mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2025-10-29 08:41:22 +00:00
225e7a1438
* kv-cache : prepare K/V buffers for separation ggml-ci * batched-bench : fix oob write ggml-ci * llama : add "virtual sequences" ggml-ci * llama : use "stream" vs "virtual sequence" ggml-ci * graph : fix stream splitting when KV cache is not used ggml-ci * kv-cache : add multi-stream save/load support ggml-ci * llama : add "--attn-streams" flag ggml-ci * kv-cache : fix handling when find_slot fails ggml-ci * kv-cache : restore find_slot impl ggml-ci * kv-cache : add comments * kv-cache : add bounds checks for sequence id ggml-ci * cont : add n_seq_max to batch allocr ggml-ci * kv-cache : perform stream copies lazily after llama_synchronize ggml-ci * kv-cache : avoid throwing exceptions across the C boundary ggml-ci * CUDA: 4D FlashAttention support (#14628) * CUDA: 4D FlashAttention support * CUDA: fix WMMA FA kernel * llama : rename attn_streams -> kv_unified ggml-ci * common : rename kv_split -> kv_unified ggml-ci --------- Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
3465 lines
146 KiB
C++
3465 lines
146 KiB
C++
#include "arg.h"
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#include "chat.h"
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#include "common.h"
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#include "gguf.h" // for reading GGUF splits
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#include "json-schema-to-grammar.h"
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#include "log.h"
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#include "sampling.h"
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// fix problem with std::min and std::max
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#if defined(_WIN32)
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#define WIN32_LEAN_AND_MEAN
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#ifndef NOMINMAX
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# define NOMINMAX
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#endif
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#include <windows.h>
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#endif
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#define JSON_ASSERT GGML_ASSERT
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#include <nlohmann/json.hpp>
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#include <algorithm>
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#include <climits>
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#include <cstdarg>
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#include <filesystem>
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#include <fstream>
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#include <regex>
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#include <set>
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#include <string>
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#include <thread>
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#include <vector>
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//#define LLAMA_USE_CURL
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#if defined(LLAMA_USE_CURL)
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#include <curl/curl.h>
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#include <curl/easy.h>
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#include <future>
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#endif
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using json = nlohmann::ordered_json;
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std::initializer_list<enum llama_example> mmproj_examples = {
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LLAMA_EXAMPLE_MTMD,
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LLAMA_EXAMPLE_SERVER,
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};
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static std::string read_file(const std::string & fname) {
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std::ifstream file(fname);
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if (!file) {
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throw std::runtime_error(string_format("error: failed to open file '%s'\n", fname.c_str()));
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}
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std::string content((std::istreambuf_iterator<char>(file)), std::istreambuf_iterator<char>());
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file.close();
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return content;
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}
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static void write_file(const std::string & fname, const std::string & content) {
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std::ofstream file(fname);
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if (!file) {
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throw std::runtime_error(string_format("error: failed to open file '%s'\n", fname.c_str()));
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}
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file << content;
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file.close();
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}
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common_arg & common_arg::set_examples(std::initializer_list<enum llama_example> examples) {
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this->examples = std::move(examples);
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return *this;
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}
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common_arg & common_arg::set_excludes(std::initializer_list<enum llama_example> excludes) {
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this->excludes = std::move(excludes);
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return *this;
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}
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common_arg & common_arg::set_env(const char * env) {
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help = help + "\n(env: " + env + ")";
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this->env = env;
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return *this;
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}
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common_arg & common_arg::set_sparam() {
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is_sparam = true;
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return *this;
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}
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bool common_arg::in_example(enum llama_example ex) {
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return examples.find(ex) != examples.end();
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}
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bool common_arg::is_exclude(enum llama_example ex) {
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return excludes.find(ex) != excludes.end();
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}
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bool common_arg::get_value_from_env(std::string & output) {
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if (env == nullptr) return false;
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char * value = std::getenv(env);
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if (value) {
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output = value;
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return true;
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}
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return false;
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}
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bool common_arg::has_value_from_env() {
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return env != nullptr && std::getenv(env);
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}
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static std::vector<std::string> break_str_into_lines(std::string input, size_t max_char_per_line) {
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std::vector<std::string> result;
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std::istringstream iss(input);
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std::string line;
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auto add_line = [&](const std::string& l) {
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if (l.length() <= max_char_per_line) {
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result.push_back(l);
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} else {
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std::istringstream line_stream(l);
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std::string word, current_line;
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while (line_stream >> word) {
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if (current_line.length() + !current_line.empty() + word.length() > max_char_per_line) {
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if (!current_line.empty()) result.push_back(current_line);
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current_line = word;
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} else {
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current_line += (!current_line.empty() ? " " : "") + word;
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}
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}
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if (!current_line.empty()) result.push_back(current_line);
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}
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};
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while (std::getline(iss, line)) {
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add_line(line);
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}
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return result;
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}
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std::string common_arg::to_string() {
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// params for printing to console
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const static int n_leading_spaces = 40;
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const static int n_char_per_line_help = 70; // TODO: detect this based on current console
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std::string leading_spaces(n_leading_spaces, ' ');
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std::ostringstream ss;
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for (const auto arg : args) {
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if (arg == args.front()) {
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if (args.size() == 1) {
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ss << arg;
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} else {
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// first arg is usually abbreviation, we need padding to make it more beautiful
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auto tmp = std::string(arg) + ", ";
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auto spaces = std::string(std::max(0, 7 - (int)tmp.size()), ' ');
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ss << tmp << spaces;
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}
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} else {
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ss << arg << (arg != args.back() ? ", " : "");
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}
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}
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if (value_hint) ss << " " << value_hint;
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if (value_hint_2) ss << " " << value_hint_2;
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if (ss.tellp() > n_leading_spaces - 3) {
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// current line is too long, add new line
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ss << "\n" << leading_spaces;
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} else {
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// padding between arg and help, same line
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ss << std::string(leading_spaces.size() - ss.tellp(), ' ');
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}
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const auto help_lines = break_str_into_lines(help, n_char_per_line_help);
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for (const auto & line : help_lines) {
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ss << (&line == &help_lines.front() ? "" : leading_spaces) << line << "\n";
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}
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return ss.str();
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}
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//
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// downloader
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//
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struct common_hf_file_res {
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std::string repo; // repo name with ":tag" removed
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std::string ggufFile;
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std::string mmprojFile;
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};
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#ifdef LLAMA_USE_CURL
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bool common_has_curl() {
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return true;
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}
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#ifdef __linux__
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#include <linux/limits.h>
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#elif defined(_WIN32)
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# if !defined(PATH_MAX)
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# define PATH_MAX MAX_PATH
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# endif
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#elif defined(_AIX)
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#include <sys/limits.h>
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#else
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#include <sys/syslimits.h>
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#endif
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#define LLAMA_CURL_MAX_URL_LENGTH 2084 // Maximum URL Length in Chrome: 2083
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//
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// CURL utils
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//
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using curl_ptr = std::unique_ptr<CURL, decltype(&curl_easy_cleanup)>;
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// cannot use unique_ptr for curl_slist, because we cannot update without destroying the old one
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struct curl_slist_ptr {
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struct curl_slist * ptr = nullptr;
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~curl_slist_ptr() {
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if (ptr) {
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curl_slist_free_all(ptr);
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}
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}
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};
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#define CURL_MAX_RETRY 3
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#define CURL_RETRY_DELAY_SECONDS 2
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static bool curl_perform_with_retry(const std::string & url, CURL * curl, int max_attempts, int retry_delay_seconds, const char * method_name) {
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int remaining_attempts = max_attempts;
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while (remaining_attempts > 0) {
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LOG_INF("%s: %s %s (attempt %d of %d)...\n", __func__ , method_name, url.c_str(), max_attempts - remaining_attempts + 1, max_attempts);
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CURLcode res = curl_easy_perform(curl);
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if (res == CURLE_OK) {
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return true;
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}
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int exponential_backoff_delay = std::pow(retry_delay_seconds, max_attempts - remaining_attempts) * 1000;
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LOG_WRN("%s: curl_easy_perform() failed: %s, retrying after %d milliseconds...\n", __func__, curl_easy_strerror(res), exponential_backoff_delay);
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remaining_attempts--;
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if (remaining_attempts == 0) break;
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std::this_thread::sleep_for(std::chrono::milliseconds(exponential_backoff_delay));
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}
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LOG_ERR("%s: curl_easy_perform() failed after %d attempts\n", __func__, max_attempts);
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return false;
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}
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// download one single file from remote URL to local path
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static bool common_download_file_single(const std::string & url, const std::string & path, const std::string & bearer_token, bool offline) {
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// Check if the file already exists locally
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auto file_exists = std::filesystem::exists(path);
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// If the file exists, check its JSON metadata companion file.
