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https://git.adityakumar.xyz/llama.cpp.git
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10f19c1121
* set default n_batch to 512 when using BLAS * spacing * alternate implementation of setting different n_batch for BLAS * set n_batch to 512 for all cases
98 lines
3.3 KiB
C++
98 lines
3.3 KiB
C++
// Various helper functions and utilities
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#pragma once
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#include "llama.h"
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#include <string>
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#include <vector>
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#include <random>
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#include <thread>
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//
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// CLI argument parsing
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//
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struct gpt_params {
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int32_t seed = -1; // RNG seed
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int32_t n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency());
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int32_t n_predict = 128; // new tokens to predict
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int32_t repeat_last_n = 64; // last n tokens to penalize
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int32_t n_parts = -1; // amount of model parts (-1 = determine from model dimensions)
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int32_t n_ctx = 512; // context size
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int32_t n_batch = 512; // batch size for prompt processing (must be >=32 to use BLAS)
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int32_t n_keep = 0; // number of tokens to keep from initial prompt
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// sampling parameters
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int32_t top_k = 40;
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float top_p = 0.95f;
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float temp = 0.80f;
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float repeat_penalty = 1.10f;
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std::string model = "models/lamma-7B/ggml-model.bin"; // model path
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std::string prompt = "";
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std::string input_prefix = ""; // string to prefix user inputs with
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std::vector<std::string> antiprompt; // string upon seeing which more user input is prompted
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std::string lora_adapter = ""; // lora adapter path
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std::string lora_base = ""; // base model path for the lora adapter
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bool memory_f16 = true; // use f16 instead of f32 for memory kv
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bool random_prompt = false; // do not randomize prompt if none provided
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bool use_color = false; // use color to distinguish generations and inputs
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bool interactive = false; // interactive mode
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bool embedding = false; // get only sentence embedding
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bool interactive_start = false; // wait for user input immediately
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bool instruct = false; // instruction mode (used for Alpaca models)
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bool ignore_eos = false; // do not stop generating after eos
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bool perplexity = false; // compute perplexity over the prompt
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bool use_mmap = true; // use mmap for faster loads
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bool use_mlock = false; // use mlock to keep model in memory
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bool mem_test = false; // compute maximum memory usage
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bool verbose_prompt = false; // print prompt tokens before generation
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};
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bool gpt_params_parse(int argc, char ** argv, gpt_params & params);
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void gpt_print_usage(int argc, char ** argv, const gpt_params & params);
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std::string gpt_random_prompt(std::mt19937 & rng);
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//
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// Vocab utils
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//
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std::vector<llama_token> llama_tokenize(struct llama_context * ctx, const std::string & text, bool add_bos);
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//
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// Console utils
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//
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#define ANSI_COLOR_RED "\x1b[31m"
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#define ANSI_COLOR_GREEN "\x1b[32m"
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#define ANSI_COLOR_YELLOW "\x1b[33m"
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#define ANSI_COLOR_BLUE "\x1b[34m"
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#define ANSI_COLOR_MAGENTA "\x1b[35m"
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#define ANSI_COLOR_CYAN "\x1b[36m"
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#define ANSI_COLOR_RESET "\x1b[0m"
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#define ANSI_BOLD "\x1b[1m"
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enum console_color_t {
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CONSOLE_COLOR_DEFAULT=0,
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CONSOLE_COLOR_PROMPT,
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CONSOLE_COLOR_USER_INPUT
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};
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struct console_state {
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bool use_color = false;
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console_color_t color = CONSOLE_COLOR_DEFAULT;
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};
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void set_console_color(console_state & con_st, console_color_t color);
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#if defined (_WIN32)
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void win32_console_init(bool enable_color);
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void win32_utf8_encode(const std::wstring & wstr, std::string & str);
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#endif
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