change sampling parameters for prediction after training to defaults …

…of common.h

and clarify what is context for prediction and what are generated tokens

examples/train-text-from-scratch/train-text-from-scratch.cpp

@@ -2799,19 +2799,19 @@ void shuffle_ints(int * begin, int * end) {
 }
 
 struct my_llama_sampler_params {
- float temp = 0.0f; // <= 0.0 disabled
- int top_k = 20; // <= 0 to use vocab size
- float top_p = 0.95f; // 1.0 = disabled
- float tfs_z = 1.00f; // 1.0 = disabled
- float typical_p = 1.00f; // 1.0 = disabled
- int repeat_last_n = 64; // last n tokens to penalize (0 = disable penalty, -1 = context size)
- float repeat_penalty = 1.0f; // 1.0 = disabled
- float alpha_presence = 0.0f; // 0.0 = disabled
- float alpha_frequency = 0.0f; // 0.0 = disabled
- int mirostat = 0; // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0
- float mirostat_tau = 5.00f; // target entropy
- float mirostat_eta = 0.10f; // learning rate
- bool penalize_nl = true; // consider newlines as a repeatable token
+ float temp  = 0.0f; // <= 0.0 disabled
+ int top_k  = 20; // <= 0 to use vocab size
+ float top_p  = 0.95f; // 1.0 = disabled
+ float tfs_z  = 1.00f; // 1.0 = disabled
+ float typical_p  = 1.00f; // 1.0 = disabled
+ int repeat_last_n  = 64; // last n tokens to penalize (0 = disable penalty, -1 = context size)
+ float repeat_penalty  = 1.0f; // 1.0 = disabled
+ float presence_penalty = 0.0f; // 0.0 = disabled
+ float frequency_penalty = 0.0f; // 0.0 = disabled
+ int mirostat  = 0; // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0
+ float mirostat_tau  = 5.00f; // target entropy
+ float mirostat_eta  = 0.10f; // learning rate
+ bool penalize_nl  = true; // consider newlines as a repeatable token
 };
 
 struct my_llama_sampler {
@@ -2871,8 +2871,8 @@ llama_token sample(struct my_llama_sampler * sampler, float * logits, const llam
  candidates_p,
  last_tokens + n_last_tokens - n_last,
  n_last,
- params.alpha_frequency,
- params.alpha_presence);
+ params.frequency_penalty,
+ params.presence_penalty);
 
  if (!params.penalize_nl) {
  logits[llama_token_nl()] = nl_logit;
@@ -4203,12 +4203,22 @@ int main(int argc, char ** argv) {
  int n_gen = params.n_predict;
  int sample_ctx = n_tokens - n_tokens/8;
 
- sampler.params.temp = 0.2f;
- sampler.params.repeat_penalty = 1.1f;
- sampler.params.mirostat = 2;
+ // use defaults from common.h
+ sampler.params.top_k = 40;
+ sampler.params.top_p = 0.95f;
+ sampler.params.tfs_z = 1.00f;
+ sampler.params.typical_p = 1.00f;
+ sampler.params.temp = 0.8f;
+ sampler.params.repeat_penalty = 1.1f;
+ sampler.params.repeat_last_n = 64;
+ sampler.params.frequency_penalty = 0.0f;
+ sampler.params.presence_penalty = 0.0f;
+ sampler.params.mirostat = 0;
+ sampler.params.mirostat_tau = 5.00f;
+ sampler.params.mirostat_eta = 0.10f;
  init_sampler(&sampler, lctx);
 
- printf("Generating %d tokens.\n", n_gen);
+ printf("[Prediction context]\n");
 
  struct ggml_tensor * tokens_input = ggml_new_tensor_1d(model.ctx, GGML_TYPE_I32, n_tokens);
  struct ggml_tensor * target_logits = ggml_new_tensor_2d(model.ctx, GGML_TYPE_F32, n_vocab, n_tokens);
@@ -4223,7 +4233,7 @@ int main(int argc, char ** argv) {
  print_token(lctx, ggml_get_i32_1d(tokens_input, i));
  }
 
- printf("---\n");
+ printf("\n[Generating %d tokens]\n", n_gen);
  for (int i=0; i<n_gen; ++i) {
  struct ggml_init_params cparams = {
  compute_size, // .mem_size