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std::string metadata_path = path + ".json";
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nlohmann::json metadata; // TODO @ngxson : get rid of this json, use regex instead
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std::string etag;
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std::string last_modified;
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if (file_exists) {
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if (offline) {
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LOG_INF("%s: using cached file (offline mode): %s\n", __func__, path.c_str());
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return true; // skip verification/downloading
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}
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// Try and read the JSON metadata file (note: stream autoclosed upon exiting this block).
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std::ifstream metadata_in(metadata_path);
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if (metadata_in.good()) {
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try {
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metadata_in >> metadata;
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LOG_DBG("%s: previous metadata file found %s: %s\n", __func__, metadata_path.c_str(), metadata.dump().c_str());
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if (metadata.contains("etag") && metadata.at("etag").is_string()) {
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etag = metadata.at("etag");
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}
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if (metadata.contains("lastModified") && metadata.at("lastModified").is_string()) {
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last_modified = metadata.at("lastModified");
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}
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} catch (const nlohmann::json::exception & e) {
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LOG_ERR("%s: error reading metadata file %s: %s\n", __func__, metadata_path.c_str(), e.what());
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}
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}
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// if we cannot open the metadata file, we assume that the downloaded file is not valid (etag and last-modified are left empty, so we will download it again)
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} else {
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if (offline) {
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LOG_ERR("%s: required file is not available in cache (offline mode): %s\n", __func__, path.c_str());
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return false;
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}
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LOG_INF("%s: no previous model file found %s\n", __func__, path.c_str());
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}
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// Send a HEAD request to retrieve the etag and last-modified headers
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struct common_load_model_from_url_headers {
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std::string etag;
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std::string last_modified;
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};
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common_load_model_from_url_headers headers;
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bool head_request_ok = false;
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bool should_download = !file_exists; // by default, we should download if the file does not exist
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// Initialize libcurl
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curl_ptr curl(curl_easy_init(), &curl_easy_cleanup);
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curl_slist_ptr http_headers;
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if (!curl) {
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LOG_ERR("%s: error initializing libcurl\n", __func__);
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return false;
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}
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// Set the URL, allow to follow http redirection
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curl_easy_setopt(curl.get(), CURLOPT_URL, url.c_str());
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curl_easy_setopt(curl.get(), CURLOPT_FOLLOWLOCATION, 1L);
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http_headers.ptr = curl_slist_append(http_headers.ptr, "User-Agent: llama-cpp");
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// Check if hf-token or bearer-token was specified
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if (!bearer_token.empty()) {
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std::string auth_header = "Authorization: Bearer " + bearer_token;
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http_headers.ptr = curl_slist_append(http_headers.ptr, auth_header.c_str());
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}
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curl_easy_setopt(curl.get(), CURLOPT_HTTPHEADER, http_headers.ptr);
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#if defined(_WIN32)
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// CURLSSLOPT_NATIVE_CA tells libcurl to use standard certificate store of
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// operating system. Currently implemented under MS-Windows.
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curl_easy_setopt(curl.get(), CURLOPT_SSL_OPTIONS, CURLSSLOPT_NATIVE_CA);
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#endif
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typedef size_t(*CURLOPT_HEADERFUNCTION_PTR)(char *, size_t, size_t, void *);
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auto header_callback = [](char * buffer, size_t /*size*/, size_t n_items, void * userdata) -> size_t {
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common_load_model_from_url_headers * headers = (common_load_model_from_url_headers *) userdata;
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static std::regex header_regex("([^:]+): (.*)\r\n");
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static std::regex etag_regex("ETag", std::regex_constants::icase);
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static std::regex last_modified_regex("Last-Modified", std::regex_constants::icase);
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std::string header(buffer, n_items);
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std::smatch match;
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if (std::regex_match(header, match, header_regex)) {
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const std::string & key = match[1];
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const std::string & value = match[2];
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if (std::regex_match(key, match, etag_regex)) {
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headers->etag = value;
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} else if (std::regex_match(key, match, last_modified_regex)) {
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headers->last_modified = value;
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}
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}
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return n_items;
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};
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curl_easy_setopt(curl.get(), CURLOPT_NOBODY, 1L); // will trigger the HEAD verb
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curl_easy_setopt(curl.get(), CURLOPT_NOPROGRESS, 1L); // hide head request progress
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curl_easy_setopt(curl.get(), CURLOPT_HEADERFUNCTION, static_cast<CURLOPT_HEADERFUNCTION_PTR>(header_callback));
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curl_easy_setopt(curl.get(), CURLOPT_HEADERDATA, &headers);
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// we only allow retrying once for HEAD requests
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// this is for the use case of using running offline (no internet), retrying can be annoying
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bool was_perform_successful = curl_perform_with_retry(url, curl.get(), 1, 0, "HEAD");
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if (!was_perform_successful) {
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head_request_ok = false;
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}
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long http_code = 0;
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curl_easy_getinfo(curl.get(), CURLINFO_RESPONSE_CODE, &http_code);
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if (http_code == 200) {
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head_request_ok = true;
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} else {
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LOG_WRN("%s: HEAD invalid http status code received: %ld\n", __func__, http_code);
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head_request_ok = false;
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}
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// if head_request_ok is false, we don't have the etag or last-modified headers
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// we leave should_download as-is, which is true if the file does not exist
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if (head_request_ok) {
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// check if ETag or Last-Modified headers are different
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// if it is, we need to download the file again
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if (!etag.empty() && etag != headers.etag) {
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LOG_WRN("%s: ETag header is different (%s != %s): triggering a new download\n", __func__, etag.c_str(), headers.etag.c_str());
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should_download = true;
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} else if (!last_modified.empty() && last_modified != headers.last_modified) {
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LOG_WRN("%s: Last-Modified header is different (%s != %s): triggering a new download\n", __func__, last_modified.c_str(), headers.last_modified.c_str());
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should_download = true;
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}
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}
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if (should_download) {
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std::string path_temporary = path + ".downloadInProgress";
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if (file_exists) {
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LOG_WRN("%s: deleting previous downloaded file: %s\n", __func__, path.c_str());
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if (remove(path.c_str()) != 0) {
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LOG_ERR("%s: unable to delete file: %s\n", __func__, path.c_str());
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return false;
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}
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}
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// Set the output file
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struct FILE_deleter {
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void operator()(FILE * f) const {
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fclose(f);
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}
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};
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std::unique_ptr<FILE, FILE_deleter> outfile(fopen(path_temporary.c_str(), "wb"));
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if (!outfile) {
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LOG_ERR("%s: error opening local file for writing: %s\n", __func__, path.c_str());
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return false;
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}
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typedef size_t(*CURLOPT_WRITEFUNCTION_PTR)(void * data, size_t size, size_t nmemb, void * fd);
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auto write_callback = [](void * data, size_t size, size_t nmemb, void * fd) -> size_t {
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return fwrite(data, size, nmemb, (FILE *)fd);
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};
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curl_easy_setopt(curl.get(), CURLOPT_NOBODY, 0L);
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curl_easy_setopt(curl.get(), CURLOPT_WRITEFUNCTION, static_cast<CURLOPT_WRITEFUNCTION_PTR>(write_callback));
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curl_easy_setopt(curl.get(), CURLOPT_WRITEDATA, outfile.get());
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// display download progress
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curl_easy_setopt(curl.get(), CURLOPT_NOPROGRESS, 0L);
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// helper function to hide password in URL
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auto llama_download_hide_password_in_url = [](const std::string & url) -> std::string {
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std::size_t protocol_pos = url.find("://");
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if (protocol_pos == std::string::npos) {
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return url; // Malformed URL
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}
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std::size_t at_pos = url.find('@', protocol_pos + 3);
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if (at_pos == std::string::npos) {
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return url; // No password in URL
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}
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return url.substr(0, protocol_pos + 3) + "********" + url.substr(at_pos);
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};
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// start the download
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LOG_INF("%s: trying to download model from %s to %s (server_etag:%s, server_last_modified:%s)...\n", __func__,
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llama_download_hide_password_in_url(url).c_str(), path.c_str(), headers.etag.c_str(), headers.last_modified.c_str());
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bool was_perform_successful = curl_perform_with_retry(url, curl.get(), CURL_MAX_RETRY, CURL_RETRY_DELAY_SECONDS, "GET");
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if (!was_perform_successful) {
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return false;
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}
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long http_code = 0;
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curl_easy_getinfo (curl.get(), CURLINFO_RESPONSE_CODE, &http_code);
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if (http_code < 200 || http_code >= 400) {
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LOG_ERR("%s: invalid http status code received: %ld\n", __func__, http_code);
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return false;
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}
|
|
|
|
// Causes file to be closed explicitly here before we rename it.
