llama.cpp/examples/server/server.cpp

#include "utils.hpp"

#include "common.h"
#include "llama.h"
#include "grammar-parser.h"

#ifndef NDEBUG
// crash the server in debug mode, otherwise send an http 500 error
#define CPPHTTPLIB_NO_EXCEPTIONS 1
#endif
// increase max payload length to allow use of larger context size
#define CPPHTTPLIB_FORM_URL_ENCODED_PAYLOAD_MAX_LENGTH 1048576
#include "httplib.h"
#include "json.hpp"

// auto generated files (update with ./deps.sh)
#include "index.html.hpp"
#include "index.js.hpp"
#include "completion.js.hpp"
#include "json-schema-to-grammar.mjs.hpp"

#include <atomic>
#include <chrono>
#include <condition_variable>
#include <cstddef>
#include <set>
#include <mutex>
#include <thread>
#include <signal.h>
#include <memory>

using json = nlohmann::json;

bool server_verbose = false;
bool server_log_json = true;

enum stop_type {
    STOP_TYPE_FULL,
    STOP_TYPE_PARTIAL,
};

enum slot_state {
    SLOT_STATE_IDLE,
    SLOT_STATE_PROCESSING,
};

enum slot_command {
    SLOT_COMMAND_NONE,
    SLOT_COMMAND_LOAD_PROMPT,
    SLOT_COMMAND_RELEASE,
};

enum server_state {
    SERVER_STATE_LOADING_MODEL,  // Server is starting up, model not fully loaded yet
    SERVER_STATE_READY,          // Server is ready and model is loaded
    SERVER_STATE_ERROR           // An error occurred, load_model failed
};

enum server_task_type {
    SERVER_TASK_TYPE_COMPLETION,
    SERVER_TASK_TYPE_CANCEL,
    SERVER_TASK_TYPE_NEXT_RESPONSE,
    SERVER_TASK_TYPE_METRICS
};

struct server_task {
    int id        = -1; // to be filled by server_queue
    int id_multi  = -1;
    int id_target = -1;

    server_task_type type;
    json data;

    bool infill    = false;
    bool embedding = false;
};

struct server_task_result {
    int id       = -1;
    int id_multi = -1;

    json data;

    bool stop;
    bool error;
};

struct server_task_multi {
    int id = -1;

    std::set<int> subtasks_remaining;
    std::vector<server_task_result> results;
};

struct slot_params {
    bool stream       = true;
    bool cache_prompt = false; // remember the prompt to avoid reprocessing all prompt

    uint32_t seed      = -1; // RNG seed
    int32_t  n_keep    =  0; // number of tokens to keep from initial prompt
    int32_t  n_predict = -1; // new tokens to predict

    std::vector<std::string> antiprompt;

    json input_prefix;
    json input_suffix;
};

struct server_params {
    int32_t port           = 8080;
    int32_t read_timeout   = 600;
    int32_t write_timeout  = 600;
    int32_t n_threads_http = -1;

    std::string hostname      = "127.0.0.1";
    std::string public_path   = "";
    std::string chat_template = "";
    std::string system_prompt = "";

    std::vector<std::string> api_keys;

#ifdef CPPHTTPLIB_OPENSSL_SUPPORT
    std::string ssl_key_file = "";
    std::string ssl_cert_file = "";
#endif

    bool slots_endpoint   = true;
    bool metrics_endpoint = false;
};

struct server_slot {
    int id;
    int id_task = -1;
    int id_multi = -1;

    struct slot_params params;

    slot_state state = SLOT_STATE_IDLE;
    slot_command command = SLOT_COMMAND_NONE;

    // used to determine the slot that has been used the longest
    int64_t t_last_used = -1;

    // generation props
    int32_t n_ctx       = 0;  // context size per slot
    int32_t n_past      = 0;
    int32_t n_decoded   = 0;
    int32_t n_remaining = -1;
    int32_t i_batch     = -1;
    int32_t n_predict   = -1; // TODO: disambiguate from params.n_predict

    int32_t n_prompt_tokens           = 0;
    int32_t n_prompt_tokens_processed = 0;

    json prompt;

    // when a task is submitted, we first tokenize the prompt and store it here
    std::vector<llama_token> prompt_tokens;

    std::string generated_text;
    std::vector<llama_token> cache_tokens;
    std::vector<completion_token_output> generated_token_probs;

    bool infill         = false;
    bool embedding      = false;
    bool has_next_token = true;
    bool truncated      = false;
    bool stopped_eos    = false;
    bool stopped_word   = false;
    bool stopped_limit  = false;

    bool oaicompat = false;

    std::string oaicompat_model;
    std::string stopping_word;

    // sampling
    llama_token sampled;
    struct llama_sampling_params sparams;
    llama_sampling_context * ctx_sampling = nullptr;

    int32_t ga_i = 0;   // group-attention state
    int32_t ga_n = 1;   // group-attention factor
    int32_t ga_w = 512; // group-attention width

    int32_t n_past_se = 0; // self-extend

    // stats
    size_t n_sent_text = 0; // number of sent text character
    size_t n_sent_token_probs = 0;

    int64_t t_start_process_prompt;
    int64_t t_start_generation;

    double t_prompt_processing; // ms
    double t_token_generation; // ms

    void reset() {
        n_prompt_tokens    = 0;
        generated_text     = "";
        truncated          = false;
        stopped_eos        = false;
        stopped_word       = false;
        stopped_limit      = false;
        stopping_word      = "";
        n_past             = 0;
        n_sent_text        = 0;
        n_sent_token_probs = 0;
        infill             = false;
        ga_i               = 0;
        n_past_se          = 0;

        generated_token_probs.clear();
    }

    bool has_budget(gpt_params &global_params) {
        if (params.n_predict == -1 && global_params.n_predict == -1) {
            return true; // limitless
        }

        n_remaining = -1;

        if (params.n_predict != -1) {
            n_remaining = params.n_predict - n_decoded;
        } else if (global_params.n_predict != -1) {
            n_remaining = global_params.n_predict - n_decoded;
        }

        return n_remaining > 0; // no budget
    }

    bool available() const {
        return state == SLOT_STATE_IDLE && command == SLOT_COMMAND_NONE;
    }

    bool is_processing() const {
        return (state == SLOT_STATE_IDLE && command == SLOT_COMMAND_LOAD_PROMPT) || state == SLOT_STATE_PROCESSING;
    }

    void add_token_string(const completion_token_output & token) {
        if (command == SLOT_COMMAND_RELEASE) {
            return;
        }
        generated_token_probs.push_back(token);
    }

    void release() {
        if (state == SLOT_STATE_PROCESSING) {
            t_token_generation = (ggml_time_us() - t_start_generation) / 1e3;
            command = SLOT_COMMAND_RELEASE;
        }
    }

    json get_formated_timings() const {
        return json {
            {"prompt_n",               n_prompt_tokens_processed},
            {"prompt_ms",              t_prompt_processing},
            {"prompt_per_token_ms",    t_prompt_processing / n_prompt_tokens_processed},
            {"prompt_per_second",      1e3 / t_prompt_processing * n_prompt_tokens_processed},

            {"predicted_n",            n_decoded},
            {"predicted_ms",           t_token_generation},
            {"predicted_per_token_ms", t_token_generation / n_decoded},
            {"predicted_per_second",   1e3 / t_token_generation * n_decoded},
        };
    }

    size_t find_stopping_strings(const std::string & text, const size_t last_token_size, const stop_type type) {
        size_t stop_pos = std::string::npos;

        for (const std::string & word : params.antiprompt) {
            size_t pos;

            if (type == STOP_TYPE_FULL) {
                const size_t tmp      = word.size() + last_token_size;
                const size_t from_pos = text.size() > tmp ? text.size() - tmp : 0;

                pos = text.find(word, from_pos);
            } else {
                pos = find_partial_stop_string(word, text);
            }

            if (pos != std::string::npos && (stop_pos == std::string::npos || pos < stop_pos)) {
                if (type == STOP_TYPE_FULL) {
                    stopped_word   = true;
                    stopping_word  = word;
                    has_next_token = false;
                }
                stop_pos = pos;
            }
        }

        return stop_pos;
    }

    void print_timings() const {
        char buffer[512];

        double t_token = t_prompt_processing / n_prompt_tokens_processed;
        double n_tokens_second = 1e3 / t_prompt_processing * n_prompt_tokens_processed;

        snprintf(buffer, 512, "prompt eval time     = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)",
                t_prompt_processing, n_prompt_tokens_processed,
                t_token, n_tokens_second);

        LOG_INFO(buffer, {
            {"id_slot",                   id},
            {"id_task",                   id_task},
            {"t_prompt_processing",       t_prompt_processing},
            {"n_prompt_tokens_processed", n_prompt_tokens_processed},
            {"t_token",                   t_token},
            {"n_tokens_second",           n_tokens_second},
        });

        t_token = t_token_generation / n_decoded;
        n_tokens_second = 1e3 / t_token_generation * n_decoded;

        snprintf(buffer, 512, "generation eval time = %10.2f ms / %5d runs   (%8.2f ms per token, %8.2f tokens per second)",
                t_token_generation, n_decoded,
                t_token, n_tokens_second);

        LOG_INFO(buffer, {
            {"id_slot",            id},
            {"id_task",            id_task},
            {"t_token_generation", t_token_generation},
            {"n_decoded",          n_decoded},
            {"t_token",            t_token},
            {"n_tokens_second",    n_tokens_second},
        });

        snprintf(buffer, 512, "          total time = %10.2f ms", t_prompt_processing + t_token_generation);

        LOG_INFO(buffer, {
            {"id_slot",             id},
            {"id_task",             id_task},
            {"t_prompt_processing", t_prompt_processing},
            {"t_token_generation",  t_token_generation},
            {"t_total",             t_prompt_processing + t_token_generation},
        });
    }
};

struct server_metrics {
    int64_t t_start = 0;

    uint64_t n_prompt_tokens_processed_total = 0;
    uint64_t t_prompt_processing_total       = 0;
    uint64_t n_tokens_predicted_total        = 0;
    uint64_t t_tokens_generation_total       = 0;

    uint64_t n_prompt_tokens_processed = 0;
    uint64_t t_prompt_processing       = 0;

    uint64_t n_tokens_predicted  = 0;
    uint64_t t_tokens_generation = 0;

    void init() {
        t_start = ggml_time_us();
    }

    void on_prompt_eval(const server_slot & slot) {
        n_prompt_tokens_processed_total += slot.n_prompt_tokens_processed;
        n_prompt_tokens_processed       += slot.n_prompt_tokens_processed;
        t_prompt_processing             += slot.t_prompt_processing;
        t_prompt_processing_total       += slot.t_prompt_processing;
    }

    void on_prediction(const server_slot & slot) {
        n_tokens_predicted_total   += slot.n_decoded;
        n_tokens_predicted         += slot.n_decoded;
        t_tokens_generation        += slot.t_token_generation;
        t_tokens_generation_total  += slot.t_token_generation;
    }

    void reset_bucket() {
        n_prompt_tokens_processed = 0;
        t_prompt_processing       = 0;
        n_tokens_predicted        = 0;
        t_tokens_generation       = 0;
    }
};

struct server_queue {
    int id = 0;
    bool running;

    // queues
    std::vector<server_task> queue_tasks;
    std::vector<server_task> queue_tasks_deferred;

    std::vector<server_task_multi> queue_multitasks;

    std::mutex mutex_tasks;
    std::condition_variable condition_tasks;

    // callback functions
    std::function<void(server_task       &)> callback_new_task;
    std::function<void(server_task_multi &)> callback_finish_multitask;
    std::function<void(void)>                callback_update_slots;

    // Add a new task to the end of the queue
    int post(server_task task) {
        std::unique_lock<std::mutex> lock(mutex_tasks);
        if (task.id == -1) {
            task.id = id++;
            LOG_VERBOSE("new task id", {{"new_id", task.id}});
        }
        queue_tasks.push_back(std::move(task));
        condition_tasks.notify_one();
        return task.id;
    }

    // Add a new task, but defer until one slot is available
    void defer(server_task task) {
        std::unique_lock<std::mutex> lock(mutex_tasks);
        queue_tasks_deferred.push_back(std::move(task));
    }

    // Get the next id for creating anew task
    int get_new_id() {
        std::unique_lock<std::mutex> lock(mutex_tasks);
        int new_id = id++;
        LOG_VERBOSE("new task id", {{"new_id", new_id}});
        return new_id;
    }

    // Register function to process a new task
    void on_new_task(std::function<void(server_task &)> callback) {
        callback_new_task = std::move(callback);
    }

    // Register function to process a multitask when it is finished
    void on_finish_multitask(std::function<void(server_task_multi&)> callback) {
        callback_finish_multitask = std::move(callback);
    }

    // Register the function to be called when all slots data is ready to be processed
    void on_update_slots(std::function<void(void)> callback) {
        callback_update_slots = std::move(callback);
    }

    // Call when the state of one slot is changed
    void notify_slot_changed() {
        // move deferred tasks back to main loop
        std::unique_lock<std::mutex> lock(mutex_tasks);
        for (auto & task : queue_tasks_deferred) {
            queue_tasks.push_back(std::move(task));
        }
        queue_tasks_deferred.clear();
    }

    // end the start_loop routine
    void terminate() {
        std::unique_lock<std::mutex> lock(mutex_tasks);
        running = false;
        condition_tasks.notify_all();
    }

    /**
     * Main loop consists of these steps:
     * - Wait until a new task arrives
     * - Process the task (i.e. maybe copy data into slot)
     * - Check if multitask is finished
     * - Update all slots
     */
    void start_loop() {
        running = true;

        while (true) {
            LOG_VERBOSE("new task may arrive", {});

            while (true) {
                std::unique_lock<std::mutex> lock(mutex_tasks);
                if (queue_tasks.empty()) {
                    lock.unlock();
                    break;
                }
                server_task task = queue_tasks.front();
                queue_tasks.erase(queue_tasks.begin());
                lock.unlock();
                LOG_VERBOSE("callback_new_task", {{"id_task", task.id}});
                callback_new_task(task);
            }

            LOG_VERBOSE("update_multitasks", {});

            // check if we have any finished multitasks
            auto queue_iterator = queue_multitasks.begin();
            while (queue_iterator != queue_multitasks.end()) {
                if (queue_iterator->subtasks_remaining.empty()) {
                    // all subtasks done == multitask is done
                    server_task_multi current_multitask = *queue_iterator;
                    callback_finish_multitask(current_multitask);
                    // remove this multitask
                    queue_iterator = queue_multitasks.erase(queue_iterator);
                } else {
                    ++queue_iterator;
                }
            }

            // all tasks in the current loop is processed, slots data is now ready
            LOG_VERBOSE("callback_update_slots", {});

            callback_update_slots();

            LOG_VERBOSE("wait for new task", {});
            {
                std::unique_lock<std::mutex> lock(mutex_tasks);
                if (queue_tasks.empty()) {
                    if (!running) {
                        LOG_VERBOSE("ending start_loop", {});
                        return;
                    }
                    condition_tasks.wait(lock, [&]{
                        return (!queue_tasks.empty() || !running);
                    });
                }
            }
        }
    }

