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Diffstat (limited to 'llama.cpp/common/arg.cpp')
| -rw-r--r-- | llama.cpp/common/arg.cpp | 3799 |
1 files changed, 3799 insertions, 0 deletions
diff --git a/llama.cpp/common/arg.cpp b/llama.cpp/common/arg.cpp new file mode 100644 index 0000000..9c85696 --- /dev/null +++ b/llama.cpp/common/arg.cpp @@ -0,0 +1,3799 @@ +#include "arg.h" + +#include "chat.h" +#include "common.h" +#include "download.h" +#include "json-schema-to-grammar.h" +#include "log.h" +#include "sampling.h" +#include "speculative.h" +#include "preset.h" + +// fix problem with std::min and std::max +#if defined(_WIN32) +#define WIN32_LEAN_AND_MEAN +#ifndef NOMINMAX +# define NOMINMAX +#endif +#include <windows.h> +#endif + +#define JSON_ASSERT GGML_ASSERT +#include <nlohmann/json.hpp> + +#include <algorithm> +#include <cinttypes> +#include <climits> +#include <cstdarg> +#include <fstream> +#include <list> +#include <regex> +#include <set> +#include <string> +#include <thread> // for hardware_concurrency +#include <vector> + +#ifndef __EMSCRIPTEN__ +#ifdef __linux__ +#include <linux/limits.h> +#elif defined(_WIN32) +# if !defined(PATH_MAX) +# define PATH_MAX MAX_PATH +# endif +#elif defined(_AIX) +#include <sys/limits.h> +#else +#include <sys/syslimits.h> +#endif +#endif + +#define LLAMA_MAX_URL_LENGTH 2084 // Maximum URL Length in Chrome: 2083 + +extern const char * LICENSES[]; + +using json = nlohmann::ordered_json; +using namespace common_arg_utils; + +static std::initializer_list<enum llama_example> mmproj_examples = { + LLAMA_EXAMPLE_MTMD, + LLAMA_EXAMPLE_SERVER, + LLAMA_EXAMPLE_CLI, +}; + +static std::string read_file(const std::string & fname) { + std::ifstream file(fname); + if (!file) { + throw std::runtime_error(string_format("error: failed to open file '%s'\n", fname.c_str())); + } + std::string content((std::istreambuf_iterator<char>(file)), std::istreambuf_iterator<char>()); + file.close(); + return content; +} + +static const std::vector<common_arg> & get_common_arg_defs() { + static const std::vector<common_arg> options = [] { + common_params params; + auto ctx = common_params_parser_init(params, LLAMA_EXAMPLE_SERVER, nullptr); + return ctx.options; + }(); + return options; +} + +common_arg & common_arg::set_examples(std::initializer_list<enum llama_example> examples) { + this->examples = examples; + return *this; +} + +common_arg & common_arg::set_excludes(std::initializer_list<enum llama_example> excludes) { + this->excludes = excludes; + return *this; +} + +common_arg & common_arg::set_env(const char * env) { + help = help + "\n(env: " + env + ")"; + this->env = env; + return *this; +} + +common_arg & common_arg::set_sparam() { + is_sparam = true; + return *this; +} + +common_arg & common_arg::set_preset_only() { + is_preset_only = true; + return *this; +} + +bool common_arg::in_example(enum llama_example ex) { + return examples.find(ex) != examples.end(); +} + +bool common_arg::is_exclude(enum llama_example ex) { + return excludes.find(ex) != excludes.end(); +} + +bool common_arg::get_value_from_env(std::string & output) const { + if (env == nullptr) return false; + if (!args_neg.empty()) { + // for compatibility, we need to check LLAMA_ARG_NO_ env as well + std::string neg_env = env; + string_replace_all(neg_env, "LLAMA_ARG_", "LLAMA_ARG_NO_"); + char * neg_value = std::getenv(neg_env.c_str()); + if (neg_value) { + output = "0"; // falsey + return true; + } + } + char * value = std::getenv(env); + if (value) { + output = value; + return true; + } + return false; +} + +bool common_arg::has_value_from_env() const { + if (env != nullptr && !args_neg.empty()) { + // for compatibility, we need to check LLAMA_ARG_NO_ env as well + std::string neg_env = env; + string_replace_all(neg_env, "LLAMA_ARG_", "LLAMA_ARG_NO_"); + if (std::getenv(neg_env.c_str())) { + return true; + } + } + return env != nullptr && std::getenv(env); +} + +static std::vector<std::string> break_str_into_lines(std::string input, size_t max_char_per_line) { + std::vector<std::string> result; + std::istringstream iss(input); + std::string line; + auto add_line = [&](const std::string& l) { + if (l.length() <= max_char_per_line) { + result.push_back(l); + } else { + std::istringstream line_stream(l); + std::string word, current_line; + while (line_stream >> word) { + if (current_line.length() + !current_line.empty() + word.length() > max_char_per_line) { + if (!current_line.empty()) result.push_back(current_line); + current_line = word; + } else { + current_line += (!current_line.empty() ? " " : "") + word; + } + } + if (!current_line.empty()) result.push_back(current_line); + } + }; + while (std::getline(iss, line)) { + add_line(line); + } + return result; +} + +std::string common_arg::to_string() const { + // params for printing to console + const static int n_leading_spaces = 40; + const static int n_char_per_line_help = 70; // TODO: detect this based on current console + std::string leading_spaces(n_leading_spaces, ' '); + + std::ostringstream ss; + auto all_args = get_args(); // also contains args_neg + for (const auto & arg : all_args) { + if (arg == all_args.front()) { + if (all_args.size() == 1) { + ss << arg; + } else { + // first arg is usually abbreviation, we need padding to make it more beautiful + auto tmp = std::string(arg) + ", "; + auto spaces = std::string(std::max(0, 7 - (int)tmp.size()), ' '); + ss << tmp << spaces; + } + } else { + ss << arg << (arg != all_args.back() ? ", " : ""); + } + } + if (value_hint) ss << " " << value_hint; + if (value_hint_2) ss << " " << value_hint_2; + if (ss.tellp() > n_leading_spaces - 3) { + // current line is too long, add new line + ss << "\n" << leading_spaces; + } else { + // padding between arg and help, same line + ss << std::string(leading_spaces.size() - ss.tellp(), ' '); + } + const auto help_lines = break_str_into_lines(help, n_char_per_line_help); + for (const auto & line : help_lines) { + ss << (&line == &help_lines.front() ? "" : leading_spaces) << line << "\n"; + } + return ss.str(); +} + +std::vector<std::string> common_arg::get_args() const { + std::vector<std::string> result; + for (const auto & arg : args) { + result.push_back(std::string(arg)); + } + for (const auto & arg : args_neg) { + result.push_back(std::string(arg)); + } + return result; +} + +std::vector<std::string> common_arg::get_env() const { + std::vector<std::string> result; + if (env) { + result.push_back(std::string(env)); + } + if (!args_neg.empty() && env) { + // for compatibility, we need to add LLAMA_ARG_NO_ variant + std::string neg_env = env; + string_replace_all(neg_env, "LLAMA_ARG_", "LLAMA_ARG_NO_"); + result.push_back(neg_env); + } + return result; +} + +// +// utils +// + +// Helper function to parse tensor buffer override strings +static void parse_tensor_buffer_overrides(const std::string & value, std::vector<llama_model_tensor_buft_override> & overrides) { + std::map<std::string, ggml_backend_buffer_type_t> buft_list; + for (size_t i = 0; i < ggml_backend_dev_count(); ++i) { + auto * dev = ggml_backend_dev_get(i); + auto * buft = ggml_backend_dev_buffer_type(dev); + if (buft) { + buft_list[ggml_backend_buft_name(buft)] = buft; + } + } + + for (const auto & override : string_split<std::string>(value, ',')) { + std::string::size_type pos = override.find('='); + if (pos == std::string::npos) { + throw std::invalid_argument("invalid value"); + } + std::string tensor_name = override.substr(0, pos); + std::string buffer_type = override.substr(pos + 1); + + if (buft_list.find(buffer_type) == buft_list.end()) { + printf("Available buffer types:\n"); + for (const auto & it : buft_list) { + printf(" %s\n", ggml_backend_buft_name(it.second)); + } + throw std::invalid_argument("unknown buffer type"); + } + // keep strings alive and avoid leaking memory by storing them in a static vector + static std::list<std::string> buft_overrides; + buft_overrides.push_back(tensor_name); + overrides.push_back({buft_overrides.back().c_str(), buft_list.at(buffer_type)}); + } +} + +static std::string clean_file_name(const std::string & fname) { + std::string clean_fname = fname; + string_replace_all(clean_fname, "\\", "_"); + string_replace_all(clean_fname, "/", "_"); + return clean_fname; +} + +static bool common_params_handle_remote_preset(common_params & params, llama_example ex) { + GGML_ASSERT(!params.model.hf_repo.empty()); + + // the returned hf_repo is without tag + auto [hf_repo, hf_tag] = common_download_split_repo_tag(params.model.hf_repo); + + // "latest" tag (default if not specified) is translated to "default" preset + if (hf_tag == "latest") { + hf_tag = "default"; + } + + const bool offline = params.offline; + std::string model_endpoint = get_model_endpoint(); + auto preset_url = model_endpoint + hf_repo + "/resolve/main/preset.ini"; + + // prepare local path for caching + auto preset_fname = clean_file_name(hf_repo + "_preset.ini"); + auto preset_path = fs_get_cache_file(preset_fname); + const int status = common_download_file_single(preset_url, preset_path, params.hf_token, offline); + const bool has_preset = status >= 200 && status < 400; + + // remote preset is optional, so we don't error out if not found + if (has_preset) { + LOG_INF("applying remote preset from %s\n", preset_url.c_str()); + common_preset_context ctx(ex, /* only_remote_allowed */ true); + common_preset global; + auto remote_presets = ctx.load_from_ini(preset_path, global); + remote_presets = ctx.cascade(global, remote_presets); + if (remote_presets.find(hf_tag) != remote_presets.end()) { + common_preset preset = remote_presets.at(hf_tag); + LOG_INF("\n%s", preset.to_ini().c_str()); // to_ini already added trailing newline + preset.apply_to_params(params); + } else { + throw std::runtime_error("Remote preset.ini does not contain [" + std::string(hf_tag) + "] section"); + } + } else { + LOG_INF("%s", "no remote preset found, skipping\n"); + } + + return has_preset; +} + +struct handle_model_result { + bool found_mmproj = false; + common_params_model mmproj; +}; + +static handle_model_result common_params_handle_model( + struct common_params_model & model, + const std::string & bearer_token, + bool offline) { + handle_model_result result; + // handle pre-fill default model path and url based on hf_repo and hf_file + { + if (!model.docker_repo.empty()) { // Handle Docker URLs by resolving them to local paths + model.path = common_docker_resolve_model(model.docker_repo); + model.name = model.docker_repo; // set name for consistency + } else if (!model.hf_repo.empty()) { + // short-hand to avoid specifying --hf-file -> default it to --model + if (model.hf_file.empty()) { + if (model.path.empty()) { + auto auto_detected = common_get_hf_file(model.hf_repo, bearer_token, offline); + if (auto_detected.repo.empty() || auto_detected.ggufFile.empty()) { + exit(1); // error message already printed + } + model.name = model.hf_repo; // repo name with tag + model.hf_repo = auto_detected.repo; // repo name without tag + model.hf_file = auto_detected.ggufFile; + if (!auto_detected.mmprojFile.empty()) { + result.found_mmproj = true; + result.mmproj.hf_repo = model.hf_repo; + result.mmproj.hf_file = auto_detected.mmprojFile; + } + } else { + model.hf_file = model.path; + } + } + + std::string model_endpoint = get_model_endpoint(); + model.url = model_endpoint + model.hf_repo + "/resolve/main/" + model.hf_file; + // make sure model path is present (for caching purposes) + if (model.path.empty()) { + // this is to avoid different repo having same file name, or same file name in different subdirs + std::string filename = clean_file_name(model.hf_repo + "_" + model.hf_file); + model.path = fs_get_cache_file(filename); + } + + } else if (!model.url.empty()) { + if (model.path.empty()) { + auto f = string_split<std::string>(model.url, '#').front(); + f = string_split<std::string>(f, '?').front(); + model.path = fs_get_cache_file(string_split<std::string>(f, '/').back()); + } + + } + } + + // then, download it if needed + if (!model.url.empty()) { + bool ok = common_download_model(model, bearer_token, offline); + if (!ok) { + LOG_ERR("error: failed to download model from %s\n", model.url.c_str()); + exit(1); + } + } + + return result; +} + +const std::vector<ggml_type> kv_cache_types = { + GGML_TYPE_F32, + GGML_TYPE_F16, + GGML_TYPE_BF16, + GGML_TYPE_Q8_0, + GGML_TYPE_Q4_0, + GGML_TYPE_Q4_1, + GGML_TYPE_IQ4_NL, + GGML_TYPE_Q5_0, + GGML_TYPE_Q5_1, +}; + +static ggml_type kv_cache_type_from_str(const std::string & s) { + for (const auto & type : kv_cache_types) { + if (ggml_type_name(type) == s) { + return type; + } + } + throw std::runtime_error("Unsupported cache type: " + s); +} + +static std::string get_all_kv_cache_types() { + std::ostringstream msg; + for (const auto & type : kv_cache_types) { + msg << ggml_type_name(type) << (&type == &kv_cache_types.back() ? "" : ", "); + } + return msg.str(); +} + +static bool parse_bool_value(const std::string & value) { + if (is_truthy(value)) { + return true; + } else if (is_falsey(value)) { + return false; + } else { + throw std::invalid_argument("invalid boolean value"); + } +} + +// +// CLI argument parsing functions +// + +static bool common_params_parse_ex(int argc, char ** argv, common_params_context & ctx_arg) { + common_params & params = ctx_arg.params; + + std::unordered_map<std::string, std::pair<common_arg *, bool>> arg_to_options; + for (auto & opt : ctx_arg.options) { + for (const auto & arg : opt.args) { + arg_to_options[arg] = {&opt, /* is_positive */ true}; + } + for (const auto & arg : opt.args_neg) { + arg_to_options[arg] = {&opt, /* is_positive */ false}; + } + } + + // handle environment variables + for (auto & opt : ctx_arg.options) { + std::string value; + if (opt.get_value_from_env(value)) { + try { + if (opt.handler_void && is_truthy(value)) { + opt.handler_void(params); + } + if (opt.handler_int) { + opt.handler_int(params, std::stoi(value)); + } + if (opt.handler_bool) { + opt.handler_bool(params, parse_bool_value(value)); + } + if (opt.handler_string) { + opt.handler_string(params, value); + continue; + } + } catch (std::exception & e) { + throw std::invalid_argument(string_format( + "error while handling environment variable \"%s\": %s\n\n", opt.env, e.what())); + } + } + } + + // handle command line arguments + auto check_arg = [&](int i) { + if (i+1 >= argc) { + throw std::invalid_argument("expected value for argument"); + } + }; + + auto parse_cli_args = [&]() { + std::set<std::string> seen_args; + + for (int i = 1; i < argc; i++) { + const std::string arg_prefix = "--"; + + std::string arg = argv[i]; + if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) { + std::replace(arg.begin(), arg.end(), '_', '-'); + } + if (arg_to_options.find(arg) == arg_to_options.end()) { + throw std::invalid_argument(string_format("error: invalid argument: %s", arg.c_str())); + } + if (!seen_args.insert(arg).second) { + LOG_WRN("DEPRECATED: argument '%s' specified multiple times, use comma-separated values instead (only last value will be used)\n", arg.c_str()); + } + auto & tmp = arg_to_options[arg]; + auto opt = *tmp.first; + bool is_positive = tmp.second; + if (opt.has_value_from_env()) { + fprintf(stderr, "warn: %s environment variable is set, but will be overwritten by command line argument %s\n", opt.env, arg.c_str()); + } + try { + if (opt.handler_void) { + opt.handler_void(params); + continue; + } + if (opt.handler_bool) { + opt.handler_bool(params, is_positive); + continue; + } + + // arg with single value + check_arg(i); + std::string val = argv[++i]; + if (opt.handler_int) { + opt.handler_int(params, std::stoi(val)); + continue; + } + if (opt.handler_string) { + opt.handler_string(params, val); + continue; + } + + // arg with 2 values + check_arg(i); + std::string val2 = argv[++i]; + if (opt.handler_str_str) { + opt.handler_str_str(params, val, val2); + continue; + } + } catch (std::exception & e) { + throw std::invalid_argument(string_format( + "error while handling argument \"%s\": %s\n\n" + "usage:\n%s\n\nto show complete usage, run with -h", + arg.c_str(), e.what(), opt.to_string().c_str())); + } + } + }; + + // parse the first time to get -hf option (used for remote preset) + parse_cli_args(); + + // maybe handle remote preset + if (!params.model.hf_repo.empty()) { + std::string cli_hf_repo = params.model.hf_repo; + bool has_preset = common_params_handle_remote_preset(params, ctx_arg.ex); + + // special case: if hf_repo explicitly set by preset, we need to preserve it (ignore CLI value) + // this is useful when we have one HF repo pointing to other HF repos (one model - multiple GGUFs) + std::string preset_hf_repo = params.model.hf_repo; + bool preset_has_hf_repo = preset_hf_repo != cli_hf_repo; + + if (has_preset) { + // re-parse CLI args to override preset values + parse_cli_args(); + } + + // preserve hf_repo from preset if needed + if (preset_has_hf_repo) { + params.model.hf_repo = preset_hf_repo; + } + } + + postprocess_cpu_params(params.cpuparams, nullptr); + postprocess_cpu_params(params.cpuparams_batch, ¶ms.cpuparams); + + postprocess_cpu_params(params.speculative.cpuparams, ¶ms.cpuparams); + postprocess_cpu_params(params.speculative.cpuparams_batch, ¶ms.cpuparams_batch); + + if (params.prompt_cache_all && (params.interactive || params.interactive_first)) { + throw std::invalid_argument("error: --prompt-cache-all not supported in interactive mode yet\n"); + } + + // handle model and download + { + auto res = common_params_handle_model(params.model, params.hf_token, params.offline); + if (params.no_mmproj) { + params.mmproj = {}; + } else if (res.found_mmproj && params.mmproj.path.empty() && params.mmproj.url.empty()) { + // optionally, handle mmproj model when -hf is specified + params.mmproj = res.mmproj; + } + // only download mmproj if the current example is using it + for (const auto & ex : mmproj_examples) { + if (ctx_arg.ex == ex) { + common_params_handle_model(params.mmproj, params.hf_token, params.offline); + break; + } + } + common_params_handle_model(params.speculative.mparams_dft, params.hf_token, params.offline); + common_params_handle_model(params.vocoder.model, params.hf_token, params.offline); + } + + // model is required (except for server) + // TODO @ngxson : maybe show a list of available models in CLI in this case + if (params.model.path.empty() && ctx_arg.ex != LLAMA_EXAMPLE_SERVER && !params.usage && !params.completion) { + throw