|
|
outfile.reset();
|
|
|
|
// Write the updated JSON metadata file.
|
|
metadata.update({
|
|
{"url", url},
|
|
{"etag", headers.etag},
|
|
{"lastModified", headers.last_modified}
|
|
});
|
|
write_file(metadata_path, metadata.dump(4));
|
|
LOG_DBG("%s: file metadata saved: %s\n", __func__, metadata_path.c_str());
|
|
|
|
if (rename(path_temporary.c_str(), path.c_str()) != 0) {
|
|
LOG_ERR("%s: unable to rename file: %s to %s\n", __func__, path_temporary.c_str(), path.c_str());
|
|
return false;
|
|
}
|
|
} else {
|
|
LOG_INF("%s: using cached file: %s\n", __func__, path.c_str());
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
// download multiple files from remote URLs to local paths
|
|
// the input is a vector of pairs <url, path>
|
|
static bool common_download_file_multiple(const std::vector<std::pair<std::string, std::string>> & urls, const std::string & bearer_token, bool offline) {
|
|
// Prepare download in parallel
|
|
std::vector<std::future<bool>> futures_download;
|
|
for (auto const & item : urls) {
|
|
futures_download.push_back(std::async(std::launch::async, [bearer_token, offline](const std::pair<std::string, std::string> & it) -> bool {
|
|
return common_download_file_single(it.first, it.second, bearer_token, offline);
|
|
}, item));
|
|
}
|
|
|
|
// Wait for all downloads to complete
|
|
for (auto & f : futures_download) {
|
|
if (!f.get()) {
|
|
return false;
|
|
}
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
static bool common_download_model(
|
|
const common_params_model & model,
|
|
const std::string & bearer_token,
|
|
bool offline) {
|
|
// Basic validation of the model.url
|
|
if (model.url.empty()) {
|
|
LOG_ERR("%s: invalid model url\n", __func__);
|
|
return false;
|
|
}
|
|
|
|
if (!common_download_file_single(model.url, model.path, bearer_token, offline)) {
|
|
return false;
|
|
}
|
|
|
|
// check for additional GGUFs split to download
|
|
int n_split = 0;
|
|
{
|
|
struct gguf_init_params gguf_params = {
|
|
/*.no_alloc = */ true,
|
|
/*.ctx = */ NULL,
|
|
};
|
|
auto * ctx_gguf = gguf_init_from_file(model.path.c_str(), gguf_params);
|
|
if (!ctx_gguf) {
|
|
LOG_ERR("\n%s: failed to load input GGUF from %s\n", __func__, model.path.c_str());
|
|
return false;
|
|
}
|
|
|
|
auto key_n_split = gguf_find_key(ctx_gguf, LLM_KV_SPLIT_COUNT);
|
|
if (key_n_split >= 0) {
|
|
n_split = gguf_get_val_u16(ctx_gguf, key_n_split);
|
|
}
|
|
|
|
gguf_free(ctx_gguf);
|
|
}
|
|
|
|
if (n_split > 1) {
|
|
char split_prefix[PATH_MAX] = {0};
|
|
char split_url_prefix[LLAMA_CURL_MAX_URL_LENGTH] = {0};
|
|
|
|
// Verify the first split file format
|
|
// and extract split URL and PATH prefixes
|
|
{
|
|
if (!llama_split_prefix(split_prefix, sizeof(split_prefix), model.path.c_str(), 0, n_split)) {
|
|
LOG_ERR("\n%s: unexpected model file name: %s n_split=%d\n", __func__, model.path.c_str(), n_split);
|
|
return false;
|
|
}
|
|
|
|
if (!llama_split_prefix(split_url_prefix, sizeof(split_url_prefix), model.url.c_str(), 0, n_split)) {
|
|
LOG_ERR("\n%s: unexpected model url: %s n_split=%d\n", __func__, model.url.c_str(), n_split);
|
|
return false;
|
|
}
|
|
}
|
|
|
|
std::vector<std::pair<std::string, std::string>> urls;
|
|
for (int idx = 1; idx < n_split; idx++) {
|
|
char split_path[PATH_MAX] = {0};
|
|
llama_split_path(split_path, sizeof(split_path), split_prefix, idx, n_split);
|
|
|
|
char split_url[LLAMA_CURL_MAX_URL_LENGTH] = {0};
|
|
llama_split_path(split_url, sizeof(split_url), split_url_prefix, idx, n_split);
|
|
|
|
if (std::string(split_path) == model.path) {
|
|
continue; // skip the already downloaded file
|
|
}
|
|
|
|
urls.push_back({split_url, split_path});
|
|
}
|
|
|
|
// Download in parallel
|
|
common_download_file_multiple(urls, bearer_token, offline);
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
std::pair<long, std::vector<char>> common_remote_get_content(const std::string & url, const common_remote_params & params) {
|
|
curl_ptr curl(curl_easy_init(), &curl_easy_cleanup);
|
|
curl_slist_ptr http_headers;
|
|
std::vector<char> res_buffer;
|
|
|
|
curl_easy_setopt(curl.get(), CURLOPT_URL, url.c_str());
|
|
curl_easy_setopt(curl.get(), CURLOPT_NOPROGRESS, 1L);
|
|
curl_easy_setopt(curl.get(), CURLOPT_FOLLOWLOCATION, 1L);
|
|
typedef size_t(*CURLOPT_WRITEFUNCTION_PTR)(void * ptr, size_t size, size_t nmemb, void * data);
|
|
auto write_callback = [](void * ptr, size_t size, size_t nmemb, void * data) -> size_t {
|
|
auto data_vec = static_cast<std::vector<char> *>(data);
|
|
data_vec->insert(data_vec->end(), (char *)ptr, (char *)ptr + size * nmemb);
|
|
return size * nmemb;
|
|
};
|
|
curl_easy_setopt(curl.get(), CURLOPT_WRITEFUNCTION, static_cast<CURLOPT_WRITEFUNCTION_PTR>(write_callback));
|
|
curl_easy_setopt(curl.get(), CURLOPT_WRITEDATA, &res_buffer);
|
|
#if defined(_WIN32)
|
|
curl_easy_setopt(curl.get(), CURLOPT_SSL_OPTIONS, CURLSSLOPT_NATIVE_CA);
|
|
#endif
|
|
if (params.timeout > 0) {
|
|
curl_easy_setopt(curl.get(), CURLOPT_TIMEOUT, params.timeout);
|
|
}
|
|
if (params.max_size > 0) {
|
|
curl_easy_setopt(curl.get(), CURLOPT_MAXFILESIZE, params.max_size);
|
|
}
|
|
http_headers.ptr = curl_slist_append(http_headers.ptr, "User-Agent: llama-cpp");
|
|
for (const auto & header : params.headers) {
|
|
http_headers.ptr = curl_slist_append(http_headers.ptr, header.c_str());
|
|
}
|
|
curl_easy_setopt(curl.get(), CURLOPT_HTTPHEADER, http_headers.ptr);
|
|
|
|
CURLcode res = curl_easy_perform(curl.get());
|
|
|
|
if (res != CURLE_OK) {
|
|
std::string error_msg = curl_easy_strerror(res);
|
|
throw std::runtime_error("error: cannot make GET request: " + error_msg);
|
|
}
|
|
|
|
long res_code;
|
|
curl_easy_getinfo(curl.get(), CURLINFO_RESPONSE_CODE, &res_code);
|
|
|
|
return { res_code, std::move(res_buffer) };
|
|
}
|
|
|
|
/**
|
|
* Allow getting the HF file from the HF repo with tag (like ollama), for example:
|
|
* - bartowski/Llama-3.2-3B-Instruct-GGUF:q4
|
|
* - bartowski/Llama-3.2-3B-Instruct-GGUF:Q4_K_M
|
|
* - bartowski/Llama-3.2-3B-Instruct-GGUF:q5_k_s
|
|
* Tag is optional, default to "latest" (meaning it checks for Q4_K_M first, then Q4, then if not found, return the first GGUF file in repo)
|
|
*
|
|
* Return pair of <repo, file> (with "repo" already having tag removed)
|
|
*
|
|
* Note: we use the Ollama-compatible HF API, but not using the blobId. Instead, we use the special "ggufFile" field which returns the value for "hf_file". This is done to be backward-compatible with existing cache files.
