diff --git a/modules/training.py b/modules/training.py
index 855ed914..7ead4b47 100644
--- a/modules/training.py
+++ b/modules/training.py
@@ -240,6 +240,21 @@ def backup_adapter(input_folder):
     except Exception as e:
         print("An error occurred in backup_adapter:", str(e))
 
+def calc_trainable_parameters(model):
+    trainable_params = 0
+    all_param = 0
+    for _, param in model.named_parameters():
+        num_params = param.numel()
+        # if using DS Zero 3 and the weights are initialized empty
+        if num_params == 0 and hasattr(param, "ds_numel"):
+            num_params = param.ds_numel
+
+        all_param += num_params
+        if param.requires_grad:
+            trainable_params += num_params
+    
+    return trainable_params,all_param
+
 
 def do_train(lora_name: str, always_override: bool, save_steps: int, micro_batch_size: int, batch_size: int, epochs: int, learning_rate: str, lr_scheduler_type: str, lora_rank: int, lora_alpha: int, lora_dropout: float, cutoff_len: int, dataset: str, eval_dataset: str, format: str, eval_steps: int, raw_text_file: str, overlap_len: int, newline_favor_len: int, higher_rank_limit: bool, warmup_steps: int, optimizer: str, hard_cut_string: str, train_only_after: str, stop_at_loss: float):
 
@@ -431,6 +446,9 @@ def do_train(lora_name: str, always_override: bool, save_steps: int, micro_batch
     if not always_override:
         backup_adapter(lora_file_path)
 
+    # == get model trainable params
+    model_trainable_params, model_all_params = calc_trainable_parameters(shared.model)
+
     try:
         logger.info("Creating LoRA model...")
         lora_model = get_peft_model(shared.model, config)
@@ -540,6 +558,12 @@ def do_train(lora_name: str, always_override: bool, save_steps: int, micro_batch
     logger.info("Starting training...")
     yield "Starting..."
 
+    lora_trainable_param, lora_all_param = calc_trainable_parameters(lora_model)
+    
+    if lora_all_param>0:
+        print(f"Trainable params: {lora_trainable_param:,d} ({100 * lora_trainable_param / lora_all_param:.4f} %), All params: {lora_all_param:,d} (Model: {model_all_params:,d})")
+
+
     train_log.update({"base_model_name": shared.model_name})
     train_log.update({"base_model_class": shared.model.__class__.__name__})
     train_log.update({"base_loaded_in_4bit": getattr(lora_model, "is_loaded_in_4bit", False)})