std::invalid_argument("error: --model is required\n"); + } + + if (params.escape) { + string_process_escapes(params.prompt); + string_process_escapes(params.input_prefix); + string_process_escapes(params.input_suffix); + for (auto & antiprompt : params.antiprompt) { + string_process_escapes(antiprompt); + } + for (auto & seq_breaker : params.sampling.dry_sequence_breakers) { + string_process_escapes(seq_breaker); + } + for (auto & pair : params.speculative.replacements) { + string_process_escapes(pair.first); + string_process_escapes(pair.second); + } + } + + if (!params.kv_overrides.empty()) { + params.kv_overrides.emplace_back(); + params.kv_overrides.back().key[0] = 0; + } + + // pad tensor_buft_overrides for llama_params_fit: + const size_t ntbo = llama_max_tensor_buft_overrides(); + while (params.tensor_buft_overrides.size() < ntbo) { + params.tensor_buft_overrides.push_back({nullptr, nullptr}); + } + + if (!params.speculative.tensor_buft_overrides.empty()) { + params.speculative.tensor_buft_overrides.push_back({nullptr, nullptr}); + } + + if (!params.chat_template.empty() && !common_chat_verify_template(params.chat_template, params.use_jinja)) { + throw std::runtime_error(string_format( + "error: the supplied chat template is not supported: %s%s\n", + params.chat_template.c_str(), + params.use_jinja ? "" : "\nnote: llama.cpp was started without --jinja, we only support commonly used templates" + )); + } + + common_log_set_verbosity_thold(params.verbosity); + + return true; +} + +static void common_params_print_usage(common_params_context & ctx_arg) { + auto print_options = [](std::vector<common_arg *> & options) { + for (common_arg * opt : options) { + printf("%s", opt->to_string().c_str()); + } + }; + + std::vector<common_arg *> common_options; + std::vector<common_arg *> sparam_options; + std::vector<common_arg *> specific_options; + for (auto & opt : ctx_arg.options) { + // in case multiple LLAMA_EXAMPLE_* are set, we prioritize the LLAMA_EXAMPLE_* matching current example + if (opt.is_sparam) { + sparam_options.push_back(&opt); + } else if (opt.in_example(ctx_arg.ex)) { + specific_options.push_back(&opt); + } else { + common_options.push_back(&opt); + } + } + printf("----- common params -----\n\n"); + print_options(common_options); + printf("\n\n----- sampling params -----\n\n"); + print_options(sparam_options); + // TODO: maybe convert enum llama_example to string + printf("\n\n----- example-specific params -----\n\n"); + print_options(specific_options); +} + +static void common_params_print_completion(common_params_context & ctx_arg) { + std::vector<common_arg *> common_options; + std::vector<common_arg *> sparam_options; + std::vector<common_arg *> specific_options; + + for (auto & opt : ctx_arg.options) { + if (opt.is_sparam) { + sparam_options.push_back(&opt); + } else if (opt.in_example(ctx_arg.ex)) { + specific_options.push_back(&opt); + } else { + common_options.push_back(&opt); + } + } + + printf("_llama_completions() {\n"); + printf(" local cur prev opts\n"); + printf(" COMPREPLY=()\n"); + printf(" cur=\"${COMP_WORDS[COMP_CWORD]}\"\n"); + printf(" prev=\"${COMP_WORDS[COMP_CWORD-1]}\"\n\n"); + + printf(" opts=\""); + auto print_options = [](const std::vector<common_arg *> & options) { + for (const common_arg * opt : options) { + for (const char * arg : opt->args) { + printf("%s ", arg); + } + } + }; + + print_options(common_options); + print_options(sparam_options); + print_options(specific_options); + printf("\"\n\n"); + + printf(" case \"$prev\" in\n"); + printf(" --model|-m)\n"); + printf(" COMPREPLY=( $(compgen -f -X '!*.gguf' -- \"$cur\") $(compgen -d -- \"$cur\") )\n"); + printf(" return 0\n"); + printf(" ;;\n"); + printf(" --grammar-file)\n"); + printf(" COMPREPLY=( $(compgen -f -X '!*.gbnf' -- \"$cur\") $(compgen -d -- \"$cur\") )\n"); + printf(" return 0\n"); + printf(" ;;\n"); + printf(" --chat-template-file)\n"); + printf(" COMPREPLY=( $(compgen -f -X '!*.jinja' -- \"$cur\") $(compgen -d -- \"$cur\") )\n"); + printf(" return 0\n"); + printf(" ;;\n"); + printf(" *)\n"); + printf(" COMPREPLY=( $(compgen -W \"${opts}\" -- \"$cur\") )\n"); + printf(" return 0\n"); + printf(" ;;\n"); + printf(" esac\n"); + printf("}\n\n"); + + std::set<std::string> executables = { + "llama-batched", + "llama-batched-bench", + "llama-bench", + "llama-cli", + "llama-completion", + "llama-convert-llama2c-to-ggml", + "llama-cvector-generator", + "llama-embedding", + "llama-eval-callback", + "llama-export-lora", + "llama-gen-docs", + "llama-gguf", + "llama-gguf-hash", + "llama-gguf-split", + "llama-gritlm", + "llama-imatrix", + "llama-infill", + "llama-mtmd-cli", + "llama-llava-clip-quantize-cli", + "llama-lookahead", + "llama-lookup", + "llama-lookup-create", + "llama-lookup-merge", + "llama-lookup-stats", + "llama-parallel", + "llama-passkey", + "llama-perplexity", + "llama-q8dot", + "llama-quantize", + "llama-qwen2vl-cli", + "llama-retrieval", + "llama-save-load-state", + "llama-server", + "llama-simple", + "llama-simple-chat", + "llama-speculative", + "llama-speculative-simple", + "llama-tokenize", + "llama-tts", + "llama-vdot" + }; + + for (const auto& exe : executables) { + printf("complete -F _llama_completions %s\n", exe.c_str()); + } +} + +static std::vector<ggml_backend_dev_t> parse_device_list(const std::string & value) { + std::vector<ggml_backend_dev_t> devices; + auto dev_names = string_split<std::string>(value, ','); + if (dev_names.empty()) { + throw std::invalid_argument("no devices specified"); + } + if (dev_names.size() == 1 && dev_names[0] == "none") { + devices.push_back(nullptr); + } else { + for (const auto & device : dev_names) { + auto * dev = ggml_backend_dev_by_name(device.c_str()); + if (!dev || ggml_backend_dev_type(dev) == GGML_BACKEND_DEVICE_TYPE_CPU) { + throw std::invalid_argument(string_format("invalid device: %s", device.c_str())); + } + devices.push_back(dev); + } + devices.push_back(nullptr); + } + return devices; +} + +static void add_rpc_devices(const std::string & servers) { + auto rpc_servers = string_split<std::string>(servers, ','); + if (rpc_servers.empty()) { + throw std::invalid_argument("no RPC servers specified"); + } + ggml_backend_reg_t rpc_reg = ggml_backend_reg_by_name("RPC"); + if (!rpc_reg) { + throw std::invalid_argument("failed to find RPC backend"); + } + typedef ggml_backend_reg_t (*ggml_backend_rpc_add_server_t)(const char * endpoint); + ggml_backend_rpc_add_server_t ggml_backend_rpc_add_server_fn = (ggml_backend_rpc_add_server_t) ggml_backend_reg_get_proc_address(rpc_reg, "ggml_backend_rpc_add_server"); + if (!ggml_backend_rpc_add_server_fn) { + throw std::invalid_argument("failed to find RPC add server function"); + } + for (const auto & server : rpc_servers) { + auto reg = ggml_backend_rpc_add_server_fn(server.c_str()); + ggml_backend_register(reg); + } +} + +bool common_params_to_map(int argc, char ** argv, llama_example ex, std::map<common_arg, std::string> & out_map) { + common_params dummy_params; + common_params_context ctx_arg = common_params_parser_init(dummy_params, ex, nullptr); + + std::unordered_map<std::string, common_arg *> arg_to_options; + for (auto & opt : ctx_arg.options) { + for (const auto & arg : opt.args) { + arg_to_options[arg] = &opt; + } + for (const auto & arg : opt.args_neg) { + arg_to_options[arg] = &opt; + } + } + + // TODO @ngxson : find a way to deduplicate this code + + // handle command line arguments + auto check_arg = [&](int i) { + if (i+1 >= argc) { + throw std::invalid_argument("expected value for argument"); + } + }; + + std::set<std::string> seen_args; + + for (int i = 1; i < argc; i++) { + const std::string arg_prefix = "--"; + + std::string arg = argv[i]; + if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) { + std::replace(arg.begin(), arg.end(), '_', '-'); + } + if (arg_to_options.find(arg) == arg_to_options.end()) { + throw std::invalid_argument(string_format("error: invalid argument: %s", arg.c_str())); + } + if (!seen_args.insert(arg).second) { + LOG_WRN("DEPRECATED: argument '%s' specified multiple times, use comma-separated values instead (only last value will be used)\n", arg.c_str()); + } + auto opt = *arg_to_options[arg]; + std::string val; + if (opt.value_hint == nullptr && opt.value_hint_2 == nullptr) { + // bool arg (need to reverse the meaning for negative args) + bool is_neg = std::find(opt.args_neg.begin(), opt.args_neg.end(), arg) != opt.args_neg.end(); + val = is_neg ? "0" : "1"; + } + if (opt.value_hint != nullptr) { + // arg with single value + check_arg(i); + val = argv[++i]; + } + if (opt.value_hint_2 != nullptr) { + // TODO: support arg with 2 values + throw std::invalid_argument("error: argument with 2 values is not yet supported\n"); + } + out_map[opt] = val; + } + + return true; +} + +bool common_params_parse(int argc, char ** argv, common_params & params, llama_example ex, void(*print_usage)(int, char **)) { + auto ctx_arg = common_params_parser_init(params, ex, print_usage); + const common_params params_org = ctx_arg.params; // the example can modify the default params + + try { + if (!common_params_parse_ex(argc, argv, ctx_arg)) { + ctx_arg.params = params_org; + return false; + } + if (ctx_arg.params.usage) { + common_params_print_usage(ctx_arg); + if (ctx_arg.print_usage) { + ctx_arg.print_usage(argc, argv); + } + exit(0); + } + if (ctx_arg.params.completion) { + common_params_print_completion(ctx_arg); + exit(0); + } + params.lr.init(); + } catch (const std::invalid_argument & ex) { + fprintf(stderr, "%s\n", ex.what()); + ctx_arg.params = params_org; + return false; + } catch (std::exception & ex) { + fprintf(stderr, "%s\n", ex.what()); + exit(1); // for other exceptions, we exit with status code 1 + } + + return true; +} + +static std::string list_builtin_chat_templates() { + std::vector<const char *> supported_tmpl; + int32_t res = llama_chat_builtin_templates(nullptr, 0); + supported_tmpl.resize(res); + res = llama_chat_builtin_templates(supported_tmpl.data(), supported_tmpl.size()); + std::ostringstream msg; + for (auto & tmpl : supported_tmpl) { + msg << tmpl << (&tmpl == &supported_tmpl.back() ? "" : ", "); + } + return msg.str(); +} + +bool common_arg_utils::is_truthy(const std::string & value) { + return value == "on" || value == "enabled" || value == "true" || value == "1"; +} + +bool common_arg_utils::is_falsey(const std::string & value) { + return value == "off" || value == "disabled" || value == "false" || value == "0"; +} + +bool common_arg_utils::is_autoy(const std::string & value) { + return value == "auto" || value == "-1"; +} + +// Simple CSV parser that handles quoted fields and escaped quotes +// example: +// input: value1,"value, with, commas","value with ""escaped"" quotes",value4 +// output: [value1] [value, with, commas] [value with "escaped" quotes] [value4] +static std::vector<std::string> parse_csv_row(const std::string& input) { + std::vector<std::string> fields; + std::string field; + bool in_quotes = false; + + for (size_t i = 0; i < input.length(); ++i) { + char ch = input[i]; + + if (ch == '"') { + if (!in_quotes) { + // start of quoted field (only valid if at beginning of field) + if (!field.empty()) { + // quote appeared in middle of unquoted field, treat as literal + field += '"'; + } else { + in_quotes = true; // start + } + } else { + if (i + 1 < input.length() && input[i + 1] == '"') { + // escaped quote: "" + field += '"'; + ++i; // skip the next quote + } else { + in_quotes = false; // end + } + } + } else if (ch == ',') { + if (in_quotes) { + field += ','; + } else { + fields.push_back(std::move(field)); + field.clear(); + } + } else { + field += ch; + } + } + + // Add the last field + fields.push_back(std::move(field)); + + return fields; +} + +common_params_context common_params_parser_init(common_params & params, llama_example ex, void(*print_usage)(int, char **)) { + // per-example default params + // we define here to make sure it's included in llama-gen-docs + if (ex == LLAMA_EXAMPLE_COMPLETION) { + params.use_jinja = false; // disable jinja by default + + } else if (ex == LLAMA_EXAMPLE_MTMD) { + params.use_jinja = false; // disable jinja by default + params.sampling.temp = 0.2; // lower temp by default for better quality + + } else if (ex == LLAMA_EXAMPLE_SERVER) { + params.n_parallel = -1; // auto by default + } + + params.use_color = tty_can_use_colors(); + + // load dynamic backends + ggml_backend_load_all(); + + common_params_context ctx_arg(params); + ctx_arg.print_usage = print_usage; + ctx_arg.ex = ex; + + std::string sampler_type_chars; + std::string sampler_type_names; + for (const auto & sampler : params.sampling.samplers) { + sampler_type_chars += common_sampler_type_to_chr(sampler); + sampler_type_names += common_sampler_type_to_str(sampler) + ";"; + } + if (!sampler_type_names.empty()) { + sampler_type_names.pop_back(); // remove last semicolon + } + + + /** + * filter options by example + * rules: + * - all examples inherit options from LLAMA_EXAMPLE_COMMON + * - if LLAMA_EXAMPLE_* is set (other than COMMON), we only show the option in the corresponding example + * - if both {LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_*,} are set, we will prioritize the LLAMA_EXAMPLE_* matching current example + */ + auto add_opt = [&](common_arg arg) { + if ((arg.in_example(ex) || arg.in_example(LLAMA_EXAMPLE_COMMON)) && !arg.is_exclude(ex)) { + ctx_arg.options.push_back(std::move(arg)); + } + }; + + + add_opt(common_arg( + {"-h", "--help", "--usage"}, + "print usage and exit", + [](common_params & params) { + params.usage = true; + } + )); + add_opt(common_arg( + {"--version"}, + "show version and build info", + [](common_params &) { + fprintf(stderr, "version: %d (%s)\n", LLAMA_BUILD_NUMBER, LLAMA_COMMIT); + fprintf(stderr, "built with %s for %s\n", LLAMA_COMPILER, LLAMA_BUILD_TARGET); + exit(0); + } + )); + add_opt(common_arg( + {"--license"}, + "show source code license and dependencies", + [](common_params &) { + for (int i = 0; LICENSES[i]; ++i) { + printf("%s\n", LICENSES[i]); + } + exit(0); + } + )); + add_opt(common_arg( + {"-cl", "--cache-list"}, + "show list of models in cache", + [](common_params &) { + printf("model cache directory: %s\n", fs_get_cache_directory().c_str()); + auto models = common_list_cached_models(); + printf("number of models in cache: %zu\n", models.size()); + for (size_t i = 0; i < models.size(); i++) { + auto & model = models[i]; + printf("%4d. %s\n", (int) i + 1, model.to_string().c_str()); + } + exit(0); + } + )); + add_opt(common_arg( + {"--completion-bash"}, + "print source-able bash completion script for llama.cpp", + [](common_params & params) { + params.completion = true; + } + )); + add_opt(common_arg( + {"--verbose-prompt"}, + string_format("print a verbose prompt before generation (default: %s)", params.verbose_prompt ? "true" : "false"), + [](common_params & params) { + params.verbose_prompt = true; + } + )); + add_opt(common_arg( + {"--display-prompt"}, + {"--no-display-prompt"}, + string_format("whether to print prompt at generation (default: %s)", params.display_prompt ? "true" : "false"), + [](common_params & params, bool value) { + params.display_prompt = value; + } + ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI})); + add_opt(common_arg( + {"-co", "--color"}, "[on|off|auto]", + "Colorize output to distinguish prompt and user input from generations ('on', 'off', or 'auto', default: 'auto')\n" + "'auto' enables colors when output is to a terminal", + [](common_params & params, const std::string & value) { + if (is_truthy(value)) { + params.use_color = true; + } else if (is_falsey(value)) { + params.use_color = false; + } else if (is_autoy(value)) { + params.use_color = tty_can_use_colors(); + } else { + throw std::invalid_argument( + string_format("error: unknown value for --color: '%s'\n", value.c_str())); + } + } + ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP})); + add_opt(common_arg( + {"-t", "--threads"}, "N", + string_format("number of CPU threads to use during generation (default: %d)", params.cpuparams.n_threads), + [](common_params & params, int value) { + params.cpuparams.n_threads = value; + if (params.cpuparams.n_threads <= 0) { + params.cpuparams.n_threads = std::thread::hardware_concurrency(); + } + } + ).set_env("LLAMA_ARG_THREADS")); + add_opt(common_arg( + {"-tb", "--threads-batch"}, "N", + "number of threads to use during batch and prompt processing (default: same as --threads)", + [](common_params & params, int value) { + params.cpuparams_batch.n_threads = value; + if (params.cpuparams_batch.n_threads <= 0) { + params.cpuparams_batch.n_threads = std::thread::hardware_concurrency(); + } + } + )); + add_opt(common_arg( + {"-C", "--cpu-mask"}, "M", + "CPU affinity mask: arbitrarily long hex. Complements cpu-range (default: \"\")", + [](common_params & params, const std::string & mask) { + params.cpuparams.mask_valid = true; + if (!parse_cpu_mask(mask, params.cpuparams.cpumask)) { + throw std::invalid_argument("invalid cpumask"); + } + } + )); + add_opt(common_arg( + {"-Cr", "--cpu-range"}, "lo-hi", + "range of CPUs for affinity. Complements --cpu-mask", + [](common_params & params, const std::string & range) { + params.cpuparams.mask_valid = true; + if (!parse_cpu_range(range, params.cpuparams.cpumask)) { + throw std::invalid_argument("invalid range"); + } + } + )); + add_opt(common_arg( + {"--cpu-strict"}, "<0|1>", + string_format("use strict CPU placement (default: %u)\n", (unsigned) params.cpuparams.strict_cpu), + [](common_params & params, const std::string & value) { + params.cpuparams.strict_cpu = std::stoul(value); + } + )); + add_opt(common_arg( + {"--prio"}, "N", + string_format("set process/thread priority : low(-1), normal(0), medium(1), high(2), realtime(3) (default: %d)\n", params.cpuparams.priority), + [](common_params & params, int prio) { + if (prio < GGML_SCHED_PRIO_LOW || prio > GGML_SCHED_PRIO_REALTIME) { + throw std::invalid_argument("invalid value"); + } + params.cpuparams.priority = (enum ggml_sched_priority) prio; + } + )); + add_opt(common_arg( + {"--poll"}, "<0...100>", + string_format("use polling level to wait for work (0 - no polling, default: %u)\n", (unsigned) params.cpuparams.poll), + [](common_params & params, const std::string & value) { + params.cpuparams.poll = std::stoul(value); + } + )); + add_opt(common_arg( + {"-Cb", "--cpu-mask-batch"}, "M", + "CPU affinity mask: arbitrarily long