    //
    // functions to manage multitasks
    //

    // add a multitask by specifying the id of all subtask (subtask is a server_task)
    void add_multitask(int id_multi, std::vector<int> & sub_ids) {
        std::lock_guard<std::mutex> lock(mutex_tasks);
        server_task_multi multi;
        multi.id = id_multi;
        std::copy(sub_ids.begin(), sub_ids.end(), std::inserter(multi.subtasks_remaining, multi.subtasks_remaining.end()));
        queue_multitasks.push_back(multi);
    }

    // updatethe remaining subtasks, while appending results to multitask
    void update_multitask(int id_multi, int id_sub, server_task_result & result) {
        std::lock_guard<std::mutex> lock(mutex_tasks);
        for (auto & multitask : queue_multitasks) {
            if (multitask.id == id_multi) {
                multitask.subtasks_remaining.erase(id_sub);
                multitask.results.push_back(result);
            }
        }
    }
};

struct server_response {
    typedef std::function<void(int, int, server_task_result &)> callback_multitask_t;
    callback_multitask_t callback_update_multitask;

    // for keeping track of all tasks waiting for the result
    std::set<int> waiting_task_ids;

    // the main result queue
    std::vector<server_task_result> queue_results;

    std::mutex mutex_results;
    std::condition_variable condition_results;

    // add the id_task to the list of tasks waiting for response
    void add_waiting_task_id(int id_task) {
        LOG_VERBOSE("waiting for task id", {{"id_task", id_task}});

        std::unique_lock<std::mutex> lock(mutex_results);
        waiting_task_ids.insert(id_task);
    }

    // when the request is finished, we can remove task associated with it
    void remove_waiting_task_id(int id_task) {
        LOG_VERBOSE("remove waiting for task id", {{"id_task", id_task}});

        std::unique_lock<std::mutex> lock(mutex_results);
        waiting_task_ids.erase(id_task);
    }

    // This function blocks the thread until there is a response for this id_task
    server_task_result recv(int id_task) {
        while (true) {
            std::unique_lock<std::mutex> lock(mutex_results);
            condition_results.wait(lock, [&]{
                return !queue_results.empty();
            });

            for (int i = 0; i < (int) queue_results.size(); i++) {
                if (queue_results[i].id == id_task) {
                    assert(queue_results[i].id_multi == -1);
                    server_task_result res = queue_results[i];
                    queue_results.erase(queue_results.begin() + i);
                    return res;
                }
            }
        }

        // should never reach here
    }

    // Register the function to update multitask
    void on_multitask_update(callback_multitask_t callback) {
        callback_update_multitask = std::move(callback);
    }

    // Send a new result to a waiting id_task
    void send(server_task_result result) {
        LOG_VERBOSE("send new result", {{"id_task", result.id}});

        std::unique_lock<std::mutex> lock(mutex_results);
        for (const auto & id_task : waiting_task_ids) {
            // LOG_TEE("waiting task id %i \n", id_task);
            // for now, tasks that have associated parent multitasks just get erased once multitask picks up the result
            if (result.id_multi == id_task) {
                LOG_VERBOSE("callback_update_multitask", {{"id_task", id_task}});
                callback_update_multitask(id_task, result.id, result);
                continue;
            }

            if (result.id == id_task) {
                LOG_VERBOSE("queue_results.push_back", {{"id_task", id_task}});
                queue_results.push_back(result);
                condition_results.notify_all();
                return;
            }
        }
    }
};

struct server_context {
    llama_model * model = nullptr;
    llama_context * ctx = nullptr;

    gpt_params params;

    llama_batch batch;

    bool clean_kv_cache = true;
    bool add_bos_token  = true;

    int32_t n_ctx; // total context for all clients / slots

    // system prompt
    bool system_need_update = false;

    std::string              system_prompt;
    std::vector<llama_token> system_tokens;

    std::string name_user;      // this should be the antiprompt
    std::string name_assistant;

    // slots / clients
    std::vector<server_slot> slots;
    json default_generation_settings_for_props;

    server_queue    queue_tasks;
    server_response queue_results;

    server_metrics metrics;

    ~server_context() {
        if (ctx) {
            llama_free(ctx);
            ctx = nullptr;
        }

        if (model) {
            llama_free_model(model);
            model = nullptr;
        }
    }

    bool load_model(const gpt_params & params_) {
        params = params_;

        // dedicate one sequence to the system prompt
        params.n_parallel += 1;

        std::tie(model, ctx) = llama_init_from_gpt_params(params);
        params.n_parallel -= 1; // but be sneaky about it
        if (model == nullptr) {
            LOG_ERROR("unable to load model", {{"model", params.model}});
            return false;
        }

        n_ctx = llama_n_ctx(ctx);

        add_bos_token = llama_should_add_bos_token(model);

        return true;
    }

    bool validate_model_chat_template() const {
        llama_chat_message chat[] = {{"user", "test"}};

        const int res = llama_chat_apply_template(model, nullptr, chat, 1, true, nullptr, 0);

        return res > 0;
    }

    void init() {
        const int32_t n_ctx_slot = n_ctx / params.n_parallel;

        LOG_INFO("initializing slots", {{"n_slots", params.n_parallel}});

        for (int i = 0; i < params.n_parallel; i++) {
            server_slot slot;

            slot.id = i;
            slot.n_ctx = n_ctx_slot;
            slot.n_predict = params.n_predict;

            LOG_INFO("new slot", {
                {"id_slot",    slot.id},
                {"n_ctx_slot", slot.n_ctx}
            });

            const int ga_n = params.grp_attn_n;
            const int ga_w = params.grp_attn_w;

            if (ga_n != 1) {
                GGML_ASSERT(ga_n > 0                    && "ga_n must be positive");                       // NOLINT
                GGML_ASSERT(ga_w % ga_n == 0            && "ga_w must be a multiple of ga_n");             // NOLINT
                //GGML_ASSERT(n_ctx_train % ga_w == 0     && "n_ctx_train must be a multiple of ga_w");    // NOLINT
                //GGML_ASSERT(n_ctx >= n_ctx_train * ga_n && "n_ctx must be at least n_ctx_train * ga_n"); // NOLINT

                LOG_INFO("slot self-extend", {
                    {"id_slot", slot.id},
                    {"ga_n",    ga_n},
                    {"ga_w",    ga_w}
                });
            }

            slot.ga_i = 0;
            slot.ga_n = ga_n;
            slot.ga_w = ga_w;

            slot.reset();

            slots.push_back(slot);
        }

        default_generation_settings_for_props = get_formated_generation(slots.front());
        default_generation_settings_for_props["seed"] = -1;

        // the update_slots() logic will always submit a maximum of n_batch tokens
        // note that n_batch can be > n_ctx (e.g. for non-causal attention models such as BERT where the KV cache is not used)
        {
            const int32_t n_batch = llama_n_batch(ctx);

            batch = llama_batch_init(n_batch, 0, params.n_parallel);
        }

        metrics.init();
    }

    std::vector<llama_token> tokenize(const json & json_prompt, bool add_bos) const {
        // TODO: currently, we tokenize using special tokens by default
        //       this is not always correct (see https://github.com/ggerganov/llama.cpp/pull/4160#issuecomment-1824826216)
        //       but it's better compared to completely ignoring ChatML and other chat templates
        const bool TMP_FORCE_SPECIAL = true;

        // If `add_bos` is true, we only add BOS, when json_prompt is a string,
        // or the first element of the json_prompt array is a string.
        std::vector<llama_token> prompt_tokens;

        if (json_prompt.is_array()) {
            bool first = true;
            for (const auto & p : json_prompt) {
                if (p.is_string()) {
                    auto s = p.template get<std::string>();

                    std::vector<llama_token> p;
                    if (first) {
                        p = ::llama_tokenize(ctx, s, add_bos, TMP_FORCE_SPECIAL);
                        first = false;
                    } else {
                        p = ::llama_tokenize(ctx, s, false, TMP_FORCE_SPECIAL);
                    }

                    prompt_tokens.insert(prompt_tokens.end(), p.begin(), p.end());
                } else {
                    if (first) {
                        first = false;
                    }

                    prompt_tokens.push_back(p.template get<llama_token>());
                }
            }
        } else {
            auto s = json_prompt.template get<std::string>();
            prompt_tokens = ::llama_tokenize(ctx, s, add_bos, TMP_FORCE_SPECIAL);
        }

        return prompt_tokens;
    }

    server_slot * get_slot(int id) {
        int64_t t_last = ggml_time_us();

        server_slot * last_used = nullptr;

        for (server_slot & slot : slots) {
            if (slot.id == id && slot.available()) {
                return &slot;
            }

            // among all available slots, find the one that has been least recently used
            if (slot.available() && slot.t_last_used < t_last) {
                last_used = &slot;
                t_last = slot.t_last_used;
            }
        }

        return last_used;
    }

    bool launch_slot_with_task(server_slot & slot, const server_task & task) {
        slot_params default_params;
        llama_sampling_params default_sparams;
        auto & data = task.data;

        if (data.count("__oaicompat") != 0) {
            slot.oaicompat = true;
            slot.oaicompat_model = json_value(data, "model", std::string(DEFAULT_OAICOMPAT_MODEL));
        } else {
            slot.oaicompat = false;
            slot.oaicompat_model = "";
        }

        slot.params.stream             = json_value(data, "stream",            false);
        slot.params.cache_prompt       = json_value(data, "cache_prompt",      false);
        slot.params.n_predict          = json_value(data, "n_predict",         default_params.n_predict);
        slot.sparams.top_k             = json_value(data, "top_k",             default_sparams.top_k);
        slot.sparams.top_p             = json_value(data, "top_p",             default_sparams.top_p);
        slot.sparams.min_p             = json_value(data, "min_p",             default_sparams.min_p);
        slot.sparams.tfs_z             = json_value(data, "tfs_z",             default_sparams.tfs_z);
        slot.sparams.typical_p         = json_value(data, "typical_p",         default_sparams.typical_p);
        slot.sparams.temp              = json_value(data, "temperature",       default_sparams.temp);
        slot.sparams.dynatemp_range    = json_value(data, "dynatemp_range",    default_sparams.dynatemp_range);
        slot.sparams.dynatemp_exponent = json_value(data, "dynatemp_exponent", default_sparams.dynatemp_exponent);
        slot.sparams.penalty_last_n    = json_value(data, "repeat_last_n",     default_sparams.penalty_last_n);
        slot.sparams.penalty_repeat    = json_value(data, "repeat_penalty",    default_sparams.penalty_repeat);
        slot.sparams.penalty_freq      = json_value(data, "frequency_penalty", default_sparams.penalty_freq);
        slot.sparams.penalty_present   = json_value(data, "presence_penalty",  default_sparams.penalty_present);
        slot.sparams.mirostat          = json_value(data, "mirostat",          default_sparams.mirostat);
        slot.sparams.mirostat_tau      = json_value(data, "mirostat_tau",      default_sparams.mirostat_tau);
        slot.sparams.mirostat_eta      = json_value(data, "mirostat_eta",      default_sparams.mirostat_eta);
        slot.sparams.penalize_nl       = json_value(data, "penalize_nl",       default_sparams.penalize_nl);
        slot.params.n_keep             = json_value(data, "n_keep",            slot.params.n_keep);
        slot.params.seed               = json_value(data, "seed",              default_params.seed);
        slot.sparams.grammar           = json_value(data, "grammar",           default_sparams.grammar);
        slot.sparams.n_probs           = json_value(data, "n_probs",           default_sparams.n_probs);
        slot.sparams.min_keep          = json_value(data, "min_keep",          default_sparams.min_keep);

        if (slot.params.cache_prompt && slot.ga_n != 1) {
            LOG_WARNING("cache_prompt is not supported with group-attention", {});
            slot.params.cache_prompt = false;
        }

        if (slot.n_predict > 0 && slot.params.n_predict > slot.n_predict) {
            // Might be better to reject the request with a 400 ?
            LOG_WARNING("Max tokens to predict exceeds server configuration", {
                {"params.n_predict", slot.params.n_predict},
                {"slot.n_predict",   slot.n_predict},
            });
            slot.params.n_predict = slot.n_predict;
        }

        // infill
        slot.params.input_prefix = json_value(data, "input_prefix", default_params.input_prefix);
        slot.params.input_suffix = json_value(data, "input_suffix", default_params.input_suffix);

        // get prompt
        {
            const auto & prompt = data.find("prompt");
            if (prompt == data.end()) {
                send_error(task, "Either \"prompt\" or \"messages\" must be provided", ERROR_TYPE_INVALID_REQUEST);
                return false;
            } else {
                slot.prompt = *prompt;
            }
            if (slot.prompt.is_array() && slot.prompt.size() == 0) {
                send_error(task, "\"prompt\" cannot be an empty array", ERROR_TYPE_INVALID_REQUEST);
                return false;
            }
        }

        // penalize user-provided tokens
        {
            slot.sparams.penalty_prompt_tokens.clear();
            slot.sparams.use_penalty_prompt_tokens = false;

            const auto & penalty_prompt = data.find("penalty_prompt");

            if (penalty_prompt != data.end()) {
                if (penalty_prompt->is_string()) {
                    const auto penalty_prompt_string = penalty_prompt->get<std::string>();
                    slot.sparams.penalty_prompt_tokens = llama_tokenize(model, penalty_prompt_string, false);

                    if (slot.params.n_predict > 0) {
                        slot.sparams.penalty_prompt_tokens.reserve(slot.sparams.penalty_prompt_tokens.size() + slot.params.n_predict);
                    }
                    slot.sparams.use_penalty_prompt_tokens = true;

                    LOG_VERBOSE("penalty_prompt_tokens", {
                        {"id_slot", slot.id},
                        {"tokens",  slot.sparams.penalty_prompt_tokens},
                    });
                }
                else if (penalty_prompt->is_array()) {
                    const auto n_tokens = penalty_prompt->size();
                    slot.sparams.penalty_prompt_tokens.reserve(n_tokens + std::max(0, slot.params.n_predict));

                    const int n_vocab = llama_n_vocab(model);
                    for (const auto & penalty_token : *penalty_prompt) {
                        if (penalty_token.is_number_integer()) {
                            const auto tok = penalty_token.get<llama_token>();
                            if (tok >= 0 && tok < n_vocab) {
                                slot.sparams.penalty_prompt_tokens.push_back(tok);
                            }
                        }
                    }
                    slot.sparams.use_penalty_prompt_tokens = true;

                    LOG_VERBOSE("penalty_prompt_tokens", {
                        {"id_slot", slot.id},
                        {"tokens",  slot.sparams.penalty_prompt_tokens},
                    });
                }
            }
        }

        {
            slot.sparams.logit_bias.clear();

            if (json_value(data, "ignore_eos", false)) {
                slot.sparams.logit_bias[llama_token_eos(model)] = -INFINITY;
            }

            const auto & logit_bias = data.find("logit_bias");
            if (logit_bias != data.end() && logit_bias->is_array()) {
                const int n_vocab = llama_n_vocab(model);
                for (const auto & el : *logit_bias) {
                    // TODO: we may want to throw errors here, in case "el" is incorrect
                    if (el.is_array() && el.size() == 2) {
                        float bias;
                        if (el[1].is_number()) {
                            bias = el[1].get<float>();
                        } else if (el[1].is_boolean() && !el[1].get<bool>()) {
                            bias = -INFINITY;
                        } else {
                            continue;
                        }

