
    fprintf(stream, "cpu_has_kompute: %s\n",     ggml_cpu_has_kompute()     ? "true" : "false");
    fprintf(stream, "cpu_has_fma: %s\n",         ggml_cpu_has_fma()         ? "true" : "false");
    fprintf(stream, "cpu_has_gpublas: %s\n",     ggml_cpu_has_gpublas()     ? "true" : "false");
    fprintf(stream, "cpu_has_neon: %s\n",        ggml_cpu_has_neon()        ? "true" : "false");
    fprintf(stream, "cpu_has_sve: %s\n",         ggml_cpu_has_sve()         ? "true" : "false");
    fprintf(stream, "cpu_has_f16c: %s\n",        ggml_cpu_has_f16c()        ? "true" : "false");
    fprintf(stream, "cpu_has_fp16_va: %s\n",     ggml_cpu_has_fp16_va()     ? "true" : "false");
    fprintf(stream, "cpu_has_wasm_simd: %s\n",   ggml_cpu_has_wasm_simd()   ? "true" : "false");
    fprintf(stream, "cpu_has_blas: %s\n",        ggml_cpu_has_blas()        ? "true" : "false");
    fprintf(stream, "cpu_has_sse3: %s\n",        ggml_cpu_has_sse3()        ? "true" : "false");
    fprintf(stream, "cpu_has_vsx: %s\n",         ggml_cpu_has_vsx()         ? "true" : "false");
    fprintf(stream, "cpu_has_matmul_int8: %s\n", ggml_cpu_has_matmul_int8() ? "true" : "false");

#ifdef NDEBUG
    fprintf(stream, "debug: false\n");
#else
    fprintf(stream, "debug: true\n");
#endif // NDEBUG

    fprintf(stream, "model_desc: %s\n", model_desc);
    fprintf(stream, "n_vocab: %d  # output size of the final layer, 32001 for some models\n", llama_n_vocab(llama_get_model(lctx)));

#ifdef __OPTIMIZE__
    fprintf(stream, "optimize: true\n");
#else
    fprintf(stream, "optimize: false\n");
#endif // __OPTIMIZE__

    fprintf(stream, "time: %s\n", timestamp.c_str());

    fprintf(stream, "\n");
    fprintf(stream, "###############\n");
    fprintf(stream, "# User Inputs #\n");
    fprintf(stream, "###############\n");
    fprintf(stream, "\n");

    fprintf(stream, "alias: %s # default: unknown\n", params.model_alias.c_str());
    fprintf(stream, "batch_size: %d # default: 512\n", params.n_batch);
    yaml_dump_string_multiline(stream, "cfg_negative_prompt", sparams.cfg_negative_prompt.c_str());
    fprintf(stream, "cfg_scale: %f # default: 1.0\n", sparams.cfg_scale);
    fprintf(stream, "chunks: %d # default: -1 (unlimited)\n", params.n_chunks);
    fprintf(stream, "color: %s # default: false\n", params.use_color ? "true" : "false");
    fprintf(stream, "ctx_size: %d # default: 512\n", params.n_ctx);
    fprintf(stream, "dry_allowed_length: %d # default: 2\n", sparams.dry_allowed_length);
    fprintf(stream, "dry_base: %.2f # default: 1.75\n", sparams.dry_base);
    fprintf(stream, "dry_multiplier: %.1f # default: 0.0\n", sparams.dry_multiplier);
    fprintf(stream, "dry_penalty_last_n: %d # default: -1 (0 = disable, -1 = context size)\n", sparams.dry_penalty_last_n);
    fprintf(stream, "escape: %s # default: false\n", params.escape ? "true" : "false");
    fprintf(stream, "file: # never logged, see prompt instead. Can still be specified for input.\n");
    fprintf(stream, "frequency_penalty: %f # default: 0.0 \n", sparams.penalty_freq);
    yaml_dump_string_multiline(stream, "grammar", sparams.grammar.grammar.c_str());
    fprintf(stream, "grammar-file: # never logged, see grammar instead. Can still be specified for input.\n");
    fprintf(stream, "hellaswag: %s # default: false\n", params.hellaswag ? "true" : "false");
    fprintf(stream, "hellaswag_tasks: %zu # default: 400\n", params.hellaswag_tasks);

    const auto logit_bias_eos = sparams.logit_bias.find(llama_token_eos(llama_get_model(lctx)));
    const bool ignore_eos = logit_bias_eos != sparams.logit_bias.end() && logit_bias_eos->second == -INFINITY;
    fprintf(stream, "ignore_eos: %s # default: false\n", ignore_eos ? "true" : "false");