hex. Complements cpu-range-batch (default: same as --cpu-mask)", + [](common_params & params, const std::string & mask) { + params.cpuparams_batch.mask_valid = true; + if (!parse_cpu_mask(mask, params.cpuparams_batch.cpumask)) { + throw std::invalid_argument("invalid cpumask"); + } + } + )); + add_opt(common_arg( + {"-Crb", "--cpu-range-batch"}, "lo-hi", + "ranges of CPUs for affinity. Complements --cpu-mask-batch", + [](common_params & params, const std::string & range) { + params.cpuparams_batch.mask_valid = true; + if (!parse_cpu_range(range, params.cpuparams_batch.cpumask)) { + throw std::invalid_argument("invalid range"); + } + } + )); + add_opt(common_arg( + {"--cpu-strict-batch"}, "<0|1>", + "use strict CPU placement (default: same as --cpu-strict)", + [](common_params & params, int value) { + params.cpuparams_batch.strict_cpu = value; + } + )); + add_opt(common_arg( + {"--prio-batch"}, "N", + string_format("set process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: %d)\n", params.cpuparams_batch.priority), + [](common_params & params, int prio) { + if (prio < 0 || prio > 3) { + throw std::invalid_argument("invalid value"); + } + params.cpuparams_batch.priority = (enum ggml_sched_priority) prio; + } + )); + add_opt(common_arg( + {"--poll-batch"}, "<0|1>", + "use polling to wait for work (default: same as --poll)", + [](common_params & params, int value) { + params.cpuparams_batch.poll = value; + } + )); + add_opt(common_arg( + {"-lcs", "--lookup-cache-static"}, "FNAME", + "path to static lookup cache to use for lookup decoding (not updated by generation)", + [](common_params & params, const std::string & value) { + params.speculative.lookup_cache_static = value; + } + ).set_examples({LLAMA_EXAMPLE_LOOKUP, LLAMA_EXAMPLE_SERVER})); + add_opt(common_arg( + {"-lcd", "--lookup-cache-dynamic"}, "FNAME", + "path to dynamic lookup cache to use for lookup decoding (updated by generation)", + [](common_params & params, const std::string & value) { + params.speculative.lookup_cache_dynamic = value; + } + ).set_examples({LLAMA_EXAMPLE_LOOKUP, LLAMA_EXAMPLE_SERVER})); + add_opt(common_arg( + {"-c", "--ctx-size"}, "N", + string_format("size of the prompt context (default: %d, 0 = loaded from model)", params.n_ctx), + [](common_params & params, int value) { + params.n_ctx = value; + if (value == 0) { + // disable context reduction in llama_params_fit if the user explicitly requests the full context size: + params.fit_params_min_ctx = UINT32_MAX; + } + } + ).set_env("LLAMA_ARG_CTX_SIZE")); + add_opt(common_arg( + {"-n", "--predict", "--n-predict"}, "N", + string_format( + ex == LLAMA_EXAMPLE_COMPLETION + ? "number of tokens to predict (default: %d, -1 = infinity, -2 = until context filled)" + : "number of tokens to predict (default: %d, -1 = infinity)", + params.n_predict), + [](common_params & params, int value) { + params.n_predict = value; + } + ).set_env("LLAMA_ARG_N_PREDICT")); + add_opt(common_arg( + {"-b", "--batch-size"}, "N", + string_format("logical maximum batch size (default: %d)", params.n_batch), + [](common_params & params, int value) { + params.n_batch = value; + } + ).set_env("LLAMA_ARG_BATCH")); + add_opt(common_arg( + {"-ub", "--ubatch-size"}, "N", + string_format("physical maximum batch size (default: %d)", params.n_ubatch), + [](common_params & params, int value) { + params.n_ubatch = value; + } + ).set_env("LLAMA_ARG_UBATCH")); + add_opt(common_arg( + {"--keep"}, "N", + string_format("number of tokens to keep from the initial prompt (default: %d, -1 = all)", params.n_keep), + [](common_params & params, int value) { + params.n_keep = value; + } + )); + add_opt(common_arg( + {"--swa-full"}, + string_format("use full-size SWA cache (default: %s)\n" + "[(more info)](https://github.com/ggml-org/llama.cpp/pull/13194#issuecomment-2868343055)", params.swa_full ? "true" : "false"), + [](common_params & params) { + params.swa_full = true; + } + ).set_env("LLAMA_ARG_SWA_FULL")); + add_opt(common_arg( + {"--ctx-checkpoints", "--swa-checkpoints"}, "N", + string_format("max number of context checkpoints to create per slot (default: %d)" + "[(more info)](https://github.com/ggml-org/llama.cpp/pull/15293)", params.n_ctx_checkpoints), + [](common_params & params, int value) { + params.n_ctx_checkpoints = value; + } + ).set_env("LLAMA_ARG_CTX_CHECKPOINTS").set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI})); + add_opt(common_arg( + {"-cram", "--cache-ram"}, "N", + string_format("set the maximum cache size in MiB (default: %d, -1 - no limit, 0 - disable)" + "[(more info)](https://github.com/ggml-org/llama.cpp/pull/16391)", params.cache_ram_mib), + [](common_params & params, int value) { + params.cache_ram_mib = value; + } + ).set_env("LLAMA_ARG_CACHE_RAM").set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI})); + add_opt(common_arg( + {"-kvu", "--kv-unified"}, + {"-no-kvu", "--no-kv-unified"}, + "use single unified KV buffer shared across all sequences (default: enabled if number of slots is auto)", + [](common_params & params, bool value) { + params.kv_unified = value; + } + ).set_env("LLAMA_ARG_KV_UNIFIED").set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_PERPLEXITY, LLAMA_EXAMPLE_BATCHED, LLAMA_EXAMPLE_BENCH})); + add_opt(common_arg( + {"--context-shift"}, + {"--no-context-shift"}, + string_format("whether to use context shift on infinite text generation (default: %s)", params.ctx_shift ? "enabled" : "disabled"), + [](common_params & params, bool value) { + params.ctx_shift = value; + } + ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_IMATRIX, LLAMA_EXAMPLE_PERPLEXITY}).set_env("LLAMA_ARG_CONTEXT_SHIFT")); + add_opt(common_arg( + {"--chunks"}, "N", + string_format("max number of chunks to process (default: %d, -1 = all)", params.n_chunks), + [](common_params & params, int value) { + params.n_chunks = value; + } + ).set_examples({LLAMA_EXAMPLE_IMATRIX, LLAMA_EXAMPLE_PERPLEXITY, LLAMA_EXAMPLE_RETRIEVAL})); + add_opt(common_arg({ "-fa", "--flash-attn" }, "[on|off|auto]", + string_format("set Flash Attention use ('on', 'off', or 'auto', default: '%s')", + llama_flash_attn_type_name(params.flash_attn_type)), + [](common_params & params, const std::string & value) { + if (is_truthy(value)) { + params.flash_attn_type = LLAMA_FLASH_ATTN_TYPE_ENABLED; + } else if (is_falsey(value)) { + params.flash_attn_type = LLAMA_FLASH_ATTN_TYPE_DISABLED; + } else if (is_autoy(value)) { + params.flash_attn_type = LLAMA_FLASH_ATTN_TYPE_AUTO; + } else { + throw std::runtime_error( + string_format("error: unknown value for --flash-attn: '%s'\n", value.c_str())); + } + }).set_env("LLAMA_ARG_FLASH_ATTN")); + add_opt(common_arg( + {"-p", "--prompt"}, "PROMPT", + "prompt to start generation with; for system message, use -sys", + [](common_params & params, const std::string & value) { + params.prompt = value; + } + ).set_excludes({LLAMA_EXAMPLE_SERVER})); + add_opt(common_arg( + {"-sys", "--system-prompt"}, "PROMPT", + "system prompt to use with model (if applicable, depending on chat template)", + [](common_params & params, const std::string & value) { + params.system_prompt = value; + } + ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_DIFFUSION, LLAMA_EXAMPLE_MTMD})); + add_opt(common_arg( + {"--perf"}, + {"--no-perf"}, + string_format("whether to enable internal libllama performance timings (default: %s)", params.no_perf ? "true" : "false"), + [](common_params & params, bool value) { + params.no_perf = !value; + params.sampling.no_perf = !value; + } + ).set_env("LLAMA_ARG_PERF")); + add_opt(common_arg( + {"--show-timings"}, + {"--no-show-timings"}, + string_format("whether to show timing information after each response (default: %s)", params.show_timings ? "true" : "false"), + [](common_params & params, bool value) { + params.show_timings = value; + } + ).set_examples({LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_SHOW_TIMINGS")); + add_opt(common_arg( + {"-f", "--file"}, "FNAME", + "a file containing the prompt (default: none)", + [](common_params & params, const std::string & value) { + params.prompt = read_file(value); + // store the external file name in params + params.prompt_file = value; + if (!params.prompt.empty() && params.prompt.back() == '\n') { + params.prompt.pop_back(); + } + } + ).set_excludes({LLAMA_EXAMPLE_SERVER})); + add_opt(common_arg( + {"-sysf", "--system-prompt-file"}, "FNAME", + "a file containing the system prompt (default: none)", + [](common_params & params, const std::string & value) { + params.system_prompt = read_file(value); + if (!params.system_prompt.empty() && params.system_prompt.back() == '\n') { + params.system_prompt.pop_back(); + } + } + ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_DIFFUSION})); + add_opt(common_arg( + {"--in-file"}, "FNAME", + "an input file (use comma-separated values to specify multiple files)", + [](common_params & params, const std::string & value) { + for (const auto & item : parse_csv_row(value)) { + std::ifstream file(item); + if (!file) { + throw std::runtime_error(string_format("error: failed to open file '%s'\n", item.c_str())); + } + params.in_files.push_back(item); + } + } + ).set_examples({LLAMA_EXAMPLE_IMATRIX})); + add_opt(common_arg( + {"-bf", "--binary-file"}, "FNAME", + "binary file containing the prompt (default: none)", + [](common_params & params, const std::string & value) { + std::ifstream file(value, std::ios::binary); + if (!file) { + throw std::runtime_error(string_format("error: failed to open file '%s'\n", value.c_str())); + } + // store the external file name in params + params.prompt_file = value; + std::ostringstream ss; + ss << file.rdbuf(); + params.prompt = ss.str(); + fprintf(stderr, "Read %zu bytes from binary file %s\n", params.prompt.size(), value.c_str()); + } + ).set_excludes({LLAMA_EXAMPLE_SERVER})); + add_opt(common_arg( + {"-e", "--escape"}, + {"--no-escape"}, + string_format("whether to process escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\) (default: %s)", params.escape ? "true" : "false"), + [](common_params & params, bool value) { + params.escape = value; + } + )); + add_opt(common_arg( + {"-ptc", "--print-token-count"}, "N", + string_format("print token count every N tokens (default: %d)", params.n_print), + [](common_params & params, int value) { + params.n_print = value; + } + ).set_examples({LLAMA_EXAMPLE_COMPLETION})); + add_opt(common_arg( + {"--prompt-cache"}, "FNAME", + "file to cache prompt state for faster startup (default: none)", + [](common_params & params, const std::string & value) { + params.path_prompt_cache = value; + } + ).set_examples({LLAMA_EXAMPLE_COMPLETION})); + add_opt(common_arg( + {"--prompt-cache-all"}, + "if specified, saves user input and generations to cache as well\n", + [](common_params & params) { + params.prompt_cache_all = true; + } + ).set_examples({LLAMA_EXAMPLE_COMPLETION})); + add_opt(common_arg( + {"--prompt-cache-ro"}, + "if specified, uses the prompt cache but does not update it", + [](common_params & params) { + params.prompt_cache_ro = true; + } + ).set_examples({LLAMA_EXAMPLE_COMPLETION})); + add_opt(common_arg( + {"-r", "--reverse-prompt"}, "PROMPT", + "halt generation at PROMPT, return control in interactive mode\n", + [](common_params & params, const std::string & value) { + params.antiprompt.emplace_back(value); + } + ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_SERVER})); + add_opt(common_arg( + {"-sp", "--special"}, + string_format("special tokens output enabled (default: %s)", params.special ? "true" : "false"), + [](common_params & params) { + params.special = true; + } + ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_SERVER})); + add_opt(common_arg( + {"-cnv", "--conversation"}, + {"-no-cnv", "--no-conversation"}, + "whether to run in conversation mode:\n" + "- does not print special tokens and suffix/prefix\n" + "- interactive mode is also enabled\n" + "(default: auto enabled if chat template is available)", + [](common_params & params, bool value) { + params.conversation_mode = value ? COMMON_CONVERSATION_MODE_ENABLED : COMMON_CONVERSATION_MODE_DISABLED; + } + ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI})); + add_opt(common_arg( + {"-st", "--single-turn"}, + "run conversation for a single turn only, then exit when done\n" + "will not be interactive if first turn is predefined with --prompt\n" + "(default: false)", + [](common_params & params) { + params.single_turn = true; + } + ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI})); + add_opt(common_arg( + {"-i", "--interactive"}, + string_format("run in interactive mode (default: %s)", params.interactive ? "true" : "false"), + [](common_params & params) { + params.interactive = true; + } + ).set_examples({LLAMA_EXAMPLE_COMPLETION})); + add_opt(common_arg( + {"-if", "--interactive-first"}, + string_format("run in interactive mode and wait for input right away (default: %s)", params.interactive_first ? "true" : "false"), + [](common_params & params) { + params.interactive_first = true; + } + ).set_examples({LLAMA_EXAMPLE_COMPLETION})); + add_opt(common_arg( + {"-mli", "--multiline-input"}, + "allows you to write or paste multiple lines without ending each in '\\'", + [](common_params & params) { + params.multiline_input = true; + } + ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI})); + add_opt(common_arg( + {"--in-prefix-bos"}, + "prefix BOS to user inputs, preceding the `--in-prefix` string", + [](common_params & params) { + params.input_prefix_bos = true; + params.enable_chat_template = false; + } + ).set_examples({LLAMA_EXAMPLE_COMPLETION})); + add_opt(common_arg( + {"--in-prefix"}, "STRING", + "string to prefix user inputs with (default: empty)", + [](common_params & params, const std::string & value) { + params.input_prefix = value; + params.enable_chat_template = false; + } + ).set_examples({LLAMA_EXAMPLE_COMPLETION})); + add_opt(common_arg( + {"--in-suffix"}, "STRING", + "string to suffix after user inputs with (default: empty)", + [](common_params & params, const std::string & value) { + params.input_suffix = value; + params.enable_chat_template = false; + } + ).set_examples({LLAMA_EXAMPLE_COMPLETION})); + add_opt(common_arg( + {"--warmup"}, + {"--no-warmup"}, + string_format("whether to perform warmup with an empty run (default: %s)", params.warmup ? "enabled" : "disabled"), + [](common_params & params, bool value) { + params.warmup = value; + } + ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_MTMD, LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_RETRIEVAL, LLAMA_EXAMPLE_PERPLEXITY, LLAMA_EXAMPLE_DEBUG})); + add_opt(common_arg( + {"--spm-infill"}, + string_format( + "use Suffix/Prefix/Middle pattern for infill (instead of Prefix/Suffix/Middle) as some models prefer this. (default: %s)", + params.spm_infill ? "enabled" : "disabled" + ), + [](common_params & params) { + params.spm_infill = true; + } + ).set_examples({LLAMA_EXAMPLE_SERVER})); + add_opt(common_arg( + {"--samplers"}, "SAMPLERS", + string_format("samplers that will be used for generation in the order, separated by \';\'\n(default: %s)", sampler_type_names.c_str()), + [](common_params & params, const std::string & value) { + const auto sampler_names = string_split<std::string>(value, ';'); + params.sampling.samplers = common_sampler_types_from_names(sampler_names, true); + params.sampling.user_sampling_config |= common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_SAMPLERS; + } + ).set_sparam()); + add_opt(common_arg( + {"-s", "--seed"}, "SEED", + string_format("RNG seed (default: %d, use random seed for %d)", params.sampling.seed, LLAMA_DEFAULT_SEED), + [](common_params & params, const std::string & value) { + params.sampling.seed = std::stoul(value); + } + ).set_sparam()); + add_opt(common_arg( + {"--sampler-seq", "--sampling-seq"}, "SEQUENCE", + string_format("simplified sequence for samplers that will be used (default: %s)", sampler_type_chars.c_str()), + [](common_params & params, const std::string & value) { + params.sampling.samplers = common_sampler_types_from_chars(value); + } + ).set_sparam()); + add_opt(common_arg( + {"--ignore-eos"}, + "ignore end of stream token and continue generating (implies --logit-bias EOS-inf)", + [](common_params & params) { + params.sampling.ignore_eos = true; + } + ).set_sparam()); + add_opt(common_arg( + {"--temp"}, "N", + string_format("temperature (default: %.2f)", (double)params.sampling.temp), + [](common_params & params, const std::string & value) { + params.sampling.temp = std::stof(value); + params.sampling.temp = std::max(params.sampling.temp, 0.0f); + params.sampling.user_sampling_config |= common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_TEMP; + } + ).set_sparam()); + add_opt(common_arg( + {"--top-k"}, "N", + string_format("top-k sampling (default: %d, 0 = disabled)", params.sampling.top_k), + [](common_params & params, int value) { + params.sampling.top_k = value; + params.sampling.user_sampling_config |= common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_TOP_K; + } + ).set_sparam().set_env("LLAMA_ARG_TOP_K")); + add_opt(common_arg( + {"--top-p"}, "N", + string_format("top-p sampling (default: %.2f, 1.0 = disabled)", (double)params.sampling.top_p), + [](common_params & params, const std::string & value) { + params.sampling.top_p = std::stof(value); + params.sampling.user_sampling_config |= common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_TOP_P; + } + ).set_sparam()); + add_opt(common_arg( + {"--min-p"}, "N", + string_format("min-p sampling (default: %.2f, 0.0 = disabled)", (double)params.sampling.min_p), + [](common_params & params, const std::string & value) { + params.sampling.min_p = std::stof(value); + params.sampling.user_sampling_config |= common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_MIN_P; + } + ).set_sparam()); + add_opt(common_arg( + {"--top-nsigma"}, "N", + string_format("top-n-sigma sampling (default: %.2f, -1.0 = disabled)", params.sampling.top_n_sigma), + [](common_params & params, const std::string & value) { + params.sampling.top_n_sigma = std::stof(value); + } + ).set_sparam()); + add_opt(common_arg( + {"--xtc-probability"}, "N", + string_format("xtc probability (default: %.2f, 0.0 = disabled)", (double)params.sampling.xtc_probability), + [](common_params & params, const std::string & value) { + params.sampling.xtc_probability = std::stof(value); + params.sampling.user_sampling_config |= common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_XTC_PROBABILITY; + } + ).set_sparam()); + add_opt(common_arg( + {"--xtc-threshold"}, "N", + string_format("xtc threshold (default: %.2f, 1.0 = disabled)", (double)params.sampling.xtc_threshold), + [](common_params & params, const std::string & value) { + params.sampling.xtc_threshold = std::stof(value); + params.sampling.user_sampling_config |= common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_XTC_THRESHOLD; + } + ).set_sparam()); + add_opt(common_arg( + {"--typical"}, "N", + string_format("locally typical sampling, parameter p (default: %.2f, 1.0 = disabled)", (double)params.sampling.typ_p), + [](common_params & params, const std::string & value) { + params.sampling.typ_p = std::stof(value); + } + ).set_sparam()); + add_opt(common_arg( + {"--repeat-last-n"}, "N", + string_format("last n tokens to consider for penalize (default: %d, 0 = disabled, -1 = ctx_size)", params.sampling.penalty_last_n), + [](common_params & params, int value) { + if (value < -1) { + throw std::runtime_error(string_format("error: invalid repeat-last-n = %d\n", value)); + } + params.sampling.penalty_last_n = value; + params.sampling.n_prev = std::max(params.sampling.n_prev, params.sampling.penalty_last_n); + params.sampling.user_sampling_config |= common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_PENALTY_LAST_N; + } + ).set_sparam()); + add_opt(common_arg( + {"--repeat-penalty"}, "N", + string_format("penalize repeat sequence of tokens (default: %.2f, 1.0 = disabled)", (double)params.sampling.penalty_repeat), + [](common_params & params, const std::string & value) { + params.sampling.penalty_repeat = std::stof(value); + params.sampling.user_sampling_config |= common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_PENALTY_REPEAT; + } + ).set_sparam()); + add_opt(common_arg( + {"--presence-penalty"}, "N", + string_format("repeat alpha presence penalty (default: %.2f, 0.0 = disabled)", (double)params.sampling.penalty_present), + [](common_params & params, const std::string & value) { + params.sampling.penalty_present = std::stof(value); + } + ).set_sparam()); + add_opt(common_arg( + {"--frequency-penalty"}, "N", + string_format("repeat alpha frequency penalty (default: %.2f, 0.0 = disabled)", (double)params.sampling.penalty_freq), + [](common_params & params, const std::string & value) { + params.sampling.penalty_freq = std::stof(value); + } + ).set_sparam()); + add_opt(common_arg( + {"--dry-multiplier"}, "N", + string_format("set DRY sampling multiplier (default: %.2f, 0.0 = disabled)", (double)params.sampling.dry_multiplier), + [](common_params & params, const std::string & value) { + params.sampling.dry_multiplier = std::stof(value); + } + ).set_sparam()); + add_opt(common_arg( + {"--dry-base"}, "N", + string_format("set DRY sampling base value (default: %.2f)", (double)params.sampling.dry_base), + [](common_params & params, const std::string & value) { + float potential_base = std::stof(value); + if (potential_base >= 1.0f) + { + params.sampling.dry_base = potential_base; + } + } + ).set_sparam()); + add_opt(common_arg( + {"--dry-allowed-length"}, "N", + string_format("set allowed length for DRY sampling (default: %d)", params.sampling.dry_allowed_length), + [](common_params & params, int value) { + params.sampling.dry_allowed_length = value; + } + ).set_sparam()); + add_opt(common_arg( + {"--dry-penalty-last-n"}, "N", + string_format("set DRY penalty for the last n tokens (default: %d, 0 = disable, -1 = context size)", params.sampling.dry_penalty_last_n), + [](common_params & params, int value) { + if (value < -1) { + throw std::runtime_error(string_format("error: invalid dry-penalty-last-n = %d\n", value)); + } + params.sampling.dry_penalty_last_n = value; + } + ).set_sparam()); + add_opt(common_arg( + {"--dry-sequence-breaker"}, "STRING", + string_format("add sequence breaker for DRY sampling, clearing out default breakers (%s) in the process; use \"none\" to not use any sequence breakers\n", + params.sampling.dry_sequence_breakers.empty() ? "none" : + std::accumulate(std::next(params.sampling.dry_sequence_breakers.begin()), + params.sampling.dry_sequence_breakers.end(), + std::string("'") + (params.sampling.dry_sequence_breakers[0] == "\n" ? "\\n" : params.sampling.dry_sequence_breakers[0]) + "'", + [](const std::string& a, const std::string& b) { + std::string formatted_b = (b == "\n") ? "\\n" : b; + return a + ", '" + formatted_b + "'"; + }).c_str()), + [](common_params & params, const std::string & value) { + static bool defaults_cleared = false; + + if (!defaults_cleared) { + params.sampling.dry_sequence_breakers.clear(); + defaults_cleared = true; + } + + if (value == "none") { + params.sampling.dry_sequence_breakers.clear(); + } else { + params.sampling.dry_sequence_breakers.emplace_back(value); + } + } + ).set_sparam()); + add_opt(common_arg( + {"--adaptive-target"}, "N", + string_format("adaptive-p: select tokens near this probability (valid range 0.0 " + "to 1.0; negative = disabled) (default: %.2f)\n" + "[(more info)](https://github.com/ggml-org/llama.cpp/pull/17927)", + (double)params.sampling.adaptive_target), + [](common_params & params, const std::string & value) { + params.sampling.adaptive_target = std::stof(value); + } + ).set_sparam()); + add_opt(common_arg( + {"--adaptive-decay"}, "N", + string_format("adaptive-p: decay rate for target adaptation over time. lower values " + "are more reactive, higher values are more stable.\n" + "(valid range 0.0 to 0.99) (default: %.2f)", + (double)params.sampling.adaptive_decay), + [](common_params & params, const std::string & value) { + params.sampling.adaptive_decay = std::stof(value); + } + ).set_sparam()); + add_opt(common_arg( + {"--dynatemp-range"}, "N", + string_format("dynamic temperature range (default: %.2f, 0.0 = disabled)", (double)params.sampling.dynatemp_range), + [](common_params & params, const std::string & value) { + params.sampling.dynatemp_range = std::stof(value); + } + ).set_sparam()); + add_opt(common_arg( + {"--dynatemp-exp"}, "N", + string_format("dynamic temperature exponent (default: %.2f)", (double)params.sampling.dynatemp_exponent), + [](common_params & params, const std::string & value) { + params.sampling.dynatemp_exponent = std::stof(value); + } + ).set_sparam()); + add_opt(common_arg( + {"--mirostat"}, "N", + string_format("use Mirostat sampling.\nTop K, Nucleus and Locally Typical samplers are ignored if used.\n" + "(default: %d, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)", params.sampling.mirostat), + [](common_params & params, int value) { + params.sampling.mirostat = value; + params.sampling.user_sampling_config |= common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_MIROSTAT; + } + ).set_sparam()); + add_opt(common_arg( + {"--mirostat-lr"}, "N", + string_format("Mirostat learning rate, parameter eta (default: %.2f)", (double)params.sampling.mirostat_eta), + [](common_params & params, const std::string & value) { + params.sampling.mirostat_eta = std::stof(value); + params.sampling.user_sampling_config |= common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_MIROSTAT_ETA; + } + ).set_sparam()); + add_opt(common_arg( + {"--mirostat-ent"}, "N", + string_format("Mirostat target entropy, parameter tau (default: %.2f)", (double)params.sampling.mirostat_tau), + [](common_params & params, const std::string & value) { + params.sampling.mirostat_tau = std::stof(value); + params.sampling.user_sampling_config |= common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_MIROSTAT_TAU; + } + ).set_sparam()); + add_opt(common_arg( + {"-l", "--logit-bias"}, "TOKEN_ID(+/-)BIAS", + "modifies the likelihood of token appearing in the completion,\n" + "i.e. `--logit-bias 15043+1` to increase likelihood of token ' Hello',\n" + "or `--logit-bias 15043-1` to decrease likelihood of token ' Hello'", + [](common_params & params, const std::string & value) { + std::stringstream ss(value); + llama_token key; + char sign; + std::string value_str; + try { + if (ss >> key && ss >> sign && std::getline(ss, value_str) && (sign == '+' || sign == '-')) { + const float bias = std::stof(value_str) * ((sign == '-') ? -1.0f : 1.0f); + params.sampling.logit_bias.push_back({key, bias}); + } else { + throw std::invalid_argument("invalid input format"); + } + } catch (const std::exception&) { + throw std::invalid_argument("invalid input format"); + } + } + ).set_sparam()); + add_opt(common_arg( + {"--grammar"}, "GRAMMAR", + string_format("BNF-like grammar to constrain generations (see samples in grammars/ dir) (default: '%s')", params.sampling.grammar.c_str()), + [](common_params & params, const std::string & value) { + params.sampling.grammar = value; + } + ).set_sparam()); + add_opt(common_arg( + {"--grammar-file"}, "FNAME", + "file to read grammar from", + [](common_params & params, const std::string & value) { + params.sampling.grammar = read_file(value); + } + ).set_sparam()); + add_opt(common_arg( + {"-j", "--json-schema"}, "SCHEMA", + "JSON schema to constrain generations (https://json-schema.org/), e.g. `{}` for any JSON object\nFor schemas w/ external $refs, use --grammar + example/json_schema_to_grammar.py instead", + [](common_params & params, const std::string & value) { + params.sampling.grammar = json_schema_to_grammar(json::parse(value)); + } + ).set_sparam()); + add_opt(common_arg( + {"-jf", "--json-schema-file"}, "FILE", + "File containing a JSON schema to constrain generations (https://json-schema.org/), e.g. `{}` for any JSON object\nFor schemas w/ external $refs, use --grammar + example/json_schema_to_grammar.py instead", + [](common_params & params, const std::string & value) { + std::ifstream file(value); + if (!file) { + throw std::runtime_error(string_format("error: failed to open file '%s'\n", value.c_str())); + } + std::string schema; + std::copy( + std::istreambuf_iterator<char>(file), + std::istreambuf_iterator<char>(), + std::back_inserter(schema) + ); + params.sampling.grammar = json_schema_to_grammar(json::parse(schema)); + } + ).set_sparam()); + add_opt(common_arg( + {"-bs", "--backend-sampling"}, + "enable backend sampling (experimental) (default: disabled)", + [](common_params & params) { + params.sampling.backend_sampling = true; + } + ).set_sparam().set_env("LLAMA_ARG_BACKEND_SAMPLING")); + add_opt(common_arg( + {"--pooling"}, "{none,mean,cls,last,rank}", + "pooling type for embeddings, use model default if unspecified", + [](common_params & params, const std::string & value) { + /**/ if (value == "none") { params.pooling_type = LLAMA_POOLING_TYPE_NONE; } + else if (value == "mean") { params.pooling_type = LLAMA_POOLING_TYPE_MEAN; } + else if (value == "cls") { params.pooling_type = LLAMA_POOLING_TYPE_CLS; } + else if (value == "last") { params.pooling_type = LLAMA_POOLING_TYPE_LAST; } + else if (value == "rank") { params.pooling_type = LLAMA_POOLING_TYPE_RANK; } + else { throw std::invalid_argument("invalid value"); } + } + ).set_examples({LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_RETRIEVAL, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_DEBUG}).set_env("LLAMA_ARG_POOLING")); + add_opt(common_arg( + {"--attention"}, "{causal,non-causal}", + "attention type for embeddings, use model default if unspecified", + [](common_params & params, const std::string & value) { + /**/ if (value == "causal") { params.attention_type = LLAMA_ATTENTION_TYPE_CAUSAL; } + else if (value == "non-causal") { params.attention_type = LLAMA_ATTENTION_TYPE_NON_CAUSAL; } + else { throw std::invalid_argument("invalid value"); } + } + ).set_examples({LLAMA_EXAMPLE_EMBEDDING})); + add_opt(common_arg( + {"--rope-scaling"}, "{none,linear,yarn}", + "RoPE frequency scaling method, defaults to linear unless specified by the model", + [](common_params & params, const std::string & value) { + /**/ if (value == "none") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_NONE; } + else if (value == "linear") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_LINEAR; } + else if (value == "yarn") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_YARN; } + else { throw std::invalid_argument("invalid value"); } + } + ).set_env("LLAMA_ARG_ROPE_SCALING_TYPE")); + add_opt(common_arg( + {"--rope-scale"}, "N", + "RoPE context scaling factor, expands context by a factor of N", + [](common_params & params, const std::string & value) { + params.rope_freq_scale = 1.0f / std::stof(value); + } + ).set_env("LLAMA_ARG_ROPE_SCALE")); + add_opt(common_arg( + {"--rope-freq-base"}, "N", + "RoPE base frequency, used by NTK-aware scaling (default: loaded from model)", + [](common_params & params, const std::string & value) { + params.rope_freq_base = std::stof(value); + } + ).set_env("LLAMA_ARG_ROPE_FREQ_BASE")); + add_opt(common_arg( + {"--rope-freq-scale"}, "N", + "RoPE frequency scaling factor, expands context by a factor of 1/N", + [](common_params & params, const std::string & value) { + params.rope_freq_scale = std::stof(value); + } + ).set_env("LLAMA_ARG_ROPE_FREQ_SCALE")); + add_opt(common_arg( + {"--yarn-orig-ctx"}, "N", + string_format("YaRN: original context size of model (default: %d = model training context size)", params.yarn_orig_ctx), + [](common_params & params, int value) { + params.yarn_orig_ctx = value; + } + ).set_env("LLAMA_ARG_YARN_ORIG_CTX")); + add_opt(common_arg( + {"--yarn-ext-factor"}, "N", + string_format("YaRN: extrapolation mix factor (default: %.2f, 0.0 = full interpolation)", (double)params.yarn_ext_factor), + [](common_params & params, const std::string & value) { + params.yarn_ext_factor = std::stof(value); + } + ).set_env("LLAMA_ARG_YARN_EXT_FACTOR")); + add_opt(common_arg( + {"--yarn-attn-factor"}, "N", + string_format("YaRN: scale sqrt(t) or attention magnitude (default: %.2f)", (double)params.yarn_attn_factor), + [](common_params & params, const std::string & value) { + params.yarn_attn_factor = std::stof(value); + } + ).set_env("LLAMA_ARG_YARN_ATTN_FACTOR")); + add_opt(common_arg( + {"--yarn-beta-slow"}, "N", + string_format("YaRN: high correction dim or alpha (default: %.2f)", (double)params.yarn_beta_slow), + [](common_params & params, const std::string & value) { + params.yarn_beta_slow = std::stof(value); + } + ).set_env("LLAMA_ARG_YARN_BETA_SLOW")); + add_opt(common_arg( + {"--yarn-beta-fast"}, "N", + string_format("YaRN: low correction dim or beta (default: %.2f)", (double)params.yarn_beta_fast), + [](common_params & params, const std::string & value) { + params.yarn_beta_fast = std::stof(value); + } + ).set_env("LLAMA_ARG_YARN_BETA_FAST")); + add_opt(common_arg( + {"-gan", "--grp-attn-n"}, "N", + string_format("group-attention factor (default: %d)", params.grp_attn_n), + [](common_params & params, int value) { + params.grp_attn_n = value; + } + ).set_env("LLAMA_ARG_GRP_ATTN_N").set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_PASSKEY})); + add_opt(common_arg( + {"-gaw", "--grp-attn-w"}, "N", + string_format("group-attention width (default: %d)", params.grp_attn_w), + [](common_params & params, int value) { + params.grp_attn_w = value; + } + ).set_env("LLAMA_ARG_GRP_ATTN_W").set_examples({LLAMA_EXAMPLE_COMPLETION})); + add_opt(common_arg( + {"-kvo", "--kv-offload"}, + {"-nkvo", "--no-kv-offload"}, + string_format("whether to enable KV cache offloading (default: %s)", params.no_kv_offload ? "disabled" : "enabled"), + [](common_params & params, bool value) { + params.no_kv_offload = !value; + } + ).set_env("LLAMA_ARG_KV_OFFLOAD")); + add_opt(common_arg( + {"--repack"}, + {"-nr", "--no-repack"}, + string_format("whether to enable weight repacking (default: %s)", params.no_extra_bufts ? "disabled" : "enabled"), + [](common_params & params, bool value) { + params.no_extra_bufts = !value; + } + ).set_env("LLAMA_ARG_REPACK")); + add_opt(common_arg( + {"--no-host"}, + "bypass host buffer allowing extra buffers to be used", + [](common_params & params) { + params.no_host = true; + } + ).set_env("LLAMA_ARG_NO_HOST")); + add_opt(common_arg( + {"-ctk", "--cache-type-k"}, "TYPE", + string_format( + "KV cache data type for K\n" + "allowed values: %s\n" + "(default: %s)", + get_all_kv_cache_types().c_str(), + ggml_type_name(params.cache_type_k) + ), + [](common_params & params, const std::string & value) { + params.cache_type_k = kv_cache_type_from_str(value); + } + ).set_env("LLAMA_ARG_CACHE_TYPE_K")); + add_opt(common_arg( + {"-ctv", "--cache-type-v"}, "TYPE", + string_format( + "KV cache data type for V\n" + "allowed values: %s\n" + "(default: %s)", + get_all_kv_cache_types().c_str(), + ggml_type_name(params.cache_type_v) + ), + [](common_params & params, const std::string & value) { + params.cache_type_v = kv_cache_type_from_str(value); + } + ).set_env("LLAMA_ARG_CACHE_TYPE_V")); + add_opt(common_arg( + {"--hellaswag"}, + "compute HellaSwag score over random tasks from datafile supplied with -f", + [](common_params & params) { + params.hellaswag = true; + } + ).set_examples({LLAMA_EXAMPLE_PERPLEXITY})); + add_opt(common_arg( + {"--hellaswag-tasks"}, "N", + string_format("number of tasks to use when computing the HellaSwag score (default: %zu)", params.hellaswag_tasks), + [](common_params & params, int value) { + params.hellaswag_tasks = value; + } + ).set_examples({LLAMA_EXAMPLE_PERPLEXITY})); + add_opt(common_arg( + {"--winogrande"}, + "compute Winogrande score over random tasks from datafile supplied with -f", + [](common_params & params) { + params.winogrande = true; + } + ).set_examples({LLAMA_EXAMPLE_PERPLEXITY})); + add_opt(common_arg( + {"--winogrande-tasks"}, "N", + string_format("number of tasks to use when computing the Winogrande score (default: %zu)", params.winogrande_tasks), + [](common_params & params, int value) { + params.winogrande_tasks = value; + } + ).set_examples({LLAMA_EXAMPLE_PERPLEXITY})); + add_opt(common_arg( + {"--multiple-choice"}, + "compute multiple choice score over random tasks from datafile supplied with -f", + [](common_params & params) { + params.multiple_choice = true; + } + ).set_examples({LLAMA_EXAMPLE_PERPLEXITY})); + add_opt(common_arg( + {"--multiple-choice-tasks"}, "N", + string_format("number of tasks to use when computing the multiple choice score (default: %zu)", params.multiple_choice_tasks), + [](common_params & params, int value) { + params.multiple_choice_tasks = value; + } + ).set_examples({LLAMA_EXAMPLE_PERPLEXITY})); + add_opt(common_arg( + {"--kl-divergence"}, + "computes KL-divergence to logits provided