llama.cpp/main.cpp

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#include "utils.h"
#include "ggml.h"
#include "llama.h"
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#include <cassert>
#include <cinttypes>
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#include <cmath>
#include <cstdio>
#include <cstring>
#include <fstream>
#include <iostream>
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#include <string>
#include <vector>
#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
#include <signal.h>
#include <unistd.h>
#elif defined (_WIN32)
#include <signal.h>
#endif
#if defined (_WIN32)
#pragma comment(lib,"kernel32.lib")
extern "C" __declspec(dllimport) void* __stdcall GetStdHandle(unsigned long nStdHandle);
extern "C" __declspec(dllimport) int __stdcall GetConsoleMode(void* hConsoleHandle, unsigned long* lpMode);
extern "C" __declspec(dllimport) int __stdcall SetConsoleMode(void* hConsoleHandle, unsigned long dwMode);
#endif
#define ANSI_COLOR_RED "\x1b[31m"
#define ANSI_COLOR_GREEN "\x1b[32m"
#define ANSI_COLOR_YELLOW "\x1b[33m"
#define ANSI_COLOR_BLUE "\x1b[34m"
#define ANSI_COLOR_MAGENTA "\x1b[35m"
#define ANSI_COLOR_CYAN "\x1b[36m"
#define ANSI_COLOR_RESET "\x1b[0m"
#define ANSI_BOLD "\x1b[1m"
/* Keep track of current color of output, and emit ANSI code if it changes. */
enum console_state {
CONSOLE_STATE_DEFAULT=0,
CONSOLE_STATE_PROMPT,
CONSOLE_STATE_USER_INPUT
};
static console_state con_st = CONSOLE_STATE_DEFAULT;
static bool con_use_color = false;
void set_console_state(console_state new_st)
{
if (!con_use_color) return;
// only emit color code if state changed
if (new_st != con_st) {
con_st = new_st;
switch(con_st) {
case CONSOLE_STATE_DEFAULT:
printf(ANSI_COLOR_RESET);
return;
case CONSOLE_STATE_PROMPT:
printf(ANSI_COLOR_YELLOW);
return;
case CONSOLE_STATE_USER_INPUT:
printf(ANSI_BOLD ANSI_COLOR_GREEN);
return;
}
}
}
std::vector<double> softmax(const std::vector<float>& logits) {
std::vector<double> probs(logits.size());
float max_logit = logits[0];
for (float v : logits) max_logit = std::max(max_logit, v);
double sum_exp = 0.0;
for (size_t i = 0; i < logits.size(); i++) {
// Subtract the maximum logit value from the current logit value for numerical stability
float logit = logits[i] - max_logit;
double exp_logit = std::exp(logit);
sum_exp += exp_logit;
probs[i] = exp_logit;
}
for (size_t i = 0; i < probs.size(); i++) probs[i] /= sum_exp;
return probs;
}
void perplexity(llama_context * ctx, const gpt_params & params) {
// Download: https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip?ref=salesforce-research
// Run `./main --perplexity -m models/7B/ggml-model-q4_0.bin -f wiki.test.raw`
// Output: `perplexity: 13.5106 [114/114]`
auto tokens = ::llama_tokenize(ctx, params.prompt, true);
int count = 0;
double nll = 0.0;
int seq_count = tokens.size() / params.n_ctx;
fprintf(stderr, "%s : calculating perplexity over %d chunks\n", __func__, seq_count);
for (int i = 0; i < seq_count; ++i) {
int start = i * params.n_ctx;
int end = start + params.n_ctx - 1;
std::vector<llama_token> embd(tokens.begin() + start, tokens.begin() + end);
auto start_t = std::chrono::high_resolution_clock::now();
if (llama_eval(ctx, embd.data(), embd.size(), 0, params.n_threads)) {
fprintf(stderr, "%s : failed to eval\n", __func__);
return;
}
auto end_t = std::chrono::high_resolution_clock::now();
if (i == 0) {
double seconds = std::chrono::duration<double>(end_t - start_t).count();
printf("%.2f seconds per pass - ETA %.2f hours\n", seconds, (seconds * seq_count) / (60.0*60.0));
}
// We get the logits for all the tokens in the context window (params.n_ctx)
// from llama_eval above. Now, based on https://huggingface.co/docs/transformers/perplexity,
// calculate the perplexity over the last half the window (so the model always has
// some context to predict the token).
