diff --git a/examples/timm/vit_clip_example.py b/examples/timm/vit_clip_example.py
deleted file mode 100755
index 8effe66..0000000
--- a/examples/timm/vit_clip_example.py
+++ /dev/null
@@ -1,62 +0,0 @@
-from hqq.engine.timm import HQQtimm
-from hqq.core.quantize import *
-
-#Model 
-#model_id = 'vit_base_patch32_clip_224.laion2b' #ViT-B-32
-#model_id = 'vit_large_patch14_clip_224.laion2b'#ViT-L-14
-model_id = 'vit_huge_patch14_clip_224.laion2b' #ViT-H-14
- 
-#Load model (on CPU)
-model = HQQtimm.create_model(model_id, pretrained=True)
-
-#Quantize settings
-#quant_config = BaseQuantizeConfig(nbits=8, group_size=128)
-quant_config = BaseQuantizeConfig(nbits=4, group_size=64)
-#quant_config = BaseQuantizeConfig(nbits=3, group_size=64)
-#quant_config = BaseQuantizeConfig(nbits=2, group_size=16, quant_scale=True)
-
-#Quantize
-model.quantize_model(quant_config=quant_config)
-
-###############################################################
-# #Save model
-# save_dir = "repo/" + model_id
-# model.save_quantized(model, save_dir=save_dir)
-
-# #Load model
-# model = HQQtimm.from_quantized(save_dir) 
-###############################################################
-import timm, torch
-import numpy as np 
-
-#Load reference model to compare with
-model_ref = timm.create_model(model_id, pretrained=True)
-model_ref = model_ref.half().cuda()
-model_ref.eval();
-
-#Pre-processing 
-mean_clip = np.array([0.4815, 0.4578, 0.4082], 'float32')
-std_clip  = np.array([0.2686, 0.2613, 0.2758], 'float32')
-def normalize_images_clip(data_np_in, BCHW=True): 
-	data_t = torch.from_numpy(data_np_in).float() if(type(data_np_in)==np.ndarray) else data_np_in.float()
-	data_t = (data_t/255. - mean_clip)/std_clip
-	data_t = data_t.swapaxes(2, 3).swapaxes(1, 2) if (BCHW) else data_t
-	return data_t
-
-###############################################################
-#Compare the compressed model with the original 
-x = np.random.rand(16, 224, 224, 3)
-x = normalize_images_clip(x).half().cuda()
-
-#Quantize
-with torch.no_grad():
-	y_q = model(x)
-	y_q /= torch.norm(y_q, p=2, dim=-1, keepdim=True)
-
-#Full-precision
-with torch.no_grad():
-	y_r = model_ref(x)
-	y_r /= torch.norm(y_r, p=2, dim=-1, keepdim=True)
-
-#We want the dot product to be as close as possible to 1 
-print('Average dot-product score', float(torch.diag(torch.matmul(y_q, y_r.t())).mean())) #~0.998 (ViT-H-14 @4bit)