    yaml_dump_string_multiline(stream, "in_prefix", params.input_prefix.c_str());
    fprintf(stream, "in_prefix_bos: %s # default: false\n", params.input_prefix_bos ? "true" : "false");
    yaml_dump_string_multiline(stream, "in_suffix", params.input_prefix.c_str());
    fprintf(stream, "interactive: %s # default: false\n", params.interactive ? "true" : "false");
    fprintf(stream, "interactive_first: %s # default: false\n", params.interactive_first ? "true" : "false");
    fprintf(stream, "keep: %d # default: 0\n", params.n_keep);
    fprintf(stream, "logdir: %s # default: unset (no logging)\n", params.logdir.c_str());

    fprintf(stream, "logit_bias:\n");
    for (std::pair<llama_token, float> lb : sparams.logit_bias) {
        if (ignore_eos && lb.first == logit_bias_eos->first) {
            continue;
        }
        fprintf(stream, "  %d: %f", lb.first, lb.second);
    }

    fprintf(stream, "lora:\n");
    for (auto & la : params.lora_adapters) {
        if (la.scale == 1.0f) {
            fprintf(stream, "  - %s\n", la.path.c_str());
        }
    }
    fprintf(stream, "lora_scaled:\n");
    for (auto & la : params.lora_adapters) {
        if (la.scale != 1.0f) {
            fprintf(stream, "  - %s: %f\n", la.path.c_str(), la.scale);
        }
    }
    fprintf(stream, "lora_init_without_apply: %s # default: false\n", params.lora_init_without_apply ? "true" : "false");
    fprintf(stream, "main_gpu: %d # default: 0\n", params.main_gpu);
    fprintf(stream, "max_gpu: %d # default: 0\n", params.max_gpu);
    fprintf(stream, "ncmoe: %d # default: 0\n", params.ncmoe);
    fprintf(stream, "fit: %d # default: false\n", params.fit);
    fprintf(stream, "fit_margin: %d # default: 0\n", params.fit_margin);
    fprintf(stream, "worst_graph_tokens: %d # default: 0\n", params.worst_graph_tokens);
    fprintf(stream, "min_keep: %d # default: 0 (disabled)\n", sparams.min_keep);
    fprintf(stream, "mirostat: %d # default: 0 (disabled)\n", sparams.mirostat);
    fprintf(stream, "mirostat_ent: %f # default: 5.0\n", sparams.mirostat_tau);
    fprintf(stream, "mirostat_lr: %f # default: 0.1\n", sparams.mirostat_eta);
    fprintf(stream, "xtc_probability: %f # default: 0.0\n", sparams.xtc_probability);
    fprintf(stream, "xtc_threshold: %f # default: 0.0\n", sparams.xtc_threshold);
    fprintf(stream, "top_n_sigma: %f # default: 0.0\n", sparams.top_n_sigma);
    fprintf(stream, "mlock: %s # default: false\n", params.use_mlock ? "true" : "false");
    fprintf(stream, "model: %s # default: %s\n", params.model.c_str(), DEFAULT_MODEL_PATH);
    fprintf(stream, "model_draft: %s # default:\n", params.speculative.model.c_str());
    fprintf(stream, "multiline_input: %s # default: false\n", params.multiline_input ? "true" : "false");
    fprintf(stream, "n_gpu_layers: %d # default: -1\n", params.n_gpu_layers);
    fprintf(stream, "n_predict: %d # default: -1 (unlimited)\n", params.n_predict);
    fprintf(stream, "n_probs: %d # only used by server binary, default: 0\n", sparams.n_probs);
    fprintf(stream, "no_mmap: %s # default: false\n", !params.use_mmap ? "true" : "false");
    fprintf(stream, "repack: %s # default: false\n", params.repack_tensors ? "true" : "false");
    fprintf(stream, "use_thp: %s # default: false\n", params.use_thp ? "true" : "false");
    fprintf(stream, "validate_quants: %s # default: false\n", params.validate_quants ? "true" : "false");
    fprintf(stream, "merge_qkv: %s # default: false\n", params.merge_qkv ? "true" : "false");
    fprintf(stream, "merge_up_gate_exps: %s # default: false\n", params.merge_up_gate_exps ? "true" : "false");
    fprintf(stream, "defer_experts: %s # default: false\n", params.defer_experts ? "true" : "false");
    fprintf(stream, "max_extra_alloc: %d # default: 256\n", params.max_extra_alloc_MiB);
    fprintf(stream, "penalize_nl: %s # default: false\n", sparams.penalize_nl ? "true" : "false");
    fprintf(stream, "ppl_output_type: %d # default: 0\n", params.ppl_output_type);
    fprintf(stream, "ppl_stride: %d # default: 0\n", params.ppl_stride);
    fprintf(stream, "presence_penalty: %f # default: 0.0\n", sparams.penalty_present);
    yaml_dump_string_multiline(stream, "prompt", params.prompt.c_str());
    fprintf(stream, "prompt_cache: %s\n", params.path_prompt_cache.c_str());
    fprintf(stream, "prompt_cache_all: %s # default: false\n", params.prompt_cache_all ? "true" : "false");
    fprintf(stream, "prompt_cache_ro: %s # default: false\n", params.prompt_cache_ro ? "true" : "false");
    yaml_dump_vector_int(stream, "prompt_tokens", prompt_tokens);
    fprintf(stream, "repeat_penalty: %f # default: 1.1\n", sparams.penalty_repeat);

