mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-25 05:48:47 +01:00
Add "--instruct" argument for usage with Alpaca (#240)
Also start adding prompts in "./prompts"
This commit is contained in:
parent
22213a17b5
commit
9e1707218a
70
main.cpp
70
main.cpp
@ -176,8 +176,6 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
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}
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}
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const ggml_type wtype2 = GGML_TYPE_F32;
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auto & ctx = model.ctx;
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size_t ctx_size = 0;
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@ -237,7 +235,6 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
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const int n_embd = hparams.n_embd;
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const int n_layer = hparams.n_layer;
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const int n_ctx = hparams.n_ctx;
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const int n_vocab = hparams.n_vocab;
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model.layers.resize(n_layer);
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@ -539,9 +536,7 @@ bool llama_eval(
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const int n_vocab = hparams.n_vocab;
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const int n_rot = hparams.n_embd/hparams.n_head;
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const int d_key = n_embd/n_head;
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// TODO: check if this size scales with n_ctx linearly and remove constant. somehow I feel it wasn't the case
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// TODO: check if this size scales with n_ctx linearly and remove constant. somehow I feel it wasn't the case
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// static size_t buf_size = hparams.n_ctx*1024*1024;
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static size_t buf_size = 512u*1024*1024;
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static void * buf = malloc(buf_size);
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@ -792,7 +787,7 @@ int main(int argc, char ** argv) {
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if (gpt_params_parse(argc, argv, params) == false) {
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return 1;
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}
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if (params.n_ctx > 2048) {
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fprintf(stderr, "%s: warning: model does not support context sizes greater than 2048 tokens (%d specified);"
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"expect poor results\n", __func__, params.n_ctx);
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@ -820,7 +815,7 @@ int main(int argc, char ** argv) {
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// load the model
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{
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const int64_t t_start_us = ggml_time_us();
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if (!llama_model_load(params.model, model, vocab, params.n_ctx)) {
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if (!llama_model_load(params.model, model, vocab, params.n_ctx)) {
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fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, params.model.c_str());
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return 1;
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}
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@ -849,9 +844,25 @@ int main(int argc, char ** argv) {
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params.n_predict = std::min(params.n_predict, model.hparams.n_ctx - (int) embd_inp.size());
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// prefix & suffix for instruct mode
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const std::vector<gpt_vocab::id> inp_pfx = ::llama_tokenize(vocab, "\n\n### Instruction:\n\n", true);
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const std::vector<gpt_vocab::id> inp_sfx = ::llama_tokenize(vocab, "\n\n### Response:\n\n", false);
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// in instruct mode, we inject a prefix and a suffix to each input by the user
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if (params.instruct) {
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fprintf(stderr, "== Instruction mode enabled ==\n");
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params.interactive = true;
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params.antiprompt = "### Instruction:\n\n";
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}
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// tokenize the reverse prompt
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std::vector<gpt_vocab::id> antiprompt_inp = ::llama_tokenize(vocab, params.antiprompt, false);
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// enable interactive mode if reverse prompt is specified
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if (!antiprompt_inp.empty()) {
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params.interactive = true;
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}
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fprintf(stderr, "\n");
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fprintf(stderr, "%s: prompt: '%s'\n", __func__, params.prompt.c_str());
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fprintf(stderr, "%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size());
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@ -872,7 +883,7 @@ int main(int argc, char ** argv) {
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fprintf(stderr, "%s: interactive mode on.\n", __func__);
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if(antiprompt_inp.size()) {
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if (antiprompt_inp.size()) {
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fprintf(stderr, "%s: reverse prompt: '%s'\n", __func__, params.antiprompt.c_str());
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fprintf(stderr, "%s: number of tokens in reverse prompt = %zu\n", __func__, antiprompt_inp.size());
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for (int i = 0; i < (int) antiprompt_inp.size(); i++) {
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@ -894,31 +905,27 @@ int main(int argc, char ** argv) {
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std::vector<gpt_vocab::id> last_n_tokens(last_n_size);
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std::fill(last_n_tokens.begin(), last_n_tokens.end(), 0);
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if (params.interactive) {
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fprintf(stderr, "== Running in interactive mode. ==\n"
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#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32)
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" - Press Ctrl+C to interject at any time.\n"
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#endif
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" - Press Return to return control to LLaMa.\n"