//
// We rely on the fact that attention in the forward pass only looks at previous
// tokens here, so the logits returned for each token are an accurate representation
// of what the model would have predicted at that point.
//
// Example, we have a context window of 512, we will compute perplexity for each of the
// last 256 tokens. Then, we split the input up into context window size chunks to
// process the entire prompt.
auto logits = llama_get_logits(ctx);
for (int j = params.n_ctx / 2; j < params.n_ctx - 1; ++j) {
// Calculate probability of next token, given the previous ones.
int n_vocab = llama_n_vocab(ctx);
std::vector<float> tok_logits(
logits + j * n_vocab,
logits + (j + 1) * n_vocab);
double prob = softmax(tok_logits)[tokens[start + j + 1]];
nll += -std::log(prob);
++count;
}
// perplexity is e^(average negative log-likelihood)
printf("[%d]%.4lf,", i + 1, std::exp(nll / count));
fflush(stdout);
}
printf("\n");
}
static bool is_interacting = false;
#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32)
void sigint_handler(int signo) {
set_console_state(CONSOLE_STATE_DEFAULT);
printf("\n"); // this also force flush stdout.
if (signo == SIGINT) {
if (!is_interacting) {
is_interacting=true;
} else {
_exit(130);
}
}
}
#endif
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int main(int argc, char ** argv) {
// has to be called once at the start of the program to init ggml stuff
ggml_time_init();
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gpt_params params;
params.model = "models/llama-7B/ggml-model.bin";
if (gpt_params_parse(argc, argv, params) == false) {
return 1;
}
if (params.n_ctx > 2048) {
fprintf(stderr, "%s: warning: model does not support context sizes greater than 2048 tokens (%d specified);"
"expect poor results\n", __func__, params.n_ctx);
}
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if (params.seed <= 0) {
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params.seed = time(NULL);
}
fprintf(stderr, "%s: seed = %d\n", __func__, params.seed);
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std::mt19937 rng(params.seed);
if (params.random_prompt) {
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params.prompt = gpt_random_prompt(rng);
}
// save choice to use color for later
// (note for later: this is a slightly awkward choice)
con_use_color = params.use_color;
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// params.prompt = R"(// this function checks if the number n is prime
//bool is_prime(int n) {)";
llama_context * ctx;
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// load the model
{
auto lparams = llama_context_default_params();
lparams.n_ctx = params.n_ctx;
lparams.n_parts = params.n_parts;
lparams.seed = params.seed;
lparams.f16_kv = params.memory_f16;
lparams.logits_all = params.perplexity;
lparams.use_mlock = params.use_mlock;
lparams.embedding = params.embedding;
ctx = llama_init_from_file(params.model.c_str(), lparams);
if (ctx == NULL) {
fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, params.model.c_str());
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return 1;
}
}
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// 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());
}
// determine the maximum memory usage needed to do inference for the given n_batch and n_predict parameters
// uncomment the "used_mem" line in llama.cpp to see the results
if (params.mem_test) {
{
const std::vector<llama_token> tmp(params.n_batch, 0);
llama_eval(ctx, tmp.data(), tmp.size(), 0, params.n_threads);
}
{
const std::vector<llama_token> tmp = { 0, };
llama_eval(ctx, tmp.data(), tmp.size(), params.n_predict - 1, params.n_threads);
}
llama_print_timings(ctx);
llama_free(ctx);
return 0;
}
if (params.perplexity) {
perplexity(ctx, params);
exit(0);
}
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int n_past = 0;
// Add a space in front of the first character to match OG llama tokenizer behavior
params.prompt.insert(0, 1, ' ');
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// tokenize the prompt
auto embd_inp = ::llama_tokenize(ctx, params.prompt, true);
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const int n_ctx = llama_n_ctx(ctx);
params.n_predict = std::min(params.n_predict, n_ctx - (int) embd_inp.size());
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// prefix & suffix for instruct mode
const auto inp_pfx = ::llama_tokenize(ctx, "\n\n### Instruction:\n\n", true);
const auto inp_sfx = ::llama_tokenize(ctx, "\n\n### Response:\n\n", false);
// in instruct mode, we inject a prefix and a suffix to each input by the user
if (params.instruct) {
