diff --git a/picotron/data_parallel/data_parallel.py b/picotron/data_parallel/data_parallel.py
index aeedfa1..e009bd9 100644
--- a/picotron/data_parallel/data_parallel.py
+++ b/picotron/data_parallel/data_parallel.py
@@ -82,9 +82,6 @@ class DataParallelBucket(nn.Module):
     def backward(self, input_tensor, output_tensor, output_tensor_grad):
         return self.module.backward(input_tensor, output_tensor, output_tensor_grad)
     
-    def get_flops(self, *args, **kwargs):
-        return self.module.get_flops(*args, **kwargs)
-    
     def register_backward_hook(self):
         """
         Registers a backward hook to manually accumulate and synchronize gradients.
diff --git a/picotron/model.py b/picotron/model.py
index ac777f8..2538785 100644
--- a/picotron/model.py
+++ b/picotron/model.py
@@ -268,13 +268,4 @@ class Llama(nn.Module):
         x = self.final_norm(x)
         logits = self.final_proj(x)
         
-        return logits  # [batch_size, seq_length, vocab_size]
-    
-    # https://github.com/karpathy/nanoGPT/blob/9755682b981a45507f6eb9b11eadef8cb83cebd5/model.py#L289-L303
-    # TODO: Need to check the formula.
-    def get_flops(self, fwdbwd_per_iter, dt, num_params):
-        L, H, T = self.num_layers , self.hidden_size, self.max_position_embeddings
-        flops_per_fwdbwd = 6 * num_params * T + 12* L* H* T ** 2
-        flops_per_iter = flops_per_fwdbwd * fwdbwd_per_iter
-        flops_achieved = flops_per_iter * (1.0/dt) # per second
-        return flops_achieved
\ No newline at end of file
+        return logits  # [batch_size, seq_length, vocab_size]
\ No newline at end of file
diff --git a/picotron/utils.py b/picotron/utils.py
index 996cdeb..952888f 100644
--- a/picotron/utils.py
+++ b/picotron/utils.py
@@ -3,6 +3,8 @@ import random
 import numpy as np
 import builtins
 import fcntl
+import picotron.process_group_manager as pgm
+import torch, torch.distributed as dist
 
 def print(*args, is_print_rank=True, **kwargs):
     """ solves multi-process interleaved print problem """
@@ -30,6 +32,49 @@ def to_readable_format(num, precision=2):
         return f"{num / 1e3:.{precision}f}K"
     else:
         return f"{num:.{precision}f}"
+
+# ref: 
+# https://github.com/karpathy/nanoGPT/blob/9755682b981a45507f6eb9b11eadef8cb83cebd5/model.py#L289
+# https://github.com/stanford-cs336/spring2024-lectures/blob/main/lecture_02.py#L950
+def get_mfu(tokens_per_second, num_params, model_config, theoretical_flops = 989.5 * 10 ** 12):
+    num_layers = model_config.num_hidden_layers
+    hidden_dim = model_config.hidden_size
+    seq_len = model_config.max_position_embeddings
+    flops_per_token = 6 * num_params + 12 * num_layers * hidden_dim * seq_len
+    mfu = tokens_per_second * flops_per_token / theoretical_flops * 100 # percentage
+    return mfu
+
+def get_num_params(model):
+    """Calculate total number of parameters accounting for tensor parallelism and pipeline parallelism.
+    
+    For TP: Parameters in attention/mlp/embed/final_proj are sharded, so multiply by tp_world_size
+    For PP: Need to gather parameter counts across pipeline stages
+    For DP: Parameters are replicated, so only count once
+    
+    Note: 
+    LayerNorm: Split across TP ranks for sequence parallelism
+    FSDP: Parameters are sharded across data parallel ranks
+    """
+    tp_world_size = pgm.process_group_manager.tp_world_size
+    
+    # Count parameters in current PP rank
+    local_num_params = 0
+    for name, param in model.named_parameters():
+        # Parameters split across TP ranks
+        # TODO: LayerNorm is also split across TP ranks for sequence parallelism
+        if any(tp_keyword in name.lower() for tp_keyword in ['attention', 'mlp', 'embed', 'final_proj']):
+            local_num_params += param.numel() * tp_world_size
+        else:
+            # Parameters replicated across TP ranks (layer norm, biases)
+            local_num_params += param.numel()
+            
+    # Gather parameter counts from all PP ranks
+    param_counts = torch.tensor(local_num_params, device='cuda')
+    
+    # Sum up parameters across all PP ranks
+    dist.all_reduce(param_counts, op=dist.ReduceOp.SUM, group=pgm.process_group_manager.pp_group)
+    
+    return param_counts.item()
     