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" - If you want to submit another line, end your input in '\\'.\n");
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" - If you want to submit another line, end your input in '\\'.\n\n");
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is_interacting = true;
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}
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int remaining_tokens = params.n_predict;
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int input_consumed = 0;
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bool input_noecho = false;
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// prompt user immediately after the starting prompt has been loaded
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if (params.interactive_start) {
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is_interacting = true;
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}
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int remaining_tokens = params.n_predict;
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// set the color for the prompt which will be output initially
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if (params.use_color) {
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printf(ANSI_COLOR_YELLOW);
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}
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while (remaining_tokens > 0) {
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while (remaining_tokens > 0 || params.interactive) {
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// predict
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if (embd.size() > 0) {
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const int64_t t_start_us = ggml_time_us();
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@ -971,13 +978,13 @@ int main(int argc, char ** argv) {
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last_n_tokens.erase(last_n_tokens.begin());
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last_n_tokens.push_back(embd_inp[input_consumed]);
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++input_consumed;
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if (embd.size() > params.n_batch) {
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if ((int) embd.size() > params.n_batch) {
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break;
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}
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}
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// reset color to default if we there is no pending user input
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if (!input_noecho && params.use_color && embd_inp.size() == input_consumed) {
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if (!input_noecho && params.use_color && (int) embd_inp.size() == input_consumed) {
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printf(ANSI_COLOR_RESET);
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}
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}
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@ -999,19 +1006,26 @@ int main(int argc, char ** argv) {
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is_interacting = true;
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}
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if (is_interacting) {
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if (params.instruct) {
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input_consumed = embd_inp.size();
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embd_inp.insert(embd_inp.end(), inp_pfx.begin(), inp_pfx.end());
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printf("\n> ");
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}
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// currently being interactive
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bool another_line=true;
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bool another_line = true;
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while (another_line) {
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fflush(stdout);
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char buf[256] = {0};
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int n_read;
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if(params.use_color) printf(ANSI_BOLD ANSI_COLOR_GREEN);
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if (params.use_color) printf(ANSI_BOLD ANSI_COLOR_GREEN);
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if (scanf("%255[^\n]%n%*c", buf, &n_read) <= 0) {
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// presumable empty line, consume the newline
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std::ignore = scanf("%*c");
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n_read=0;
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}
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if(params.use_color) printf(ANSI_COLOR_RESET);
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if (params.use_color) printf(ANSI_COLOR_RESET);
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if (n_read > 0 && buf[n_read-1]=='\\') {
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another_line = true;
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@ -1026,6 +1040,10 @@ int main(int argc, char ** argv) {
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std::vector<gpt_vocab::id> line_inp = ::llama_tokenize(vocab, buf, false);
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embd_inp.insert(embd_inp.end(), line_inp.begin(), line_inp.end());
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if (params.instruct) {
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embd_inp.insert(embd_inp.end(), inp_sfx.begin(), inp_sfx.end());
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}
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remaining_tokens -= line_inp.size();
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input_noecho = true; // do not echo this again
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@ -1037,8 +1055,12 @@ int main(int argc, char ** argv) {
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// end of text token
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if (embd.back() == 2) {
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fprintf(stderr, " [end of text]\n");
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break;
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if (params.interactive) {
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is_interacting = true;
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} else {
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fprintf(stderr, " [end of text]\n");
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break;
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}
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}
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}
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1
prompts/alpaca.txt
Normal file
1
prompts/alpaca.txt
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@ -0,0 +1 @@
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Below is an instruction that describes a task. Write a response that appropriately completes the request.