                        if (el[0].is_number_integer()) {
                            llama_token tok = el[0].get<llama_token>();
                            if (tok >= 0 && tok < n_vocab) {
                                slot.sparams.logit_bias[tok] = bias;
                            }
                        } else if (el[0].is_string()) {
                            auto toks = llama_tokenize(model, el[0].get<std::string>(), false);
                            for (auto tok : toks) {
                                slot.sparams.logit_bias[tok] = bias;
                            }
                        }
                    }
                }
            }
        }

        {
            slot.params.antiprompt.clear();

            const auto & stop = data.find("stop");
            if (stop != data.end() && stop->is_array()) {
                for (const auto & word : *stop) {
                    if (!word.empty()) {
                        slot.params.antiprompt.push_back(word);
                    }
                }
            }
        }

        {
            const auto & samplers_sequence = data.find("samplers");
            if (samplers_sequence != data.end() && samplers_sequence->is_array()) {
                std::vector<std::string> sampler_names;
                for (const auto & sampler_name : *samplers_sequence) {
                    if (sampler_name.is_string()) {
                        sampler_names.emplace_back(sampler_name);
                    }
                }
                slot.sparams.samplers_sequence = sampler_types_from_names(sampler_names, false);
            } else {
                slot.sparams.samplers_sequence = default_sparams.samplers_sequence;
            }
        }

        {
            if (slot.ctx_sampling != nullptr) {
                llama_sampling_free(slot.ctx_sampling);
            }
            slot.ctx_sampling = llama_sampling_init(slot.sparams);
            if (slot.ctx_sampling == nullptr) {
                // for now, the only error that may happen here is invalid grammar
                send_error(task, "Failed to parse grammar", ERROR_TYPE_INVALID_REQUEST);
                return false;
            }
            llama_set_rng_seed(ctx, slot.params.seed);
        }

        slot.command = SLOT_COMMAND_LOAD_PROMPT;
        slot.prompt_tokens.clear();

        LOG_INFO("slot is processing task", {
            {"id_slot", slot.id},
            {"id_task", slot.id_task},
        });

        return true;
    }

    void kv_cache_clear() {
        LOG_VERBOSE("clearing KV cache", {});

        // clear the entire KV cache
        llama_kv_cache_clear(ctx);
        clean_kv_cache = false;
    }

    void system_prompt_update() {
        LOG_VERBOSE("system prompt update", {
            {"system_prompt", system_prompt},
        });

        kv_cache_clear();
        system_tokens.clear();

        if (!system_prompt.empty()) {
            system_tokens = ::llama_tokenize(ctx, system_prompt, add_bos_token);

            llama_batch_clear(batch);

            for (int i = 0; i < (int)system_tokens.size(); ++i) {
                llama_batch_add(batch, system_tokens[i], i, { 0 }, false);
            }

            const int32_t n_batch = llama_n_batch(ctx);

            for (int32_t i = 0; i < batch.n_tokens; i += n_batch) {
                const int32_t n_tokens = std::min(params.n_batch, batch.n_tokens - i);
                llama_batch batch_view = {
                    n_tokens,
                    batch.token    + i,
                    nullptr,
                    batch.pos      + i,
                    batch.n_seq_id + i,
                    batch.seq_id   + i,
                    batch.logits   + i,
                    0, 0, 0, // unused
                };

                if (llama_decode(ctx, batch_view) != 0) {
                    LOG_TEE("%s: llama_decode() failed\n", __func__);
                    return;
                }
            }

            // assign the system KV cache to all parallel sequences
            for (int32_t i = 1; i <= params.n_parallel; ++i) {
                llama_kv_cache_seq_cp(ctx, 0, i, -1, -1);
            }
        }

        system_need_update = false;
    }

    void system_prompt_set(const json & sys_props) {
        system_prompt  = sys_props.value("prompt", "");
        name_user      = sys_props.value("anti_prompt", "");
        name_assistant = sys_props.value("assistant_name", "");

        LOG_VERBOSE("system prompt process", {
            {"system_prompt",  system_prompt},
            {"name_user",      name_user},
            {"name_assistant", name_assistant},
        });

        // release all slots
        for (server_slot & slot : slots) {
            slot.release();
        }

        system_need_update = true;
    }

    bool process_token(completion_token_output & result, server_slot & slot) {
        // remember which tokens were sampled - used for repetition penalties during sampling
        const std::string token_str = llama_token_to_piece(ctx, result.tok);
        slot.sampled = result.tok;

        // search stop word and delete it
        slot.generated_text += token_str;
        slot.has_next_token = true;

        if (slot.ctx_sampling->params.use_penalty_prompt_tokens && result.tok != -1) {
            // we can change penalty_prompt_tokens because it is always created from scratch each request
            slot.ctx_sampling->params.penalty_prompt_tokens.push_back(result.tok);
        }

        // check if there is incomplete UTF-8 character at the end
        bool incomplete = false;
        for (unsigned i = 1; i < 5 && i <= slot.generated_text.size(); ++i) {
            unsigned char c = slot.generated_text[slot.generated_text.size() - i];
            if ((c & 0xC0) == 0x80) {
                // continuation byte: 10xxxxxx
                continue;
            }
            if ((c & 0xE0) == 0xC0) {
                // 2-byte character: 110xxxxx ...
                incomplete = i < 2;
            } else if ((c & 0xF0) == 0xE0) {
                // 3-byte character: 1110xxxx ...
                incomplete = i < 3;
            } else if ((c & 0xF8) == 0xF0) {
                // 4-byte character: 11110xxx ...
                incomplete = i < 4;
            }
            // else 1-byte character or invalid byte
            break;
        }

        if (!incomplete) {
            size_t pos = std::min(slot.n_sent_text, slot.generated_text.size());

            const std::string str_test = slot.generated_text.substr(pos);
            bool is_stop_full = false;

            size_t stop_pos = slot.find_stopping_strings(str_test, token_str.size(), STOP_TYPE_FULL);
            if (stop_pos != std::string::npos) {
                is_stop_full = true;
                slot.generated_text.erase(
                    slot.generated_text.begin() + pos + stop_pos,
                    slot.generated_text.end());
                pos = std::min(slot.n_sent_text, slot.generated_text.size());
            } else {
                is_stop_full = false;
                stop_pos = slot.find_stopping_strings(str_test, token_str.size(), STOP_TYPE_PARTIAL);
            }

            // check if there is any token to predict
            if (stop_pos == std::string::npos || (!slot.has_next_token && !is_stop_full && stop_pos > 0)) {
                // no send the stop word in the response
                result.text_to_send = slot.generated_text.substr(pos, std::string::npos);
                slot.n_sent_text += result.text_to_send.size();
                // add the token to slot queue and cache
            }

            slot.add_token_string(result);
            if (slot.params.stream) {
                send_partial_response(slot, result);
            }
        }

        if (incomplete) {
            slot.has_next_token = true;
        }

        // check the limits
        if (slot.n_decoded > 0 && slot.has_next_token && !slot.has_budget(params)) {
            slot.stopped_limit  = true;
            slot.has_next_token = false;

            LOG_VERBOSE("stopped by limit", {
                {"id_slot",   slot.id},
                {"id_task",   slot.id_task},
                {"n_decoded", slot.n_decoded},
                {"n_predict", slot.params.n_predict},
            });
        }

        if (result.tok == llama_token_eos(model)) {
            slot.stopped_eos    = true;
            slot.has_next_token = false;

            LOG_VERBOSE("eos token found", {});
        }

        LOG_VERBOSE("next token", {
            {"id_slot",        slot.id},
            {"id_task",        slot.id_task},
            {"token",          result.tok},
            {"token_text",     tokens_to_output_formatted_string(ctx, result.tok)},
            {"has_next_token", slot.has_next_token},
            {"n_remain",       slot.n_remaining},
            {"n_decoded",      slot.n_decoded},
            {"stopped_eos",    slot.stopped_eos},
            {"stopped_word",   slot.stopped_word},
            {"stopped_limit",  slot.stopped_limit},
            {"stopping_word",  slot.stopping_word},
        });

        return slot.has_next_token; // continue
    }

    json get_formated_generation(const server_slot & slot) const {
        const auto eos_bias = slot.sparams.logit_bias.find(llama_token_eos(model));
        const bool ignore_eos = eos_bias != slot.sparams.logit_bias.end() && eos_bias->second < 0.0f && std::isinf(eos_bias->second);

        std::vector<std::string> samplers_sequence;
        samplers_sequence.reserve(slot.sparams.samplers_sequence.size());
        for (const auto & sampler_type : slot.sparams.samplers_sequence) {
            samplers_sequence.emplace_back(sampler_type_to_name_string(sampler_type));
        }

        return json {
            {"n_ctx",                     slot.n_ctx},
            {"n_predict",                 slot.n_predict},
            {"model",                     params.model_alias},
            {"seed",                      slot.params.seed},
            {"temperature",               slot.sparams.temp},
            {"dynatemp_range",            slot.sparams.dynatemp_range},
            {"dynatemp_exponent",         slot.sparams.dynatemp_exponent},
            {"top_k",                     slot.sparams.top_k},
            {"top_p",                     slot.sparams.top_p},
            {"min_p",                     slot.sparams.min_p},
            {"tfs_z",                     slot.sparams.tfs_z},
            {"typical_p",                 slot.sparams.typical_p},
            {"repeat_last_n",             slot.sparams.penalty_last_n},
            {"repeat_penalty",            slot.sparams.penalty_repeat},
            {"presence_penalty",          slot.sparams.penalty_present},
            {"frequency_penalty",         slot.sparams.penalty_freq},
            {"penalty_prompt_tokens",     slot.sparams.penalty_prompt_tokens},
            {"use_penalty_prompt_tokens", slot.sparams.use_penalty_prompt_tokens},
            {"mirostat",                  slot.sparams.mirostat},
            {"mirostat_tau",              slot.sparams.mirostat_tau},
            {"mirostat_eta",              slot.sparams.mirostat_eta},
            {"penalize_nl",               slot.sparams.penalize_nl},
            {"stop",                      slot.params.antiprompt},
            {"n_predict",                 slot.params.n_predict}, // TODO: fix duplicate key n_predict
            {"n_keep",                    params.n_keep},
            {"ignore_eos",                ignore_eos},
            {"stream",                    slot.params.stream},
            {"logit_bias",                slot.sparams.logit_bias},
            {"n_probs",                   slot.sparams.n_probs},
            {"min_keep",                  slot.sparams.min_keep},
            {"grammar",                   slot.sparams.grammar},
            {"samplers",                  samplers_sequence}
        };
    }

    void send_error(const server_task & task, const std::string & error, const enum error_type type = ERROR_TYPE_SERVER) {
        send_error(task.id, task.id_multi, error, type);
    }

    void send_error(const server_slot & slot, const std::string & error, const enum error_type type = ERROR_TYPE_SERVER) {
        send_error(slot.id_task, slot.id_multi, error, type);
    }

    void send_error(const int id_task, const int id_multi, const std::string & error, const enum error_type type = ERROR_TYPE_SERVER) {
        LOG_TEE("task %i - error: %s\n", id_task, error.c_str());

        server_task_result res;
        res.id       = id_task;
        res.id_multi = id_multi;
        res.stop     = false;
        res.error    = true;
        res.data     = format_error_response(error, type);

        queue_results.send(res);
    }

    void send_partial_response(server_slot & slot, completion_token_output tkn) {
        server_task_result res;
        res.id       = slot.id_task;
        res.id_multi = slot.id_multi;
        res.error    = false;
        res.stop     = false;
        res.data     = json {
            {"content",    tkn.text_to_send},
            {"stop",       false},
            {"id_slot",    slot.id},
            {"multimodal", false}
        };

        if (slot.sparams.n_probs > 0) {
            const std::vector<llama_token> to_send_toks = llama_tokenize(ctx, tkn.text_to_send, false);
            const size_t probs_pos      = std::min(slot.n_sent_token_probs,                       slot.generated_token_probs.size());
            const size_t probs_stop_pos = std::min(slot.n_sent_token_probs + to_send_toks.size(), slot.generated_token_probs.size());

            std::vector<completion_token_output> probs_output;
            if (probs_pos < probs_stop_pos) {
                probs_output = std::vector<completion_token_output>(
                        slot.generated_token_probs.begin() + probs_pos,
                        slot.generated_token_probs.begin() + probs_stop_pos);
            }
            slot.n_sent_token_probs = probs_stop_pos;

            res.data["completion_probabilities"] = probs_vector_to_json(ctx, probs_output);
        }

        if (slot.oaicompat) {
            res.data["oaicompat_token_ctr"] = slot.n_decoded;
            res.data["model"] = slot.oaicompat_model;
        }

        queue_results.send(res);
    }

    void send_final_response(const server_slot & slot) {
        server_task_result res;
        res.id       = slot.id_task;
        res.id_multi = slot.id_multi;
        res.error    = false;
        res.stop     = true;
        res.data     = json {
            {"content",             !slot.params.stream ? slot.generated_text : ""},
            {"id_slot",             slot.id},
            {"stop",                true},
            {"model",               params.model_alias},
            {"tokens_predicted",    slot.n_decoded},
            {"tokens_evaluated",    slot.n_prompt_tokens},
            {"generation_settings", get_formated_generation(slot)},
            {"prompt",              slot.prompt},
            {"truncated",           slot.truncated},
            {"stopped_eos",         slot.stopped_eos},
            {"stopped_word",        slot.stopped_word},
            {"stopped_limit",       slot.stopped_limit},
            {"stopping_word",       slot.stopping_word},
            {"tokens_cached",       slot.n_past},
            {"timings",             slot.get_formated_timings()}
        };

        if (slot.sparams.n_probs > 0) {
            std::vector<completion_token_output> probs;
            if (!slot.params.stream && slot.stopped_word) {
                const std::vector<llama_token> stop_word_toks = llama_tokenize(ctx, slot.stopping_word, false);

                probs = std::vector<completion_token_output>(
                        slot.generated_token_probs.begin(),
                        slot.generated_token_probs.end() - stop_word_toks.size());
            } else {
                probs = std::vector<completion_token_output>(
                        slot.generated_token_probs.begin(),
                        slot.generated_token_probs.end());
            }

            res.data["completion_probabilities"] = probs_vector_to_json(ctx, probs);
        }

        if (slot.oaicompat) {
            res.data["oaicompat_token_ctr"] = slot.n_decoded;
            res.data["model"] = slot.oaicompat_model;
        }

        queue_results.send(res);
    }

    void send_embedding(const server_slot & slot, const llama_batch & batch) {
        server_task_result res;
        res.id       = slot.id_task;
        res.id_multi = slot.id_multi;
        res.error    = false;
        res.stop     = true;

        const int n_embd = llama_n_embd(model);

        std::vector<float> embd_res(n_embd, 0.0f);

        for (int i = 0; i < batch.n_tokens; ++i) {
            if (!batch.logits[i] || batch.seq_id[i][0] != slot.id + 1) {
                continue;
            }

            const float * embd = llama_get_embeddings_seq(ctx, batch.seq_id[i][0]);
            if (embd == NULL) {
                embd = llama_get_embeddings_ith(ctx, i);
            }

            if (embd == NULL) {
                LOG_ERROR("failed to get embeddings", {
                    {"token",  batch.token [i]},
                        {"seq_id", batch.seq_id[i][0]}
                });

                res.data = json {
                    {"embedding", std::vector<float>(n_embd, 0.0f)},
                };

                continue;
            }

            llama_embd_normalize(embd, embd_res.data(), n_embd);

            res.data = json {
                {"embedding", embd_res},
            };
        }

        queue_results.send(res);
    }

    void request_completion(int id_task, int id_multi, json data, bool infill, bool embedding) {
        server_task task;
        task.id        = id_task;
        task.id_multi  = id_multi;
        task.id_target = 0;
        task.data      = std::move(data);
        task.infill    = infill;
        task.embedding = embedding;
        task.type      = SERVER_TASK_TYPE_COMPLETION;