 def assert_no_meta_tensors(model):
     meta_tensors = []
diff --git a/train.py b/train.py
index f334085..85344be 100644
--- a/train.py
+++ b/train.py
@@ -1,11 +1,9 @@
 """Training script for LLaMA model.
-torchrun --nproc_per_node 1 --master_addr localhost --master_port 25500 train.py --use_wandb
-torchrun --nproc_per_node 2 --master_addr localhost --master_port 25500 train.py --dp_size 2 --use_wandb 
-torchrun --nproc_per_node 4 --master_addr localhost --master_port 25500 train.py --tp_size 2 --pp_size 2 --use_wandb 
-torchrun --nproc_per_node 4 --master_addr localhost --master_port 25500 train.py --tp_size 2 --pp_size 2 --load_path ckpt/150
-torchrun --nproc_per_node 8 --master_addr localhost --master_port 25500 train.py --tp_size 2 --dp_size 2 --pp_size 2 --use_wandb
-CUDA_DEVICE_MAX_CONNECTIONS=1 debugpy-run -p 5678 -m torch.distributed.run -- --nproc_per_node=2 --nnodes=1 --rdzv_backend=c10d --rdzv_endpoint=localhost:29400 train.py --dp_size 2
-CUDA_DEVICE_MAX_CONNECTIONS=1 torchrun    --nproc_per_node=4    --nnodes=1    --rdzv_backend=c10d    --rdzv_endpoint=localhost:29400    --max_restarts=0    --tee=3    train.py
+CUDA_DEVICE_MAX_CONNECTIONS=1 torchrun --nproc_per_node 1 --master_addr localhost --master_port 25500 train.py --config tmp/dummy/360M_131K.json
+CUDA_DEVICE_MAX_CONNECTIONS=1 torchrun --nproc_per_node 2 --master_addr localhost --master_port 25500 train.py --config tmp/dummy/360M_131K.json
+CUDA_DEVICE_MAX_CONNECTIONS=1 torchrun --nproc_per_node 4 --master_addr localhost --master_port 25500 train.py --config tmp/dummy/360M_131K.json
+CUDA_DEVICE_MAX_CONNECTIONS=1 torchrun --nproc_per_node 8 --master_addr localhost --master_port 25500 train.py --config tmp/dummy/360M_131K.json
+CUDA_DEVICE_MAX_CONNECTIONS=1 debugpy-run -p 5678 -m torch.distributed.run -- --nproc_per_node=2 --nnodes=1 --rdzv_backend=c10d --rdzv_endpoint=localhost:29400 train.py --config tmp/dummy/360M_131K.json
 #VERBOSE=0 torchrun --nproc_per_node 4 --master_addr localhost --master_port 25500 train.py --pp_size 2 --dp_size 2
 """
 import os
@@ -22,7 +20,7 @@ from transformers import AutoConfig
 from picotron.context_parallel.context_parallel import apply_context_parallel
 from picotron.tensor_parallel.tensor_parallel import apply_tensor_parallel
 import picotron.process_group_manager as pgm
-from picotron.utils import set_all_seed, print, to_readable_format
+from picotron.utils import set_all_seed, print, to_readable_format, get_mfu, get_num_params
 from picotron.checkpoint import CheckpointManager
 from picotron.checkpoint import init_model_with_dematerialized_weights, init_model_with_materialized_weights
 from picotron.data import MicroBatchDataLoader
@@ -117,7 +115,7 @@ if __name__ == "__main__":
 
     dist.barrier()
 
-    print("init dataloader time:", time.time()-start_time, is_print_rank=is_wandb_rank)
+    print(f"init dataloader time: {time.time()-start_time:.2f}s", is_print_rank=is_wandb_rank)
     tokens_per_step = data_loader.global_batch_size * config["training"]["seq_length"]
     
     if pgm.process_group_manager.global_rank == 0:
@@ -172,9 +170,11 @@ if __name__ == "__main__":
     if pgm.process_group_manager.cp_dp_world_size > 1:
         model = DataParallelBucket(model)
     
-    print("init model parallel time:", time.time()-start_time, is_print_rank=is_wandb_rank)
+    print(f"init model parallel time: {time.time()-start_time:.2f}s", is_print_rank=is_wandb_rank)
     
     model.train()
+    num_params = get_num_params(model)
+    print(f"Number of parameters: {to_readable_format(num_params)}", is_print_rank=is_wandb_rank)
     
     tensor_shapes = (data_loader.micro_batch_size, data_loader.seq_length_per_gpu, model_config.hidden_size)
     
@@ -224,15 +224,22 @@ if __name__ == "__main__":
             model.reset()
 
         step_duration = time.time() - step_start_time
+        tokens_per_second = tokens_per_step / step_duration
+        mfu = get_mfu(tokens_per_second / world_size, num_params, model_config)
         
         if is_wandb_rank:
-            print(f"[rank {pgm.process_group_manager.global_rank}] Step: {step}, Loss: {loss:.4f}, "
-                f"Global batch size: {to_readable_format(tokens_per_step)}, "
-                f"Tokens/s: {to_readable_format(tokens_per_step / step_duration)}, "
-                f"Tokens/s/GPU: {to_readable_format(tokens_per_step / step_duration / world_size)}, "
-                f"Tokens: {to_readable_format(trained_tokens)}{('/' + to_readable_format(config['training']['max_tokens'])) if config['training']['max_tokens'] else ''}, "
-                f"Memory usage: {torch.cuda.memory_reserved() / 1e9:.2f}GB"
-            , is_print_rank=is_wandb_rank)
+            print(
+                f"[rank {pgm.process_group_manager.global_rank}] "
+                f"Step: {step:<5d} | "
+                f"Loss: {loss:6.4f} | "
+                f"Global batch size: {to_readable_format(tokens_per_step):>7s} | "
+                f"Tokens/s: {to_readable_format(tokens_per_second):>7s} | "
+                f"Tokens/s/GPU: {to_readable_format(tokens_per_second / world_size):>7s} | "
+                f"Tokens: {to_readable_format(trained_tokens):>7s}{('/' + to_readable_format(config['training']['max_tokens'])) if config['training']['max_tokens'] else ''} | "
+                f"MFU: {mfu:5.2f}% | "
+                f"Memory usage: {torch.cuda.memory_reserved() / 1e9:6.2f}GB",
+                is_print_rank=is_wandb_rank
+            )
         
             if config["logging"]["use_wandb"]:
                 wandb.log({