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prompts/chat-with-bob.txt
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7
prompts/chat-with-bob.txt
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@ -0,0 +1,7 @@
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Transcript of a dialog, where the User interacts with an Assistant named Bob. Bob is helpful, kind, honest, good at writing, and never fails to answer the User's requests immediately and with precision.
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User: Hello, Bob.
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Bob: Hello. How may I help you today?
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User: Please tell me the largest city in Europe.
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Bob: Sure. The largest city in Europe is Moscow, the capital of Russia.
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User:
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18
utils.cpp
18
utils.cpp
@ -38,13 +38,8 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
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} else if (arg == "-p" || arg == "--prompt") {
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params.prompt = argv[++i];
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} else if (arg == "-f" || arg == "--file") {
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std::ifstream file(argv[++i]);
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std::copy(std::istreambuf_iterator<char>(file),
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std::istreambuf_iterator<char>(),
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back_inserter(params.prompt));
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std::copy(std::istreambuf_iterator<char>(file), std::istreambuf_iterator<char>(), back_inserter(params.prompt));
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} else if (arg == "-n" || arg == "--n_predict") {
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params.n_predict = std::stoi(argv[++i]);
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} else if (arg == "--top_k") {
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@ -65,9 +60,8 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
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params.model = argv[++i];
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} else if (arg == "-i" || arg == "--interactive") {
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params.interactive = true;
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} else if (arg == "--interactive-start") {
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params.interactive = true;
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params.interactive_start = true;
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} else if (arg == "-ins" || arg == "--instruct") {
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params.instruct = true;
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} else if (arg == "--color") {
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params.use_color = true;
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} else if (arg == "-r" || arg == "--reverse-prompt") {
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@ -85,13 +79,13 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
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return true;
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}
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void gpt_print_usage(int argc, char ** argv, const gpt_params & params) {
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void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
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fprintf(stderr, "usage: %s [options]\n", argv[0]);
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fprintf(stderr, "\n");
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fprintf(stderr, "options:\n");
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fprintf(stderr, " -h, --help show this help message and exit\n");
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fprintf(stderr, " -i, --interactive run in interactive mode\n");
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fprintf(stderr, " --interactive-start run in interactive mode and poll user input at startup\n");
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fprintf(stderr, " -ins, --instruct run in instruction mode (use with Alpaca models)\n");
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fprintf(stderr, " -r PROMPT, --reverse-prompt PROMPT\n");
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fprintf(stderr, " in interactive mode, poll user input upon seeing PROMPT\n");
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fprintf(stderr, " --color colorise output to distinguish prompt and user input from generations\n");
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@ -398,7 +392,7 @@ gpt_vocab::id llama_sample_top_p_top_k(
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logits_id.push_back(std::make_pair(logits[i]*scale*repeat_penalty, i));
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} else {
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logits_id.push_back(std::make_pair(logits[i]*scale/repeat_penalty, i));
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}
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}
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} else {
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logits_id.push_back(std::make_pair(logits[i]*scale, i));
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}
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8
utils.h
8
utils.h
@ -27,14 +27,14 @@ struct gpt_params {
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int32_t n_batch = 8; // batch size for prompt processing
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std::string model = "models/lamma-7B/ggml-model.bin"; // model path
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std::string prompt;
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std::string model = "models/lamma-7B/ggml-model.bin"; // model path
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std::string prompt = "";
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std::string antiprompt = ""; // string upon seeing which more user input is prompted
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bool use_color = false; // use color to distinguish generations and inputs
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bool interactive = false; // interactive mode
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bool interactive_start = false; // reverse prompt immediately
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std::string antiprompt = ""; // string upon seeing which more user input is prompted
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bool instruct = false; // instruction mode (used for Alpaca models)
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};
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bool gpt_params_parse(int argc, char ** argv, gpt_params & params);
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