via --kl-divergence-base", + [](common_params & params) { + params.kl_divergence = true; + } + ).set_examples({LLAMA_EXAMPLE_PERPLEXITY})); + add_opt(common_arg( + {"--save-all-logits", "--kl-divergence-base"}, "FNAME", + "set logits file", + [](common_params & params, const std::string & value) { + params.logits_file = value; + } + ).set_examples({LLAMA_EXAMPLE_PERPLEXITY})); + add_opt(common_arg( + {"--ppl-stride"}, "N", + string_format("stride for perplexity calculation (default: %d)", params.ppl_stride), + [](common_params & params, int value) { + params.ppl_stride = value; + } + ).set_examples({LLAMA_EXAMPLE_PERPLEXITY})); + add_opt(common_arg( + {"--ppl-output-type"}, "<0|1>", + string_format("output type for perplexity calculation (default: %d)", params.ppl_output_type), + [](common_params & params, int value) { + params.ppl_output_type = value; + } + ).set_examples({LLAMA_EXAMPLE_PERPLEXITY})); + add_opt(common_arg( + {"-dt", "--defrag-thold"}, "N", + string_format("KV cache defragmentation threshold (DEPRECATED)"), + [](common_params & params, const std::string & value) { + GGML_UNUSED(params); + GGML_UNUSED(value); + LOG_WRN("DEPRECATED: --defrag-thold is deprecated and no longer necessary to specify\n"); + } + ).set_env("LLAMA_ARG_DEFRAG_THOLD")); + if (ex == LLAMA_EXAMPLE_SERVER) { + // this is to make sure this option appears in the server-specific section of the help message + add_opt(common_arg( + {"-np", "--parallel"}, "N", + string_format("number of server slots (default: %d, -1 = auto)", params.n_parallel), + [](common_params & params, int value) { + if (value == 0) { + throw std::invalid_argument("error: invalid value for n_parallel\n"); + } + params.n_parallel = value; + } + ).set_env("LLAMA_ARG_N_PARALLEL").set_examples({LLAMA_EXAMPLE_SERVER})); + } else { + add_opt(common_arg( + {"-np", "--parallel"}, "N", + string_format("number of parallel sequences to decode (default: %d)", params.n_parallel), + [](common_params & params, int value) { + params.n_parallel = value; + } + ).set_env("LLAMA_ARG_N_PARALLEL")); + } + add_opt(common_arg( + {"-ns", "--sequences"}, "N", + string_format("number of sequences to decode (default: %d)", params.n_sequences), + [](common_params & params, int value) { + params.n_sequences = value; + } + ).set_examples({LLAMA_EXAMPLE_PARALLEL})); + add_opt(common_arg( + {"-cb", "--cont-batching"}, + {"-nocb", "--no-cont-batching"}, + string_format("whether to enable continuous batching (a.k.a dynamic batching) (default: %s)", params.cont_batching ? "enabled" : "disabled"), + [](common_params & params, bool value) { + params.cont_batching = value; + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_CONT_BATCHING")); + add_opt(common_arg( + {"-mm", "--mmproj"}, "FILE", + "path to a multimodal projector file. see tools/mtmd/README.md\n" + "note: if -hf is used, this argument can be omitted", + [](common_params & params, const std::string & value) { + params.mmproj.path = value; + } + ).set_examples(mmproj_examples).set_env("LLAMA_ARG_MMPROJ")); + add_opt(common_arg( + {"-mmu", "--mmproj-url"}, "URL", + "URL to a multimodal projector file. see tools/mtmd/README.md", + [](common_params & params, const std::string & value) { + params.mmproj.url = value; + } + ).set_examples(mmproj_examples).set_env("LLAMA_ARG_MMPROJ_URL")); + add_opt(common_arg( + {"--mmproj-auto"}, + {"--no-mmproj", "--no-mmproj-auto"}, + string_format("whether to use multimodal projector file (if available), useful when using -hf (default: %s)", params.no_mmproj ? "disabled" : "enabled"), + [](common_params & params, bool value) { + params.no_mmproj = !value; + } + ).set_examples(mmproj_examples).set_env("LLAMA_ARG_MMPROJ_AUTO")); + add_opt(common_arg( + {"--mmproj-offload"}, + {"--no-mmproj-offload"}, + string_format("whether to enable GPU offloading for multimodal projector (default: %s)", params.mmproj_use_gpu ? "enabled" : "disabled"), + [](common_params & params, bool value) { + params.mmproj_use_gpu = value; + } + ).set_examples(mmproj_examples).set_env("LLAMA_ARG_MMPROJ_OFFLOAD")); + add_opt(common_arg( + {"--image", "--audio"}, "FILE", + "path to an image or audio file. use with multimodal models, use comma-separated values for multiple files\n", + [](common_params & params, const std::string & value) { + for (const auto & item : parse_csv_row(value)) { + params.image.emplace_back(item); + } + } + ).set_examples({LLAMA_EXAMPLE_MTMD, LLAMA_EXAMPLE_CLI})); + add_opt(common_arg( + {"--image-min-tokens"}, "N", + "minimum number of tokens each image can take, only used by vision models with dynamic resolution (default: read from model)", + [](common_params & params, int value) { + params.image_min_tokens = value; + } + ).set_examples(mmproj_examples).set_env("LLAMA_ARG_IMAGE_MIN_TOKENS")); + add_opt(common_arg( + {"--image-max-tokens"}, "N", + "maximum number of tokens each image can take, only used by vision models with dynamic resolution (default: read from model)", + [](common_params & params, int value) { + params.image_max_tokens = value; + } + ).set_examples(mmproj_examples).set_env("LLAMA_ARG_IMAGE_MAX_TOKENS")); + if (llama_supports_rpc()) { + add_opt(common_arg( + {"--rpc"}, "SERVERS", + "comma separated list of RPC servers (host:port)", + [](common_params & params, const std::string & value) { + add_rpc_devices(value); + GGML_UNUSED(params); + } + ).set_env("LLAMA_ARG_RPC")); + } + add_opt(common_arg( + {"--mlock"}, + "force system to keep model in RAM rather than swapping or compressing", + [](common_params & params) { + params.use_mlock = true; + } + ).set_env("LLAMA_ARG_MLOCK")); + add_opt(common_arg( + {"--mmap"}, + {"--no-mmap"}, + string_format("whether to memory-map model. (if mmap disabled, slower load but may reduce pageouts if not using mlock) (default: %s)", params.use_mmap ? "enabled" : "disabled"), + [](common_params & params, bool value) { + params.use_mmap = value; + } + ).set_env("LLAMA_ARG_MMAP")); + add_opt(common_arg( + {"-dio", "--direct-io"}, + {"-ndio", "--no-direct-io"}, + string_format("use DirectIO if available. (default: %s)", params.use_direct_io ? "enabled" : "disabled"), + [](common_params & params, bool value) { + params.use_direct_io = value; + } + ).set_env("LLAMA_ARG_DIO")); + add_opt(common_arg( + {"--numa"}, "TYPE", + "attempt optimizations that help on some NUMA systems\n" + "- distribute: spread execution evenly over all nodes\n" + "- isolate: only spawn threads on CPUs on the node that execution started on\n" + "- numactl: use the CPU map provided by numactl\n" + "if run without this previously, it is recommended to drop the system page cache before using this\n" + "see https://github.com/ggml-org/llama.cpp/issues/1437", + [](common_params & params, const std::string & value) { + /**/ if (value == "distribute" || value == "") { params.numa = GGML_NUMA_STRATEGY_DISTRIBUTE; } + else if (value == "isolate") { params.numa = GGML_NUMA_STRATEGY_ISOLATE; } + else if (value == "numactl") { params.numa = GGML_NUMA_STRATEGY_NUMACTL; } + else { throw std::invalid_argument("invalid value"); } + } + ).set_env("LLAMA_ARG_NUMA")); + add_opt(common_arg( + {"-dev", "--device"}, "<dev1,dev2,..>", + "comma-separated list of devices to use for offloading (none = don't offload)\n" + "use --list-devices to see a list of available devices", + [](common_params & params, const std::string & value) { + params.devices = parse_device_list(value); + } + ).set_env("LLAMA_ARG_DEVICE")); + add_opt(common_arg( + {"--list-devices"}, + "print list of available devices and exit", + [](common_params &) { + std::vector<ggml_backend_dev_t> devices; + for (size_t i = 0; i < ggml_backend_dev_count(); ++i) { + auto * dev = ggml_backend_dev_get(i); + if (ggml_backend_dev_type(dev) != GGML_BACKEND_DEVICE_TYPE_CPU) { + devices.push_back(dev); + } + } + printf("Available devices:\n"); + for (auto * dev : devices) { + size_t free, total; + ggml_backend_dev_memory(dev, &free, &total); + printf(" %s: %s (%zu MiB, %zu MiB free)\n", ggml_backend_dev_name(dev), ggml_backend_dev_description(dev), total / 1024 / 1024, free / 1024 / 1024); + } + exit(0); + } + )); + add_opt(common_arg( + {"-ot", "--override-tensor"}, "<tensor name pattern>=<buffer type>,...", + "override tensor buffer type", [](common_params & params, const std::string & value) { + parse_tensor_buffer_overrides(value, params.tensor_buft_overrides); + } + ).set_env("LLAMA_ARG_OVERRIDE_TENSOR")); + add_opt(common_arg( + {"-otd", "--override-tensor-draft"}, "<tensor name pattern>=<buffer type>,...", + "override tensor buffer type for draft model", [](common_params & params, const std::string & value) { + parse_tensor_buffer_overrides(value, params.speculative.tensor_buft_overrides); + } + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI})); + add_opt(common_arg( + {"-cmoe", "--cpu-moe"}, + "keep all Mixture of Experts (MoE) weights in the CPU", + [](common_params & params) { + params.tensor_buft_overrides.push_back(llm_ffn_exps_cpu_override()); + } + ).set_env("LLAMA_ARG_CPU_MOE")); + add_opt(common_arg( + {"-ncmoe", "--n-cpu-moe"}, "N", + "keep the Mixture of Experts (MoE) weights of the first N layers in the CPU", + [](common_params & params, int value) { + if (value < 0) { + throw std::invalid_argument("invalid value"); + } + for (int i = 0; i < value; ++i) { + // keep strings alive and avoid leaking memory by storing them in a static vector + static std::list<std::string> buft_overrides; + buft_overrides.push_back(llm_ffn_exps_block_regex(i)); + params.tensor_buft_overrides.push_back({buft_overrides.back().c_str(), ggml_backend_cpu_buffer_type()}); + } + } + ).set_env("LLAMA_ARG_N_CPU_MOE")); + add_opt(common_arg( + {"-cmoed", "--cpu-moe-draft"}, + "keep all Mixture of Experts (MoE) weights in the CPU for the draft model", + [](common_params & params) { + params.speculative.tensor_buft_overrides.push_back(llm_ffn_exps_cpu_override()); + } + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_CPU_MOE_DRAFT")); + add_opt(common_arg( + {"-ncmoed", "--n-cpu-moe-draft"}, "N", + "keep the Mixture of Experts (MoE) weights of the first N layers in the CPU for the draft model", + [](common_params & params, int value) { + if (value < 0) { + throw std::invalid_argument("invalid value"); + } + for (int i = 0; i < value; ++i) { + static std::list<std::string> buft_overrides_draft; + buft_overrides_draft.push_back(llm_ffn_exps_block_regex(i)); + params.speculative.tensor_buft_overrides.push_back({buft_overrides_draft.back().c_str(), ggml_backend_cpu_buffer_type()}); + } + } + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_N_CPU_MOE_DRAFT")); + GGML_ASSERT(params.n_gpu_layers < 0); // string_format would need to be extended for a default >= 0 + add_opt(common_arg( + {"-ngl", "--gpu-layers", "--n-gpu-layers"}, "N", + string_format("max. number of layers to store in VRAM, either an exact number, 'auto', or 'all' (default: %s)", params.n_gpu_layers == -1 ? "auto" : "all"), + [](common_params & params, const std::string & value) { + if (value == "auto") { + params.n_gpu_layers = -1; + } else if (value == "all") { + params.n_gpu_layers = -2; + } else { + params.n_gpu_layers = std::stoi(value); + } + if (!llama_supports_gpu_offload()) { + fprintf(stderr, "warning: no usable GPU found, --gpu-layers option will be ignored\n"); + fprintf(stderr, "warning: one possible reason is that llama.cpp was compiled without GPU support\n"); + fprintf(stderr, "warning: consult docs/build.md for compilation instructions\n"); + } + } + ).set_env("LLAMA_ARG_N_GPU_LAYERS")); + add_opt(common_arg( + {"-sm", "--split-mode"}, "{none,layer,row}", + "how to split the model across multiple GPUs, one of:\n" + "- none: use one GPU only\n" + "- layer (default): split layers and KV across GPUs\n" + "- row: split rows across GPUs", + [](common_params & params, const std::string & value) { + std::string arg_next = value; + if (arg_next == "none") { + params.split_mode = LLAMA_SPLIT_MODE_NONE; + } else if (arg_next == "layer") { + params.split_mode = LLAMA_SPLIT_MODE_LAYER; + } else if (arg_next == "row") { + params.split_mode = LLAMA_SPLIT_MODE_ROW; + } else { + throw std::invalid_argument("invalid value"); + } + if (!llama_supports_gpu_offload()) { + fprintf(stderr, "warning: llama.cpp was compiled without support for GPU offload. Setting the split mode has no effect.\n"); + } + } + ).set_env("LLAMA_ARG_SPLIT_MODE")); + add_opt(common_arg( + {"-ts", "--tensor-split"}, "N0,N1,N2,...", + "fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1", + [](common_params & params, const std::string & value) { + std::string arg_next = value; + + // split string by , and / + const std::regex regex{ R"([,/]+)" }; + std::sregex_token_iterator it{ arg_next.begin(), arg_next.end(), regex, -1 }; + std::vector<std::string> split_arg{ it, {} }; + if (split_arg.size() >= llama_max_devices()) { + throw std::invalid_argument( + string_format("got %zu input configs, but system only has %zu devices", split_arg.size(), llama_max_devices()) + ); + } + for (size_t i = 0; i < llama_max_devices(); ++i) { + if (i < split_arg.size()) { + params.tensor_split[i] = std::stof(split_arg[i]); + } else { + params.tensor_split[i] = 0.0f; + } + } + if (!llama_supports_gpu_offload()) { + fprintf(stderr, "warning: llama.cpp was compiled without support for GPU offload. Setting a tensor split has no effect.\n"); + } + } + ).set_env("LLAMA_ARG_TENSOR_SPLIT")); + add_opt(common_arg( + {"-mg", "--main-gpu"}, "INDEX", + string_format("the GPU to use for the model (with split-mode = none), or for intermediate results and KV (with split-mode = row) (default: %d)", params.main_gpu), + [](common_params & params, int value) { + params.main_gpu = value; + if (!llama_supports_gpu_offload()) { + fprintf(stderr, "warning: llama.cpp was compiled without support for GPU offload. Setting the main GPU has no effect.\n"); + } + } + ).set_env("LLAMA_ARG_MAIN_GPU")); + add_opt(common_arg( + { "-fit", "--fit" }, "[on|off]", + string_format("whether to adjust unset arguments to fit in device memory ('on' or 'off', default: '%s')", params.fit_params ? "on" : "off"), + [](common_params & params, const std::string & value) { + if (is_truthy(value)) { + params.fit_params = true; + } else if (is_falsey(value)) { + params.fit_params = false; + } else { + throw std::runtime_error( + string_format("error: unkown value for --fit: '%s'\n", value.c_str())); + } + } + ).set_env("LLAMA_ARG_FIT")); + add_opt(common_arg( + { "-fitt", "--fit-target" }, "MiB0,MiB1,MiB2,...", + string_format("target margin per device for --fit, comma-separated list of values, " + "single value is broadcast across all devices, default: %zu", params.fit_params_target[0]/(1024*1024)), + [](common_params & params, const std::string & value) { + std::string arg_next = value; + + // split string by , and / + const std::regex regex{ R"([,/]+)" }; + std::sregex_token_iterator it{ arg_next.begin(), arg_next.end(), regex, -1 }; + std::vector<std::string> split_arg{ it, {} }; + if (split_arg.size() >= llama_max_devices()) { + throw std::invalid_argument( + string_format("got %zu input configs, but system only has %zu devices", split_arg.size(), llama_max_devices()) + ); + } + if (split_arg.size() == 1) { + std::fill(params.fit_params_target.begin(), params.fit_params_target.end(), std::stoul(split_arg[0]) * 1024*1024); + return; + } + for (size_t i = 0; i < split_arg.size(); i++) { + params.fit_params_target[i] = std::stoul(split_arg[i]) * 1024*1024; + } + } + ).set_env("LLAMA_ARG_FIT_TARGET")); + add_opt(common_arg( + { "-fitc", "--fit-ctx" }, "N", + string_format("minimum ctx size that can be set by --fit option, default: %" PRIu32, params.fit_params_min_ctx), + [](common_params & params, int value) { + params.fit_params_min_ctx = value; + } + ).set_env("LLAMA_ARG_FIT_CTX")); + add_opt(common_arg( + {"--check-tensors"}, + string_format("check model tensor data for invalid values (default: %s)", params.check_tensors ? "true" : "false"), + [](common_params & params) { + params.check_tensors = true; + } + )); + add_opt(common_arg( + {"--override-kv"}, "KEY=TYPE:VALUE,...", + "advanced option to override model metadata by key. to specify multiple overrides, either use comma-separated values.\n" + "types: int, float, bool, str. example: --override-kv tokenizer.ggml.add_bos_token=bool:false,tokenizer.ggml.add_eos_token=bool:false", + [](common_params & params, const std::string & value) { + for (const auto & item : parse_csv_row(value)) { + if (!string_parse_kv_override(item.c_str(), params.kv_overrides)) { + throw std::runtime_error(string_format("error: Invalid type for KV override: %s\n", item.c_str())); + } + } + } + )); + add_opt(common_arg( + {"--op-offload"}, + {"--no-op-offload"}, + string_format("whether to offload host tensor operations to device (default: %s)", params.no_op_offload ? "false" : "true"), + [](common_params & params, bool value) { + params.no_op_offload = !value; + } + )); + add_opt(common_arg( + {"--lora"}, "FNAME", + "path to LoRA adapter (use comma-separated values to load multiple adapters)", + [](common_params & params, const std::string & value) { + for (const auto & item : parse_csv_row(value)) { + params.lora_adapters.push_back({ item, 1.0, "", "", nullptr }); + } + } + // we define this arg on both COMMON and EXPORT_LORA, so when showing help message of export-lora, it will be categorized as "example-specific" arg + ).set_examples({LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_EXPORT_LORA})); + add_opt(common_arg( + {"--lora-scaled"}, "FNAME:SCALE,...", + "path to LoRA adapter with user defined scaling (format: FNAME:SCALE,...)