|
|
*/
|
|
static struct common_hf_file_res common_get_hf_file(const std::string & hf_repo_with_tag, const std::string & bearer_token, bool offline) {
|
|
auto parts = string_split<std::string>(hf_repo_with_tag, ':');
|
|
std::string tag = parts.size() > 1 ? parts.back() : "latest";
|
|
std::string hf_repo = parts[0];
|
|
if (string_split<std::string>(hf_repo, '/').size() != 2) {
|
|
throw std::invalid_argument("error: invalid HF repo format, expected <user>/<model>[:quant]\n");
|
|
}
|
|
|
|
std::string url = get_model_endpoint() + "v2/" + hf_repo + "/manifests/" + tag;
|
|
|
|
// headers
|
|
std::vector<std::string> headers;
|
|
headers.push_back("Accept: application/json");
|
|
if (!bearer_token.empty()) {
|
|
headers.push_back("Authorization: Bearer " + bearer_token);
|
|
}
|
|
// Important: the User-Agent must be "llama-cpp" to get the "ggufFile" field in the response
|
|
// User-Agent header is already set in common_remote_get_content, no need to set it here
|
|
|
|
// we use "=" to avoid clashing with other component, while still being allowed on windows
|
|
std::string cached_response_fname = "manifest=" + hf_repo + "=" + tag + ".json";
|
|
string_replace_all(cached_response_fname, "/", "_");
|
|
std::string cached_response_path = fs_get_cache_file(cached_response_fname);
|
|
|
|
// make the request
|
|
common_remote_params params;
|
|
params.headers = headers;
|
|
long res_code = 0;
|
|
std::string res_str;
|
|
bool use_cache = false;
|
|
if (!offline) {
|
|
try {
|
|
auto res = common_remote_get_content(url, params);
|
|
res_code = res.first;
|
|
res_str = std::string(res.second.data(), res.second.size());
|
|
} catch (const std::exception & e) {
|
|
LOG_WRN("error: failed to get manifest at %s: %s\n", url.c_str(), e.what());
|
|
}
|
|
}
|
|
if (res_code == 0) {
|
|
if (std::filesystem::exists(cached_response_path)) {
|
|
LOG_WRN("trying to read manifest from cache: %s\n", cached_response_path.c_str());
|
|
res_str = read_file(cached_response_path);
|
|
res_code = 200;
|
|
use_cache = true;
|
|
} else {
|
|
throw std::runtime_error(
|
|
offline ? "error: failed to get manifest (offline mode)"
|
|
: "error: failed to get manifest (check your internet connection)");
|
|
}
|
|
}
|
|
std::string ggufFile;
|
|
std::string mmprojFile;
|
|
|
|
if (res_code == 200 || res_code == 304) {
|
|
// extract ggufFile.rfilename in json, using regex
|
|
{
|
|
std::regex pattern("\"ggufFile\"[\\s\\S]*?\"rfilename\"\\s*:\\s*\"([^\"]+)\"");
|
|
std::smatch match;
|
|
if (std::regex_search(res_str, match, pattern)) {
|
|
ggufFile = match[1].str();
|
|
}
|
|
}
|
|
// extract mmprojFile.rfilename in json, using regex
|
|
{
|
|
std::regex pattern("\"mmprojFile\"[\\s\\S]*?\"rfilename\"\\s*:\\s*\"([^\"]+)\"");
|
|
std::smatch match;
|
|
if (std::regex_search(res_str, match, pattern)) {
|
|
mmprojFile = match[1].str();
|
|
}
|
|
}
|
|
if (!use_cache) {
|
|
// if not using cached response, update the cache file
|
|
write_file(cached_response_path, res_str);
|
|
}
|
|
} else if (res_code == 401) {
|
|
throw std::runtime_error("error: model is private or does not exist; if you are accessing a gated model, please provide a valid HF token");
|
|
} else {
|
|
throw std::runtime_error(string_format("error from HF API, response code: %ld, data: %s", res_code, res_str.c_str()));
|
|
}
|
|
|
|
// check response
|
|
if (ggufFile.empty()) {
|
|
throw std::runtime_error("error: model does not have ggufFile");
|
|
}
|
|
|
|
return { hf_repo, ggufFile, mmprojFile };
|
|
}
|
|
|
|
#else
|
|
|
|
bool common_has_curl() {
|
|
return false;
|
|
}
|
|
|
|
static bool common_download_file_single(const std::string &, const std::string &, const std::string &, bool) {
|
|
LOG_ERR("error: built without CURL, cannot download model from internet\n");
|
|
return false;
|
|
}
|
|
|
|
static bool common_download_file_multiple(const std::vector<std::pair<std::string, std::string>> &, const std::string &, bool) {
|
|
LOG_ERR("error: built without CURL, cannot download model from the internet\n");
|
|
return false;
|
|
}
|
|
|
|
static bool common_download_model(
|
|
const common_params_model &,
|
|
const std::string &,
|
|
bool) {
|
|
LOG_ERR("error: built without CURL, cannot download model from the internet\n");
|
|
return false;
|
|
}
|
|
|
|
static struct common_hf_file_res common_get_hf_file(const std::string &, const std::string &, bool) {
|
|
LOG_ERR("error: built without CURL, cannot download model from the internet\n");
|
|
return {};
|
|
}
|
|
|
|
std::pair<long, std::vector<char>> common_remote_get_content(const std::string & url, const common_remote_params &) {
|
|
if (!url.empty()) {
|
|
throw std::runtime_error("error: built without CURL, cannot download model from the internet");
|
|
}
|
|
|
|
return {};
|
|
}
|
|
|
|
#endif // LLAMA_USE_CURL
|
|
|
|
//
|
|
// utils
|
|
//
|
|
|
|
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,
|
|
const std::string & model_path_default,
|
|
bool offline) {
|
|
handle_model_result result;
|
|
// handle pre-fill default model path and url based on hf_repo and hf_file
|
|
{
|
|
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); // built without CURL, error message already printed
|
|
}
|
|
model.hf_repo = auto_detected.repo;
|
|
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 = model.hf_repo + "_" + model.hf_file;
|
|
// to make sure we don't have any slashes in the filename
|
|
string_replace_all(filename, "/", "_");
|
|
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());
|
|
}
|
|
|
|
} else if (model.path.empty()) {
|
|
model.path = model_path_default;
|
|
}
|
|
}
|
|
|
|
// 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();
|
|
}
|
|
|
|
//
|
|
// CLI argument parsing functions
|
|
//
|
|
|
|
static bool common_params_parse_ex(int argc, char ** argv, common_params_context & ctx_arg) {
|
|
std::string arg;
|
|
const std::string arg_prefix = "--";
|
|
common_params & params = ctx_arg.params;
|
|
|
|
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;
|
|
}
|
|
}
|
|
|
|
// handle environment variables
|
|
for (auto & opt : ctx_arg.options) {
|
|
std::string value;
|
|
if (opt.get_value_from_env(value)) {
|
|
try {
|
|
if (opt.handler_void && (value == "1" || value == "true")) {
|
|
opt.handler_void(params);
|
|
}
|
|
if (opt.handler_int) {
|
|
opt.handler_int(params, std::stoi(value));
|
|
}
|
|
if (opt.handler_string) {
|
|
opt.handler_string(params, value);
|
|
continue;
|
|
}
|
|
} catch (std::exception & e) {
|
|
throw std::invalid_argument(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");
|
|
}
|
|
};
|
|
|
|
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()));
|
|
}
|
|
auto opt = *arg_to_options[arg];
|
|
if (opt.has_value_from_env()) {
|
|
fprintf(stderr, "warn: %s environment variable is set, but will be overwritten by command line argument %s\n", opt.env, arg.c_str());
|
|
}
|
|
try {
|
|
if (opt.handler_void) {
|
|
opt.handler_void(params);
|
|
continue;
|
|
}
|
|
|
|
// arg with single value
|
|
check_arg(i);
|
|
std::string val = argv[++i];
|
|
if (opt.handler_int) {
|
|
opt.handler_int(params, std::stoi(val));
|
|
continue;
|
|
}
|
|
if (opt.handler_string) {
|
|
opt.handler_string(params, val);
|
|
continue;
|
|
}
|
|
|
|
// arg with 2 values
|
|
check_arg(i);
|
|
std::string val2 = argv[++i];
|
|
if (opt.handler_str_str) {
|
|
opt.handler_str_str(params, val, val2);