    fprintf(stream, "reverse_prompt:\n");
    for (std::string ap : params.antiprompt) {
        size_t pos = 0;
        while ((pos = ap.find('\n', pos)) != std::string::npos) {
            ap.replace(pos, 1, "\\n");
            pos += 1;
        }

        fprintf(stream, "  - %s\n", ap.c_str());
    }

    fprintf(stream, "rope_freq_base: %f # default: 10000.0\n", params.rope_freq_base);
    fprintf(stream, "rope_freq_scale: %f # default: 1.0\n", params.rope_freq_scale);
    fprintf(stream, "seed: %u # default: -1 (random seed)\n", params.seed);
    fprintf(stream, "simple_io: %s # default: false\n", params.simple_io ? "true" : "false");
    fprintf(stream, "cont_batching: %s # default: false\n", params.cont_batching ? "true" : "false");
    fprintf(stream, "flash_attn: %s # default: false\n", params.flash_attn ? "true" : "false");
    fprintf(stream, "mla_attn: %d # default: 0\n", params.mla_attn);
    fprintf(stream, "attn_max_batch: %d # default: 0\n", params.attn_max_batch);
    fprintf(stream, "fused_moe: %s # default: false\n", params.fused_moe_up_gate ? "true" : "false");
    fprintf(stream, "grouped_expert_routing: %s # default: false\n", params.grouped_expert_routing ? "true" : "false");
    fprintf(stream, "fused_up_gate: %s # default: true\n", params.fused_up_gate ? "true" : "false");
    fprintf(stream, "fused_mmad: %s # default: true\n", params.fused_mmad ? "true" : "false");
    fprintf(stream, "rope_cache: %s # default: false\n", params.rope_cache ? "true" : "false");
    fprintf(stream, "graph_reuse: %s # default: false\n", params.graph_reuse ? "true" : "false");
    fprintf(stream, "k_cache_hadamard: %s # default: false\n", params.k_cache_hadamard ? "true" : "false");
    fprintf(stream, "v_cache_hadamard: %s # default: false\n", params.v_cache_hadamard ? "true" : "false");
    fprintf(stream, "split_mode_graph_scheduling: %s # default: false\n", params.split_mode_graph_scheduling ? "true" : "false");
    //fprintf(stream, "split_mode_f16: %s # default: true\n", params.split_mode_f16 ? "true" : "false");
    fprintf(stream, "reduce_type: %s # default f16\n", params.reduce_type.c_str());
    fprintf(stream, "scheduler_async: %s # default: false\n", params.scheduler_async ? "true" : "false");
    fprintf(stream, "ser: %d,%g # defaulr: -1,0\n", params.min_experts, params.thresh_experts);
    fprintf(stream, "temp: %f # default: 0.8\n", sparams.temp);

    const std::vector<float> tensor_split_vector(params.tensor_split, params.tensor_split + llama_max_devices());
    yaml_dump_vector_float(stream, "tensor_split", tensor_split_vector);

    fprintf(stream, "tfs: %f # default: 1.0\n", sparams.tfs_z);
    fprintf(stream, "threads: %d # default: %u\n", params.n_threads, std::thread::hardware_concurrency());
    fprintf(stream, "top_k: %d # default: 40\n", sparams.top_k);
    fprintf(stream, "top_p: %f # default: 0.95\n", sparams.top_p);
    fprintf(stream, "min_p: %f # default: 0.0\n", sparams.min_p);
    fprintf(stream, "typical_p: %f # default: 1.0\n", sparams.typical_p);
    fprintf(stream, "adaptive_target: %f # default: -1.0\n", sparams.adaptive_target);
    fprintf(stream, "adaptive_decay: %f # default: 0.9\n", sparams.adaptive_decay);
    fprintf(stream, "adaptive_updt_w_cur: %s # default: false\n", sparams.adaptive_updt_w_cur ? "true" : "false");
    fprintf(stream, "verbose_prompt: %s # default: false\n", params.verbose_prompt ? "true" : "false");
    fprintf(stream, "display_prompt: %s # default: true\n", params.display_prompt ? "true" : "false");
}

