// Various helper functions and utilities #pragma once #include "llama.h" #include #include #include #include #include // // CLI argument parsing // struct gpt_params { int32_t seed = -1; // RNG seed int32_t n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency()); int32_t n_predict = -1; // new tokens to predict int32_t n_parts = -1; // amount of model parts (-1 = determine from model dimensions) int32_t n_ctx = 512; // context size int32_t n_batch = 512; // batch size for prompt processing (must be >=32 to use BLAS) int32_t n_keep = 0; // number of tokens to keep from initial prompt // sampling parameters std::unordered_map logit_bias; // logit bias for specific tokens int32_t top_k = 40; // <= 0 to use vocab size float top_p = 0.95f; // 1.0 = disabled float tfs_z = 1.00f; // 1.0 = disabled float typical_p = 1.00f; // 1.0 = disabled float temp = 0.80f; // 1.0 = disabled float repeat_penalty = 1.10f; // 1.0 = disabled int32_t repeat_last_n = 64; // last n tokens to penalize (0 = disable penalty, -1 = context size) float frequency_penalty = 0.00f; // 0.0 = disabled float presence_penalty = 0.00f; // 0.0 = disabled int mirostat = 0; // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0 float mirostat_tau = 5.00f; // target entropy float mirostat_eta = 0.10f; // learning rate std::string model = "models/lamma-7B/ggml-model.bin"; // model path std::string prompt = ""; std::string path_session = ""; // path to file for saving/loading model eval state std::string input_prefix = ""; // string to prefix user inputs with std::vector antiprompt; // string upon seeing which more user input is prompted std::string lora_adapter = ""; // lora adapter path std::string lora_base = ""; // base model path for the lora adapter bool memory_f16 = true; // use f16 instead of f32 for memory kv bool random_prompt = false; // do not randomize prompt if none provided bool use_color = false; // use color to distinguish generations and inputs bool interactive = false; // interactive mode bool embedding = false; // get only sentence embedding bool interactive_first = false; // wait for user input immediately bool instruct = false; // instruction mode (used for Alpaca models) bool penalize_nl = true; // consider newlines as a repeatable token bool perplexity = false; // compute perplexity over the prompt bool use_mmap = true; // use mmap for faster loads bool use_mlock = false; // use mlock to keep model in memory bool mem_test = false; // compute maximum memory usage bool verbose_prompt = false; // print prompt tokens before generation }; bool gpt_params_parse(int argc, char ** argv, gpt_params & params); void gpt_print_usage(int argc, char ** argv, const gpt_params & params); std::string gpt_random_prompt(std::mt19937 & rng); // // Vocab utils // std::vector llama_tokenize(struct llama_context * ctx, const std::string & text, bool add_bos); // // Console utils // #define ANSI_COLOR_RED "\x1b[31m" #define ANSI_COLOR_GREEN "\x1b[32m" #define ANSI_COLOR_YELLOW "\x1b[33m" #define ANSI_COLOR_BLUE "\x1b[34m" #define ANSI_COLOR_MAGENTA "\x1b[35m" #define ANSI_COLOR_CYAN "\x1b[36m" #define ANSI_COLOR_RESET "\x1b[0m" #define ANSI_BOLD "\x1b[1m" enum console_color_t { CONSOLE_COLOR_DEFAULT=0, CONSOLE_COLOR_PROMPT, CONSOLE_COLOR_USER_INPUT }; struct console_state { bool use_color = false; console_color_t color = CONSOLE_COLOR_DEFAULT; }; void set_console_color(console_state & con_st, console_color_t color); #if defined (_WIN32) void win32_console_init(bool enable_color); void win32_utf8_encode(const std::wstring & wstr, std::string & str); #endif