|
|
continue;
|
|
}
|
|
} catch (std::exception & e) {
|
|
throw std::invalid_argument(string_format(
|
|
"error while handling argument \"%s\": %s\n\n"
|
|
"usage:\n%s\n\nto show complete usage, run with -h",
|
|
arg.c_str(), e.what(), arg_to_options[arg]->to_string().c_str()));
|
|
}
|
|
}
|
|
|
|
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, DEFAULT_MODEL_PATH, 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 (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.model, params.hf_token, "", params.offline);
|
|
common_params_handle_model(params.vocoder.model, params.hf_token, "", params.offline);
|
|
}
|
|
|
|
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);
|
|
}
|
|
}
|
|
|
|
if (!params.kv_overrides.empty()) {
|
|
params.kv_overrides.emplace_back();
|
|
params.kv_overrides.back().key[0] = 0;
|
|
}
|
|
|
|
if (!params.tensor_buft_overrides.empty()) {
|
|
params.tensor_buft_overrides.push_back({nullptr, nullptr});
|
|
}
|
|
|
|
if (!params.chat_template.empty() && !common_chat_verify_template(params.chat_template, params.use_jinja)) {
|
|
throw std::runtime_error(string_format(
|
|
"error: the supplied chat template is not supported: %s%s\n",
|
|
params.chat_template.c_str(),
|
|
params.use_jinja ? "" : "\nnote: llama.cpp was started without --jinja, we only support commonly used templates"
|
|
));
|
|
}
|
|
|
|
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)\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-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-run",
|
|
"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_GPU) {
|
|
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(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_dev_t (*ggml_backend_rpc_add_device_t)(const char * endpoint);
|
|
ggml_backend_rpc_add_device_t ggml_backend_rpc_add_device_fn = (ggml_backend_rpc_add_device_t) ggml_backend_reg_get_proc_address(rpc_reg, "ggml_backend_rpc_add_device");
|
|
if (!ggml_backend_rpc_add_device_fn) {
|
|
throw std::invalid_argument("failed to find RPC device add function");
|
|
}
|
|
for (const auto & server : rpc_servers) {
|
|
ggml_backend_dev_t dev = ggml_backend_rpc_add_device_fn(server.c_str());
|
|
if (dev) {
|
|
ggml_backend_device_register(dev);
|
|
} else {
|
|
throw std::invalid_argument("failed to register RPC device");
|
|
}
|
|
}
|
|
}
|
|
|
|
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);
|
|
}
|
|
} 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();
|
|
}
|
|
|
|
common_params_context common_params_parser_init(common_params & params, llama_example ex, void(*print_usage)(int, char **)) {
|
|
// 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) + ";";
|
|
}
|
|
sampler_type_names.pop_back();
|
|
|
|
|
|
/**
|
|
* filter options by example
|
|
* rules:
|
|
* - all examples inherit options from LLAMA_EXAMPLE_COMMON
|
|
* - if LLAMA_EXAMPLE_* is set (other than COMMON), we only show the option in the corresponding example
|
|
* - if both {LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_*,} are set, we will prioritize the LLAMA_EXAMPLE_* matching current example
|
|
*/
|
|
auto add_opt = [&](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(
|
|
{"--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(
|
|
{"--no-display-prompt"},
|
|
string_format("don't print prompt at generation (default: %s)", !params.display_prompt ? "true" : "false"),
|
|
[](common_params & params) {
|
|
params.display_prompt = false;
|
|
}
|
|
).set_examples({LLAMA_EXAMPLE_MAIN}));
|
|
add_opt(common_arg(
|
|
{"-co", "--color"},
|
|
string_format("colorise output to distinguish prompt and user input from generations (default: %s)", params.use_color ? "true" : "false"),
|
|
[](common_params & params) {
|
|
params.use_color = true;
|
|
}
|
|
).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP}));
|
|
add_opt(common_arg(
|
|
{"-t", "--threads"}, "N",
|
|
string_format("number of threads to use during generation (default: %d)", params.cpuparams.n_threads),
|
|
[](common_params & params, int value) {
|
|
params.cpuparams.n_threads = value;
|
|
if (params.cpuparams.n_threads <= 0) {
|
|
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.lookup_cache_static = value;
|
|
}
|
|
).set_examples({LLAMA_EXAMPLE_LOOKUP}));
|
|
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.lookup_cache_dynamic = value;
|
|
}
|
|
).set_examples({LLAMA_EXAMPLE_LOOKUP}));
|
|
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;
|
|
}
|
|
).set_env("LLAMA_ARG_CTX_SIZE"));
|
|
add_opt(common_arg(
|
|
{"-n", "--predict", "--n-predict"}, "N",
|
|
string_format(
|
|
ex == LLAMA_EXAMPLE_MAIN
|
|
? "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(
|
|
{"--kv-unified", "-kvu"},
|
|
string_format("use single unified KV buffer for the KV cache of all sequences (default: %s)\n"
|
|
"[(more info)](https://github.com/ggml-org/llama.cpp/pull/14363)", params.kv_unified ? "true" : "false"),
|
|
[](common_params & params) {
|
|
params.kv_unified = true;
|
|
}
|
|
).set_env("LLAMA_ARG_KV_SPLIT"));
|
|
add_opt(common_arg(
|
|
{"--no-context-shift"},
|
|
string_format("disables context shift on infinite text generation (default: %s)", params.ctx_shift ? "disabled" : "enabled"),
|
|
[](common_params & params) {
|
|
params.ctx_shift = false;
|
|
}
|
|
).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_IMATRIX, LLAMA_EXAMPLE_PERPLEXITY}).set_env("LLAMA_ARG_NO_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"},
|
|
string_format("enable Flash Attention (default: %s)", params.flash_attn ? "enabled" : "disabled"),
|
|
[](common_params & params) {
|
|
params.flash_attn = true;
|
|
}
|
|
).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_MAIN}));
|
|
add_opt(common_arg(
|
|
{"--no-perf"},
|
|
string_format("disable internal libllama performance timings (default: %s)", params.no_perf ? "true" : "false"),
|
|
[](common_params & params) {
|
|
params.no_perf = true;
|
|
params.sampling.no_perf = true;
|
|
}
|
|
).set_env("LLAMA_ARG_NO_PERF"));
|
|
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_MAIN}));
|
|
add_opt(common_arg(
|
|
{"--in-file"}, "FNAME",
|
|
"an input file (repeat to specify multiple files)",
|
|
[](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()));
|
|
}
|
|
params.in_files.push_back(value);
|
|
}
|
|
).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"},
|
|
string_format("process escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\) (default: %s)", params.escape ? "true" : "false"),
|
|
[](common_params & params) {
|
|
params.escape = true;
|
|
}
|
|
));
|
|
add_opt(common_arg(
|
|
{"--no-escape"},
|
|
"do not process escape sequences",
|
|
[](common_params & params) {
|
|
params.escape = false;
|
|
}
|
|
));
|
|
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_MAIN}));
|
|
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_MAIN}));
|
|
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_MAIN}));
|
|
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_MAIN}));
|
|
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_MAIN}));
|
|
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_MAIN, LLAMA_EXAMPLE_SERVER}));
|
|
add_opt(common_arg(
|
|
{"-cnv", "--conversation"},
|
|
"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) {
|
|
params.conversation_mode = COMMON_CONVERSATION_MODE_ENABLED;
|
|
}
|
|
