#define ANSI_COLOR_RESET "\x1b[0m"
#define ANSI_BOLD "\x1b[1m"
+static const int EOS_TOKEN_ID = 2;
+
// determine number of model parts based on the dimension
static const std::map<int, int> LLAMA_N_PARTS = {
{ 4096, 1 },
{
const int64_t t_start_sample_us = ggml_time_us();
+ if (params.ignore_eos) {
+ // set the logit of the eos token to zero to avoid sampling it
+ logits[logits.size() - n_vocab + EOS_TOKEN_ID] = 0;
+ }
+
id = llama_sample_top_p_top_k(vocab, logits.data() + (logits.size() - n_vocab), last_n_tokens, repeat_penalty, top_k, top_p, temp, rng);
last_n_tokens.erase(last_n_tokens.begin());
}
// end of text token
- if (embd.back() == 2) {
+
+ if (embd.back() == EOS_TOKEN_ID) {
if (params.interactive) {
is_interacting = true;
} else {
params.use_color = true;
} else if (arg == "-r" || arg == "--reverse-prompt") {
params.antiprompt = argv[++i];
+ } else if (arg == "--ignore-eos") {
+ params.ignore_eos = true;
} else if (arg == "-h" || arg == "--help") {
gpt_print_usage(argc, argv, params);
exit(0);
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, " -b N, --batch_size N batch size for prompt processing (default: %d)\n", params.n_batch);
bool interactive = false; // interactive mode
bool instruct = false; // instruction mode (used for Alpaca models)
+
+ bool ignore_eos = false; // do not stop generating after eos
};
bool gpt_params_parse(int argc, char ** argv, gpt_params & params);