params.interactive = true;
params.antiprompt.push_back("### Instruction:\n\n");
}
// enable interactive mode if reverse prompt is specified
if (params.antiprompt.size() != 0) {
params.interactive = true;
}
if (params.interactive_start) {
params.interactive = true;
}
Replace EOS with newline to prevent context/memory being flushed by EOS in interactive mode (#333) * Improve interactive mode's coherence after EOS Aims to improve coherence and ability to resume the interactive session when the user is given input back after an end of text token is reached. Not sure what token 13 is or why it seems to help. See conversation for examples. * Make newline token a constant * dynamically determine newline token * relocate previous newline token const * cleanup whitespace * print a new line on end of text in interactive this may need to be looked into further when not using a reverse prompt * only print manual newline with reverse prompt fix formatting of reverse prompts so they don't end up at the end of the current line while not introducing unnecessary new lines otherwise * alternate approach to replace end of text tokens * Inject the reverse prompt again after eos in interactive mode * tokenize reverse prompt when needed makes this PR compatible with https://github.com/ggerganov/llama.cpp/pull/330 * tokenize and inject only first reverse prompt thanks to tjohnman * tokenize first reverse prompt once * add newline token * add newline token * tokenize/inject reverse prompt for refactor this doesn't seem right though * tokenize nothing for antiprompt if no reverse * Update main.cpp * Update main.cpp * tokenize and inject reverse prompt as needed this doesn't seem to work if the reverse prompt is tokenized outside earlier on * not needed * remove newline token * remove newline token * tokenize newline token * add space to comment * Update main.cpp Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> --------- Co-authored-by: Slaren <2141330+slaren@users.noreply.github.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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// determine newline token
auto llama_token_newline = ::llama_tokenize(ctx, "\n", false);
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());
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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]));
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}
fprintf(stderr, "\n");
if (params.interactive) {
#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
struct sigaction sigint_action;
sigint_action.sa_handler = sigint_handler;
sigemptyset (&sigint_action.sa_mask);
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sigint_action.sa_flags = 0;
sigaction(SIGINT, &sigint_action, NULL);
#elif defined (_WIN32)
signal(SIGINT, sigint_handler);
#endif
fprintf(stderr, "%s: interactive mode on.\n", __func__);
if(params.antiprompt.size()) {
for (auto antiprompt : params.antiprompt) {
fprintf(stderr, "Reverse prompt: '%s'\n", antiprompt.c_str());
}
}
if (!params.input_prefix.empty()) {
fprintf(stderr, "Input prefix: '%s'\n", params.input_prefix.c_str());
}
}
fprintf(stderr, "sampling parameters: temp = %f, top_k = %d, top_p = %f, repeat_last_n = %i, repeat_penalty = %f\n", params.temp, params.top_k, params.top_p, params.repeat_last_n, params.repeat_penalty);
fprintf(stderr, "\n\n");
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std::vector<llama_token> embd;
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int last_n_size = params.repeat_last_n;
std::vector<llama_token> last_n_tokens(last_n_size);
std::fill(last_n_tokens.begin(), last_n_tokens.end(), 0);
if (params.interactive) {
fprintf(stderr, "== Running in interactive mode. ==\n"
#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32)
" - Press Ctrl+C to interject at any time.\n"
#endif
" - Press Return to return control to LLaMa.\n"
" - If you want to submit another line, end your input in '\\'.\n\n");
is_interacting = params.interactive_start || params.instruct;
}
int input_consumed = 0;
bool input_noecho = false;
int remaining_tokens = params.n_predict;
#if defined (_WIN32)
if (params.use_color) {
// Enable ANSI colors on Windows 10+
unsigned long dwMode = 0;
void* hConOut = GetStdHandle((unsigned long)-11); // STD_OUTPUT_HANDLE (-11)
if (hConOut && hConOut != (void*)-1 && GetConsoleMode(hConOut, &dwMode) && !(dwMode & 0x4)) {
SetConsoleMode(hConOut, dwMode | 0x4); // ENABLE_VIRTUAL_TERMINAL_PROCESSING (0x4)
}
}
#endif
// the first thing we will do is to output the prompt, so set color accordingly
set_console_state(CONSOLE_STATE_PROMPT);
if (params.embedding){
embd = embd_inp;
if (embd.size() > 0) {