//
// Argparse utils
//

std::tuple<uint32_t, uint32_t, std::string, float> argparse_allowlist_unicode_rule(std::string argstr) {
    // format:
    //   LOWER..UPPER,SCRIPT:BIAS

    auto subs = string_split(argstr, ":");
    float bias = subs.size() == 1 ? 0 : std::stof(subs[1]);

    subs = string_split(subs[0], ",");
    std::string script = std::all_of(subs.back().begin(), subs.back().end(), [](char c) {
        return std::isalpha(c);
    }) ? string_lower(subs.back()) : "*";
    if (script == "ascii") {
        return { 0x000000, 0x00007F, "*", bias };
    }

    uint32_t first = 0;
    uint32_t last = -1;
    if ((script == "*") || (subs.size() > 1)) {
        subs = string_split(subs.front(), ".");
        if (!subs.front().empty()) {
            first = std::stoul(subs.front());
        }
        if (!subs.back().empty()) {
            last = std::stoul(subs.back());
        }
    }

    return { std::min(first, last), std::max(first, last), script, bias };
}

void argparse_expiring_logit_bias(const std::string& content, common_params_sampling& sparams) {
    decltype(sparams.elb_params) elb_params = { { { }, "" } };

    int32_t saved_duration = 0;
    std::vector<std::string> saved_phrases;
    std::vector<float> saved_biases;
    bool saved_is_range = false;

    for (auto line: string_split(content, "\n")) {
        string_strip(line);
        const char c0 = line.empty() ? '#' : line[0];
        if (c0 == '#') {
            // comment
            continue;   // next line
        }

        auto n_char = line.length();
        const char cE = line[n_char - 1];

        if (n_char > 1) {
            if ('(' == c0 && cE == ')') {
                const bool is_nested = '(' == line[1] && line[n_char - 2] == ')';
                if (is_nested) {
                    if (n_char == 4) {
                        // (())
                        saved_phrases.clear();
                        saved_biases.clear();
                        continue;   // next line
                    }
                    n_char -= 2;
                    line = line.substr(1, n_char);
                }

                auto qqpos = line.find('"');

                // (DURATION : ...)
                int32_t duration = is_nested ? -1 : 1;
                const auto cpos = line.find(':');
                if ((cpos != std::string::npos) && (1 < cpos) && (cpos < qqpos)) {
                    auto sub = line.substr(1, cpos - 1);
                    duration = std::stoi(sub);
                }
                if (duration == 0) {
                    continue;   // next line
                }

                // (... "PHRASE" ... "PHRASE" ...)
                std::vector<std::string> phrases;
                auto pos = line.find('"', qqpos + 1);
                while (pos != std::string::npos) {
                    if (line[pos - 1] == '\\') {
                        pos = line.find('"', pos + 1);
                    } else {
                        auto phrase = line.substr(qqpos + 1, pos - qqpos - 1);
                        string_process_escapes(phrase);
                        phrases.push_back(std::move(phrase));
                        qqpos = line.find('"', pos + 1);
                        if (qqpos == std::string::npos) {
                            break;
                        }
                        pos = line.find('"', qqpos + 1);
                    }
                }
                if (phrases.empty()) {
                    continue;   // next line
                }

                // (... : BIAS ...)
                std::vector<float> biases;
                bool is_range = false;
                const auto rcpos = line.rfind(':');
                if ((rcpos != std::string::npos) && (line.rfind('"') < rcpos)) {
                    auto sub = line.substr(rcpos + 1, n_char - rcpos - 2);
                    if (sub.find("~") != std::string::npos) {
                        // (... : BIAS ~ BIAS)
                        const auto splits = string_split(sub, '~');
                        auto split = splits.front();
                        biases.push_back(std::stof(split));
                        split = splits.back();
                        biases.push_back(std::stof(split));
                        is_range = true;
                    } else {
                        // (... : BIAS, BIAS, ..., BIAS)
                        for (auto split: string_split(sub, ',')) {
                            if (!split.empty()) {
                                biases.push_back(std::stof(split));
                            }
                        }
                    }
                }
                if (biases.empty()) {
                    continue;   // next line
                }

                if (is_nested) {
                    saved_duration = duration;
                    saved_phrases = std::move(phrases);
                    saved_biases = std::move(biases);
                    saved_is_range = is_range;
                } else {
                    elb_params.back().entries.push_back({ std::move(phrases), std::move(biases), duration, is_range });
                }
                continue;   // next line
            }
        }

        // exitword
        if ('"' == c0 && cE == '"') {
            line = line.substr(1, n_char - 2);
        }
        string_process_escapes(line);
        elb_params.back().exitword = std::move(line);
        if (!saved_phrases.empty() && !saved_biases.empty() && (saved_duration != 0)) {
            elb_params.back().entries.push_back({ saved_phrases, saved_biases, saved_duration, saved_is_range });
        }
        elb_params.push_back({ { }, "" });
    }

    sparams.elb_params = std::move(elb_params);
}