// determine sensible default number of threads.

// std::thread::hardware_concurrency may not be equal to the number of cores, or may return 0.

#ifdef __linux__

std::ifstream cpuinfo("/proc/cpuinfo");

params.n_threads = std::count(std::istream_iterator<std::string>(cpuinfo),

std::istream_iterator<std::string>(),

std::string("processor"));

#endif

if (params.n_threads == 0) {

params.n_threads = std::max(1, (int32_t) std::thread::hardware_concurrency());

}

for (int i = 1; i < argc; i++) {

std::string arg = argv[i];

if (arg == "-s" || arg == "--seed") {

params.seed = std::stoi(argv[++i]);

} else if (arg == "-t" || arg == "--threads") {

params.n_threads = std::stoi(argv[++i]);

} else if (arg == "-p" || arg == "--prompt") {

params.prompt = argv[++i];

} else if (arg == "-f" || arg == "--file") {

std::ifstream file(argv[++i]);

std::copy(std::istreambuf_iterator<char>(file), std::istreambuf_iterator<char>(), back_inserter(params.prompt));

if (params.prompt.back() == '\n') {

params.prompt.pop_back();

}

} else if (arg == "-n" || arg == "--n_predict") {

params.n_predict = std::stoi(argv[++i]);

} else if (arg == "--top_k") {

params.top_k = std::stoi(argv[++i]);

} else if (arg == "-c" || arg == "--ctx_size") {

params.n_ctx = std::stoi(argv[++i]);

} else if (arg == "--memory_f16") {

params.memory_f16 = true;

} else if (arg == "--top_p") {

params.top_p = std::stof(argv[++i]);

} else if (arg == "--temp") {

params.temp = std::stof(argv[++i]);

} else if (arg == "--repeat_last_n") {

params.repeat_last_n = std::stoi(argv[++i]);

} else if (arg == "--repeat_penalty") {

params.repeat_penalty = std::stof(argv[++i]);

} else if (arg == "-b" || arg == "--batch_size") {

params.n_batch = std::stoi(argv[++i]);

} else if (arg == "-m" || arg == "--model") {

params.model = argv[++i];

} else if (arg == "-i" || arg == "--interactive") {

params.interactive = true;

} else if (arg == "-ins" || arg == "--instruct") {

params.instruct = true;

} else if (arg == "--color") {

params.use_color = true;

} else if (arg == "-r" || arg == "--reverse-prompt") {

params.antiprompt.push_back(argv[++i]);

} else if (arg == "--perplexity") {

params.perplexity = true;

} else if (arg == "--ignore-eos") {

params.ignore_eos = true;

} else if (arg == "--n_parts") {

params.n_parts = std::stoi(argv[++i]);

} else if (arg == "-h" || arg == "--help") {

gpt_print_usage(argc, argv, params);

exit(0);

} else if (arg == "--random-prompt") {

params.random_prompt = true;

} else {

fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());

gpt_print_usage(argc, argv, params);

exit(0);

}

}

return true;

fprintf(stderr, "usage: %s [options]\n", argv[0]);

fprintf(stderr, "\n");

fprintf(stderr, "options:\n");

fprintf(stderr, " -h, --help show this help message and exit\n");

fprintf(stderr, " -i, --interactive run in interactive mode\n");

fprintf(stderr, " -ins, --instruct run in instruction mode (use with Alpaca models)\n");

fprintf(stderr, " -r PROMPT, --reverse-prompt PROMPT\n");

fprintf(stderr, " in interactive mode, poll user input upon seeing PROMPT (can be\n");

fprintf(stderr, " specified more than once for multiple prompts).\n");

fprintf(stderr, " --color colorise output to distinguish prompt and user input from generations\n");

fprintf(stderr, " -s SEED, --seed SEED RNG seed (default: -1, use random seed for <= 0)\n");

fprintf(stderr, " -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads);

fprintf(stderr, " -p PROMPT, --prompt PROMPT\n");

fprintf(stderr, " prompt to start generation with (default: empty)\n");

fprintf(stderr, " --random-prompt start with a randomized prompt.\n");

fprintf(stderr, " -f FNAME, --file FNAME\n");

fprintf(stderr, " prompt file to start generation.\n");

fprintf(stderr, " -n N, --n_predict N number of tokens to predict (default: %d)\n", params.n_predict);

fprintf(stderr, " --top_k N top-k sampling (default: %d)\n", params.top_k);

fprintf(stderr, " --top_p N top-p sampling (default: %.1f)\n", params.top_p);

fprintf(stderr, " --repeat_last_n N last n tokens to consider for penalize (default: %d)\n", params.repeat_last_n);

fprintf(stderr, " --repeat_penalty N penalize repeat sequence of tokens (default: %.1f)\n", params.repeat_penalty);

fprintf(stderr, " -c N, --ctx_size N size of the prompt context (default: %d)\n", params.n_ctx);

fprintf(stderr, " --ignore-eos ignore end of stream token and continue generating\n");

fprintf(stderr, " --memory_f16 use f16 instead of f32 for memory key+value\n");

fprintf(stderr, " --temp N temperature (default: %.1f)\n", params.temp);

fprintf(stderr, " --n_parts N number of model parts (default: -1 = determine from dimensions)\n");

fprintf(stderr, " -b N, --batch_size N batch size for prompt processing (default: %d)\n", params.n_batch);

fprintf(stderr, " --perplexity compute perplexity over the prompt\n");

fprintf(stderr, " -m FNAME, --model FNAME\n");

fprintf(stderr, " model path (default: %s)\n", params.model.c_str());

fprintf(stderr, "\n");

const int r = rng() % 10;

switch (r) {

case 0: return "So";

case 1: return "Once upon a time";

case 2: return "When";

case 3: return "The";

case 4: return "After";

case 5: return "If";

case 6: return "import";

case 7: return "He";

case 8: return "She";

case 9: return "They";

default: return "To";

}

return "The";

std::vector<llama_token> res(8096);

int n = llama_tokenize(ctx, text.c_str(), res.data(), res.size(), add_bos);

res.resize(n);

return res;