16bc66d947
* llama.cpp : split llama_context_params into model and context params ggml-ci * fix metal build * fix freq_base/scale default to model value * llama-bench : keep the same model between tests when possible * move n_threads to llama_context_params, add n_threads_batch * fix mpi build * remove kv_size(), cuda scratch fixes * remove low-vram option * add n_threads_batch to system info, refactor to get_system_info() * add documentation about --threads-batch to the READMEs * llama-bench fix * main : fix rope freq/scale warning * llama.cpp : add llama_get_model common : add llama_tokenize from model * remove duplicated ctx/model functions ggml-ci * cuda : print total VRAM used
36 lines
969 B
C++
36 lines
969 B
C++
#include "embd-input.h"
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#include <stdlib.h>
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#include <random>
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#include <string.h>
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int main(int argc, char** argv) {
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auto mymodel = create_mymodel(argc, argv);
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int N = 10;
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int max_tgt_len = 500;
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int n_embd = llama_n_embd(llama_get_model(mymodel->ctx));
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// add random float embd to test evaluation
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float * data = new float[N*n_embd];
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std::default_random_engine e;
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std::uniform_real_distribution<float> u(0,1);
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for (int i=0;i<N*n_embd;i++) {
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data[i] = u(e);
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}
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eval_string(mymodel, "user: what is the color of the flag of UN?");
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eval_float(mymodel, data, N);
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eval_string(mymodel, "assistant:");
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eval_string(mymodel, mymodel->params.prompt.c_str());
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const char* tmp;
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for (int i=0; i<max_tgt_len; i++) {
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tmp = sampling(mymodel);
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if (strcmp(tmp, "</s>")==0) break;
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printf("%s", tmp);
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fflush(stdout);
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}
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printf("\n");
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free_mymodel(mymodel);
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return 0;
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}
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