\n" + "note: use comma-separated values", + [](common_params & params, const std::string & value) { + for (const auto & item : parse_csv_row(value)) { + auto parts = string_split<std::string>(item, ':'); + if (parts.size() != 2) { + throw std::invalid_argument("lora-scaled format: FNAME:SCALE"); + } + params.lora_adapters.push_back({ parts[0], std::stof(parts[1]), "", "", nullptr }); + } + } + // we define this arg on both COMMON and EXPORT_LORA, so when showing help message of export-lora, it will be categorized as "example-specific" arg + ).set_examples({LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_EXPORT_LORA})); + add_opt(common_arg( + {"--control-vector"}, "FNAME", + "add a control vector\nnote: use comma-separated values to add multiple control vectors", + [](common_params & params, const std::string & value) { + for (const auto & item : parse_csv_row(value)) { + params.control_vectors.push_back({ 1.0f, item, }); + } + } + )); + add_opt(common_arg( + {"--control-vector-scaled"}, "FNAME:SCALE,...", + "add a control vector with user defined scaling SCALE\n" + "note: use comma-separated values (format: FNAME:SCALE,...)", + [](common_params & params, const std::string & value) { + for (const auto & item : parse_csv_row(value)) { + auto parts = string_split<std::string>(item, ':'); + if (parts.size() != 2) { + throw std::invalid_argument("control-vector-scaled format: FNAME:SCALE"); + } + params.control_vectors.push_back({ std::stof(parts[1]), parts[0] }); + } + } + )); + add_opt(common_arg( + {"--control-vector-layer-range"}, "START", "END", + "layer range to apply the control vector(s) to, start and end inclusive", + [](common_params & params, const std::string & start, const std::string & end) { + params.control_vector_layer_start = std::stoi(start); + params.control_vector_layer_end = std::stoi(end); + } + )); + add_opt(common_arg( + {"-a", "--alias"}, "STRING", + "set alias for model name (to be used by REST API)", + [](common_params & params, const std::string & value) { + params.model_alias = value; + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_ALIAS")); + add_opt(common_arg( + {"-m", "--model"}, "FNAME", + ex == LLAMA_EXAMPLE_EXPORT_LORA + ? "model path from which to load base model" + : "model path to load", + [](common_params & params, const std::string & value) { + params.model.path = value; + } + ).set_examples({LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_EXPORT_LORA}).set_env("LLAMA_ARG_MODEL")); + add_opt(common_arg( + {"-mu", "--model-url"}, "MODEL_URL", + "model download url (default: unused)", + [](common_params & params, const std::string & value) { + params.model.url = value; + } + ).set_env("LLAMA_ARG_MODEL_URL")); + add_opt(common_arg( + { "-dr", "--docker-repo" }, "[<repo>/]<model>[:quant]", + "Docker Hub model repository. repo is optional, default to ai/. quant is optional, default to :latest.\n" + "example: gemma3\n" + "(default: unused)", + [](common_params & params, const std::string & value) { + params.model.docker_repo = value; + } + ).set_env("LLAMA_ARG_DOCKER_REPO")); + add_opt(common_arg( + {"-hf", "-hfr", "--hf-repo"}, "<user>/<model>[:quant]", + "Hugging Face model repository; quant is optional, case-insensitive, default to Q4_K_M, or falls back to the first file in the repo if Q4_K_M doesn't exist.\n" + "mmproj is also downloaded automatically if available. to disable, add --no-mmproj\n" + "example: unsloth/phi-4-GGUF:q4_k_m\n" + "(default: unused)", + [](common_params & params, const std::string & value) { + params.model.hf_repo = value; + } + ).set_env("LLAMA_ARG_HF_REPO")); + add_opt(common_arg( + {"-hfd", "-hfrd", "--hf-repo-draft"}, "<user>/<model>[:quant]", + "Same as --hf-repo, but for the draft model (default: unused)", + [](common_params & params, const std::string & value) { + params.speculative.mparams_dft.hf_repo = value; + } + ).set_env("LLAMA_ARG_HFD_REPO")); + add_opt(common_arg( + {"-hff", "--hf-file"}, "FILE", + "Hugging Face model file. If specified, it will override the quant in --hf-repo (default: unused)", + [](common_params & params, const std::string & value) { + params.model.hf_file = value; + } + ).set_env("LLAMA_ARG_HF_FILE")); + add_opt(common_arg( + {"-hfv", "-hfrv", "--hf-repo-v"}, "<user>/<model>[:quant]", + "Hugging Face model repository for the vocoder model (default: unused)", + [](common_params & params, const std::string & value) { + params.vocoder.model.hf_repo = value; + } + ).set_env("LLAMA_ARG_HF_REPO_V")); + add_opt(common_arg( + {"-hffv", "--hf-file-v"}, "FILE", + "Hugging Face model file for the vocoder model (default: unused)", + [](common_params & params, const std::string & value) { + params.vocoder.model.hf_file = value; + } + ).set_env("LLAMA_ARG_HF_FILE_V")); + add_opt(common_arg( + {"-hft", "--hf-token"}, "TOKEN", + "Hugging Face access token (default: value from HF_TOKEN environment variable)", + [](common_params & params, const std::string & value) { + params.hf_token = value; + } + ).set_env("HF_TOKEN")); + add_opt(common_arg( + {"--context-file"}, "FNAME", + "file to load context from (use comma-separated values to specify multiple files)", + [](common_params & params, const std::string & value) { + for (const auto & item : parse_csv_row(value)) { + std::ifstream file(item, std::ios::binary); + if (!file) { + throw std::runtime_error(string_format("error: failed to open file '%s'\n", item.c_str())); + } + params.context_files.push_back(item); + } + } + ).set_examples({LLAMA_EXAMPLE_RETRIEVAL})); + add_opt(common_arg( + {"--chunk-size"}, "N", + string_format("minimum length of embedded text chunks (default: %d)", params.chunk_size), + [](common_params & params, int value) { + params.chunk_size = value; + } + ).set_examples({LLAMA_EXAMPLE_RETRIEVAL})); + add_opt(common_arg( + {"--chunk-separator"}, "STRING", + string_format("separator between chunks (default: '%s')", params.chunk_separator.c_str()), + [](common_params & params, const std::string & value) { + params.chunk_separator = value; + } + ).set_examples({LLAMA_EXAMPLE_RETRIEVAL})); + add_opt(common_arg( + {"--junk"}, "N", + string_format("number of times to repeat the junk text (default: %d)", params.n_junk), + [](common_params & params, int value) { + params.n_junk = value; + } + ).set_examples({LLAMA_EXAMPLE_PASSKEY, LLAMA_EXAMPLE_PARALLEL})); + add_opt(common_arg( + {"--pos"}, "N", + string_format("position of the passkey in the junk text (default: %d)", params.i_pos), + [](common_params & params, int value) { + params.i_pos = value; + } + ).set_examples({LLAMA_EXAMPLE_PASSKEY})); + add_opt(common_arg( + {"-o", "--output", "--output-file"}, "FNAME", + string_format("output file (default: '%s')", params.out_file.c_str()), + [](common_params & params, const std::string & value) { + params.out_file = value; + } + ).set_examples({LLAMA_EXAMPLE_IMATRIX, LLAMA_EXAMPLE_CVECTOR_GENERATOR, LLAMA_EXAMPLE_EXPORT_LORA, LLAMA_EXAMPLE_TTS, LLAMA_EXAMPLE_FINETUNE})); + add_opt(common_arg( + {"-ofreq", "--output-frequency"}, "N", + string_format("output the imatrix every N iterations (default: %d)", params.n_out_freq), + [](common_params & params, int value) { + params.n_out_freq = value; + } + ).set_examples({LLAMA_EXAMPLE_IMATRIX})); + add_opt(common_arg( + {"--output-format"}, "{gguf,dat}", + string_format("output format for imatrix file (default: %s)", params.imat_dat > 0 ? "dat" : "gguf"), + [](common_params & params, const std::string & value) { + /**/ if (value == "gguf") { params.imat_dat = -1; } + else if (value == "dat") { params.imat_dat = 1; } + else { throw std::invalid_argument("invalid output format"); } + } + ).set_examples({LLAMA_EXAMPLE_IMATRIX})); + add_opt(common_arg( + {"--save-frequency"}, "N", + string_format("save an imatrix copy every N iterations (default: %d)", params.n_save_freq), + [](common_params & params, int value) { + params.n_save_freq = value; + } + ).set_examples({LLAMA_EXAMPLE_IMATRIX})); + add_opt(common_arg( + {"--process-output"}, + string_format("collect data for the output tensor (default: %s)", params.process_output ? "true" : "false"), + [](common_params & params) { + params.process_output = true; + } + ).set_examples({LLAMA_EXAMPLE_IMATRIX})); + add_opt(common_arg( + {"--ppl"}, + {"--no-ppl"}, + string_format("whether to compute perplexity (default: %s)", params.compute_ppl ? "true" : "false"), + [](common_params & params, bool value) { + params.compute_ppl = value; + } + ).set_examples({LLAMA_EXAMPLE_IMATRIX})); + add_opt(common_arg( + {"--chunk", "--from-chunk"}, "N", + string_format("start processing the input from chunk N (default: %d)", params.i_chunk), + [](common_params & params, int value) { + params.i_chunk = value; + } + ).set_examples({LLAMA_EXAMPLE_IMATRIX})); + add_opt(common_arg( + {"--show-statistics"}, + string_format("show imatrix statistics and then exit (default: %s)", params.show_statistics ? "true" : "false"), + [](common_params & params) { + params.show_statistics = true; + } + ).set_examples({LLAMA_EXAMPLE_IMATRIX})); + add_opt(common_arg( + {"--parse-special"}, + string_format("parse special tokens (chat, tool, etc) (default: %s)", params.parse_special ? "true" : "false"), + [](common_params & params) { + params.parse_special = true; + } + ).set_examples({LLAMA_EXAMPLE_IMATRIX})); + add_opt(common_arg( + {"-pps"}, + string_format("is the prompt shared across parallel sequences (default: %s)", params.is_pp_shared ? "true" : "false"), + [](common_params & params) { + params.is_pp_shared = true; + } + ).set_examples({LLAMA_EXAMPLE_BENCH, LLAMA_EXAMPLE_PARALLEL})); + add_opt(common_arg( + {"-tgs"}, + string_format("is the text generation separated across the different sequences (default: %s)", params.is_tg_separate ? "true" : "false"), + [](common_params & params) { + params.is_tg_separate = true; + } + ).set_examples({LLAMA_EXAMPLE_BENCH, LLAMA_EXAMPLE_PARALLEL})); + add_opt(common_arg( + {"-npp"}, "n0,n1,...", + "number of prompt tokens", + [](common_params & params, const std::string & value) { + auto p = string_split<int>(value, ','); + params.n_pp.insert(params.n_pp.end(), p.begin(), p.end()); + } + ).set_examples({LLAMA_EXAMPLE_BENCH})); + add_opt(common_arg( + {"-ntg"}, "n0,n1,...", + "number of text generation tokens", + [](common_params & params, const std::string & value) { + auto p = string_split<int>(value, ','); + params.n_tg.insert(params.n_tg.end(), p.begin(), p.end()); + } + ).set_examples({LLAMA_EXAMPLE_BENCH})); + add_opt(common_arg( + {"-npl"}, "n0,n1,...", + "number of parallel prompts", + [](common_params & params, const std::string & value) { + auto p = string_split<int>(value, ','); + params.n_pl.insert(params.n_pl.end(), p.begin(), p.end()); + } + ).set_examples({LLAMA_EXAMPLE_BENCH})); + add_opt(common_arg( + {"--embd-normalize"}, "N", + string_format("normalisation for embeddings (default: %d) (-1=none, 0=max absolute int16, 1=taxicab, 2=euclidean, >2=p-norm)", params.embd_normalize), + [](common_params & params, int value) { + params.embd_normalize = value; + } + ).set_examples({LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_DEBUG})); + add_opt(common_arg( + {"--embd-output-format"}, "FORMAT", + "empty = default, \"array\" = [[],[]...], \"json\" = openai style, \"json+\" = same \"json\" + cosine similarity matrix, \"raw\" = plain whitespace-delimited output (one embedding per line)", + [](common_params & params, const std::string & value) { + params.embd_out = value; + } + ).set_examples({LLAMA_EXAMPLE_EMBEDDING})); + add_opt(common_arg( + {"--embd-separator"}, "STRING", + "separator of embeddings (default \\n) for example \"<#sep#>\"", + [](common_params & params, const std::string & value) { + params.embd_sep = value; + } + ).set_examples({LLAMA_EXAMPLE_EMBEDDING})); + add_opt(common_arg( + {"--cls-separator"}, "STRING", + "separator of classification sequences (default \\t) for example \"<#seq#>\"", + [](common_params & params, const std::string & value) { + params.cls_sep = value; + } + ).set_examples({LLAMA_EXAMPLE_EMBEDDING})); + add_opt(common_arg( + {"--host"}, "HOST", + string_format("ip address to listen, or bind to an UNIX socket if the address ends with .sock (default: %s)", params.hostname.c_str()), + [](common_params & params, const std::string & value) { + params.hostname = value; + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_HOST")); + add_opt(common_arg( + {"--port"}, "PORT", + string_format("port to listen (default: %d)", params.port), + [](common_params & params, int value) { + params.port = value; + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_PORT")); + add_opt(common_arg( + {"--path"}, "PATH", + string_format("path to serve static files from (default: %s)", params.public_path.c_str()), + [](common_params & params, const std::string & value) { + params.public_path = value; + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_STATIC_PATH")); + add_opt(common_arg( + {"--api-prefix"}, "PREFIX", + string_format("prefix path the server serves from, without the trailing slash (default: %s)", params.api_prefix.c_str()), + [](common_params & params, const std::string & value) { + params.api_prefix = value; + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_API_PREFIX")); + add_opt(common_arg( + {"--webui-config"}, "JSON", + "JSON that provides default WebUI settings (overrides WebUI defaults)", + [](common_params & params, const std::string & value) { + params.webui_config_json = value; + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_WEBUI_CONFIG")); + add_opt(common_arg( + {"--webui-config-file"}, "PATH", + "JSON file that provides default WebUI settings (overrides WebUI defaults)", + [](common_params & params, const std::string & value) { + params.webui_config_json = read_file(value); + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_WEBUI_CONFIG_FILE")); + add_opt(common_arg( + {"--webui"}, + {"--no-webui"}, + string_format("whether to enable the Web UI (default: %s)", params.webui ? "enabled" : "disabled"), + [](common_params & params, bool value) { + params.webui = value; + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_WEBUI")); + add_opt(common_arg( + {"--embedding", "--embeddings"}, + string_format("restrict to only support embedding use case; use only with dedicated embedding models (default: %s)", params.embedding ? "enabled" : "disabled"), + [](common_params & params) { + params.embedding = true; + } + ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_DEBUG}).set_env("LLAMA_ARG_EMBEDDINGS")); + add_opt(common_arg( + {"--rerank", "--reranking"}, + string_format("enable reranking endpoint on server (default: %s)", "disabled"), + [](common_params & params) { + params.embedding = true; + params.pooling_type = LLAMA_POOLING_TYPE_RANK; + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_RERANKING")); + add_opt(common_arg( + {"--api-key"}, "KEY", + "API key to use for authentication, multiple keys can be provided as a comma-separated list (default: none)", + [](common_params & params, const std::string & value) { + for (const auto & key : parse_csv_row(value)) { + if (!key.empty()) { + params.api_keys.push_back(key); + } + } + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_API_KEY")); + add_opt(common_arg( + {"--api-key-file"}, "FNAME", + "path to file containing API keys (default: none)", + [](common_params & params, const std::string & value) { + std::ifstream key_file(value); + if (!key_file) { + throw std::runtime_error(string_format("error: failed to open file '%s'\n", value.c_str())); + } + std::string key; + while (std::getline(key_file, key)) { + if (!key.empty()) { + params.api_keys.push_back(key); + } + } + key_file.close(); + } + ).set_examples({LLAMA_EXAMPLE_SERVER})); + add_opt(common_arg( + {"--ssl-key-file"}, "FNAME", + "path to file a PEM-encoded SSL private key", + [](common_params & params, const std::string & value) { + params.ssl_file_key = value; + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_SSL_KEY_FILE")); + add_opt(common_arg( + {"--ssl-cert-file"}, "FNAME", + "path to file a PEM-encoded SSL certificate", + [](common_params & params, const std::string & value) { + params.ssl_file_cert = value; + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_SSL_CERT_FILE")); + add_opt(common_arg( + {"--chat-template-kwargs"}, "STRING", + "sets additional params for the json template parser, must be a valid json object string, e.g. '{\"key1\":\"value1\",\"key2\":\"value2\"}'", + [](common_params & params, const std::string & value) { + auto parsed = json::parse(value); + for (const auto & item : parsed.items()) { + params.default_template_kwargs[item.key()] = item.value().dump(); + } + } + ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_CHAT_TEMPLATE_KWARGS")); + add_opt(common_arg( + {"-to", "--timeout"}, "N", + string_format("server read/write timeout in seconds (default: %d)", params.timeout_read), + [](common_params & params, int value) { + params.timeout_read = value; + params.timeout_write = value; + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_TIMEOUT")); + add_opt(common_arg( + {"--threads-http"}, "N", + string_format("number of threads used to process HTTP requests (default: %d)", params.n_threads_http), + [](common_params & params, int value) { + params.n_threads_http = value; + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_THREADS_HTTP")); + add_opt(common_arg( + {"--cache-prompt"}, + {"--no-cache-prompt"}, + string_format("whether to enable prompt caching (default: %s)", params.cache_prompt ? "enabled" : "disabled"), + [](common_params & params, bool value) { + params.cache_prompt = value; + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_CACHE_PROMPT")); + add_opt(common_arg( + {"--cache-reuse"}, "N", + string_format( + "min chunk size to attempt reusing from the cache via KV shifting, requires prompt caching to be enabled (default: %d)\n" + "[(card)](https://ggml.ai/f0.png)", params.n_cache_reuse + ), + [](common_params & params, int value) { + params.n_cache_reuse = value; + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_CACHE_REUSE")); + add_opt(common_arg( + {"--metrics"}, + string_format("enable prometheus compatible metrics endpoint (default: %s)", params.endpoint_metrics ? "enabled" : "disabled"), + [](common_params & params) { + params.endpoint_metrics = true; + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_ENDPOINT_METRICS")); + add_opt(common_arg( + {"--props"}, + string_format("enable changing global properties via POST /props (default: %s)", params.endpoint_props ? "enabled" : "disabled"), + [](common_params & params) { + params.endpoint_props = true; + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_ENDPOINT_PROPS")); + add_opt(common_arg( + {"--slots"}, + {"--no-slots"}, + string_format("expose slots monitoring endpoint (default: %s)", params.endpoint_slots ? "enabled" : "disabled"), + [](common_params & params, bool value) { + params.endpoint_slots = value; + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_ENDPOINT_SLOTS")); + add_opt(common_arg( + {"--slot-save-path"}, "PATH", + "path to save slot kv cache (default: disabled)", + [](common_params & params, const std::string & value) { + params.slot_save_path = value; + if (!fs_is_directory(params.slot_save_path)) { + throw std::invalid_argument("not