        // when a completion task's prompt array is not a singleton, we split it into multiple requests
        // otherwise, it's a single-prompt task, we actually queue it
        // if there's numbers in the prompt array it will be treated as an array of tokens
        if (task.data.count("prompt") != 0 && task.data.at("prompt").size() > 1) {
            bool numbers = false;
            for (const auto & e : task.data.at("prompt")) {
                if (e.is_number()) {
                    numbers = true;
                    break;
                }
            }

            // NOTE: split_multiprompt_task() does not handle a mix of strings and numbers,
            // it will completely stall the server. I don't know where the bug for this is.
            //
            // if there are numbers, it needs to be treated like a single prompt,
            // queue_tasks handles a mix of strings and numbers just fine.
            if (numbers) {
                queue_tasks.post(task);
            } else {
                split_multiprompt_task(id_task, task);
            }
        } else {
            queue_tasks.post(task);
        }
    }

    void request_cancel(int id_task) {
        server_task task;
        task.type      = SERVER_TASK_TYPE_CANCEL;
        task.id_target = id_task;

        queue_tasks.post(task);
    }

    void split_multiprompt_task(int id_multi, const server_task & multiprompt_task) {
        const int prompt_count = multiprompt_task.data.at("prompt").size();
        if (prompt_count <= 1) {
            send_error(multiprompt_task, "error while handling multiple prompts");
            return;
        }

        // generate all the ID for subtask
        std::vector<int> subtask_ids(prompt_count);
        for (int i = 0; i < prompt_count; i++) {
            subtask_ids[i] = queue_tasks.get_new_id();
        }

        // queue up the multitask so we can track its subtask progression
        queue_tasks.add_multitask(id_multi, subtask_ids);

        // add subtasks
        for (int i = 0; i < prompt_count; i++) {
            json subtask_data = multiprompt_task.data;
            subtask_data["prompt"] = subtask_data["prompt"][i];

            // subtasks inherit everything else (infill mode, embedding mode, etc.)
            request_completion(subtask_ids[i], id_multi, subtask_data, multiprompt_task.infill, multiprompt_task.embedding);
        }
    }

    void process_single_task(const server_task & task) {
        switch (task.type) {
            case SERVER_TASK_TYPE_COMPLETION:
                {
                    server_slot * slot = get_slot(json_value(task.data, "id_slot", -1));
                    if (slot == nullptr) {
                        // if no slot is available, we defer this task for processing later
                        LOG_VERBOSE("no slot is available", {{"id_task", task.id}});
                        queue_tasks.defer(task);
                        break;
                    }

                    if (task.data.contains("system_prompt")) {
                        system_prompt_set(task.data["system_prompt"]);

                        for (server_slot & slot : slots) {
                            slot.n_past    = 0;
                            slot.n_past_se = 0;
                        }
                    }

                    slot->reset();

                    slot->id_task   = task.id;
                    slot->id_multi  = task.id_multi;
                    slot->infill    = task.infill;
                    slot->embedding = task.embedding;

                    if (!launch_slot_with_task(*slot, task)) {
                        LOG_ERROR("error while launching slot", task.data);
                        break;
                    }
                } break;
            case SERVER_TASK_TYPE_CANCEL:
                {
                    // release slot linked with the task id
                    for (auto & slot : slots) {
                        if (slot.id_task == task.id_target) {
                            slot.release();
                            break;
                        }
                    }
                } break;
            case SERVER_TASK_TYPE_NEXT_RESPONSE:
                {
                    // do nothing
                } break;
            case SERVER_TASK_TYPE_METRICS:
                {
                    json slots_data = json::array();

                    int n_idle_slots       = 0;
                    int n_processing_slots = 0;

                    for (server_slot & slot : slots) {
                        json slot_data = get_formated_generation(slot);
                        slot_data["id"]         = slot.id;
                        slot_data["id_task"]    = slot.id_task;
                        slot_data["state"]      = slot.state;
                        slot_data["prompt"]     = slot.prompt;
                        slot_data["next_token"] = {
                            {"has_next_token", slot.has_next_token},
                            {"n_remain",       slot.n_remaining},
                            {"n_decoded",      slot.n_decoded},
                            {"stopped_eos",    slot.stopped_eos},
                            {"stopped_word",   slot.stopped_word},
                            {"stopped_limit",  slot.stopped_limit},
                            {"stopping_word",  slot.stopping_word},
                        };

                        if (slot_data["state"] == SLOT_STATE_IDLE) {
                            n_idle_slots++;
                        } else {
                            n_processing_slots++;
                        }

                        slots_data.push_back(slot_data);
                    }
                    LOG_INFO("slot data", {
                        {"id_task",            task.id},
                        {"n_idle_slots",       n_idle_slots},
                        {"n_processing_slots", n_processing_slots}
                    });

                    LOG_VERBOSE("slot data", {
                        {"id_task",            task.id},
                        {"n_idle_slots",       n_idle_slots},
                        {"n_processing_slots", n_processing_slots},
                        {"slots",              slots_data}
                    });

                    server_task_result res;
                    res.id       = task.id;
                    res.id_multi = task.id_multi;
                    res.stop     = true;
                    res.error    = false;
                    res.data     = {
                        { "idle",                            n_idle_slots       },
                        { "processing",                      n_processing_slots },
                        { "deferred",                        queue_tasks.queue_tasks_deferred.size() },
                        { "t_start",                         metrics.t_start},

                        { "n_prompt_tokens_processed_total", metrics.n_prompt_tokens_processed_total},
                        { "t_tokens_generation_total",       metrics.t_tokens_generation_total},
                        { "n_tokens_predicted_total",        metrics.n_tokens_predicted_total},
                        { "t_prompt_processing_total",       metrics.t_prompt_processing_total},

                        { "n_prompt_tokens_processed",       metrics.n_prompt_tokens_processed},
                        { "t_prompt_processing",             metrics.t_prompt_processing},
                        { "n_tokens_predicted",              metrics.n_tokens_predicted},
                        { "t_tokens_generation",             metrics.t_tokens_generation},

                        { "kv_cache_tokens_count",           llama_get_kv_cache_token_count(ctx)},
                        { "kv_cache_used_cells",             llama_get_kv_cache_used_cells(ctx)},

                        { "slots",                           slots_data },
                    };

                    if (json_value(task.data, "reset_bucket", false)) {
                        metrics.reset_bucket();
                    }
                    queue_results.send(res);
                } break;
        }
    }

    void on_finish_multitask(const server_task_multi & multitask) {
        // all subtasks done == multitask is done
        server_task_result result;
        result.id    = multitask.id;
        result.stop  = true;
        result.error = false;

        // collect json results into one json result
        std::vector<json> result_jsons;
        for (const auto & subres : multitask.results) {
            result_jsons.push_back(subres.data);
            result.error = result.error && subres.error;
        }
        result.data = json {
            { "results", result_jsons }
        };

        queue_results.send(result);
    }

    void update_slots() {
        if (system_need_update) {
            system_prompt_update();
        }

        // release slots
        for (auto & slot : slots) {
            if (slot.command == SLOT_COMMAND_RELEASE) {
                slot.state       = SLOT_STATE_IDLE;
                slot.command     = SLOT_COMMAND_NONE;
                slot.t_last_used = ggml_time_us();

                LOG_INFO("slot released", {
                    {"id_slot",         slot.id},
                    {"id_task",         slot.id_task},
                    {"n_ctx",           n_ctx},
                    {"n_past",          slot.n_past},
                    {"n_system_tokens", system_tokens.size()},
                    {"n_cache_tokens",  slot.cache_tokens.size()},
                    {"truncated",       slot.truncated}
                });

                queue_tasks.notify_slot_changed();
            }
        }

        // check if all slots are idle
        {
            bool all_idle = true;

            for (auto & slot : slots) {
                if (slot.state != SLOT_STATE_IDLE || slot.command != SLOT_COMMAND_NONE) {
                    all_idle = false;
                    break;
                }
            }

            if (all_idle) {
                LOG_INFO("all slots are idle", {});
                if (system_prompt.empty() && clean_kv_cache) {
                    kv_cache_clear();
                }

                return;
            }
        }

        {
            LOG_VERBOSE("posting NEXT_RESPONSE", {});

            server_task task;
            task.type      = SERVER_TASK_TYPE_NEXT_RESPONSE;
            task.id_target = -1;

            queue_tasks.post(task);
        }

        // apply context-shift if needed
        // TODO: simplify and improve
        for (server_slot & slot : slots) {
            if (slot.ga_n == 1) {
                if (slot.is_processing() && (int) system_tokens.size() + slot.n_past >= slot.n_ctx - 1) {
                    // Shift context
                    const int n_keep    = slot.params.n_keep + add_bos_token;
                    const int n_left    = (int) system_tokens.size() + slot.n_past - n_keep;
                    const int n_discard = n_left / 2;

                    LOG_INFO("slot context shift", {
                        {"id_slot",         slot.id},
                        {"id_task",         slot.id_task},
                        {"n_keep",          n_keep},
                        {"n_left",          n_left},
                        {"n_discard",       n_discard},
                        {"n_ctx",           n_ctx},
                        {"n_past",          slot.n_past},
                        {"n_system_tokens", system_tokens.size()},
                        {"n_cache_tokens",  slot.cache_tokens.size()}
                    });

                    llama_kv_cache_seq_rm (ctx, slot.id + 1, n_keep            , n_keep + n_discard);
                    llama_kv_cache_seq_add(ctx, slot.id + 1, n_keep + n_discard, system_tokens.size() + slot.n_past, -n_discard);

                    if (slot.params.cache_prompt) {
                        for (size_t i = n_keep + n_discard; i < slot.cache_tokens.size(); i++) {
                            slot.cache_tokens[i - n_discard] = slot.cache_tokens[i];
                        }

                        slot.cache_tokens.resize(slot.cache_tokens.size() - n_discard);
                    }

                    slot.n_past -= n_discard;

                    slot.truncated = true;
                }
            }
        }

        // start populating the batch for this iteration
        llama_batch_clear(batch);

        // frist, add sampled tokens from any ongoing sequences
        for (auto & slot : slots) {
            if (slot.state == SLOT_STATE_IDLE) {
                continue;
            }

            slot.i_batch = batch.n_tokens;

            const int32_t slot_npast = slot.n_past_se > 0 ? slot.n_past_se : slot.n_past;

            // TODO: we always have to take into account the "system_tokens"
            //       this is not great and needs to be improved somehow
            llama_batch_add(batch, slot.sampled, system_tokens.size() + slot_npast, { slot.id + 1 }, true);

            slot.n_past += 1;

            if (slot.params.cache_prompt) {
                slot.cache_tokens.push_back(slot.sampled);
            }

            LOG_VERBOSE("slot decode token", {
                {"id_slot",         slot.id},
                {"id_task",         slot.id_task},
                {"n_ctx",           n_ctx},
                {"n_past",          slot.n_past},
                {"n_system_tokens", system_tokens.size()},
                {"n_cache_tokens",  slot.cache_tokens.size()},
                {"truncated",       slot.truncated}
            });
        }

        // process in chunks of params.n_batch
        int32_t n_batch = llama_n_batch(ctx);
        int32_t n_ubatch = llama_n_ubatch(ctx);

        // next, batch any pending prompts without exceeding n_batch
        if (params.cont_batching || batch.n_tokens == 0) {
            for (auto & slot : slots) {
                // this slot still has a prompt to be processed
                if (slot.state == SLOT_STATE_IDLE && slot.command == SLOT_COMMAND_LOAD_PROMPT) {
                    auto & prompt_tokens = slot.prompt_tokens;

                    // we haven't tokenized the prompt yet - do it now:
                    if (prompt_tokens.empty()) {
                        LOG_VERBOSE("tokenizing prompt", {
                            {"id_slot", slot.id},
                            {"id_task", slot.id_task}
                        });

                        slot.t_start_process_prompt = ggml_time_us();
                        slot.t_start_generation = 0;

                        if (slot.infill) {
                            bool suff_rm_leading_spc = true;
                            if (params.input_suffix.find_first_of(' ') == 0 && params.input_suffix.size() > 1) {
                                params.input_suffix.erase(0, 1);
                                suff_rm_leading_spc = false;
                            }

                            auto prefix_tokens = tokenize(slot.params.input_prefix, false);
                            auto suffix_tokens = tokenize(slot.params.input_suffix, false);

                            const int space_token = 29871; // TODO: this should not be hardcoded
                            if (suff_rm_leading_spc && !suffix_tokens.empty() && suffix_tokens[0] == space_token) {
                                suffix_tokens.erase(suffix_tokens.begin());
                            }

                            prefix_tokens.insert(prefix_tokens.begin(), llama_token_prefix(model));
                            prefix_tokens.insert(prefix_tokens.begin(), llama_token_bos(model)); // always add BOS
                            prefix_tokens.insert(prefix_tokens.end(),   llama_token_suffix(model));
                            prefix_tokens.insert(prefix_tokens.end(),   suffix_tokens.begin(), suffix_tokens.end());
                            prefix_tokens.push_back(llama_token_middle(model));
                            prompt_tokens = prefix_tokens;
                        } else {
                            prompt_tokens = tokenize(slot.prompt, system_prompt.empty() && add_bos_token);  // add BOS if there isn't system prompt
                        }

                        slot.n_past = 0;
                        slot.n_prompt_tokens = prompt_tokens.size();

                        LOG_VERBOSE("prompt tokenized", {
                            {"id_slot",         slot.id},
                            {"id_task",         slot.id_task},
                            {"n_ctx",           slot.n_ctx},
                            {"n_keep",          slot.params.n_keep},
                            {"n_prompt_tokens", slot.n_prompt_tokens},
                            {"prompt_tokens",   tokens_to_str(ctx, prompt_tokens.cbegin(), prompt_tokens.cend())},
                        });

                        // empty prompt passed -> release the slot and send empty response
                        if (prompt_tokens.empty()) {
                            LOG_INFO("empty prompt - releasing slot", {
                                {"id_slot", slot.id},
                                {"id_task", slot.id_task}
                            });

                            slot.state = SLOT_STATE_PROCESSING;
                            slot.command = SLOT_COMMAND_NONE;
                            slot.release();
                            slot.print_timings();
                            send_final_response(slot);
                            continue;
                        }

                        if (slot.embedding) {
                            // this prompt is too large to process - discard it
                            if (slot.n_prompt_tokens > n_ubatch) {
                                slot.state = SLOT_STATE_PROCESSING;
                                slot.command = SLOT_COMMAND_NONE;
                                slot.release();
                                slot.print_timings();
                                send_final_response(slot);
                                continue;
                            }
                        } else {
                            if (slot.params.n_keep < 0) {
                                slot.params.n_keep = slot.n_prompt_tokens;
                            }
                            slot.params.n_keep = std::min(slot.n_ctx - 4, slot.params.n_keep);