).set_examples({LLAMA_EXAMPLE_MAIN}));
|
|
add_opt(common_arg(
|
|
{"-no-cnv", "--no-conversation"},
|
|
"force disable conversation mode (default: false)",
|
|
[](common_params & params) {
|
|
params.conversation_mode = COMMON_CONVERSATION_MODE_DISABLED;
|
|
}
|
|
).set_examples({LLAMA_EXAMPLE_MAIN}));
|
|
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_MAIN}));
|
|
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_MAIN}));
|
|
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_MAIN}));
|
|
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_MAIN}));
|
|
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_MAIN}));
|
|
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_MAIN}));
|
|
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_MAIN}));
|
|
add_opt(common_arg(
|
|
{"--no-warmup"},
|
|
"skip warming up the model with an empty run",
|
|
[](common_params & params) {
|
|
params.warmup = false;
|
|
}
|
|
).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_RETRIEVAL}));
|
|
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);
|
|
}
|
|
).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(
|
|
{"--sampling-seq", "--sampler-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: %.1f)", (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);
|
|
}
|
|
).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;
|
|
}
|
|
).set_sparam());
|
|
add_opt(common_arg(
|
|
{"--top-p"}, "N",
|
|
string_format("top-p sampling (default: %.1f, 1.0 = disabled)", (double)params.sampling.top_p),
|
|
[](common_params & params, const std::string & value) {
|
|
params.sampling.top_p = std::stof(value);
|
|
}
|
|
).set_sparam());
|
|
add_opt(common_arg(
|
|
{"--min-p"}, "N",
|
|
string_format("min-p sampling (default: %.1f, 0.0 = disabled)", (double)params.sampling.min_p),
|
|
[](common_params & params, const std::string & value) {
|
|
params.sampling.min_p = std::stof(value);
|
|
}
|
|
).set_sparam());
|
|
add_opt(common_arg(
|
|
{"--top-nsigma"}, "N",
|
|
string_format("top-n-sigma sampling (default: %.1f, -1.0 = disabled)", params.sampling.top_n_sigma),
|
|
[](common_params & params, const std::string & value) {
|
|
params.sampling.top_n_sigma = std::stof(value);
|
|
}
|
|
).set_examples({LLAMA_EXAMPLE_MAIN}).set_sparam());
|
|
add_opt(common_arg(
|
|
{"--xtc-probability"}, "N",
|
|
string_format("xtc probability (default: %.1f, 0.0 = disabled)", (double)params.sampling.xtc_probability),
|
|
[](common_params & params, const std::string & value) {
|
|
params.sampling.xtc_probability = std::stof(value);
|
|
}
|
|
).set_sparam());
|
|
add_opt(common_arg(
|
|
{"--xtc-threshold"}, "N",
|
|
string_format("xtc threshold (default: %.1f, 1.0 = disabled)", (double)params.sampling.xtc_threshold),
|
|
[](common_params & params, const std::string & value) {
|
|
params.sampling.xtc_threshold = std::stof(value);
|
|
}
|
|
).set_sparam());
|
|
add_opt(common_arg(
|
|
{"--typical"}, "N",
|
|
string_format("locally typical sampling, parameter p (default: %.1f, 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);
|
|
}
|
|
).set_sparam());
|
|
add_opt(common_arg(
|
|
{"--repeat-penalty"}, "N",
|
|
string_format("penalize repeat sequence of tokens (default: %.1f, 1.0 = disabled)", (double)params.sampling.penalty_repeat),
|
|
[](common_params & params, const std::string & value) {
|
|
params.sampling.penalty_repeat = std::stof(value);
|
|
}
|
|
).set_sparam());
|
|
add_opt(common_arg(
|
|
{"--presence-penalty"}, "N",
|
|
string_format("repeat alpha presence penalty (default: %.1f, 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: %.1f, 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: %.1f, 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(
|
|
{"--dynatemp-range"}, "N",
|
|
string_format("dynamic temperature range (default: %.1f, 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: %.1f)", (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;
|
|
}
|
|
).set_sparam());
|
|
add_opt(common_arg(
|
|
{"--mirostat-lr"}, "N",
|
|
string_format("Mirostat learning rate, parameter eta (default: %.1f)", (double)params.sampling.mirostat_eta),
|
|
[](common_params & params, const std::string & value) {
|
|
params.sampling.mirostat_eta = std::stof(value);
|
|
}
|
|
).set_sparam());
|
|
add_opt(common_arg(
|
|
{"--mirostat-ent"}, "N",
|
|
string_format("Mirostat target entropy, parameter tau (default: %.1f)", (double)params.sampling.mirostat_tau),
|
|
[](common_params & params, const std::string & value) {
|
|
params.sampling.mirostat_tau = std::stof(value);
|
|
}
|
|
).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(
|
|
{"--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}).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: %.1f, 0.0 = full interpolation)", (double)params.yarn_ext_factor),
|
|
[](common_params & params, const std::string & value) {
|
|
params.yarn_ext_factor = std::stof(value);
|
|
}
|
|
).set_env("LLAMA_ARG_YARN_EXT_FACTOR"));
|
|
add_opt(common_arg(
|
|
{"--yarn-attn-factor"}, "N",
|
|
string_format("YaRN: scale sqrt(t) or attention magnitude (default: %.1f)", (double)params.yarn_attn_factor),
|
|
[](common_params & params, const std::string & value) {
|
|
params.yarn_attn_factor = std::stof(value);
|
|
}
|
|
).set_env("LLAMA_ARG_YARN_ATTN_FACTOR"));
|
|
add_opt(common_arg(
|
|
{"--yarn-beta-slow"}, "N",
|
|
string_format("YaRN: high correction dim or alpha (default: %.1f)", (double)params.yarn_beta_slow),
|
|
[](common_params & params, const std::string & value) {
|
|
params.yarn_beta_slow = std::stof(value);
|
|
}
|
|
).set_env("LLAMA_ARG_YARN_BETA_SLOW"));
|
|
add_opt(common_arg(
|
|
{"--yarn-beta-fast"}, "N",
|
|
string_format("YaRN: low correction dim or beta (default: %.1f)", (double)params.yarn_beta_fast),
|
|
[](common_params & params, const std::string & value) {
|
|
params.yarn_beta_fast = std::stof(value);
|
|
}
|
|
).set_env("LLAMA_ARG_YARN_BETA_FAST"));
|
|
add_opt(common_arg(
|
|
{"-gan", "--grp-attn-n"}, "N",
|
|
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_MAIN, 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_MAIN}));
|
|
add_opt(common_arg(
|
|
{"-nkvo", "--no-kv-offload"},
|
|
"disable KV offload",
|
|
[](common_params & params) {
|
|
params.no_kv_offload = true;
|
|
}
|
|
).set_env("LLAMA_ARG_NO_KV_OFFLOAD"));
|
|
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 (default: %.1f, < 0 - disabled)", (double)params.defrag_thold),
|
|
[](common_params & params, const std::string & value) {
|
|
params.defrag_thold = std::stof(value);
|
|
}
|
|
).set_env("LLAMA_ARG_DEFRAG_THOLD"));
|
|
add_opt(common_arg(
|
|
{"-np", "--parallel"}, "N",
|
|
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"},
|
|
string_format("enable continuous batching (a.k.a dynamic batching) (default: %s)", params.cont_batching ? "enabled" : "disabled"),
|
|
[](common_params & params) {
|
|
params.cont_batching = true;
|
|
}
|
|
).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_CONT_BATCHING"));
|
|
add_opt(common_arg(
|
|
{"-nocb", "--no-cont-batching"},
|
|
"disable continuous batching",
|
|
[](common_params & params) {
|
|
params.cont_batching = false;
|
|
}
|
|
).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_NO_CONT_BATCHING"));
|
|
add_opt(common_arg(
|
|
{"--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(
|
|
{"--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(