if (llama_eval(ctx, embd.data(), embd.size(), n_past, params.n_threads)) {
fprintf(stderr, "%s : failed to eval\n", __func__);
return 1;
}
}
const auto embeddings = llama_get_embeddings(ctx);
// TODO: print / use the embeddings
if (params.use_color) {
printf(ANSI_COLOR_RESET);
}
return 0;
}
while (remaining_tokens > 0 || params.interactive) {
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// predict
if (embd.size() > 0) {
if (llama_eval(ctx, embd.data(), embd.size(), n_past, params.n_threads)) {
fprintf(stderr, "%s : failed to eval\n", __func__);
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return 1;
}
}
n_past += embd.size();
embd.clear();
if ((int) embd_inp.size() <= input_consumed && !is_interacting) {
// out of user input, sample next token
const float top_k = params.top_k;
const float top_p = params.top_p;
const float temp = params.temp;
const float repeat_penalty = params.repeat_penalty;
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llama_token id = 0;
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{
auto logits = llama_get_logits(ctx);
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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;
// TODO: this does not work of params.logits_all == true
assert(params.perplexity == false);
logits[llama_token_eos()] = 0;
}
id = llama_sample_top_p_top_k(ctx, last_n_tokens.data(), last_n_tokens.size(), top_k, top_p, temp, repeat_penalty);
last_n_tokens.erase(last_n_tokens.begin());
last_n_tokens.push_back(id);
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}
Replace EOS with newline to prevent context/memory being flushed by EOS in interactive mode (#333) * Improve interactive mode's coherence after EOS Aims to improve coherence and ability to resume the interactive session when the user is given input back after an end of text token is reached. Not sure what token 13 is or why it seems to help. See conversation for examples. * Make newline token a constant * dynamically determine newline token * relocate previous newline token const * cleanup whitespace * print a new line on end of text in interactive this may need to be looked into further when not using a reverse prompt * only print manual newline with reverse prompt fix formatting of reverse prompts so they don't end up at the end of the current line while not introducing unnecessary new lines otherwise * alternate approach to replace end of text tokens * Inject the reverse prompt again after eos in interactive mode * tokenize reverse prompt when needed makes this PR compatible with https://github.com/ggerganov/llama.cpp/pull/330 * tokenize and inject only first reverse prompt thanks to tjohnman * tokenize first reverse prompt once * add newline token * add newline token * tokenize/inject reverse prompt for refactor this doesn't seem right though * tokenize nothing for antiprompt if no reverse * Update main.cpp * Update main.cpp * tokenize and inject reverse prompt as needed this doesn't seem to work if the reverse prompt is tokenized outside earlier on * not needed * remove newline token * remove newline token * tokenize newline token * add space to comment * Update main.cpp Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> --------- Co-authored-by: Slaren <2141330+slaren@users.noreply.github.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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// replace end of text token with newline token when in interactive mode
if (id == llama_token_eos() && params.interactive && !params.instruct) {
Replace EOS with newline to prevent context/memory being flushed by EOS in interactive mode (#333) * Improve interactive mode's coherence after EOS Aims to improve coherence and ability to resume the interactive session when the user is given input back after an end of text token is reached. Not sure what token 13 is or why it seems to help. See conversation for examples. * Make newline token a constant * dynamically determine newline token * relocate previous newline token const * cleanup whitespace * print a new line on end of text in interactive this may need to be looked into further when not using a reverse prompt * only print manual newline with reverse prompt fix formatting of reverse prompts so they don't end up at the end of the current line while not introducing unnecessary new lines otherwise * alternate approach to replace end of text tokens * Inject the reverse prompt again after eos in interactive mode * tokenize reverse prompt when needed makes this PR compatible with https://github.com/ggerganov/llama.cpp/pull/330 * tokenize and inject only first reverse prompt thanks to tjohnman * tokenize first reverse prompt once * add newline token * add newline token * tokenize/inject reverse prompt for refactor this doesn't seem right though * tokenize nothing for antiprompt if no reverse * Update main.cpp * Update main.cpp * tokenize and inject reverse prompt as needed this doesn't seem to work if the reverse prompt is tokenized outside earlier on * not needed * remove newline token * remove newline token * tokenize newline token * add space to comment * Update main.cpp Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> --------- Co-authored-by: Slaren <2141330+slaren@users.noreply.github.