                            // if input prompt is too big, truncate it (if group attention self-extend is disabled)
                            if (slot.ga_n == 1 && slot.n_prompt_tokens >= slot.n_ctx) {
                                const int n_left = slot.n_ctx - slot.params.n_keep;

                                const int n_block_size = n_left / 2;
                                const int erased_blocks = (slot.n_prompt_tokens - slot.params.n_keep - n_block_size) / n_block_size;

                                std::vector<llama_token> new_tokens(
                                        prompt_tokens.begin(),
                                        prompt_tokens.begin() + slot.params.n_keep);

                                new_tokens.insert(
                                        new_tokens.end(),
                                        prompt_tokens.begin() + slot.params.n_keep + erased_blocks * n_block_size,
                                        prompt_tokens.end());

                                prompt_tokens = std::move(new_tokens);

                                slot.truncated = true;
                                slot.n_prompt_tokens = prompt_tokens.size();

                                LOG_VERBOSE("input truncated", {
                                    {"id_slot",         slot.id},
                                    {"id_task",         slot.id_task},
                                    {"n_ctx",           slot.n_ctx},
                                    {"n_keep",          slot.params.n_keep},
                                    {"n_left",          n_left},
                                    {"n_prompt_tokens", slot.n_prompt_tokens},
                                    {"prompt_tokens",   tokens_to_str(ctx, prompt_tokens.cbegin(), prompt_tokens.cend())},
                                });

                                GGML_ASSERT(slot.n_prompt_tokens < slot.n_ctx);
                            }

                            llama_sampling_reset(slot.ctx_sampling);

                            if (!slot.params.cache_prompt) {
                                slot.n_past_se = 0;
                                slot.ga_i      = 0;
                            } else {
                                GGML_ASSERT(slot.ga_n == 1);

                                // reuse any previously computed tokens that are common with the new prompt
                                slot.n_past = common_part(slot.cache_tokens, prompt_tokens);

                                // push the prompt into the sampling context (do not apply grammar)
                                for (int i = 0; i < slot.n_past; ++i) {
                                    llama_sampling_accept(slot.ctx_sampling, ctx, slot.cache_tokens[i], false);
                                }
                            }
                        }

                        if (slot.n_past == slot.n_prompt_tokens && slot.n_past > 0) {
                            // we have to evaluate at least 1 token to generate logits.
                            LOG_INFO("we have to evaluate at least 1 token to generate logits", {
                                { "id_slot", slot.id },
                                { "id_task", slot.id_task }
                            });

                            slot.n_past--;
                            if (slot.ga_i > 0) {
                                slot.n_past_se--;
                            }
                        }

                        slot.n_prompt_tokens_processed = 0;
                    }

                    if (slot.embedding) {
                        // cannot fit the prompt in the current batch - will try next iter
                        if (batch.n_tokens + slot.n_prompt_tokens > n_batch) {
                            continue;
                        }
                    }

                    // keep only the common part
                    int p0 = (int) system_tokens.size() + slot.n_past;
                    if (!llama_kv_cache_seq_rm(ctx, slot.id + 1, p0, -1)) {
                        // could not partially delete (likely using a non-Transformer model)
                        llama_kv_cache_seq_rm(ctx, slot.id + 1, -1, -1);

                        p0 = (int) system_tokens.size();
                        if (p0 != 0) {
                            // copy over the system prompt when there is one
                            llama_kv_cache_seq_cp(ctx, 0, slot.id + 1, -1, -1);
                        }

                        // there is no common part left (except for the system prompt)
                        slot.n_past = 0;
                        slot.n_past_se = 0;
                        slot.ga_i = 0;
                        // TODO: is the system prompt ever in the sampling context?
                        llama_sampling_reset(slot.ctx_sampling);
                    }

                    // remove the non-common part from the cache
                    slot.cache_tokens.resize(slot.n_past);

                    LOG_INFO("kv cache rm [p0, end)", {
                        { "id_slot", slot.id },
                        { "id_task", slot.id_task },
                        { "p0",      p0 }
                    });

                    int32_t slot_npast = slot.n_past_se > 0 ? slot.n_past_se : slot.n_past;

                    int32_t ga_i = slot.ga_i;
                    int32_t ga_n = slot.ga_n;
                    int32_t ga_w = slot.ga_w;

                    // add prompt tokens for processing in the current batch
                    // TODO: the self-extend stuff here is a mess - simplify and/or abstract it somehow
                    for (; slot.n_past < slot.n_prompt_tokens && batch.n_tokens < n_batch; ++slot.n_past) {
                        if (slot.ga_n != 1) {
                            while (slot_npast >= ga_i + ga_w) {
                                const int bd = (ga_w/ga_n)*(ga_n - 1);
                                slot_npast -= bd;
                                ga_i += ga_w/ga_n;
                            }
                        }

                        llama_batch_add(batch, prompt_tokens[slot.n_past], system_tokens.size() + slot_npast, { slot.id + 1 }, false);

                        if (slot.params.cache_prompt) {
                            slot.cache_tokens.push_back(prompt_tokens[slot.n_past]);
                        }

                        slot.n_prompt_tokens_processed++;
                        slot_npast++;
                    }

                    LOG_VERBOSE("prompt processing progress", {
                        {"id_slot",  slot.id},
                        {"n_past",   slot.n_past},
                        {"n_ctx",    n_ctx},
                        {"n_tokens", batch.n_tokens},
                        {"progress", (float) slot.n_prompt_tokens_processed / slot.n_prompt_tokens},
                    });

                    // entire prompt has been processed - start decoding new tokens
                    if (slot.n_past == slot.n_prompt_tokens) {
                        slot.state   = SLOT_STATE_PROCESSING;
                        slot.command = SLOT_COMMAND_NONE;

                        GGML_ASSERT(batch.n_tokens > 0);

                        // extract the logits only for the last token
                        batch.logits[batch.n_tokens - 1] = true;

                        slot.n_decoded = 0;
                        slot.i_batch   = batch.n_tokens - 1;

                        LOG_VERBOSE("prompt done", {
                            {"id_slot",  slot.id},
                            {"n_past",   slot.n_past},
                            {"n_ctx",    n_ctx},
                            {"n_tokens", batch.n_tokens},
                        });
                    }
                }

                if (batch.n_tokens >= n_batch) {
                    break;
                }
            }
        }

        if (batch.n_tokens == 0) {
            LOG_VERBOSE("no tokens to decode", {});
            return;
        }

        LOG_VERBOSE("decoding batch", {
            {"n_tokens", batch.n_tokens},
        });

        // process the created batch of tokens
        for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch) {
            const int32_t n_tokens = std::min(n_batch, batch.n_tokens - i);

            for (auto & slot : slots) {
                if (slot.ga_n != 1) {
                    // context extension via Self-Extend
                    // TODO: simplify and/or abstract this
                    while (slot.n_past_se >= slot.ga_i + slot.ga_w) {
                        const int ib = (slot.ga_n * slot.ga_i) / slot.ga_w;
                        const int bd = (slot.ga_w / slot.ga_n) * (slot.ga_n - 1);
                        const int dd = (slot.ga_w / slot.ga_n) - ib * bd - slot.ga_w;

                        LOG_TEE("\n");
                        LOG_TEE("shift: [%6d, %6d] + %6d -> [%6d, %6d]\n", slot.ga_i, slot.n_past_se, ib * bd, slot.ga_i + ib * bd, slot.n_past_se + ib * bd);
                        LOG_TEE("div:   [%6d, %6d] / %6d -> [%6d, %6d]\n", slot.ga_i + ib * bd, slot.ga_i + ib * bd + slot.ga_w, slot.ga_n, (slot.ga_i + ib * bd) / slot.ga_n, (slot.ga_i + ib * bd + slot.ga_w) / slot.ga_n);
                        LOG_TEE("shift: [%6d, %6d] + %6d -> [%6d, %6d]\n", slot.ga_i + ib * bd + slot.ga_w, slot.n_past_se + ib * bd, dd, slot.ga_i + ib * bd + slot.ga_w + dd, slot.n_past_se + ib * bd + dd);

                        llama_kv_cache_seq_add(ctx, slot.id + 1, slot.ga_i, slot.n_past_se, ib * bd);
                        llama_kv_cache_seq_div(ctx, slot.id + 1, slot.ga_i + ib * bd, slot.ga_i + ib * bd + slot.ga_w, slot.ga_n);
                        llama_kv_cache_seq_add(ctx, slot.id + 1, slot.ga_i + ib * bd + slot.ga_w, slot.n_past_se + ib * bd, dd);

                        slot.n_past_se -= bd;

                        slot.ga_i += slot.ga_w / slot.ga_n;

                        LOG_TEE("\nn_past_old = %d, n_past = %d, ga_i = %d\n\n", slot.n_past_se + bd, slot.n_past_se, slot.ga_i);
                    }

                    slot.n_past_se += n_tokens;
                }
            }

            llama_batch batch_view = {
                n_tokens,
                batch.token    + i,
                nullptr,
                batch.pos      + i,
                batch.n_seq_id + i,
                batch.seq_id   + i,
                batch.logits   + i,
                0, 0, 0, // unused
            };

            const int ret = llama_decode(ctx, batch_view);

            if (ret != 0) {
                if (n_batch == 1 || ret < 0) {
                    // if you get here, it means the KV cache is full - try increasing it via the context size
                    LOG_TEE("%s : failed to decode the batch, n_batch = %d, ret = %d\n", __func__, n_batch, ret);
                    for (auto & slot : slots) {
                        slot.state = SLOT_STATE_PROCESSING;
                        slot.command = SLOT_COMMAND_NONE;
                        slot.release();
                        send_error(slot, "Input prompt is too big compared to KV size. Please try increasing KV size.");
                    }
                    break; // break loop of n_batch
                }

                LOG_TEE("%s : failed to find free space in the KV cache, retrying with smaller n_batch = %d\n", __func__, n_batch / 2);

                // retry with half the batch size to try to find a free slot in the KV cache
                n_batch /= 2;
                i -= n_batch;

                continue; // continue loop of n_batch
            }

            for (auto & slot : slots) {
                if (slot.state != SLOT_STATE_PROCESSING || slot.i_batch < (int) i || slot.i_batch >= (int) (i + n_tokens)) {
                    continue; // continue loop of slots
                }

                // prompt evaluated for embedding
                if (slot.embedding) {
                    send_embedding(slot, batch_view);
                    slot.release();
                    slot.i_batch = -1;
                    continue; // continue loop of slots
                }

                completion_token_output result;
                const llama_token id = llama_sampling_sample(slot.ctx_sampling, ctx, NULL, slot.i_batch - i);

                llama_sampling_accept(slot.ctx_sampling, ctx, id, true);

                slot.n_decoded += 1;
                if (slot.n_decoded == 1) {
                    slot.t_start_generation = ggml_time_us();
                    slot.t_prompt_processing = (slot.t_start_generation - slot.t_start_process_prompt) / 1e3;
                    metrics.on_prompt_eval(slot);
                }

                llama_token_data_array cur_p = { slot.ctx_sampling->cur.data(), slot.ctx_sampling->cur.size(), false };
                result.tok = id;

                const int32_t n_probs = slot.sparams.n_probs;
                if (slot.sparams.temp <= 0 && n_probs > 0) {
                    // for llama_sample_token_greedy we need to sort candidates
                    llama_sample_softmax(ctx, &cur_p);
                }

                for (size_t i = 0; i < std::min(cur_p.size, (size_t) n_probs); ++i) {
                    result.probs.push_back({
                        cur_p.data[i].id,
                        cur_p.data[i].p
                    });
                }

                if (!process_token(result, slot)) {
                    slot.release();
                    slot.print_timings();
                    send_final_response(slot);
                    metrics.on_prediction(slot);
                }

                slot.i_batch = -1;
            }
        }

        LOG_VERBOSE("run slots completed", {});
    }

    json model_meta() const {
        return json {
            {"vocab_type",  llama_vocab_type    (model)},
            {"n_vocab",     llama_n_vocab       (model)},
            {"n_ctx_train", llama_n_ctx_train   (model)},
            {"n_embd",      llama_n_embd        (model)},
            {"n_params",    llama_model_n_params(model)},
            {"size",        llama_model_size    (model)},
        };
    }
};