|
|
{"--no-mmproj"},
|
|
"explicitly disable multimodal projector, useful when using -hf",
|
|
[](common_params & params) {
|
|
params.no_mmproj = true;
|
|
}
|
|
).set_examples(mmproj_examples).set_env("LLAMA_ARG_NO_MMPROJ"));
|
|
add_opt(common_arg(
|
|
{"--no-mmproj-offload"},
|
|
"do not offload multimodal projector to GPU",
|
|
[](common_params & params) {
|
|
params.mmproj_use_gpu = false;
|
|
}
|
|
).set_examples(mmproj_examples).set_env("LLAMA_ARG_NO_MMPROJ_OFFLOAD"));
|
|
add_opt(common_arg(
|
|
{"--image", "--audio"}, "FILE",
|
|
"path to an image or audio file. use with multimodal models, can be repeated if you have multiple files\n",
|
|
[](common_params & params, const std::string & value) {
|
|
params.image.emplace_back(value);
|
|
}
|
|
).set_examples({LLAMA_EXAMPLE_MTMD}));
|
|
if (llama_supports_rpc()) {
|
|
add_opt(common_arg(
|
|
{"--rpc"}, "SERVERS",
|
|
"comma separated list of RPC servers",
|
|
[](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(
|
|
{"--no-mmap"},
|
|
"do not memory-map model (slower load but may reduce pageouts if not using mlock)",
|
|
[](common_params & params) {
|
|
params.use_mmap = false;
|
|
}
|
|
).set_env("LLAMA_ARG_NO_MMAP"));
|
|
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> rpc_devices;
|
|
std::vector<ggml_backend_dev_t> all_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_GPU) {
|
|
ggml_backend_reg_t reg = ggml_backend_dev_backend_reg(dev);
|
|
if (ggml_backend_reg_name(reg) == std::string("RPC")) {
|
|
rpc_devices.push_back(dev);
|
|
} else {
|
|
all_devices.push_back(dev);
|
|
}
|
|
}
|
|
}
|
|
// insert RPC devices in front
|
|
all_devices.insert(all_devices.begin(), rpc_devices.begin(), rpc_devices.end());
|
|
printf("Available devices:\n");
|
|
for (size_t i = 0; i < all_devices.size(); ++i) {
|
|
auto * dev = all_devices[i];
|
|
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(
|
|
{"--override-tensor", "-ot"}, "<tensor name pattern>=<buffer type>,...",
|
|
"override tensor buffer type", [](common_params & params, const std::string & value) {
|
|
/* static */ std::map<std::string, ggml_backend_buffer_type_t> buft_list;
|
|
if (buft_list.empty()) {
|
|
// enumerate all the devices and add their buffer types to the list
|
|
for (size_t i = 0; i < ggml_backend_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");
|
|
}
|
|
// FIXME: this leaks memory
|
|
params.tensor_buft_overrides.push_back({strdup(tensor_name.c_str()), buft_list.at(buffer_type)});
|
|
}
|
|
}
|
|
));
|
|
add_opt(common_arg(
|
|
{"-ngl", "--gpu-layers", "--n-gpu-layers"}, "N",
|
|
"number of layers to store in VRAM",
|
|
[](common_params & params, int value) {
|
|
params.n_gpu_layers = 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 %d input configs, but system only has %d devices", (int)split_arg.size(), (int)llama_max_devices())
|
|
);
|
|
}
|
|
for (size_t i = 0; i < llama_max_devices(); ++i) {
|
|
if (i < split_arg.size()) {
|
|
params.tensor_split[i] = std::stof(split_arg[i]);
|
|
} else {
|
|
params.tensor_split[i] = 0.0f;
|
|
}
|
|
}
|
|
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(
|
|
{"--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. may be specified multiple times.\n"
|
|
"types: int, float, bool, str. example: --override-kv tokenizer.ggml.add_bos_token=bool:false",
|
|
[](common_params & params, const std::string & value) {
|
|
if (!string_parse_kv_override(value.c_str(), params.kv_overrides)) {
|
|
throw std::runtime_error(string_format("error: Invalid type for KV override: %s\n", value.c_str()));
|
|
}
|
|
}
|
|
));
|
|
add_opt(common_arg(
|
|
{"--no-op-offload"},
|
|
string_format("disable offloading host tensor operations to device (default: %s)", params.no_op_offload ? "true" : "false"),
|
|
[](common_params & params) {
|
|
params.no_op_offload = true;
|
|
}
|
|
));
|
|
add_opt(common_arg(
|
|
{"--lora"}, "FNAME",
|
|
"path to LoRA adapter (can be repeated to use multiple adapters)",
|
|
[](common_params & params, const std::string & value) {
|
|
params.lora_adapters.push_back({ std::string(value), 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 (can be repeated to use multiple adapters)",
|
|
[](common_params & params, const std::string & fname, const std::string & scale) {
|
|
params.lora_adapters.push_back({ fname, std::stof(scale), 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: this argument can be repeated to add multiple control vectors",
|
|
[](common_params & params, const std::string & value) {
|
|
params.control_vectors.push_back({ 1.0f, value, });
|
|
}
|
|
));
|
|
add_opt(common_arg(
|
|
{"--control-vector-scaled"}, "FNAME", "SCALE",
|
|
"add a control vector with user defined scaling SCALE\n"
|
|
"note: this argument can be repeated to add multiple scaled control vectors",
|
|
[](common_params & params, const std::string & fname, const std::string & scale) {
|
|
params.control_vectors.push_back({ std::stof(scale), fname });
|
|
}
|
|
));
|
|
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
|
|
? std::string("model path from which to load base model")
|
|
: string_format(
|
|
"model path (default: `models/$filename` with filename from `--hf-file` "
|
|
"or `--model-url` if set, otherwise %s)", DEFAULT_MODEL_PATH
|
|
),
|
|
[](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(
|
|
{"-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.model.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 (repeat to specify multiple files)",
|
|
[](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()));
|
|
}
|
|
params.context_files.push_back(value);
|
|
}
|
|
).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}));
|
|
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(
|
|
{"--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(
|
|
{"--no-ppl"},
|
|
string_format("do not compute perplexity (default: %s)", params.compute_ppl ? "true" : "false"),
|
|
[](common_params & params) {
|
|
params.compute_ppl = false;
|
|
}
|
|
).set_examples({LLAMA_EXAMPLE_IMATRIX}));
|
|
add_opt(common_arg(
|
|
{"--chunk", "--from-chunk"}, "N",
|
|
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(
|
|
{"--parse-special"},
|
|
string_format("prase 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(
|
|
{"-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}));
|
|
add_opt(common_arg(
|
|
{"--embd-output-format"}, "FORMAT",
|
|
"empty = default, \"array\" = [[],[]...], \"json\" = openai style, \"json+\" = same \"json\" + cosine similarity matrix",
|
|
[](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(
|
|
{"--no-webui"},
|
|
string_format("Disable the Web UI (default: %s)", params.webui ? "enabled" : "disabled"),
|
|
[](common_params & params) {
|
|
params.webui = false;
|
|
}
|
|
).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_NO_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}).set_env("LLAMA_ARG_EMBEDDINGS"));
|
|
add_opt(common_arg(
|
|
{"--reranking", "--rerank"},
|
|
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 (default: none)",
|
|
[](common_params & params, const std::string & value) {
|
|
params.api_keys.push_back(value);
|
|
}
|
|
).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",
|
|
string_format("sets additional params for the json template parser"),
|
|
[](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}).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-reuse"}, "N",