a directory: " + value); + } + // if doesn't end with DIRECTORY_SEPARATOR, add it + if (!params.slot_save_path.empty() && params.slot_save_path[params.slot_save_path.size() - 1] != DIRECTORY_SEPARATOR) { + params.slot_save_path += DIRECTORY_SEPARATOR; + } + } + ).set_examples({LLAMA_EXAMPLE_SERVER})); + add_opt(common_arg( + {"--media-path"}, "PATH", + "directory for loading local media files; files can be accessed via file:// URLs using relative paths (default: disabled)", + [](common_params & params, const std::string & value) { + params.media_path = value; + if (!fs_is_directory(params.media_path)) { + throw std::invalid_argument("not a directory: " + value); + } + // if doesn't end with DIRECTORY_SEPARATOR, add it + if (!params.media_path.empty() && params.media_path[params.media_path.size() - 1] != DIRECTORY_SEPARATOR) { + params.media_path += DIRECTORY_SEPARATOR; + } + } + ).set_examples({LLAMA_EXAMPLE_SERVER})); + add_opt(common_arg( + {"--models-dir"}, "PATH", + "directory containing models for the router server (default: disabled)", + [](common_params & params, const std::string & value) { + params.models_dir = value; + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_MODELS_DIR")); + add_opt(common_arg( + {"--models-preset"}, "PATH", + "path to INI file containing model presets for the router server (default: disabled)", + [](common_params & params, const std::string & value) { + params.models_preset = value; + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_MODELS_PRESET")); + add_opt(common_arg( + {"--models-max"}, "N", + string_format("for router server, maximum number of models to load simultaneously (default: %d, 0 = unlimited)", params.models_max), + [](common_params & params, int value) { + params.models_max = value; + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_MODELS_MAX")); + add_opt(common_arg( + {"--models-autoload"}, + {"--no-models-autoload"}, + string_format("for router server, whether to automatically load models (default: %s)", params.models_autoload ? "enabled" : "disabled"), + [](common_params & params, bool value) { + params.models_autoload = value; + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_MODELS_AUTOLOAD")); + add_opt(common_arg( + {"--jinja"}, + {"--no-jinja"}, + string_format("whether to use jinja template engine for chat (default: %s)", params.use_jinja ? "enabled" : "disabled"), + [](common_params & params, bool value) { + params.use_jinja = value; + } + ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_MTMD}).set_env("LLAMA_ARG_JINJA")); + add_opt(common_arg( + {"--reasoning-format"}, "FORMAT", + "controls whether thought tags are allowed and/or extracted from the response, and in which format they're returned; one of:\n" + "- none: leaves thoughts unparsed in `message.content`\n" + "- deepseek: puts thoughts in `message.reasoning_content`\n" + "- deepseek-legacy: keeps `<think>` tags in `message.content` while also populating `message.reasoning_content`\n" + "(default: auto)", + [](common_params & params, const std::string & value) { + params.reasoning_format = common_reasoning_format_from_name(value); + } + ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_THINK")); + add_opt(common_arg( + {"--reasoning-budget"}, "N", + "controls the amount of thinking allowed; currently only one of: -1 for unrestricted thinking budget, or 0 to disable thinking (default: -1)", + [](common_params & params, int value) { + if (value != 0 && value != -1) { throw std::invalid_argument("invalid value"); } + params.reasoning_budget = value; + } + ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_THINK_BUDGET")); + add_opt(common_arg( + {"--chat-template"}, "JINJA_TEMPLATE", + string_format( + "set custom jinja chat template (default: template taken from model's metadata)\n" + "if suffix/prefix are specified, template will be disabled\n" + "only commonly used templates are accepted (unless --jinja is set before this flag):\n" + "list of built-in templates:\n%s", list_builtin_chat_templates().c_str() + ), + [](common_params & params, const std::string & value) { + params.chat_template = value; + } + ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_MTMD}).set_env("LLAMA_ARG_CHAT_TEMPLATE")); + add_opt(common_arg( + {"--chat-template-file"}, "JINJA_TEMPLATE_FILE", + string_format( + "set custom jinja chat template file (default: template taken from model's metadata)\n" + "if suffix/prefix are specified, template will be disabled\n" + "only commonly used templates are accepted (unless --jinja is set before this flag):\n" + "list of built-in templates:\n%s", list_builtin_chat_templates().c_str() + ), + [](common_params & params, const std::string & value) { + params.chat_template = read_file(value); + } + ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_CHAT_TEMPLATE_FILE")); + add_opt(common_arg( + {"--prefill-assistant"}, + {"--no-prefill-assistant"}, + string_format( + "whether to prefill the assistant's response if the last message is an assistant message (default: prefill enabled)\n" + "when this flag is set, if the last message is an assistant message then it will be treated as a full message and not prefilled\n" + ), + [](common_params & params, bool value) { + params.prefill_assistant = value; + } + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_PREFILL_ASSISTANT")); + add_opt(common_arg( + {"-sps", "--slot-prompt-similarity"}, "SIMILARITY", + string_format("how much the prompt of a request must match the prompt of a slot in order to use that slot (default: %.2f, 0.0 = disabled)\n", params.slot_prompt_similarity), + [](common_params & params, const std::string & value) { + params.slot_prompt_similarity = std::stof(value); + } + ).set_examples({LLAMA_EXAMPLE_SERVER})); + add_opt(common_arg( + {"--lora-init-without-apply"}, + string_format("load LoRA adapters without applying them (apply later via POST /lora-adapters) (default: %s)", params.lora_init_without_apply ? "enabled" : "disabled"), + [](common_params & params) { + params.lora_init_without_apply = true; + } + ).set_examples({LLAMA_EXAMPLE_SERVER})); + add_opt(common_arg( + {"--sleep-idle-seconds"}, "SECONDS", + string_format("number of seconds of idleness after which the server will sleep (default: %d; -1 = disabled)", params.sleep_idle_seconds), + [](common_params & params, int value) { + if (value == 0 || value < -1) { + throw std::invalid_argument("invalid value: cannot be 0 or less than -1"); + } + params.sleep_idle_seconds = value; + } + ).set_examples({LLAMA_EXAMPLE_SERVER})); + add_opt(common_arg( + {"--simple-io"}, + "use basic IO for better compatibility in subprocesses and limited consoles", + [](common_params & params) { + params.simple_io = true; + } + ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI})); + add_opt(common_arg( + {"--positive-file"}, "FNAME", + string_format("positive prompts file, one prompt per line (default: '%s')", params.cvector_positive_file.c_str()), + [](common_params & params, const std::string & value) { + params.cvector_positive_file = value; + } + ).set_examples({LLAMA_EXAMPLE_CVECTOR_GENERATOR})); + add_opt(common_arg( + {"--negative-file"}, "FNAME", + string_format("negative prompts file, one prompt per line (default: '%s')", params.cvector_negative_file.c_str()), + [](common_params & params, const std::string & value) { + params.cvector_negative_file = value; + } + ).set_examples({LLAMA_EXAMPLE_CVECTOR_GENERATOR})); + add_opt(common_arg( + {"--pca-batch"}, "N", + string_format("batch size used for PCA. Larger batch runs faster, but uses more memory (default: %d)", params.n_pca_batch), + [](common_params & params, int value) { + params.n_pca_batch = value; + } + ).set_examples({LLAMA_EXAMPLE_CVECTOR_GENERATOR})); + add_opt(common_arg( + {"--pca-iter"}, "N", + string_format("number of iterations used for PCA (default: %d)", params.n_pca_iterations), + [](common_params & params, int value) { + params.n_pca_iterations = value; + } + ).set_examples({LLAMA_EXAMPLE_CVECTOR_GENERATOR})); + add_opt(common_arg( + {"--method"}, "{pca, mean}", + "dimensionality reduction method to be used (default: pca)", + [](common_params & params, const std::string & value) { + /**/ if (value == "pca") { params.cvector_dimre_method = DIMRE_METHOD_PCA; } + else if (value == "mean") { params.cvector_dimre_method = DIMRE_METHOD_MEAN; } + else { throw std::invalid_argument("invalid value"); } + } + ).set_examples({LLAMA_EXAMPLE_CVECTOR_GENERATOR})); + add_opt(common_arg( + {"--output-format"}, "{md,jsonl}", + "output format for batched-bench results (default: md)", + [](common_params & params, const std::string & value) { + /**/ if (value == "jsonl") { params.batched_bench_output_jsonl = true; } + else if (value == "md") { params.batched_bench_output_jsonl = false; } + else { throw std::invalid_argument("invalid value"); } + } + ).set_examples({LLAMA_EXAMPLE_BENCH})); + add_opt(common_arg( + {"--log-disable"}, + "Log disable", + [](common_params &) { + common_log_pause(common_log_main()); + } + )); + add_opt(common_arg( + {"--log-file"}, "FNAME", + "Log to file", + [](common_params &, const std::string & value) { + common_log_set_file(common_log_main(), value.c_str()); + } + ).set_env("LLAMA_LOG_FILE")); + add_opt(common_arg( + {"--log-colors"}, "[on|off|auto]", + "Set colored logging ('on', 'off', or 'auto', default: 'auto')\n" + "'auto' enables colors when output is to a terminal", + [](common_params &, const std::string & value) { + if (is_truthy(value)) { + common_log_set_colors(common_log_main(), LOG_COLORS_ENABLED); + } else if (is_falsey(value)) { + common_log_set_colors(common_log_main(), LOG_COLORS_DISABLED); + } else if (is_autoy(value)) { + common_log_set_colors(common_log_main(), LOG_COLORS_AUTO); + } else { + throw std::invalid_argument( + string_format("error: unknown value for --log-colors: '%s'\n", value.c_str())); + } + } + ).set_env("LLAMA_LOG_COLORS")); + add_opt(common_arg( + {"-v", "--verbose", "--log-verbose"}, + "Set verbosity level to infinity (i.e. log all messages, useful for debugging)", + [](common_params & params) { + params.verbosity = INT_MAX; + } + )); + add_opt(common_arg( + {"--offline"}, + "Offline mode: forces use of cache, prevents network access", + [](common_params & params) { + params.offline = true; + } + ).set_env("LLAMA_OFFLINE")); + add_opt(common_arg( + {"-lv", "--verbosity", "--log-verbosity"}, "N", + string_format("Set the verbosity threshold. Messages with a higher verbosity will be ignored. Values:\n" + " - 0: generic output\n" + " - 1: error\n" + " - 2: warning\n" + " - 3: info\n" + " - 4: debug\n" + "(default: %d)\n", params.verbosity), + [](common_params & params, int value) { + params.verbosity = value; + } + ).set_env("LLAMA_LOG_VERBOSITY")); + add_opt(common_arg( + {"--log-prefix"}, + "Enable prefix in log messages", + [](common_params &) { + common_log_set_prefix(common_log_main(), true); + } + ).set_env("LLAMA_LOG_PREFIX")); + add_opt(common_arg( + {"--log-timestamps"}, + "Enable timestamps in log messages", + [](common_params &) { + common_log_set_timestamps(common_log_main(), true); + } + ).set_env("LLAMA_LOG_TIMESTAMPS")); + + // speculative parameters + add_opt(common_arg( + {"-td", "--threads-draft"}, "N", + "number of threads to use during generation (default: same as --threads)", + [](common_params & params, int value) { + params.speculative.cpuparams.n_threads = value; + if (params.speculative.cpuparams.n_threads <= 0) { + params.speculative.cpuparams.n_threads = std::thread::hardware_concurrency(); + } + } + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER})); + add_opt(common_arg( + {"-tbd", "--threads-batch-draft"}, "N", + "number of threads to use during batch and prompt processing (default: same as --threads-draft)", + [](common_params & params, int value) { + params.speculative.cpuparams_batch.n_threads = value; + if (params.speculative.cpuparams_batch.n_threads <= 0) { + params.speculative.cpuparams_batch.n_threads = std::thread::hardware_concurrency(); + } + } + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER})); + add_opt(common_arg( + {"-Cd", "--cpu-mask-draft"}, "M", + "Draft model CPU affinity mask. Complements cpu-range-draft (default: same as --cpu-mask)", + [](common_params & params, const std::string & mask) { + params.speculative.cpuparams.mask_valid = true; + if (!parse_cpu_mask(mask, params.speculative.cpuparams.cpumask)) { + throw std::invalid_argument("invalid cpumask"); + } + } + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE})); + add_opt(common_arg( + {"-Crd", "--cpu-range-draft"}, "lo-hi", + "Ranges of CPUs for affinity. Complements --cpu-mask-draft", + [](common_params & params, const std::string & range) { + params.speculative.cpuparams.mask_valid = true; + if (!parse_cpu_range(range, params.speculative.cpuparams.cpumask)) { + throw std::invalid_argument("invalid range"); + } + } + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE})); + add_opt(common_arg( + {"--cpu-strict-draft"}, "<0|1>", + "Use strict CPU placement for draft model (default: same as --cpu-strict)", + [](common_params & params, int value) { + params.speculative.cpuparams.strict_cpu = value; + } + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE})); + add_opt(common_arg( + {"--prio-draft"}, "N", + string_format("set draft process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: %d)\n", params.speculative.cpuparams.priority), + [](common_params & params, int prio) { + if (prio < 0 || prio > 3) { + throw std::invalid_argument("invalid value"); + } + params.speculative.cpuparams.priority = (enum ggml_sched_priority) prio; + } + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE})); + add_opt(common_arg( + {"--poll-draft"}, "<0|1>", + "Use polling to wait for draft model work (default: same as --poll])", + [](common_params & params, int value) { + params.speculative.cpuparams.poll = value; + } + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE})); + add_opt(common_arg( + {"-Cbd", "--cpu-mask-batch-draft"}, "M", + "Draft model CPU affinity mask. Complements cpu-range-draft (default: same as --cpu-mask)", + [](common_params & params, const std::string & mask) { + params.speculative.cpuparams_batch.mask_valid = true; + if (!parse_cpu_mask(mask, params.speculative.cpuparams_batch.cpumask)) { + throw std::invalid_argument("invalid cpumask"); + } + } + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE})); + add_opt(common_arg( + {"-Crbd", "--cpu-range-batch-draft"}, "lo-hi", + "Ranges of CPUs for affinity. Complements --cpu-mask-draft-batch)", + [](common_params & params, const std::string & range) { + params.speculative.cpuparams_batch.mask_valid = true; + if (!parse_cpu_range(range, params.speculative.cpuparams_batch.cpumask)) { + throw std::invalid_argument("invalid cpumask"); + } + } + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE})); + add_opt(common_arg( + {"--cpu-strict-batch-draft"}, "<0|1>", + "Use strict CPU placement for draft model (default: --cpu-strict-draft)", + [](common_params & params, int value) { + params.speculative.cpuparams_batch.strict_cpu = value; + } + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE})); + add_opt(common_arg( + {"--prio-batch-draft"}, "N", + string_format("set draft process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: %d)\n", params.speculative.cpuparams_batch.priority), + [](common_params & params, int prio) { + if (prio < 0 || prio > 3) { + throw std::invalid_argument("invalid value"); + } + params.speculative.cpuparams_batch.priority = (enum ggml_sched_priority) prio; + } + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE})); + add_opt(common_arg( + {"--poll-batch-draft"}, "<0|1>", + "Use polling to wait for draft model work (default: --poll-draft)", + [](common_params & params, int value) { + params.speculative.cpuparams_batch.poll = value; + } + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE})); + add_opt(common_arg( + {"--draft", "--draft-n", "--draft-max"}, "N", + string_format("number of tokens to draft for speculative decoding (default: %d)", params.speculative.n_max), + [](common_params & params, int value) { + params.speculative.n_max = value; + } + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_DRAFT_MAX")); + add_opt(common_arg( + {"--draft-min", "--draft-n-min"}, "N", + string_format("minimum number of draft tokens to use for speculative decoding (default: %d)", params.speculative.n_min), + [](common_params & params, int value) { + params.speculative.n_min = value; + } + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_DRAFT_MIN")); + add_opt(common_arg( + {"--draft-p-split"}, "P", + string_format("speculative decoding split probability (default: %.2f)", (double)params.speculative.p_split), + [](common_params & params, const std::string & value) { + params.speculative.p_split = std::stof(value); + } + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}).set_env("LLAMA_ARG_DRAFT_P_SPLIT")); + add_opt(common_arg( + {"--draft-p-min"}, "P", + string_format("minimum speculative decoding probability (greedy) (default: %.2f)", (double)params.speculative.p_min), + [](common_params & params, const std::string & value) { + params.speculative.p_min = std::stof(value); + } + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_DRAFT_P_MIN")); + add_opt(common_arg( + {"-cd", "--ctx-size-draft"}, "N", + string_format("size of the prompt context for the draft model (default: %d, 0 = loaded from model)", params.speculative.n_ctx), + [](common_params & params, int value) { + params.speculative.n_ctx = value; + } + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_CTX_SIZE_DRAFT")); + add_opt(common_arg( + {"-devd", "--device-draft"}, "<dev1,dev2,..