static void server_print_usage(const char * argv0, const gpt_params & params, const server_params & sparams) {
    printf("usage: %s [options]\n", argv0);
    printf("\n");
    printf("options:\n");
    printf("  -h, --help                show this help message and exit\n");
    printf("  -v, --verbose             verbose output (default: %s)\n", server_verbose ? "enabled" : "disabled");
    printf("  -t N, --threads N         number of threads to use during computation (default: %d)\n", params.n_threads);
    printf("  -tb N, --threads-batch N  number of threads to use during batch and prompt processing (default: same as --threads)\n");
    printf("  --threads-http N          number of threads in the http server pool to process requests (default: max(hardware concurrency - 1, --parallel N + 2))\n");
    printf("  -c N, --ctx-size N        size of the prompt context (default: %d)\n", params.n_ctx);
    printf("  --rope-scaling {none,linear,yarn}\n");
    printf("                            RoPE frequency scaling method, defaults to linear unless specified by the model\n");
    printf("  --rope-freq-base N        RoPE base frequency (default: loaded from model)\n");
    printf("  --rope-freq-scale N       RoPE frequency scaling factor, expands context by a factor of 1/N\n");
    printf("  --yarn-ext-factor N       YaRN: extrapolation mix factor (default: 1.0, 0.0 = full interpolation)\n");
    printf("  --yarn-attn-factor N      YaRN: scale sqrt(t) or attention magnitude (default: 1.0)\n");
    printf("  --yarn-beta-slow N        YaRN: high correction dim or alpha (default: %.1f)\n", params.yarn_beta_slow);
    printf("  --yarn-beta-fast N        YaRN: low correction dim or beta (default: %.1f)\n", params.yarn_beta_fast);
    printf("  --pooling {none,mean,cls} pooling type for embeddings, use model default if unspecified\n");
    printf("  -dt N, --defrag-thold N\n");
    printf("                            KV cache defragmentation threshold (default: %.1f, < 0 - disabled)\n", params.defrag_thold);
    printf("  -b N, --batch-size N      logical maximum batch size (default: %d)\n", params.n_batch);
    printf("  -ub N, --ubatch-size N    physical maximum batch size (default: %d)\n", params.n_ubatch);
    printf("  --memory-f32              use f32 instead of f16 for memory key+value (default: disabled)\n");
    printf("                            not recommended: doubles context memory required and no measurable increase in quality\n");
    if (llama_supports_mlock()) {
        printf("  --mlock                   force system to keep model in RAM rather than swapping or compressing\n");
    }
    if (llama_supports_mmap()) {
        printf("  --no-mmap                 do not memory-map model (slower load but may reduce pageouts if not using mlock)\n");
    }
    printf("  --numa TYPE               attempt optimizations that help on some NUMA systems\n");
    printf("                              - distribute: spread execution evenly over all nodes\n");
    printf("                              - isolate: only spawn threads on CPUs on the node that execution started on\n");
    printf("                              - numactl: use the CPU map provided my numactl\n");
    if (llama_supports_gpu_offload()) {
        printf("  -ngl N, --n-gpu-layers N\n");
        printf("                            number of layers to store in VRAM\n");
        printf("  -sm SPLIT_MODE, --split-mode SPLIT_MODE\n");
        printf("                            how to split the model across multiple GPUs, one of:\n");
        printf("                              - none: use one GPU only\n");
        printf("                              - layer (default): split layers and KV across GPUs\n");
        printf("                              - row: split rows across GPUs\n");
        printf("  -ts SPLIT --tensor-split SPLIT\n");
        printf("                            fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1\n");
        printf("  -mg i, --main-gpu i       the GPU to use for the model (with split-mode = none),\n");
        printf("                            or for intermediate results and KV (with split-mode = row)\n");
    }
    printf("  -m FNAME, --model FNAME\n");
    printf("                            model path (default: %s)\n", params.model.c_str());
    printf("  -a ALIAS, --alias ALIAS\n");
    printf("                            set an alias for the model, will be added as `model` field in completion response\n");
    printf("  --lora FNAME              apply LoRA adapter (implies --no-mmap)\n");
    printf("  --lora-base FNAME         optional model to use as a base for the layers modified by the LoRA adapter\n");
    printf("  --host                    ip address to listen (default  (default: %s)\n", sparams.hostname.c_str());
    printf("  --port PORT               port to listen (default  (default: %d)\n", sparams.port);
    printf("  --path PUBLIC_PATH        path from which to serve static files (default: disabled)\n");
    printf("  --api-key API_KEY         optional api key to enhance server security. If set, requests must include this key for access.\n");
    printf("  --api-key-file FNAME      path to file containing api keys delimited by new lines. If set, requests must include one of the keys for access.\n");
#ifdef CPPHTTPLIB_OPENSSL_SUPPORT
    printf("  --ssl-key-file FNAME      path to file a PEM-encoded SSL private key\n");
    printf("  --ssl-cert-file FNAME     path to file a PEM-encoded SSL certificate\n");
#endif
    printf("  -to N, --timeout N        server read/write timeout in seconds (default: %d)\n", sparams.read_timeout);
    printf("  --embeddings              enable embedding vector output (default: %s)\n", params.embedding ? "enabled" : "disabled");
    printf("  -np N, --parallel N       number of slots for process requests (default: %d)\n", params.n_parallel);
    printf("  -cb, --cont-batching      enable continuous batching (a.k.a dynamic batching) (default: disabled)\n");
    printf("  -spf FNAME, --system-prompt-file FNAME\n");
    printf("                            set a file to load a system prompt (initial prompt of all slots), this is useful for chat applications.\n");
    printf("  -ctk TYPE, --cache-type-k TYPE\n");
    printf("                            KV cache data type for K (default: f16)\n");
    printf("  -ctv TYPE, --cache-type-v TYPE\n");
    printf("                            KV cache data type for V (default: f16)\n");
    printf("  --log-format              log output format: json or text (default: json)\n");
    printf("  --log-disable             disables logging to a file.\n");
    printf("  --slots-endpoint-disable  disables slots monitoring endpoint.\n");
    printf("  --metrics                 enable prometheus compatible metrics endpoint (default: %s).\n", sparams.metrics_endpoint ? "enabled" : "disabled");
    printf("\n");
    printf("  -n, --n-predict           maximum tokens to predict (default: %d)\n", params.n_predict);
    printf("  --override-kv KEY=TYPE:VALUE\n");
    printf("                            advanced option to override model metadata by key. may be specified multiple times.\n");
    printf("                            types: int, float, bool. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n");
    printf("  -gan N, --grp-attn-n N    set the group attention factor to extend context size through self-extend(default: 1=disabled), used together with group attention width `--grp-attn-w`\n");
    printf("  -gaw N, --grp-attn-w N    set the group attention width to extend context size through self-extend(default: 512), used together with group attention factor `--grp-attn-n`\n");
    printf("  --chat-template JINJA_TEMPLATE\n");
    printf("                            set custom jinja chat template (default: template taken from model's metadata)\n");
    printf("                            only commonly used templates are accepted:\n");
    printf("                            https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template\n");
    printf("\n");
}

static void server_params_parse(int argc, char ** argv, server_params & sparams, gpt_params & params) {
    gpt_params    default_params;
    server_params default_sparams;

    std::string arg;
    bool invalid_param = false;

    for (int i = 1; i < argc; i++) {
        arg = argv[i];
        if (arg == "--port") {
            if (++i >= argc) {
                invalid_param = true;
                break;
            }
            sparams.port = std::stoi(argv[i]);
        } else if (arg == "--host") {
            if (++i >= argc) {
                invalid_param = true;
                break;
            }
            sparams.hostname = argv[i];
        } else if (arg == "--path") {
            if (++i >= argc) {
                invalid_param = true;
                break;
            }
            sparams.public_path = argv[i];
        } else if (arg == "--api-key") {
            if (++i >= argc) {
                invalid_param = true;
                break;
            }
            sparams.api_keys.push_back(argv[i]);
        } else if (arg == "--api-key-file") {
            if (++i >= argc) {
                invalid_param = true;
                break;
            }
            std::ifstream key_file(argv[i]);
            if (!key_file) {
                fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
                invalid_param = true;
                break;
            }
            std::string key;
            while (std::getline(key_file, key)) {
               if (key.size() > 0) {
                   sparams.api_keys.push_back(key);
               }
            }
            key_file.close();

        }
#ifdef CPPHTTPLIB_OPENSSL_SUPPORT
        else if (arg == "--ssl-key-file") {
            if (++i >= argc) {
                invalid_param = true;
                break;
            }
            sparams.ssl_key_file = argv[i];
        } else if (arg == "--ssl-cert-file") {
            if (++i >= argc) {
                invalid_param = true;
                break;
            }
            sparams.ssl_cert_file = argv[i];
        }
#endif
        else if (arg == "--timeout" || arg == "-to") {
            if (++i >= argc) {
                invalid_param = true;
                break;
            }
            sparams.read_timeout = std::stoi(argv[i]);
            sparams.write_timeout = std::stoi(argv[i]);
        } else if (arg == "-m" || arg == "--model") {
            if (++i >= argc) {
                invalid_param = true;
                break;
            }
            params.model = argv[i];
        } else if (arg == "-a" || arg == "--alias") {
            if (++i >= argc) {
                invalid_param = true;
                break;
            }
            params.model_alias = argv[i];
        } else if (arg == "-h" || arg == "--help") {
            server_print_usage(argv[0], default_params, default_sparams);
            exit(0);
        } else if (arg == "-c" || arg == "--ctx-size" || arg == "--ctx_size") {
            if (++i >= argc) {
                invalid_param = true;
                break;
            }
            params.n_ctx = std::stoi(argv[i]);
        } else if (arg == "--rope-scaling") {
            if (++i >= argc) {
                invalid_param = true;
                break;
            }
            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; break; }
        } else if (arg == "--rope-freq-base") {
            if (++i >= argc) {
                invalid_param = true;
                break;
            }
            params.rope_freq_base = std::stof(argv[i]);
        } else if (arg == "--rope-freq-scale") {
            if (++i >= argc) {
                invalid_param = true;
                break;
            }
            params.rope_freq_scale = std::stof(argv[i]);
        } else if (arg == "--yarn-ext-factor") {
            if (++i >= argc) {
                invalid_param = true;
                break;
            }
            params.yarn_ext_factor = std::stof(argv[i]);
        }
        else if (arg == "--yarn-attn-factor") {
            if (++i >= argc) {
                invalid_param = true;
                break;
            }
            params.yarn_attn_factor = std::stof(argv[i]);
        } else if (arg == "--yarn-beta-fast") {
            if (++i >= argc) {
                invalid_param = true;
                break;
            }
            params.yarn_beta_fast = std::stof(argv[i]);
        } else if (arg == "--yarn-beta-slow") {
            if (++i >= argc) {
                invalid_param = true;
                break;
            }
            params.yarn_beta_slow = std::stof(argv[i]);
        } else if (arg == "--pooling") {
            if (++i >= argc) {
                invalid_param = true;
                break;
            }
            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 { invalid_param = true; break; }
        } else if (arg == "--defrag-thold" || arg == "-dt") {
            if (++i >= argc) {
                invalid_param = true;
                break;
            }
            params.defrag_thold = std::stof(argv[i]);
        } else if (arg == "--threads" || arg == "-t") {
            if (++i >= argc)
            {
                invalid_param = true;
                break;
            }
            params.n_threads = std::stoi(argv[i]);
        } else if (arg == "--grp-attn-n" || arg == "-gan") {
            if (++i >= argc) {
                invalid_param = true;
                break;
            }

            params.grp_attn_n = std::stoi(argv[i]);
        } else if (arg == "--grp-attn-w" || arg == "-gaw") {
            if (++i >= argc) {
                invalid_param = true;
                break;
            }

            params.grp_attn_w = std::stoi(argv[i]);
        } else if (arg == "--threads-batch" || arg == "-tb") {
            if (++i >= argc) {
                invalid_param = true;
                break;
            }
            params.n_threads_batch = std::stoi(argv[i]);
        } else if (arg == "--threads-http") {
            if (++i >= argc) {
                invalid_param = true;
                break;
            }
            sparams.n_threads_http = std::stoi(argv[i]);
        } else if (arg == "-b" || arg == "--batch-size") {
            if (++i >= argc) {
                invalid_param = true;
                break;
            }
            params.n_batch = std::stoi(argv[i]);
        } else if (arg == "-ub" || arg == "--ubatch-size") {
            if (++i >= argc) {
                invalid_param = true;
                break;
            }
            params.n_ubatch = std::stoi(argv[i]);
        } else if (arg == "--gpu-layers" || arg == "-ngl" || arg == "--n-gpu-layers") {
            if (++i >= argc) {
                invalid_param = true;
                break;
            }
            if (llama_supports_gpu_offload()) {
                params.n_gpu_layers = std::stoi(argv[i]);
            } else {
                LOG_WARNING(
                    "Not compiled with GPU offload support, --n-gpu-layers option will be ignored. "
                    "See main README.md for information on enabling GPU BLAS support",
                    {{"n_gpu_layers", params.n_gpu_layers}});
            }
        } else if (arg == "--split-mode" || arg == "-sm") {
            if (++i >= argc) {
                invalid_param = true;
                break;
            }
            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 == "row") {
                params.split_mode = LLAMA_SPLIT_MODE_ROW;
            } else {
                invalid_param = true;
                break;
            }
#ifndef GGML_USE_CUBLAS
            fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. Setting the split mode has no effect.\n");
#endif // GGML_USE_CUBLAS
        } else if (arg == "--tensor-split" || arg == "-ts") {
            if (++i >= argc) {
                invalid_param = true;
                break;
            }
#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_SYCL)
            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, {}};
            GGML_ASSERT(split_arg.size() <= llama_max_devices());

            for (size_t i_device = 0; i_device < llama_max_devices(); ++i_device) {
                if (i_device < split_arg.size()) {
                    params.tensor_split[i_device] = std::stof(split_arg[i_device]);
                } else {
                    params.tensor_split[i_device] = 0.0f;
                }
            }
#else
            LOG_WARNING("llama.cpp was compiled without cuBLAS. It is not possible to set a tensor split.\n", {});
#endif // GGML_USE_CUBLAS
        } else if (arg == "--main-gpu" || arg == "-mg") {
            if (++i >= argc) {
                invalid_param = true;
                break;
            }
#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_SYCL)
            params.main_gpu = std::stoi(argv[i]);
#else
            LOG_WARNING("llama.cpp was compiled without cuBLAS. It is not possible to set a main GPU.", {});
#endif
        } else if (arg == "--lora") {
            if (++i >= argc) {
                invalid_param = true;
                break;
            }
            params.lora_adapter.emplace_back(argv[i], 1.0f);
            params.use_mmap = false;
        } else if (arg == "--lora-scaled") {
            if (++i >= argc) {
                invalid_param = true;
                break;
            }
            const char * lora_adapter = argv[i];
            if (++i >= argc) {
                invalid_param = true;
                break;
            }
            params.lora_adapter.emplace_back(lora_adapter, std::stof(argv[i]));
            params.use_mmap = false;
        } else if (arg == "--lora-base") {
            if (++i >= argc) {
                invalid_param = true;
                break;
            }
            params.lora_base = argv[i];
        } else if (arg == "-v" || arg == "--verbose") {
#if SERVER_VERBOSE != 1
            LOG_WARNING("server.cpp is not built with verbose logging.", {});
#else
            server_verbose = true;
#endif
        } else if (arg == "--mlock") {
            params.use_mlock = true;
        } else if (arg == "--no-mmap") {
            params.use_mmap = false;
        } else if (arg == "--numa") {
            if (++i >= argc) {
                invalid_param = true;
                break;
            } else {
                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; break; }
            }
        } else if (arg == "--embedding" || arg == "--embeddings") {
            params.embedding = true;
        } else if (arg == "-cb" || arg == "--cont-batching") {
            params.cont_batching = true;
        } else if (arg == "-np" || arg == "--parallel") {
            if (++i >= argc) {
                invalid_param = true;
                break;
            }
            params.n_parallel = std::stoi(argv[i]);
        } else if (arg == "-n" || arg == "--n-predict") {
            if (++i >= argc) {
                invalid_param = true;
                break;
            }
            params.n_predict = std::stoi(argv[i]);
        } else if (arg == "-spf" || arg == "--system-prompt-file") {
            if (++i >= argc) {
                invalid_param = true;
                break;
            }
            std::ifstream file(argv[i]);
            if (!file) {
                fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
                invalid_param = true;
                break;
            }
            std::string system_prompt;
            std::copy(
                std::istreambuf_iterator<char>(file),
                std::istreambuf_iterator<char>(),
                std::back_inserter(system_prompt)
            );
            sparams.system_prompt = system_prompt;
        } else if (arg == "-ctk" || arg == "--cache-type-k") {
            params.cache_type_k = argv[++i];
        } else if (arg == "-ctv" || arg == "--cache-type-v") {
            params.cache_type_v = argv[++i];
        } else if (arg == "--log-format") {
            if (++i >= argc) {
                invalid_param = true;
                break;
            }
            if (std::strcmp(argv[i], "json") == 0) {
                server_log_json = true;
            } else if (std::strcmp(argv[i], "text") == 0) {
                server_log_json = false;
            } else {
                invalid_param = true;
                break;
            }
        } else if (arg == "--log-disable") {
            log_set_target(stdout);
            LOG_INFO("logging to file is disabled.", {});
        } else if (arg == "--slots-endpoint-disable") {
            sparams.slots_endpoint = false;
        } else if (arg == "--metrics") {
            sparams.metrics_endpoint = true;
        } else if (arg == "--chat-template") {
            if (++i >= argc) {
                invalid_param = true;
                break;
            }
            if (!verify_custom_template(argv[i])) {
                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;
                break;
            }
            sparams.chat_template = argv[i];
        } else if (arg == "--override-kv") {
            if (++i >= argc) {
                invalid_param = true;
                break;
            }
            char * sep = strchr(argv[i], '=');
            if (sep == nullptr || sep - argv[i] >= 128) {
                fprintf(stderr, "error: Malformed KV override: %s\n", argv[i]);
                invalid_param = true;
                break;
            }

            struct llama_model_kv_override kvo;
            std::strncpy(kvo.key, argv[i], sep - argv[i]);
            kvo.key[sep - argv[i]] = 0;
            sep++;
            if (strncmp(sep, "int:", 4) == 0) {
                sep += 4;
                kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT;
                kvo.int_value = std::atol(sep);
            } else if (strncmp(sep, "float:", 6) == 0) {
                sep += 6;
                kvo.tag = LLAMA_KV_OVERRIDE_TYPE_FLOAT;
                kvo.float_value = 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.bool_value = true;
                } else if (std::strcmp(sep, "false") == 0) {
                    kvo.bool_value = false;
                } else {
                    fprintf(stderr, "error: Invalid boolean value for KV override: %s\n", argv[i]);
                    invalid_param = true;
                    break;
                }
            } else {
                fprintf(stderr, "error: Invalid type for KV override: %s\n", argv[i]);
                invalid_param = true;
                break;
            }
            params.kv_overrides.push_back(kvo);
        } else {
            fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
            server_print_usage(argv[0], default_params, default_sparams);
            exit(1);
        }
    }