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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id = llama_token_newline.front();
if (params.antiprompt.size() != 0) {
// tokenize and inject first reverse prompt
const auto first_antiprompt = ::llama_tokenize(ctx, params.antiprompt.front(), false);
embd_inp.insert(embd_inp.end(), first_antiprompt.begin(), first_antiprompt.end());
}
}
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// add it to the context
embd.push_back(id);
// echo this to console
input_noecho = false;
// decrement remaining sampling budget
--remaining_tokens;
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} else {
// some user input remains from prompt or interaction, forward it to processing
while ((int) embd_inp.size() > input_consumed) {
embd.push_back(embd_inp[input_consumed]);
last_n_tokens.erase(last_n_tokens.begin());
last_n_tokens.push_back(embd_inp[input_consumed]);
++input_consumed;
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if ((int) embd.size() >= params.n_batch) {
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break;
}
}
}
// display text
if (!input_noecho) {
for (auto id : embd) {
printf("%s", llama_token_to_str(ctx, id));
}
fflush(stdout);
}
// reset color to default if we there is no pending user input
if (!input_noecho && (int)embd_inp.size() == input_consumed) {
set_console_state(CONSOLE_STATE_DEFAULT);
}
// in interactive mode, and not currently processing queued inputs;
// check if we should prompt the user for more
if (params.interactive && (int) embd_inp.size() <= input_consumed) {
// check for reverse prompt
std::string last_output;
for (auto id : last_n_tokens) {
last_output += llama_token_to_str(ctx, id);
}
// Check if each of the reverse prompts appears at the end of the output.
for (std::string & antiprompt : params.antiprompt) {
if (last_output.find(antiprompt.c_str(), last_output.length() - antiprompt.length(), antiprompt.length()) != std::string::npos) {
is_interacting = true;
set_console_state(CONSOLE_STATE_USER_INPUT);
fflush(stdout);
break;
}
}
if (n_past > 0 && is_interacting) {
// potentially set color to indicate we are taking user input
set_console_state(CONSOLE_STATE_USER_INPUT);
if (params.instruct) {
input_consumed = embd_inp.size();
embd_inp.insert(embd_inp.end(), inp_pfx.begin(), inp_pfx.end());
printf("\n> ");
}
std::string buffer;
if (!params.input_prefix.empty()) {
buffer += params.input_prefix;
printf("%s", buffer.c_str());
}
std::string line;
bool another_line = true;
do {
std::getline(std::cin, line);
if (line.empty() || line.back() != '\\') {
another_line = false;
} else {
line.pop_back(); // Remove the continue character
}
buffer += line + '\n'; // Append the line to the result
} while (another_line);
// done taking input, reset color
set_console_state(CONSOLE_STATE_DEFAULT);
auto line_inp = ::llama_tokenize(ctx, buffer, false);
embd_inp.insert(embd_inp.end(), line_inp.begin(), line_inp.end());
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if (params.instruct) {
embd_inp.insert(embd_inp.end(), inp_sfx.begin(), inp_sfx.end());
}
remaining_tokens -= line_inp.size();
input_noecho = true; // do not echo this again
}
if (n_past > 0) {
is_interacting = false;
}
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}
// end of text token
if (embd.back() == llama_token_eos()) {
if (params.instruct) {
is_interacting = true;
} else {
fprintf(stderr, " [end of text]\n");
break;
}
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}
// In interactive mode, respect the maximum number of tokens and drop back to user input when reached.
if (params.interactive && remaining_tokens <= 0) {
remaining_tokens = params.n_predict;
is_interacting = true;
}
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}
#if defined (_WIN32)
signal(SIGINT, SIG_DFL);
#endif
llama_print_timings(ctx);
llama_free(ctx);
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set_console_state(CONSOLE_STATE_DEFAULT);
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return 0;
}