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Runtime error
| int main(int argc, char ** argv) { | |
| gpt_params params; | |
| if (gpt_params_parse(argc, argv, params) == false) { | |
| return 1; | |
| } | |
| params.embedding = true; | |
| if (params.n_ctx > 2048) { | |
| fprintf(stderr, "%s: warning: model might not support context sizes greater than 2048 tokens (%d specified);" | |
| "expect poor results\n", __func__, params.n_ctx); | |
| } | |
| fprintf(stderr, "%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT); | |
| if (params.seed == LLAMA_DEFAULT_SEED) { | |
| params.seed = time(NULL); | |
| } | |
| fprintf(stderr, "%s: seed = %u\n", __func__, params.seed); | |
| std::mt19937 rng(params.seed); | |
| if (params.random_prompt) { | |
| params.prompt = gpt_random_prompt(rng); | |
| } | |
| llama_backend_init(params.numa); | |
| llama_model * model; | |
| llama_context * ctx; | |
| // load the model | |
| std::tie(model, ctx) = llama_init_from_gpt_params(params); | |
| if (model == NULL) { | |
| fprintf(stderr, "%s: error: unable to load model\n", __func__); | |
| return 1; | |
| } | |
| // print system information | |
| { | |
| fprintf(stderr, "\n"); | |
| fprintf(stderr, "system_info: n_threads = %d / %d | %s\n", | |
| params.n_threads, std::thread::hardware_concurrency(), llama_print_system_info()); | |
| } | |
| int n_past = 0; | |
| // Add a space in front of the first character to match OG llama tokenizer behavior | |
| params.prompt.insert(0, 1, ' '); | |
| // tokenize the prompt | |
| auto embd_inp = ::llama_tokenize(ctx, params.prompt, true); | |
| if (params.verbose_prompt) { | |
| fprintf(stderr, "\n"); | |
| fprintf(stderr, "%s: prompt: '%s'\n", __func__, params.prompt.c_str()); | |
| fprintf(stderr, "%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size()); | |
| for (int i = 0; i < (int) embd_inp.size(); i++) { | |
| fprintf(stderr, "%6d -> '%s'\n", embd_inp[i], llama_token_to_str(ctx, embd_inp[i])); | |
| } | |
| fprintf(stderr, "\n"); | |
| } | |
| if (params.embedding){ | |
| if (embd_inp.size() > 0) { | |
| if (llama_eval(ctx, embd_inp.data(), embd_inp.size(), n_past, params.n_threads)) { | |
| fprintf(stderr, "%s : failed to eval\n", __func__); | |
| return 1; | |
| } | |
| } | |
| const int n_embd = llama_n_embd(ctx); | |
| const auto embeddings = llama_get_embeddings(ctx); | |
| for (int i = 0; i < n_embd; i++) { | |
| printf("%f ", embeddings[i]); | |
| } | |
| printf("\n"); | |
| } | |
| llama_print_timings(ctx); | |
| llama_free(ctx); | |
| llama_free_model(model); | |
| llama_backend_free(); | |
| return 0; | |
| } | |