diff --git a/lib/data.py b/lib/data.py
index b6842c40..b15ca375 100644
--- a/lib/data.py
+++ b/lib/data.py
@@ -40,8 +40,8 @@ def get_wikitext2(nsamples, seed, seqlen, tokenizer):
 # Load and process c4 dataset
 def get_c4(nsamples, seed, seqlen, tokenizer):
     # Load train and validation datasets
-    traindata = load_dataset('allenai/c4', 'allenai--c4', data_files={'train': 'en/c4-train.00000-of-01024.json.gz'}, split='train')
-    valdata = load_dataset('allenai/c4', 'allenai--c4', data_files={'validation': 'en/c4-validation.00000-of-00008.json.gz'}, split='validation')
+    traindata = load_dataset('allenai/c4', data_files={'train': 'en/c4-train.00000-of-01024.json.gz'}, split='train')
+    valdata = load_dataset('allenai/c4', data_files={'validation': 'en/c4-validation.00000-of-00008.json.gz'}, split='validation')
 
     # Generate samples from training set
     random.seed(seed)
@@ -70,4 +70,4 @@ def get_loaders(name, nsamples=128, seed=0, seqlen=2048, tokenizer=None):
     if 'wikitext2' in name:
         return get_wikitext2(nsamples, seed, seqlen, tokenizer)
     if "c4" in name:
-        return get_c4(nsamples, seed, seqlen, tokenizer)
\ No newline at end of file
+        return get_c4(nsamples, seed, seqlen, tokenizer)    
\ No newline at end of file
diff --git a/lib/eval.py b/lib/eval.py
index 5214d50f..2eed305f 100644
--- a/lib/eval.py
+++ b/lib/eval.py
@@ -11,7 +11,7 @@
 
 
 # Function to evaluate perplexity (ppl) on a specified model and tokenizer
-def eval_ppl(args, model, tokenizer, device=torch.device("cuda:0")):
+def eval_ppl(model, tokenizer, device=torch.device("cuda:0")):
     # Set dataset
     dataset = "wikitext2"
 
diff --git a/lib/prune.py b/lib/prune.py
index 01d981c4..0bfa9339 100644
--- a/lib/prune.py
+++ b/lib/prune.py
@@ -2,6 +2,7 @@
 import heapq 
 import torch 
 import torch.nn as nn 
+import tqdm
 from .sparsegpt import SparseGPT 
 from .layerwrapper import WrappedGPT
 from .data import get_loaders 
@@ -128,14 +129,14 @@ def prune_wanda(args, model, tokenizer, device=torch.device("cuda:0"), prune_n=0
     use_cache = model.config.use_cache 
     model.config.use_cache = False 
 
-    print("loading calibdation data")
+    print("loading calibration data")
     dataloader, _ = get_loaders("c4",nsamples=args.nsamples,seed=args.seed,seqlen=model.seqlen,tokenizer=tokenizer)
     print("dataset loading complete")
     with torch.no_grad():
         inps, outs, attention_mask, position_ids = prepare_calibration_input(model, dataloader, device)
 
     layers = model.model.layers
-    for i in range(len(layers)):
+    for i in tqdm.tqdm(range(len(layers))):
         layer = layers[i]
         subset = find_layers(layer)
 
@@ -162,7 +163,7 @@ def tmp(_, inp, out):
             h.remove()
 
         for name in subset:
-            print(f"pruning layer {i} name {name}")
+            #print(f"pruning layer {i} name {name}")
             W_metric = torch.abs(subset[name].weight.data) * torch.sqrt(wrapped_layers[name].scaler_row.reshape((1,-1)))
 
             W_mask = (torch.zeros_like(W_metric) == 1)  ## initialize a mask to be all False
diff --git a/lib/prune_opt.py b/lib/prune_opt.py
index 5910bcf3..804cec7b 100644
--- a/lib/prune_opt.py
+++ b/lib/prune_opt.py
@@ -2,6 +2,7 @@
 import heapq 
 import torch 
 import torch.nn as nn 
+import tqdm
 from .sparsegpt import SparseGPT 
 from .layerwrapper import WrappedGPT
 from .data import get_loaders 
@@ -125,14 +126,14 @@ def prune_wanda(args, model, tokenizer, device=torch.device("cuda:0"), prune_n=0
     use_cache = model.config.use_cache 
     model.config.use_cache = False 
 
-    print("loading calibdation data")
+    print("loading calibration data")
     dataloader, _ = get_loaders("c4",nsamples=args.nsamples,seed=args.seed,seqlen=model.seqlen,tokenizer=tokenizer)
     print("dataset loading complete")
     with torch.no_grad():
         inps, outs, attention_mask = prepare_calibration_input(model, dataloader, device)
 
     layers = model.model.decoder.layers
-    for i in range(len(layers)):
+    for i in tqdm.tqdm(range(len(layers))):
         layer = layers[i]
         subset = find_layers(layer)
 
@@ -159,7 +160,7 @@ def tmp(_, inp, out):
             h.remove()
 
         for name in subset:
-            print(f"pruning layer {i} name {name}")
+            #print(f"pruning layer {i} name {name}")
             W_metric = torch.abs(subset[name].weight.data) * torch.sqrt(wrapped_layers[name].scaler_row.reshape((1,-1)))
 
             W_mask = (torch.zeros_like(W_metric) == 1)  ## initialize a mask to be all False