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

    if (invalid_param) {
        fprintf(stderr, "error: invalid parameter for argument: %s\n", arg.c_str());
        server_print_usage(argv[0], default_params, default_sparams);
        exit(1);
    }
}

static void log_server_request(const httplib::Request & req, const httplib::Response & res) {
    // skip GH copilot requests when using default port
    if (req.path == "/v1/health" || req.path == "/v1/completions") {
        return;
    }

    LOG_INFO("request", {
        {"remote_addr", req.remote_addr},
        {"remote_port", req.remote_port},
        {"status",      res.status},
        {"method",      req.method},
        {"path",        req.path},
        {"params",      req.params},
    });

    LOG_VERBOSE("request", {
        {"request",  req.body},
        {"response", res.body},
    });
}

std::function<void(int)> shutdown_handler;
std::atomic_flag is_terminating = ATOMIC_FLAG_INIT;

inline void signal_handler(int signal) {
    if (is_terminating.test_and_set()) {
        // in case it hangs, we can force terminate the server by hitting Ctrl+C twice
        // this is for better developer experience, we can remove when the server is stable enough
        fprintf(stderr, "Received second interrupt, terminating immediately.\n");
        exit(1);
    }

    shutdown_handler(signal);
}

int main(int argc, char ** argv) {
#if SERVER_VERBOSE != 1
    log_disable();
#endif
    // own arguments required by this example
    gpt_params    params;
    server_params sparams;

    // struct that contains llama context and inference
    server_context ctx_server;

    server_params_parse(argc, argv, sparams, params);

    if (!sparams.system_prompt.empty()) {
        ctx_server.system_prompt_set(json::parse(sparams.system_prompt));
    }

    if (params.model_alias == "unknown") {
        params.model_alias = params.model;
    }

    llama_backend_init();
    llama_numa_init(params.numa);

    LOG_INFO("build info", {
        {"build",  LLAMA_BUILD_NUMBER},
        {"commit", LLAMA_COMMIT}
    });

    LOG_INFO("system info", {
        {"n_threads",       params.n_threads},
        {"n_threads_batch", params.n_threads_batch},
        {"total_threads",   std::thread::hardware_concurrency()},
        {"system_info",     llama_print_system_info()},
    });

    std::unique_ptr<httplib::Server> svr;
#ifdef CPPHTTPLIB_OPENSSL_SUPPORT
    if (sparams.ssl_key_file != "" && sparams.ssl_cert_file != "") {
        LOG_INFO("Running with SSL", {{"key", sparams.ssl_key_file}, {"cert", sparams.ssl_cert_file}});
        svr.reset(
            new httplib::SSLServer(sparams.ssl_cert_file.c_str(), sparams.ssl_key_file.c_str())
        );
    } else {
        LOG_INFO("Running without SSL", {});
        svr.reset(new httplib::Server());
    }
#else
    svr.reset(new httplib::Server());
#endif

    std::atomic<server_state> state{SERVER_STATE_LOADING_MODEL};

    svr->set_default_headers({{"Server", "llama.cpp"}});

    // CORS preflight
    svr->Options(R"(.*)", [](const httplib::Request & req, httplib::Response & res) {
        res.set_header("Access-Control-Allow-Origin",      req.get_header_value("Origin"));
        res.set_header("Access-Control-Allow-Credentials", "true");
        res.set_header("Access-Control-Allow-Methods",     "POST");
        res.set_header("Access-Control-Allow-Headers",     "*");
        return res.set_content("", "application/json; charset=utf-8");
    });

    svr->set_logger(log_server_request);

    auto res_error = [](httplib::Response & res, json error_data) {
        json final_response {{"error", error_data}};
        res.set_content(final_response.dump(), "application/json; charset=utf-8");
        res.status = json_value(error_data, "code", 500);
    };

    svr->set_exception_handler([&res_error](const httplib::Request &, httplib::Response & res, std::exception_ptr ep) {
        std::string message;
        try {
            std::rethrow_exception(std::move(ep));
        } catch (std::exception & e) {
            message = e.what();
        } catch (...) {
            message = "Unknown Exception";
        }

        json formatted_error = format_error_response(message, ERROR_TYPE_SERVER);
        LOG_VERBOSE("Got exception", formatted_error);
        res_error(res, formatted_error);
    });

    svr->set_error_handler([&res_error](const httplib::Request &, httplib::Response & res) {
        if (res.status == 404) {
            res_error(res, format_error_response("File Not Found", ERROR_TYPE_NOT_FOUND));
        }
        // for other error codes, we skip processing here because it's already done by res_error()
    });

    // set timeouts and change hostname and port
    svr->set_read_timeout (sparams.read_timeout);
    svr->set_write_timeout(sparams.write_timeout);

    if (!svr->bind_to_port(sparams.hostname, sparams.port)) {
        fprintf(stderr, "\ncouldn't bind to server socket: hostname=%s port=%d\n\n", sparams.hostname.c_str(), sparams.port);
        return 1;
    }

    std::unordered_map<std::string, std::string> log_data;

    log_data["hostname"] = sparams.hostname;
    log_data["port"]     = std::to_string(sparams.port);

    if (sparams.api_keys.size() == 1) {
        auto key = sparams.api_keys[0];
        log_data["api_key"] = "api_key: ****" + key.substr(std::max((int)(key.length() - 4), 0));
    } else if (sparams.api_keys.size() > 1) {
        log_data["api_key"] = "api_key: " + std::to_string(sparams.api_keys.size()) + " keys loaded";
    }

    // load the model
    if (!ctx_server.load_model(params)) {
        state.store(SERVER_STATE_ERROR);
        return 1;
    } else {
        ctx_server.init();
        state.store(SERVER_STATE_READY);
    }

    LOG_INFO("model loaded", {});

    const auto model_meta = ctx_server.model_meta();

    // if a custom chat template is not supplied, we will use the one that comes with the model (if any)
    if (sparams.chat_template.empty()) {
        if (!ctx_server.validate_model_chat_template()) {
            LOG_ERROR("The chat template that comes with this model is not yet supported, falling back to chatml. This may cause the model to output suboptimal responses", {});
            sparams.chat_template = "chatml";
        }
    }

    // print sample chat example to make it clear which template is used
    {
        json chat;
        chat.push_back({{"role", "system"},    {"content", "You are a helpful assistant"}});
        chat.push_back({{"role", "user"},      {"content", "Hello"}});
        chat.push_back({{"role", "assistant"}, {"content", "Hi there"}});
        chat.push_back({{"role", "user"},      {"content", "How are you?"}});

        const std::string chat_example = format_chat(ctx_server.model, sparams.chat_template, chat);

        LOG_INFO("chat template", {
            {"chat_example", chat_example},
            {"built_in", sparams.chat_template.empty()},
        });
    }

    //
    // Middlewares
    //

    auto middleware_validate_api_key = [&sparams, &res_error](const httplib::Request & req, httplib::Response & res) {
        // TODO: should we apply API key to all endpoints, including "/health" and "/models"?
        static const std::set<std::string> protected_endpoints = {
            "/props",
            "/completion",
            "/completions",
            "/v1/completions",
            "/chat/completions",
            "/v1/chat/completions",
            "/infill",
            "/tokenize",
            "/detokenize",
            "/embedding",
            "/embeddings",
            "/v1/embeddings",
        };

        // If API key is not set, skip validation
        if (sparams.api_keys.empty()) {
            return true;
        }

        // If path is not in protected_endpoints list, skip validation
        if (protected_endpoints.find(req.path) == protected_endpoints.end()) {
            return true;
        }

        // Check for API key in the header
        auto auth_header = req.get_header_value("Authorization");

        std::string prefix = "Bearer ";
        if (auth_header.substr(0, prefix.size()) == prefix) {
            std::string received_api_key = auth_header.substr(prefix.size());
            if (std::find(sparams.api_keys.begin(), sparams.api_keys.end(), received_api_key) != sparams.api_keys.end()) {
                return true; // API key is valid
            }
        }

        // API key is invalid or not provided
        // TODO: make another middleware for CORS related logic
        res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
        res_error(res, format_error_response("Invalid API Key", ERROR_TYPE_AUTHENTICATION));

        LOG_WARNING("Unauthorized: Invalid API Key", {});

        return false;
    };

    // register server middlewares
    svr->set_pre_routing_handler([&middleware_validate_api_key](const httplib::Request & req, httplib::Response & res) {
        if (!middleware_validate_api_key(req, res)) {
            return httplib::Server::HandlerResponse::Handled;
        }
        return httplib::Server::HandlerResponse::Unhandled;
    });

    //
    // Route handlers (or controllers)
    //

    const auto handle_health = [&](const httplib::Request & req, httplib::Response & res) {
        server_state current_state = state.load();
        switch (current_state) {
            case SERVER_STATE_READY:
                {
                    // request slots data using task queue
                    server_task task;
                    task.id   = ctx_server.queue_tasks.get_new_id();
                    task.type = SERVER_TASK_TYPE_METRICS;
                    task.id_target = -1;

                    ctx_server.queue_results.add_waiting_task_id(task.id);
                    ctx_server.queue_tasks.post(task);

                    // get the result
                    server_task_result result = ctx_server.queue_results.recv(task.id);
                    ctx_server.queue_results.remove_waiting_task_id(task.id);

                    const int n_idle_slots       = result.data["idle"];
                    const int n_processing_slots = result.data["processing"];

                    json health = {
                        {"status",           "ok"},
                        {"slots_idle",       n_idle_slots},
                        {"slots_processing", n_processing_slots}
                    };

                    res.status = 200; // HTTP OK
                    if (sparams.slots_endpoint && req.has_param("include_slots")) {
                        health["slots"] = result.data["slots"];
                    }

                    if (n_idle_slots == 0) {
                        health["status"] = "no slot available";
                        if (req.has_param("fail_on_no_slot")) {
                            res.status = 503; // HTTP Service Unavailable
                        }
                    }

                    res.set_content(health.dump(), "application/json");
                    break;
                }
            case SERVER_STATE_LOADING_MODEL:
                {
                    res_error(res, format_error_response("Loading model", ERROR_TYPE_UNAVAILABLE));
                } break;
            case SERVER_STATE_ERROR:
                {
                    res_error(res, format_error_response("Model failed to load", ERROR_TYPE_SERVER));
                } break;
        }
    };

    const auto handle_slots = [&](const httplib::Request &, httplib::Response & res) {
        if (!sparams.slots_endpoint) {
            res_error(res, format_error_response("This server does not support slots endpoint.", ERROR_TYPE_NOT_SUPPORTED));
            return;
        }

        // request slots data using task queue
        server_task task;
        task.id = ctx_server.queue_tasks.get_new_id();
        task.id_multi  = -1;
        task.id_target = -1;
        task.type = SERVER_TASK_TYPE_METRICS;

        ctx_server.queue_results.add_waiting_task_id(task.id);
        ctx_server.queue_tasks.post(task);

        // get the result
        server_task_result result = ctx_server.queue_results.recv(task.id);
        ctx_server.queue_results.remove_waiting_task_id(task.id);

        res.set_content(result.data["slots"].dump(), "application/json");
        res.status = 200; // HTTP OK
    };

    const auto handle_metrics = [&](const httplib::Request &, httplib::Response & res) {
        if (!sparams.metrics_endpoint) {
            res_error(res, format_error_response("This server does not support metrics endpoint.", ERROR_TYPE_NOT_SUPPORTED));
            return;
        }

        // request slots data using task queue
        server_task task;
        task.id = ctx_server.queue_tasks.get_new_id();
        task.id_multi  = -1;
        task.id_target = -1;
        task.type = SERVER_TASK_TYPE_METRICS;
        task.data.push_back({{"reset_bucket", true}});

        ctx_server.queue_results.add_waiting_task_id(task.id);
        ctx_server.queue_tasks.post(task);

        // get the result
        server_task_result result = ctx_server.queue_results.recv(task.id);
        ctx_server.queue_results.remove_waiting_task_id(task.id);

        json data = result.data;

        const uint64_t n_prompt_tokens_processed = data["n_prompt_tokens_processed"];
        const uint64_t t_prompt_processing       = data["t_prompt_processing"];

        const uint64_t n_tokens_predicted  = data["n_tokens_predicted"];
        const uint64_t t_tokens_generation = data["t_tokens_generation"];

        const int32_t kv_cache_used_cells = data["kv_cache_used_cells"];

