diff --git a/llama/bridge/LlamaPredictOperation.mm b/llama/bridge/LlamaPredictOperation.mm
index f3955d7..8c67836 100644
--- a/llama/bridge/LlamaPredictOperation.mm
+++ b/llama/bridge/LlamaPredictOperation.mm
@@ -89,8 +89,6 @@ struct llama_model {
 
 // load the model's weights from a file
 bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab & vocab, int n_ctx) {
-  printf("%s: loading model from '%s' - please wait ...\n", __func__, fname.c_str());
-
   auto fin = std::ifstream(fname, std::ios::binary);
   if (!fin) {
     fprintf(stderr, "%s: failed to open '%s'\n", __func__, fname.c_str());
@@ -127,17 +125,6 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
 
     n_ff = ((2*(4*hparams.n_embd)/3 + hparams.n_mult - 1)/hparams.n_mult)*hparams.n_mult;
     n_parts = LLAMA_N_PARTS.at(hparams.n_embd);
-
-    printf("%s: n_vocab = %d\n", __func__, hparams.n_vocab);
-    printf("%s: n_ctx   = %d\n", __func__, hparams.n_ctx);
-    printf("%s: n_embd  = %d\n", __func__, hparams.n_embd);
-    printf("%s: n_mult  = %d\n", __func__, hparams.n_mult);
-    printf("%s: n_head  = %d\n", __func__, hparams.n_head);
-    printf("%s: n_layer = %d\n", __func__, hparams.n_layer);
-    printf("%s: n_rot   = %d\n", __func__, hparams.n_rot);
-    printf("%s: f16     = %d\n", __func__, hparams.f16);
-    printf("%s: n_ff    = %d\n", __func__, n_ff);
-    printf("%s: n_parts = %d\n", __func__, n_parts);
   }
 
   // load vocab
@@ -220,8 +207,6 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
     ctx_size += n_ctx*n_layer*n_embd*ggml_type_sizef(GGML_TYPE_F32); // memory_v
 
     ctx_size += (5 + 10*n_layer)*256; // object overhead
-
-    printf("%s: ggml ctx size = %6.2f MB\n", __func__, ctx_size/(1024.0*1024.0));
   }
 
   // create the ggml context
@@ -307,8 +292,6 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
     model.memory_v = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_elements);
 
     const size_t memory_size = ggml_nbytes(model.memory_k) + ggml_nbytes(model.memory_v);
-
-    printf("%s: memory_size = %8.2f MB, n_mem = %d\n", __func__, memory_size/1024.0/1024.0, n_mem);
   }
 
   const size_t file_offset = fin.tellg();
@@ -326,8 +309,6 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
       fname_part += "." + std::to_string(i);
     }
 
-    printf("%s: loading model part %d/%d from '%s'\n", __func__, i+1, n_parts, fname_part.c_str());
-
     fin = std::ifstream(fname_part, std::ios::binary);
     fin.seekg(file_offset);
 
@@ -336,8 +317,6 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
       int n_tensors = 0;
       size_t total_size = 0;
 
-      printf("%s: ", __func__);
-
       while (true) {
         int32_t n_dims;
         int32_t length;
@@ -436,7 +415,6 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
 
         if (0) {
           static const char * ftype_str[] = { "f32", "f16", "q4_0", "q4_1", };
-          printf("%24s - [%5d, %5d], type = %6s, split = %d\n", name.data(), ne[0], ne[1], ftype_str[ftype], split_type);
         }
 
         size_t bpe = 0;
@@ -498,17 +476,7 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
 
           total_size += ggml_nbytes(tensor)/n_parts;
         }
-
-        //printf("%42s - [%5d, %5d], type = %6s, %6.2f MB\n", name.data(), ne[0], ne[1], ftype == 0 ? "float" : "f16", ggml_nbytes(tensor)/1024.0/1024.0);
-        if (++n_tensors % 8 == 0) {
-          printf(".");
-          fflush(stdout);
-        }
       }
-
-      printf(" done\n");
-
-      printf("%s: model size = %8.2f MB / num tensors = %d\n", __func__, total_size/1024.0/1024.0, n_tensors);
     }
 
     fin.close();
@@ -794,8 +762,6 @@ NSError *makeLlamaError(LlamaErrorCode errorCode, NSString *description)
   ggml_time_init();
   const int64_t t_main_start_us = ggml_time_us();
 
-  printf("%s: seed = %d\n", __func__, _params.seed);
-
   std::mt19937 rng(_params.seed);
   if (_params.prompt.empty()) {
     _params.prompt = gpt_random_prompt(rng);
@@ -841,16 +807,6 @@ NSError *makeLlamaError(LlamaErrorCode errorCode, NSString *description)
   // tokenize the reverse prompt
   std::vector<gpt_vocab::id> antiprompt_inp = ::llama_tokenize(vocab, _params.antiprompt, false);
 
-  printf("\n");
-  printf("%s: prompt: '%s'\n", __func__, _params.prompt.c_str());
-  printf("%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size());
-  for (int i = 0; i < (int) embd_inp.size(); i++) {
-    printf("%6d -> '%s'\n", embd_inp[i], vocab.id_to_token.at(embd_inp[i]).c_str());
-  }
-  printf("\n");
-  printf("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);
-  printf("\n\n");
-
   std::vector<gpt_vocab::id> embd;
 
   // determine the required inference memory per token:
@@ -926,28 +882,10 @@ NSError *makeLlamaError(LlamaErrorCode errorCode, NSString *description)
       NSString *token = [[NSString alloc] initWithCString:vocab.id_to_token[id].c_str() encoding:NSUTF8StringEncoding];
       [self postEvent:[_LlamaEvent outputTokenWithToken:token]];
     }
-
-    // end of text token
-    if (embd.back() == 2) {
-      printf(" [end of text]\n");
-      break;
-    }
   }
 
   [self postEvent:[_LlamaEvent completed]];
 
-  // report timing
-  {
-    const int64_t t_main_end_us = ggml_time_us();
-
-    printf("\n\n");
-    printf("%s: mem per token = %8zu bytes\n", __func__, mem_per_token);
-    printf("%s:     load time = %8.2f ms\n", __func__, t_load_us/1000.0f);
-    printf("%s:   sample time = %8.2f ms\n", __func__, t_sample_us/1000.0f);
-    printf("%s:  predict time = %8.2f ms / %.2f ms per token\n", __func__, t_predict_us/1000.0f, t_predict_us/1000.0f/n_past);
-    printf("%s:    total time = %8.2f ms\n", __func__, (t_main_end_us - t_main_start_us)/1000.0f);
-  }
-
   ggml_free(model.ctx);
 }