const llama_model * model = llama_get_model(ctx);

const llama_vocab * vocab = llama_model_get_vocab(model);

const bool add_bos = llama_vocab_get_add_bos(vocab);

std::vector<llama_token> tokens = common_tokenize(ctx, params.prompt, add_bos, true);

if (tokens.empty()) {

LOG_ERR("%s : there are not input tokens to process - (try to provide a prompt with '-p')\n", __func__);

return false;

}

LOG_INF("number of input tokens = %zu\n", tokens.size());

for (size_t i = 0; i < tokens.size(); ++i) {

LOG_INF(" %d\n", tokens[i]);

}

if (llama_decode(ctx, llama_batch_get_one(tokens.data(), tokens.size()))) {

LOG_ERR("%s : failed to eval\n", __func__);

return false;

}

return true;

std::setlocale(LC_NUMERIC, "C");

common_debug_cb_user_data cb_data;

common_params params;

common_init();

if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON)) {

return 1;

}

llama_backend_init();

llama_numa_init(params.numa);

// pass the callback to the backend scheduler

// it will be executed for each node during the graph computation

params.cb_eval = common_debug_cb_eval;

params.cb_eval_user_data = &cb_data;

params.warmup = false;

// init

auto llama_init = common_init_from_params(params);

auto * model = llama_init->model();

auto * ctx = llama_init->context();

if (model == nullptr || ctx == nullptr) {

LOG_ERR("%s : failed to init\n", __func__);

return 1;

}

// print system information

{

LOG_INF("\n");

LOG_INF("%s\n", common_params_get_system_info(params).c_str());

LOG_INF("\n");

}

bool OK = run(ctx, params);

if (!OK) {

return 1;

}

LOG("\n");

llama_perf_context_print(ctx);

llama_backend_free();

return 0;