        // metrics definition: https://prometheus.io/docs/practices/naming/#metric-names
        json all_metrics_def = json {
            {"counter", {{
                    {"name",  "prompt_tokens_total"},
                    {"help",  "Number of prompt tokens processed."},
                    {"value",  (uint64_t) data["n_prompt_tokens_processed_total"]}
            }, {
                    {"name",  "prompt_seconds_total"},
                    {"help",  "Prompt process time"},
                    {"value",  (uint64_t) data["t_prompt_processing_total"] / 1.e3}
            }, {
                    {"name",  "tokens_predicted_total"},
                    {"help",  "Number of generation tokens processed."},
                    {"value",  (uint64_t) data["n_tokens_predicted_total"]}
            }, {
                    {"name",  "tokens_predicted_seconds_total"},
                    {"help",  "Predict process time"},
                    {"value",  (uint64_t) data["t_tokens_generation_total"] / 1.e3}
            }}},
            {"gauge", {{
                    {"name",  "prompt_tokens_seconds"},
                    {"help",  "Average prompt throughput in tokens/s."},
                    {"value",  n_prompt_tokens_processed ? 1.e3 / t_prompt_processing * n_prompt_tokens_processed : 0.}
            },{
                    {"name",  "predicted_tokens_seconds"},
                    {"help",  "Average generation throughput in tokens/s."},
                    {"value",  n_tokens_predicted ? 1.e3 / t_tokens_generation * n_tokens_predicted : 0.}
            },{
                    {"name",  "kv_cache_usage_ratio"},
                    {"help",  "KV-cache usage. 1 means 100 percent usage."},
                    {"value",  1. * kv_cache_used_cells / params.n_ctx}
            },{
                    {"name",  "kv_cache_tokens"},
                    {"help",  "KV-cache tokens."},
                    {"value",  (uint64_t) data["kv_cache_tokens_count"]}
            },{
                    {"name",  "requests_processing"},
                    {"help",  "Number of request processing."},
                    {"value",  (uint64_t) data["processing"]}
            },{
                    {"name",  "requests_deferred"},
                    {"help",  "Number of request deferred."},
                    {"value",  (uint64_t) data["deferred"]}
            }}}
        };

        std::stringstream prometheus;

        for (const auto & el : all_metrics_def.items()) {
            const auto & type        = el.key();
            const auto & metrics_def = el.value();

            for (const auto & metric_def : metrics_def) {
                const std::string name = metric_def["name"];
                const std::string help = metric_def["help"];

                auto value = json_value(metric_def, "value", 0.);
                prometheus << "# HELP llamacpp:" << name << " " << help  << "\n"
                            << "# TYPE llamacpp:" << name << " " << type  << "\n"
                            << "llamacpp:"        << name << " " << value << "\n";
            }
        }

        const int64_t t_start = data["t_start"];
        res.set_header("Process-Start-Time-Unix", std::to_string(t_start));

        res.set_content(prometheus.str(), "text/plain; version=0.0.4");
        res.status = 200; // HTTP OK
    };

    const auto handle_props = [&ctx_server](const httplib::Request & req, httplib::Response & res) {
        res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
        json data = {
            { "user_name",                   ctx_server.name_user.c_str() },
            { "assistant_name",              ctx_server.name_assistant.c_str() },
            { "default_generation_settings", ctx_server.default_generation_settings_for_props },
            { "total_slots",                 ctx_server.params.n_parallel }
        };

        res.set_content(data.dump(), "application/json; charset=utf-8");
    };

    const auto handle_completions = [&ctx_server, &res_error](const httplib::Request & req, httplib::Response & res) {
        res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));

        json data = json::parse(req.body);

        const int id_task = ctx_server.queue_tasks.get_new_id();

        ctx_server.queue_results.add_waiting_task_id(id_task);
        ctx_server.request_completion(id_task, -1, data, false, false);

        if (!json_value(data, "stream", false)) {
            server_task_result result = ctx_server.queue_results.recv(id_task);
            if (!result.error && result.stop) {
                res.set_content(result.data.dump(-1, ' ', false, json::error_handler_t::replace), "application/json; charset=utf-8");
            } else {
                res_error(res, result.data);
            }

            ctx_server.queue_results.remove_waiting_task_id(id_task);
        } else {
            const auto chunked_content_provider = [id_task, &ctx_server](size_t, httplib::DataSink & sink) {
                while (true) {
                    server_task_result result = ctx_server.queue_results.recv(id_task);
                    if (!result.error) {
                        const std::string str =
                            "data: " +
                            result.data.dump(-1, ' ', false, json::error_handler_t::replace) +
                            "\n\n";

                        LOG_VERBOSE("data stream", {
                            { "to_send", str }
                        });

                        if (!sink.write(str.c_str(), str.size())) {
                            ctx_server.queue_results.remove_waiting_task_id(id_task);
                            return false;
                        }

                        if (result.stop) {
                            break;
                        }
                    } else {
                        const std::string str =
                            "error: " +
                            result.data.dump(-1, ' ', false, json::error_handler_t::replace) +
                            "\n\n";

                        LOG_VERBOSE("data stream", {
                            { "to_send", str }
                        });

                        if (!sink.write(str.c_str(), str.size())) {
                            ctx_server.queue_results.remove_waiting_task_id(id_task);
                            return false;
                        }

                        break;
                    }
                }

                ctx_server.queue_results.remove_waiting_task_id(id_task);
                sink.done();

                return true;
            };

            auto on_complete = [id_task, &ctx_server] (bool) {
                // cancel
                ctx_server.request_cancel(id_task);
                ctx_server.queue_results.remove_waiting_task_id(id_task);
            };

            res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete);
        }
    };

    const auto handle_models = [&params, &model_meta](const httplib::Request & req, httplib::Response & res) {
        res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));

        json models = {
            {"object", "list"},
            {"data", {
                 {
                     {"id",       params.model_alias},
                     {"object",   "model"},
                     {"created",  std::time(0)},
                     {"owned_by", "llamacpp"},
                     {"meta",     model_meta}
                 },
             }}
        };

        res.set_content(models.dump(), "application/json; charset=utf-8");
    };

    const auto handle_chat_completions = [&ctx_server, &sparams, &res_error](const httplib::Request & req, httplib::Response & res) {
        res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
        json data = oaicompat_completion_params_parse(ctx_server.model, json::parse(req.body), sparams.chat_template);

        const int id_task = ctx_server.queue_tasks.get_new_id();

        ctx_server.queue_results.add_waiting_task_id(id_task);
        ctx_server.request_completion(id_task, -1, data, false, false);

        const auto completion_id = gen_chatcmplid();
        if (!json_value(data, "stream", false)) {
            server_task_result result = ctx_server.queue_results.recv(id_task);

            if (!result.error && result.stop) {
                json result_oai = format_final_response_oaicompat(data, result.data, completion_id);

                res.set_content(result_oai.dump(-1, ' ', false, json::error_handler_t::replace), "application/json; charset=utf-8");
            } else {
                res_error(res, result.data);
            }
            ctx_server.queue_results.remove_waiting_task_id(id_task);
        } else {
            const auto chunked_content_provider = [id_task, &ctx_server, completion_id](size_t, httplib::DataSink & sink) {
                while (true) {
                    server_task_result result = ctx_server.queue_results.recv(id_task);
                    if (!result.error) {
                        std::vector<json> result_array = format_partial_response_oaicompat(result.data, completion_id);

                        for (auto it = result_array.begin(); it != result_array.end(); ++it) {
                            if (!it->empty()) {
                                const std::string str =
                                    "data: " +
                                    it->dump(-1, ' ', false, json::error_handler_t::replace) +
                                    "\n\n";
                                LOG_VERBOSE("data stream", {{"to_send", str}});
                                if (!sink.write(str.c_str(), str.size())) {
                                    ctx_server.queue_results.remove_waiting_task_id(id_task);
                                    return false;
                                }
                            }
                        }
                        if (result.stop) {
                            break;
                        }
                    } else {
                        const std::string str =
                            "error: " +
                            result.data.dump(-1, ' ', false, json::error_handler_t::replace) +
                            "\n\n";
                        LOG_VERBOSE("data stream", {{"to_send", str}});
                        if (!sink.write(str.c_str(), str.size())) {
                            ctx_server.queue_results.remove_waiting_task_id(id_task);
                            return false;
                        }
                        break;
                    }
                }
                sink.done();
                ctx_server.queue_results.remove_waiting_task_id(id_task);
                return true;
            };

            auto on_complete = [id_task, &ctx_server](bool) {
                // cancel request
                ctx_server.request_cancel(id_task);
                ctx_server.queue_results.remove_waiting_task_id(id_task);
            };

            res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete);
        }
    };

    const auto handle_infill = [&ctx_server, &res_error](const httplib::Request & req, httplib::Response & res) {
        res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));

        json data = json::parse(req.body);

        const int id_task = ctx_server.queue_tasks.get_new_id();

        ctx_server.queue_results.add_waiting_task_id(id_task);
        ctx_server.request_completion(id_task, -1, data, true, false);

        if (!json_value(data, "stream", false)) {
            server_task_result result = ctx_server.queue_results.recv(id_task);
            if (!result.error && result.stop) {
                res.set_content(result.data.dump(-1, ' ', false, json::error_handler_t::replace), "application/json; charset=utf-8");
            } else {
                res_error(res, result.data);
            }

            ctx_server.queue_results.remove_waiting_task_id(id_task);
        } else {
            const auto chunked_content_provider = [id_task, &ctx_server](size_t, httplib::DataSink & sink) {
                while (true) {
                    server_task_result result = ctx_server.queue_results.recv(id_task);
                    if (!result.error) {
                        const std::string str =
                            "data: " +
                            result.data.dump(-1, ' ', false, json::error_handler_t::replace) +
                            "\n\n";

                        LOG_VERBOSE("data stream", {
                            { "to_send", str }
                        });

                        if (!sink.write(str.c_str(), str.size())) {
                            ctx_server.queue_results.remove_waiting_task_id(id_task);
                            return false;
                        }

                        if (result.stop) {
                            break;
                        }
                    } else {
                        break;
                    }
                }

                ctx_server.queue_results.remove_waiting_task_id(id_task);
                sink.done();

                return true;
            };

            auto on_complete = [id_task, &ctx_server] (bool) {
                ctx_server.request_cancel(id_task);
            };

            res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete);
        }
    };

    const auto handle_tokenize = [&ctx_server](const httplib::Request & req, httplib::Response & res) {
        res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
        const json body = json::parse(req.body);

        std::vector<llama_token> tokens;
        if (body.count("content") != 0) {
            tokens = ctx_server.tokenize(body["content"], false);
        }
        const json data = format_tokenizer_response(tokens);
        return res.set_content(data.dump(), "application/json; charset=utf-8");
    };

    const auto handle_detokenize = [&ctx_server](const httplib::Request & req, httplib::Response & res) {
        res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
        const json body = json::parse(req.body);

        std::string content;
        if (body.count("tokens") != 0) {
            const std::vector<llama_token> tokens = body["tokens"];
            content = tokens_to_str(ctx_server.ctx, tokens.cbegin(), tokens.cend());
        }

        const json data = format_detokenized_response(content);
        return res.set_content(data.dump(), "application/json; charset=utf-8");
    };

    const auto handle_embeddings = [&params, &ctx_server, &res_error](const httplib::Request & req, httplib::Response & res) {
        res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
        if (!params.embedding) {
            res.status = 501;
            res.set_content("This server does not support embeddings. Start it with `--embeddings`", "text/plain; charset=utf-8");
            return;
        }

        const json body = json::parse(req.body);
        bool is_openai = false;

        // an input prompt can be a string or a list of tokens (integer)
        json prompt;
        if (body.count("input") != 0) {
            is_openai = true;
            prompt = body["input"];
        } else if (body.count("content") != 0) {
            // with "content", we only support single prompt
            prompt = std::vector<std::string>{body["content"]};
        } else {
            res_error(res, format_error_response("\"input\" or \"content\" must be provided", ERROR_TYPE_INVALID_REQUEST));
            return;
        }

        // create and queue the task
        json responses;
        {
            const int id_task = ctx_server.queue_tasks.get_new_id();
            ctx_server.queue_results.add_waiting_task_id(id_task);
            ctx_server.request_completion(id_task, -1, {{"prompt", prompt}}, false, true);

            // get the result
            server_task_result result = ctx_server.queue_results.recv(id_task);
            ctx_server.queue_results.remove_waiting_task_id(id_task);
            if (!result.error) {
                if (result.data.count("results")) {
                    // result for multi-task
                    responses = result.data["results"];
                } else {
                    // result for single task
                    responses = std::vector<json>{result.data};
                }
            } else {
                // error received, ignore everything else
                res_error(res, result.data);
                return;
            }
        }

        // write JSON response
        json root = is_openai
            ? format_embeddings_response_oaicompat(body, responses)
            : responses[0];
        return res.set_content(root.dump(), "application/json; charset=utf-8");
    };

    auto handle_static_file = [](unsigned char * content, size_t len, const char * mime_type) {
        return [content, len, mime_type](const httplib::Request &, httplib::Response & res) {
            res.set_content(reinterpret_cast<const char*>(content), len, mime_type);
            return false;
        };
    };

    //
    // Router
    //

    // register static assets routes
    if (!sparams.public_path.empty()) {
        // Set the base directory for serving static files
        svr->set_base_dir(sparams.public_path);
    }

    // using embedded static files
    svr->Get("/", handle_static_file(index_html, index_html_len, "text/html; charset=utf-8"));
    svr->Get("/index.js", handle_static_file(index_js, index_js_len, "text/javascript; charset=utf-8"));
    svr->Get("/completion.js", handle_static_file(completion_js, completion_js_len, "text/javascript; charset=utf-8"));
    svr->Get("/json-schema-to-grammar.mjs", handle_static_file(
        json_schema_to_grammar_mjs, json_schema_to_grammar_mjs_len, "text/javascript; charset=utf-8"));

    // register API routes
    svr->Get ("/health",              handle_health);
    svr->Get ("/slots",               handle_slots);
    svr->Get ("/metrics",             handle_metrics);
    svr->Get ("/props",               handle_props);
    svr->Get ("/v1/models",           handle_models);
    svr->Post("/completion",          handle_completions); // legacy
    svr->Post("/completions",         handle_completions);
    svr->Post("/v1/completions",      handle_completions);
    svr->Post("/chat/completions",    handle_chat_completions);
    svr->Post("/v1/chat/completions", handle_chat_completions);
    svr->Post("/infill",              handle_infill);
    svr->Post("/embedding",           handle_embeddings); // legacy
    svr->Post("/embeddings",          handle_embeddings);
    svr->Post("/v1/embeddings",       handle_embeddings);
    svr->Post("/tokenize",            handle_tokenize);
    svr->Post("/detokenize",          handle_detokenize);

    //
    // Start the server
    //
    if (sparams.n_threads_http < 1) {
        // +2 threads for monitoring endpoints
        sparams.n_threads_http = std::max(params.n_parallel + 2, (int32_t) std::thread::hardware_concurrency() - 1);
    }
    log_data["n_threads_http"] =  std::to_string(sparams.n_threads_http);
    svr->new_task_queue = [&sparams] { return new httplib::ThreadPool(sparams.n_threads_http); };

    LOG_INFO("HTTP server listening", log_data);

    // run the HTTP server in a thread - see comment below
    std::thread t([&]() {
        if (!svr->listen_after_bind()) {
            state.store(SERVER_STATE_ERROR);
            return 1;
        }

        return 0;
    });

    ctx_server.queue_tasks.on_new_task(std::bind(
        &server_context::process_single_task, &ctx_server, std::placeholders::_1));
    ctx_server.queue_tasks.on_finish_multitask(std::bind(
        &server_context::on_finish_multitask, &ctx_server, std::placeholders::_1));
    ctx_server.queue_tasks.on_update_slots(std::bind(
        &server_context::update_slots, &ctx_server));
    ctx_server.queue_results.on_multitask_update(std::bind(
        &server_queue::update_multitask,
        &ctx_server.queue_tasks,
        std::placeholders::_1,
        std::placeholders::_2,
        std::placeholders::_3
    ));

    shutdown_handler = [&](int) {
        ctx_server.queue_tasks.terminate();
    };

#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
    struct sigaction sigint_action;
    sigint_action.sa_handler = signal_handler;
    sigemptyset (&sigint_action.sa_mask);
    sigint_action.sa_flags = 0;
    sigaction(SIGINT, &sigint_action, NULL);
#elif defined (_WIN32)
    auto console_ctrl_handler = +[](DWORD ctrl_type) -> BOOL {
        return (ctrl_type == CTRL_C_EVENT) ? (signal_handler(SIGINT), true) : false;
    };
    SetConsoleCtrlHandler(reinterpret_cast<PHANDLER_ROUTINE>(console_ctrl_handler), true);
#endif

    ctx_server.queue_tasks.start_loop();

    svr->stop();
    t.join();

    llama_backend_free();

    return 0;
}