|
|
string_format(
|
|
"min chunk size to attempt reusing from the cache via KV shifting (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(
|
|
{"--slots"},
|
|
string_format("enable slots monitoring endpoint (default: %s)", params.endpoint_slots ? "enabled" : "disabled"),
|
|
[](common_params & params) {
|
|
params.endpoint_slots = true;
|
|
}
|
|
).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_ENDPOINT_SLOTS"));
|
|
add_opt(common_arg(
|
|
{"--props"},
|
|
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(
|
|
{"--no-slots"},
|
|
"disables slots monitoring endpoint",
|
|
[](common_params & params) {
|
|
params.endpoint_slots = false;
|
|
}
|
|
).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_NO_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 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(
|
|
{"--jinja"},
|
|
"use jinja template for chat (default: disabled)",
|
|
[](common_params & params) {
|
|
params.use_jinja = true;
|
|
}
|
|
).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_MAIN}).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` (except in streaming mode, which behaves as `none`)\n"
|
|
"(default: deepseek)",
|
|
[](common_params & params, const std::string & value) {
|
|
/**/ if (value == "deepseek") { params.reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK; }
|
|
else if (value == "deepseek-legacy") { params.reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK_LEGACY; }
|
|
else if (value == "none") { params.reasoning_format = COMMON_REASONING_FORMAT_NONE; }
|
|
else { throw std::invalid_argument("invalid value"); }
|
|
}
|
|
).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_MAIN}).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_MAIN}).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_MAIN, 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_MAIN, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_CHAT_TEMPLATE_FILE"));
|
|
add_opt(common_arg(
|
|
{"--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) {
|
|
params.prefill_assistant = false;
|
|
}
|
|
).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_NO_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(
|
|
{"--simple-io"},
|
|
"use basic IO for better compatibility in subprocesses and limited consoles",
|
|
[](common_params & params) {
|
|
params.simple_io = true;
|
|
}
|
|
).set_examples({LLAMA_EXAMPLE_MAIN}));
|
|
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());
|
|
}
|
|
));
|
|
add_opt(common_arg(
|
|
{"--log-colors"},
|
|
"Enable colored logging",
|
|
[](common_params &) {
|
|
common_log_set_colors(common_log_main(), true);
|
|
}
|
|
).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;
|
|
common_log_set_verbosity_thold(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",
|
|
"Set the verbosity threshold. Messages with a higher verbosity will be ignored.",
|
|
[](common_params & params, int value) {
|
|
params.verbosity = value;
|
|
common_log_set_verbosity_thold(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}));
|
|
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}));
|
|
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-max", "--draft", "--draft-n"}, "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}).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}).set_env("LLAMA_ARG_DRAFT_MIN"));
|
|
add_opt(common_arg(
|
|
{"--draft-p-split"}, "P",
|
|
string_format("speculative decoding split probability (default: %.1f)", (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: %.1f)", (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}).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}).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}));
|
|
add_opt(common_arg(
|
|
{"-ngld", "--gpu-layers-draft", "--n-gpu-layers-draft"}, "N",
|
|
"number of layers to store in VRAM for the draft model",
|
|
[](common_params & params, int value) {
|
|
params.speculative.n_gpu_layers = 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}).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.model.path = value;
|
|
}
|
|
).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_MODEL_DRAFT"));
|
|
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}));
|
|
|
|
// model-specific
|
|
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-bge-small-en-default"},
|
|
string_format("use default bge-small-en-v1.5 model (note: can download weights from the internet)"),
|
|
[](common_params & params) {
|
|
params.model.hf_repo = "ggml-org/bge-small-en-v1.5-Q8_0-GGUF";
|
|
params.model.hf_file = "bge-small-en-v1.5-q8_0.gguf";
|
|
params.pooling_type = LLAMA_POOLING_TYPE_NONE;
|
|
params.embd_normalize = 2;
|
|
params.n_ctx = 512;
|
|
params.verbose_prompt = true;
|
|
params.embedding = true;
|
|
}
|
|
).set_examples({LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_SERVER}));
|
|
|
|
add_opt(common_arg(
|
|
{"--embd-e5-small-en-default"},
|
|
string_format("use default e5-small-v2 model (note: can download weights from the internet)"),
|
|
[](common_params & params) {
|
|
params.model.hf_repo = "ggml-org/e5-small-v2-Q8_0-GGUF";
|
|
params.model.hf_file = "e5-small-v2-q8_0.gguf";
|
|
params.pooling_type = LLAMA_POOLING_TYPE_NONE;
|
|
params.embd_normalize = 2;
|
|
params.n_ctx = 512;
|
|
params.verbose_prompt = true;
|
|
params.embedding = true;
|
|
}
|
|
).set_examples({LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_SERVER}));
|
|
|
|
add_opt(common_arg(
|
|
{"--embd-gte-small-default"},
|
|
string_format("use default gte-small model (note: can download weights from the internet)"),
|
|
[](common_params & params) {
|
|
params.model.hf_repo = "ggml-org/gte-small-Q8_0-GGUF";
|
|
params.model.hf_file = "gte-small-q8_0.gguf";
|
|
params.pooling_type = LLAMA_POOLING_TYPE_NONE;
|
|
params.embd_normalize = 2;
|
|
params.n_ctx = 512;
|
|
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_gpu_layers = 99;
|
|
params.flash_attn = true;
|
|
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_gpu_layers = 99;
|
|
params.flash_attn = true;
|
|
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_gpu_layers = 99;
|
|
params.flash_attn = true;
|
|
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.model.hf_repo = "ggml-org/Qwen2.5-Coder-0.5B-Q8_0-GGUF";
|
|
params.speculative.model.hf_file = "qwen2.5-coder-0.5b-q8_0.gguf";
|
|
params.speculative.n_gpu_layers = 99;
|
|
params.port = 8012;
|
|
params.n_gpu_layers = 99;
|
|
params.flash_attn = true;
|
|
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.model.hf_repo = "ggml-org/Qwen2.5-Coder-0.5B-Q8_0-GGUF";
|
|
params.speculative.model.hf_file = "qwen2.5-coder-0.5b-q8_0.gguf";
|
|
params.speculative.n_gpu_layers = 99;
|
|
params.port = 8012;
|
|
params.n_gpu_layers = 99;
|
|
params.flash_attn = true;
|
|
params.n_ubatch = 1024;
|
|
params.n_batch = 1024;
|
|
params.n_ctx = 0;
|
|
params.n_cache_reuse = 256;
|
|
}
|
|
).set_examples({LLAMA_EXAMPLE_SERVER}));
|
|
|
|
// diffusion parameters
|
|
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-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=MASKGIT_PLUS, 2=TOPK_MARGIN, 3=ENTROPY (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("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-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 }));
|
|
|
|
return ctx_arg;
|
|
}
|