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#include "llama.h"
#include "models.h"
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <getopt.h>
static void show_help(const char *prog) {
printf("Usage: %s [OPTIONS]\n", prog);
printf("Options:\n");
printf(" -m, --model <name> Specify model to use (default: first model)\n");
printf(" -p, --prompt <text> Specify prompt text (default: \"What is 2+2?\")\n");
printf(" -h, --help Show this help message\n");
}
int main(int argc, char **argv) {
const char *model_name = NULL;
const char *prompt = NULL;
int n_predict = 64;
static struct option long_options[] = {
{"model", required_argument, 0, 'm'},
{"prompt", required_argument, 0, 'p'},
{"help", no_argument, 0, 'h'},
{0, 0, 0, 0}
};
int opt;
int option_index = 0;
while ((opt = getopt_long(argc, argv, "m:p:h", long_options, &option_index)) != -1) {
switch (opt) {
case 'm':
model_name = optarg;
break;
case 'p':
prompt = optarg;
break;
case 'h':
show_help(argv[0]);
return 0;
default:
fprintf(stderr, "Usage: %s [-m model] [-p prompt] [-h]\n", argv[0]);
return 1;
}
}
if (prompt == NULL) {
printf("Prompt must be provided. Exiting...");
return 1;
}
const model_config *cfg = NULL;
if (model_name != NULL) {
cfg = get_model_by_name(model_name);
if (cfg == NULL) {
fprintf(stderr, "Error: unknown model '%s'\n", model_name);
return 1;
}
} else {
cfg = &models[0];
}
ggml_backend_load_all();
struct llama_model_params model_params = llama_model_default_params();
model_params.n_gpu_layers = cfg->n_gpu_layers;
model_params.use_mmap = cfg->use_mmap;
struct llama_model *model = llama_model_load_from_file(cfg->filepath, model_params);
if (model == NULL) {
fprintf(stderr, "Error: unable to load model from %s\n", cfg->filepath);
return 1;
}
const struct llama_vocab *vocab = llama_model_get_vocab(model);
int n_prompt = -llama_tokenize(vocab, prompt, strlen(prompt), NULL, 0, true, true);
llama_token *prompt_tokens = (llama_token *)malloc(n_prompt * sizeof(llama_token));
if (llama_tokenize(vocab, prompt, strlen(prompt), prompt_tokens, n_prompt, true, true) < 0) {
fprintf(stderr, "Error: failed to tokenize the prompt\n");
free(prompt_tokens);
llama_model_free(model);
return 1;
}
struct llama_context_params ctx_params = llama_context_default_params();
ctx_params.n_ctx = cfg->n_ctx;
ctx_params.n_batch = cfg->n_batch;
ctx_params.embeddings = cfg->embeddings;
struct llama_context *ctx = llama_init_from_model(model, ctx_params);
if (ctx == NULL) {
fprintf(stderr, "Error: failed to create the llama_context\n");
free(prompt_tokens);
llama_model_free(model);
return 1;
}
struct llama_sampler_chain_params sparams = llama_sampler_chain_default_params();
struct llama_sampler *smpl = llama_sampler_chain_init(sparams);
llama_sampler_chain_add(smpl, llama_sampler_init_temp(cfg->temperature));
llama_sampler_chain_add(smpl, llama_sampler_init_min_p(cfg->min_p, 1));
llama_sampler_chain_add(smpl, llama_sampler_init_dist(cfg->seed));
struct llama_batch batch = llama_batch_get_one(prompt_tokens, n_prompt);
if (llama_model_has_encoder(model)) {
if (llama_encode(ctx, batch)) {
fprintf(stderr, "Error: failed to encode prompt\n");
return 1;
}
llama_token decoder_start = llama_model_decoder_start_token(model);
if (decoder_start == LLAMA_TOKEN_NULL) {
decoder_start = llama_vocab_bos(vocab);
}
batch = llama_batch_get_one(&decoder_start, 1);
}
printf("Prompt: %s\n", prompt);
printf("Response: ");
fflush(stdout);
int n_pos = 0;
llama_token new_token_id;
while (n_pos + batch.n_tokens < n_prompt + n_predict) {
if (llama_decode(ctx, batch)) {
fprintf(stderr, "Error: failed to decode\n");
break;
}
n_pos += batch.n_tokens;
new_token_id = llama_sampler_sample(smpl, ctx, -1);
if (llama_vocab_is_eog(vocab, new_token_id)) {
break;
}
char buf[128];
int n = llama_token_to_piece(vocab, new_token_id, buf, sizeof(buf), 0, true);
if (n < 0) {
fprintf(stderr, "Error: failed to convert token to piece\n");
break;
}
printf("%.*s", n, buf);
fflush(stdout);
batch = llama_batch_get_one(&new_token_id, 1);
}
printf("\n");
free(prompt_tokens);
llama_sampler_free(smpl);
llama_free(ctx);
llama_model_free(model);
return 0;
}
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