diff --git a/examples/train-text-from-scratch/train-text-from-scratch.cpp b/examples/train-text-from-scratch/train-text-from-scratch.cpp
index 770b41e7b..2c17d0b99 100644
--- a/examples/train-text-from-scratch/train-text-from-scratch.cpp
+++ b/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