>", + "comma-separated list of devices to use for offloading the draft model (none = don't offload)\n" + "use --list-devices to see a list of available devices", + [](common_params & params, const std::string & value) { + params.speculative.devices = parse_device_list(value); + } + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI})); + GGML_ASSERT(params.speculative.n_gpu_layers < 0); // string_format would need to be extended for a default >= 0 + add_opt(common_arg( + {"-ngld", "--gpu-layers-draft", "--n-gpu-layers-draft"}, "N", + string_format("max. number of draft model layers to store in VRAM, either an exact number, 'auto', or 'all' (default: %s)", + params.speculative.n_gpu_layers == -1 ? "auto" : "all"), + [](common_params & params, const std::string & value) { + if (value == "auto") { + params.speculative.n_gpu_layers = -1; + } else if (value == "all") { + params.speculative.n_gpu_layers = -2; + } else { + params.speculative.n_gpu_layers = std::stoi(value); + } + if (!llama_supports_gpu_offload()) { + fprintf(stderr, "warning: no usable GPU found, --gpu-layers-draft option will be ignored\n"); + fprintf(stderr, "warning: one possible reason is that llama.cpp was compiled without GPU support\n"); + fprintf(stderr, "warning: consult docs/build.md for compilation instructions\n"); + } + } + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_N_GPU_LAYERS_DRAFT")); + add_opt(common_arg( + {"-md", "--model-draft"}, "FNAME", + "draft model for speculative decoding (default: unused)", + [](common_params & params, const std::string & value) { + params.speculative.mparams_dft.path = value; + } + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_MODEL_DRAFT")); + add_opt(common_arg( + {"--spec-replace"}, "TARGET", "DRAFT", + "translate the string in TARGET into DRAFT if the draft model and main model are not compatible", + [](common_params & params, const std::string & tgt, const std::string & dft) { + params.speculative.replacements.push_back({ tgt, dft }); + } + ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI})); + add_opt(common_arg( + {"--spec-type"}, "[none|ngram-cache|ngram-simple|ngram-map-k|ngram-map-k4v|ngram-mod]", + string_format("type of speculative decoding to use when no draft model is provided (default: %s)\n", + common_speculative_type_to_str(params.speculative.type).c_str()), + [](common_params & params, const std::string & value) { + if (value == "none") { + params.speculative.type = COMMON_SPECULATIVE_TYPE_NONE; + } else if (value == "ngram-cache") { + params.speculative.type = COMMON_SPECULATIVE_TYPE_NGRAM_CACHE; + } else if (value == "ngram-simple") { + params.speculative.type = COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE; + } else if (value == "ngram-map-k") { + params.speculative.type = COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K; + } else if (value == "ngram-map-k4v") { + params.speculative.type = COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V; + } else if (value == "ngram-mod") { + params.speculative.type = COMMON_SPECULATIVE_TYPE_NGRAM_MOD; + } else { + throw std::invalid_argument("unknown speculative decoding type without draft model"); + } + } + ).set_examples({LLAMA_EXAMPLE_SERVER})); + add_opt(common_arg( + {"--spec-ngram-size-n"}, "N", + string_format("ngram size N for ngram-simple/ngram-map speculative decoding, length of lookup n-gram (default: %d)", params.speculative.ngram_size_n), + [](common_params & params, int value) { + if (value < 1 || value > 1024) { + throw std::invalid_argument("ngram size N must be between 1 and 1024 inclusive"); + } + params.speculative.ngram_size_n = value; + } + ).set_examples({LLAMA_EXAMPLE_SERVER})); + add_opt(common_arg( + {"--spec-ngram-size-m"}, "N", + string_format("ngram size M for ngram-simple/ngram-map speculative decoding, length of draft m-gram (default: %d)", params.speculative.ngram_size_m), + [](common_params & params, int value) { + if (value < 1 || value > 1024) { + throw std::invalid_argument("ngram size M must be between 1 and 1024 inclusive"); + } + params.speculative.ngram_size_m = value; + } + ).set_examples({LLAMA_EXAMPLE_SERVER})); + add_opt(common_arg( + {"--spec-ngram-min-hits"}, "N", + string_format("minimum hits for ngram-map speculative decoding (default: %d)", params.speculative.ngram_min_hits), + [](common_params & params, int value) { + if (value < 1) { + throw std::invalid_argument("ngram min hits must be at least 1"); + } + params.speculative.ngram_min_hits = value; + } + ).set_examples({LLAMA_EXAMPLE_SERVER})); + add_opt(common_arg( + {"-ctkd", "--cache-type-k-draft"}, "TYPE", + string_format( + "KV cache data type for K for the draft model\n" + "allowed values: %s\n" + "(default: %s)", + get_all_kv_cache_types().c_str(), + ggml_type_name(params.speculative.cache_type_k) + ), + [](common_params & params, const std::string & value) { + params.speculative.cache_type_k = kv_cache_type_from_str(value); + } + ).set_env("LLAMA_ARG_CACHE_TYPE_K_DRAFT")); + add_opt(common_arg( + {"-ctvd", "--cache-type-v-draft"}, "TYPE", + string_format( + "KV cache data type for V for the draft model\n" + "allowed values: %s\n" + "(default: %s)", + get_all_kv_cache_types().c_str(), + ggml_type_name(params.speculative.cache_type_v) + ), + [](common_params & params, const std::string & value) { + params.speculative.cache_type_v = kv_cache_type_from_str(value); + } + ).set_env("LLAMA_ARG_CACHE_TYPE_V_DRAFT")); + + add_opt(common_arg( + {"-mv", "--model-vocoder"}, "FNAME", + "vocoder model for audio generation (default: unused)", + [](common_params & params, const std::string & value) { + params.vocoder.model.path = value; + } + ).set_examples({LLAMA_EXAMPLE_TTS, LLAMA_EXAMPLE_SERVER})); + add_opt(common_arg( + {"--tts-use-guide-tokens"}, + "Use guide tokens to improve TTS word recall", + [](common_params & params) { + params.vocoder.use_guide_tokens = true; + } + ).set_examples({LLAMA_EXAMPLE_TTS, LLAMA_EXAMPLE_SERVER})); + add_opt(common_arg( + {"--tts-speaker-file"}, "FNAME", + "speaker file path for audio generation", + [](common_params & params, const std::string & value) { + params.vocoder.speaker_file = value; + } + ).set_examples({LLAMA_EXAMPLE_TTS})); + + add_opt(common_arg( + {"--diffusion-steps"}, "N", + string_format("number of diffusion steps (default: %d)", params.diffusion.steps), + [](common_params & params, int value) { params.diffusion.steps = value; } + ).set_examples({ LLAMA_EXAMPLE_DIFFUSION })); + add_opt(common_arg( + {"--diffusion-visual"}, + string_format("enable visual diffusion mode (show progressive generation) (default: %s)", params.diffusion.visual_mode ? "true" : "false"), + [](common_params & params) { params.diffusion.visual_mode = true; } + ).set_examples({ LLAMA_EXAMPLE_DIFFUSION })); + add_opt(common_arg( + {"--diffusion-eps"}, "F", + string_format("epsilon for timesteps (default: %.6f)", (double) params.diffusion.eps), + [](common_params & params, const std::string & value) { params.diffusion.eps = std::stof(value); } + ).set_examples({ LLAMA_EXAMPLE_DIFFUSION })); + add_opt(common_arg( + {"--diffusion-algorithm"}, "N", + string_format("diffusion algorithm: 0=ORIGIN, 1=ENTROPY_BASED, 2=MARGIN_BASED, 3=RANDOM, 4=LOW_CONFIDENCE (default: %d)", params.diffusion.algorithm), + [](common_params & params, int value) { params.diffusion.algorithm = value; } + ).set_examples({ LLAMA_EXAMPLE_DIFFUSION })); + add_opt(common_arg( + {"--diffusion-alg-temp"}, "F", + string_format("dream algorithm temperature (default: %.3f)", (double) params.diffusion.alg_temp), + [](common_params & params, const std::string & value) { params.diffusion.alg_temp = std::stof(value); } + ).set_examples({ LLAMA_EXAMPLE_DIFFUSION })); + add_opt(common_arg( + {"--diffusion-block-length"}, "N", + string_format("llada block length for generation (default: %d)", params.diffusion.block_length), + [](common_params & params, int value) { params.diffusion.block_length = value; } + ).set_examples({ LLAMA_EXAMPLE_DIFFUSION })); + add_opt(common_arg( + {"--diffusion-cfg-scale"}, "F", + string_format("llada classifier-free guidance scale (default: %.3f)", (double) params.diffusion.cfg_scale), + [](common_params & params, const std::string & value) { params.diffusion.cfg_scale = std::stof(value); } + ).set_examples({ LLAMA_EXAMPLE_DIFFUSION })); + add_opt(common_arg( + {"--diffusion-add-gumbel-noise"}, "F", + string_format("add gumbel noise to the logits if temp > 0.0 (default: %s)", params.diffusion.add_gumbel_noise ? "true" : "false"), + [](common_params & params, const std::string & value) { params.diffusion.add_gumbel_noise = std::stof(value); } + ).set_examples({ LLAMA_EXAMPLE_DIFFUSION })); + add_opt(common_arg( + { "-lr", "--learning-rate" }, "ALPHA", + string_format("adamw or sgd optimizer alpha (default: %.2g); note: sgd alpha recommended ~10x (no momentum)", (double) params.lr.lr0), + [](common_params & params, const std::string & value) { params.lr.lr0 = std::stof(value); } + ).set_examples({ LLAMA_EXAMPLE_FINETUNE })); + add_opt(common_arg({ "-lr-min", "--learning-rate-min" }, "ALPHA", + string_format("(if >0) final learning rate after decay (if -decay-epochs is set, default=%.2g)", + (double) params.lr.lr_min), + [](common_params & params, const std::string & value) { params.lr.lr_min = std::stof(value); } + ).set_examples({ LLAMA_EXAMPLE_FINETUNE })); + add_opt(common_arg( + {"-decay-epochs", "--learning-rate-decay-epochs"}, "ALPHA", + string_format("(if >0) decay learning rate to -lr-min after this many epochs (exponential decay, default=%.2g)", (double) params.lr.decay_epochs), + [](common_params & params, const std::string & value) { params.lr.decay_epochs = std::stof(value); } + ).set_examples({ LLAMA_EXAMPLE_FINETUNE })); + add_opt(common_arg( + {"-wd", "--weight-decay"}, "WD", + string_format("adamw or sgd optimizer weight decay (0 is off; recommend very small e.g. 1e-9) (default: %.2g).", (double) params.lr.wd), + [](common_params & params, const std::string & value) { params.lr.wd = std::stof(value); } + ).set_examples({ LLAMA_EXAMPLE_FINETUNE })); + add_opt(common_arg( + {"-val-split", "--val-split"}, "FRACTION", + string_format("fraction of data to use as validation set for training (default: %.2g).", (double) params.val_split), + [](common_params & params, const std::string & value) { params.val_split = std::stof(value); } + ).set_examples({ LLAMA_EXAMPLE_FINETUNE })); + add_opt(common_arg( + {"-epochs", "--epochs"}, "N", + string_format("optimizer max # of epochs (default: %d)", params.lr.epochs), + [](common_params & params, int epochs) { params.lr.epochs = epochs; } + ).set_examples({ LLAMA_EXAMPLE_FINETUNE })); + add_opt(common_arg( + {"-opt", "--optimizer"}, "sgd|adamw", "adamw or sgd", + [](common_params & params, const std::string & name) { + params.optimizer = common_opt_get_optimizer(name.c_str()); + if (params.optimizer == GGML_OPT_OPTIMIZER_TYPE_COUNT) { + throw std::invalid_argument("invalid --optimizer, valid options: adamw, sgd"); + } + } + ).set_examples({ LLAMA_EXAMPLE_FINETUNE })); + add_opt(common_arg( + {"--save-logits"}, + string_format("save final logits to files for verification (default: %s)", params.save_logits ? "true" : "false"), + [](common_params & params) { + params.save_logits = true; + } + ).set_examples({LLAMA_EXAMPLE_DEBUG})); + add_opt(common_arg( + {"--logits-output-dir"}, "PATH", + string_format("directory for saving logits output files (default: %s)", params.logits_output_dir.c_str()), + [](common_params & params, const std::string & value) { + params.logits_output_dir = value; + } + ).set_examples({LLAMA_EXAMPLE_DEBUG})); + add_opt(common_arg( + {"--tensor-filter"}, "REGEX", + "filter tensor names for debug output (regex pattern, can be specified multiple times)", + [](common_params & params, const std::string & value) { + params.tensor_filter.push_back(value); + } + ).set_examples({LLAMA_EXAMPLE_DEBUG})); + + // presets + add_opt(common_arg( + {"--tts-oute-default"}, + string_format("use default OuteTTS models (note: can download weights from the internet)"), + [](common_params & params) { + params.model.hf_repo = "OuteAI/OuteTTS-0.2-500M-GGUF"; + params.model.hf_file = "OuteTTS-0.2-500M-Q8_0.gguf"; + params.vocoder.model.hf_repo = "ggml-org/WavTokenizer"; + params.vocoder.model.hf_file = "WavTokenizer-Large-75-F16.gguf"; + } + ).set_examples({LLAMA_EXAMPLE_TTS})); + + add_opt(common_arg( + {"--embd-gemma-default"}, + string_format("use default EmbeddingGemma model (note: can download weights from the internet)"), + [](common_params & params) { + params.model.hf_repo = "ggml-org/embeddinggemma-300M-qat-q4_0-GGUF"; + params.model.hf_file = "embeddinggemma-300M-qat-Q4_0.gguf"; + params.port = 8011; + params.n_ubatch = 2048; + params.n_batch = 2048; + params.n_parallel = 32; + params.n_ctx = 2048*params.n_parallel; + params.verbose_prompt = true; + params.embedding = true; + } + ).set_examples({LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_SERVER})); + + add_opt(common_arg( + {"--fim-qwen-1.5b-default"}, + string_format("use default Qwen 2.5 Coder 1.5B (note: can download weights from the internet)"), + [](common_params & params) { + params.model.hf_repo = "ggml-org/Qwen2.5-Coder-1.5B-Q8_0-GGUF"; + params.model.hf_file = "qwen2.5-coder-1.5b-q8_0.gguf"; + params.port = 8012; + params.n_ubatch = 1024; + params.n_batch = 1024; + params.n_ctx = 0; + params.n_cache_reuse = 256; + } + ).set_examples({LLAMA_EXAMPLE_SERVER})); + + add_opt(common_arg( + {"--fim-qwen-3b-default"}, + string_format("use default Qwen 2.5 Coder 3B (note: can download weights from the internet)"), + [](common_params & params) { + params.model.hf_repo = "ggml-org/Qwen2.5-Coder-3B-Q8_0-GGUF"; + params.model.hf_file = "qwen2.5-coder-3b-q8_0.gguf"; + params.port = 8012; + params.n_ubatch = 1024; + params.n_batch = 1024; + params.n_ctx = 0; + params.n_cache_reuse = 256; + } + ).set_examples({LLAMA_EXAMPLE_SERVER})); + + add_opt(common_arg( + {"--fim-qwen-7b-default"}, + string_format("use default Qwen 2.5 Coder 7B (note: can download weights from the internet)"), + [](common_params & params) { + params.model.hf_repo = "ggml-org/Qwen2.5-Coder-7B-Q8_0-GGUF"; + params.model.hf_file = "qwen2.5-coder-7b-q8_0.gguf"; + params.port = 8012; + params.n_ubatch = 1024; + params.n_batch = 1024; + params.n_ctx = 0; + params.n_cache_reuse = 256; + } + ).set_examples({LLAMA_EXAMPLE_SERVER})); + + add_opt(common_arg( + {"--fim-qwen-7b-spec"}, + string_format("use Qwen 2.5 Coder 7B + 0.5B draft for speculative decoding (note: can download weights from the internet)"), + [](common_params & params) { + params.model.hf_repo = "ggml-org/Qwen2.5-Coder-7B-Q8_0-GGUF"; + params.model.hf_file = "qwen2.5-coder-7b-q8_0.gguf"; + params.speculative.mparams_dft.hf_repo = "ggml-org/Qwen2.5-Coder-0.5B-Q8_0-GGUF"; + params.speculative.mparams_dft.hf_file = "qwen2.5-coder-0.5b-q8_0.gguf"; + params.port = 8012; + params.n_ubatch = 1024; + params.n_batch = 1024; + params.n_ctx = 0; + params.n_cache_reuse = 256; + } + ).set_examples({LLAMA_EXAMPLE_SERVER})); + + add_opt(common_arg( + {"--fim-qwen-14b-spec"}, + string_format("use Qwen 2.5 Coder 14B + 0.5B draft for speculative decoding (note: can download weights from the internet)"), + [](common_params & params) { + params.model.hf_repo = "ggml-org/Qwen2.5-Coder-14B-Q8_0-GGUF"; + params.model.hf_file = "qwen2.5-coder-14b-q8_0.gguf"; + params.speculative.mparams_dft.hf_repo = "ggml-org/Qwen2.5-Coder-0.5B-Q8_0-GGUF"; + params.speculative.mparams_dft.hf_file = "qwen2.5-coder-0.5b-q8_0.gguf"; + params.port = 8012; + params.n_ubatch = 1024; + params.n_batch = 1024; + params.n_ctx = 0; + params.n_cache_reuse = 256; + } + ).set_examples({LLAMA_EXAMPLE_SERVER})); + + add_opt(common_arg( + {"--fim-qwen-30b-default"}, + string_format("use default Qwen 3 Coder 30B A3B Instruct (note: can download weights from the internet)"), + [](common_params & params) { + params.model.hf_repo = "ggml-org/Qwen3-Coder-30B-A3B-Instruct-Q8_0-GGUF"; + params.model.hf_file = "qwen3-coder-30b-a3b-instruct-q8_0.gguf"; + params.port = 8012; + params.n_ubatch = 1024; + params.n_batch = 1024; + params.n_ctx = 0; + params.n_cache_reuse = 256; + } + ).set_examples({LLAMA_EXAMPLE_SERVER})); + + add_opt(common_arg( + {"--gpt-oss-20b-default"}, + string_format("use gpt-oss-20b (note: can download weights from the internet)"), + [](common_params & params) { + params.model.hf_repo = "ggml-org/gpt-oss-20b-GGUF"; + params.model.hf_file = "gpt-oss-20b-mxfp4.gguf"; + params.port = 8013; + params.n_ubatch = 2048; + params.n_batch = 32768; + params.n_parallel = 2; + params.n_ctx = 131072*params.n_parallel; + params.sampling.temp = 1.0f; + params.sampling.top_p = 1.0f; + params.sampling.top_k = 0; + params.sampling.min_p = 0.01f; + params.use_jinja = true; + //params.default_template_kwargs["reasoning_effort"] = "\"high\""; + } + ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI})); + + add_opt(common_arg( + {"--gpt-oss-120b-default"}, + string_format("use gpt-oss-120b (note: can download weights from the internet)"), + [](common_params & params) { + params.model.hf_repo = "ggml-org/gpt-oss-120b-GGUF"; + params.port = 8013; + params.n_ubatch = 2048; + params.n_batch = 32768; + params.n_parallel = 2; + params.n_ctx = 131072*params.n_parallel; + params.sampling.temp = 1.0f; + params.sampling.top_p = 1.0f; + params.sampling.top_k = 0; + params.sampling.min_p = 0.01f; + params.use_jinja = true; + //params.default_template_kwargs["reasoning_effort"] = "\"high\""; + } + ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI})); + + add_opt(common_arg( + {"--vision-gemma-4b-default"}, + string_format("use Gemma 3 4B QAT (note: can download weights from the internet)"), + [](common_params & params) { + params.model.hf_repo = "ggml-org/gemma-3-4b-it-qat-GGUF"; + params.port = 8014; + params.n_ctx = 0; + params.use_jinja = true; + } + ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI})); + + add_opt(common_arg( + {"--vision-gemma-12b-default"}, + string_format("use Gemma 3 12B QAT (note: can download weights from the internet)"), + [](common_params & params) { + params.model.hf_repo = "ggml-org/gemma-3-12b-it-qat-GGUF"; + params.port = 8014; + params.n_ctx = 0; + params.use_jinja = true; + } + ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI})); + + return ctx_arg; +} + +void common_params_add_preset_options(std::vector<common_arg> & args) { + // arguments below won't be treated as CLI args, only preset options + args.push_back(common_arg( + {"load-on-startup"}, "NAME", + "in server router mode, autoload this model on startup", + [](common_params &, const std::string &) { /* unused */ } + ).set_env(COMMON_ARG_PRESET_LOAD_ON_STARTUP).set_preset_only()); + + args.push_back(common_arg( + {"stop-timeout"}, "SECONDS", + "in server router mode, force-kill model instance after this many seconds of graceful shutdown", + [](common_params &, int) { /* unused */ } + ).set_env(COMMON_ARG_PRESET_STOP_TIMEOUT).set_preset_only()); + + // args.push_back(common_arg( + // {"pin"}, + // "in server router mode, do not unload this model if models_max is exceeded", + // [](common_params &) { /* unused */ } + // ).set_preset_only()); +} |