diff --git a/ConFIG/grad_operator.py b/ConFIG/grad_operator.py
index f0d96b9..898bec1 100644
--- a/ConFIG/grad_operator.py
+++ b/ConFIG/grad_operator.py
@@ -30,8 +30,8 @@ def ConFIG_update_double(grad_1:torch.Tensor,grad_2:torch.Tensor,
         
     Examples:
         ```python
-        from ConFIG.grad_operator import ConFIG_update_double
-        from ConFIG.utils import get_gradient_vector,apply_gradient_vector
+        from conflictfree.grad_operator import ConFIG_update_double
+        from conflictfree.utils import get_gradient_vector,apply_gradient_vector
         optimizer=torch.Adam(network.parameters(),lr=1e-3)
         for input_i in dataset:
             grads=[] # we record gradients rather than losses
@@ -93,8 +93,8 @@ def ConFIG_update(
         
     Examples:
         ```python
-        from ConFIG.grad_operator import ConFIG_update
-        from ConFIG.utils import get_gradient_vector,apply_gradient_vector
+        from conflictfree.grad_operator import ConFIG_update
+        from conflictfree.utils import get_gradient_vector,apply_gradient_vector
         optimizer=torch.Adam(network.parameters(),lr=1e-3)
         for input_i in dataset:
             grads=[] # we record gradients rather than losses
@@ -191,8 +191,8 @@ class ConFIGOperator(GradientOperator):
     
     Examples:
         ```python
-        from ConFIG.grad_operator import ConFIGOperator
-        from ConFIG.utils import get_gradient_vector,apply_gradient_vector
+        from conflictfree.grad_operator import ConFIGOperator
+        from conflictfree.utils import get_gradient_vector,apply_gradient_vector
         optimizer=torch.Adam(network.parameters(),lr=1e-3)
         operator=ConFIGOperator() # initialize operator
         for input_i in dataset:
diff --git a/ConFIG/momentum_operator.py b/ConFIG/momentum_operator.py
index a49c1c3..32f1fbd 100644
--- a/ConFIG/momentum_operator.py
+++ b/ConFIG/momentum_operator.py
@@ -1,9 +1,9 @@
 from torch import Tensor
 from torch.nn.modules import Module
-from ConFIG import Sequence
-from ConFIG.grad_operator import ConFIGOperator, GradientOperator
-from ConFIG.utils import Sequence
-from ConFIG.loss_recorder import LatestLossRecorder, LossRecorder
+from conflictfree import Sequence
+from conflictfree.grad_operator import ConFIGOperator, GradientOperator
+from conflictfree.utils import Sequence
+from conflictfree.loss_recorder import LatestLossRecorder, LossRecorder
 from . import *
 from .utils import *
 from .loss_recorder import *
@@ -152,8 +152,8 @@ class PseudoMomentumOperator(MomentumOperator):
             
     Examples:
     ```python
-    from ConFIG.momentum_operator import PseudoMomentumOperator
-    from ConFIG.utils import get_gradient_vector,apply_gradient_vector
+    from conflictfree.momentum_operator import PseudoMomentumOperator
+    from conflictfree.utils import get_gradient_vector,apply_gradient_vector
     optimizer=torch.Adam(network.parameters(),lr=1e-3)
     operator=PseudoMomentumOperator(num_vector=len(loss_fns)) # initialize operator, the only difference here is we need to specify the number of gradient vectors.
     for input_i in dataset:
diff --git a/README.md b/README.md
index faf66c3..86bbd46 100644
--- a/README.md
+++ b/README.md
@@ -65,8 +65,10 @@ url={https://arxiv.org/abs/2408.11104},
 
 ## Installation
 
-* Install through `pip`: `pip install git+https://github.com/tum-pbs/ConFIG`
-* Install locally: Download and run `pip install .` or `install.sh` in terminal.
+* Install through `pip`: `pip install conflictfree`
+* Install from repository online: `pip install git+https://github.com/tum-pbs/ConFIG`
+* Install from repository offline: Download the repository and run `pip install .` or `install.sh` in terminal.
+* Install from released wheel: Download the wheel and run `pip install conflictfree-x.x.x-py3-none-any.whl` in terminal.
 
 ## Usage
 
@@ -88,8 +90,8 @@ for input_i in dataset:
 With `ConFIG`:
 
 ```python
-from ConFIG.grad_operator import ConFIG_update
-from ConFIG.utils import get_gradient_vector,apply_gradient_vector
+from conflictfree.grad_operator import ConFIG_update
+from conflictfree.utils import get_gradient_vector,apply_gradient_vector
 optimizer=torch.Adam(network.parameters(),lr=1e-3)
 for input_i in dataset:
     grads=[]
diff --git a/docs/examples/mtl_toy.ipynb b/docs/examples/mtl_toy.ipynb
index 9896612..b1d4a97 100644
--- a/docs/examples/mtl_toy.ipynb
+++ b/docs/examples/mtl_toy.ipynb
@@ -316,7 +316,7 @@
    ],
    "source": [
     "from tqdm import tqdm\n",
-    "from ConFIG.grad_operator import ConFIG_update\n",
+    "from conflictfree.grad_operator import ConFIG_update\n",
     "scale=[0.1,1]\n",
     "trajectories = [];losses = []\n",
     "for init in inits:\n",
@@ -397,7 +397,7 @@
    ],
    "source": [
     "from tqdm import tqdm\n",
-    "from ConFIG.momentum_operator import PseudoMomentumOperator\n",
+    "from conflictfree.momentum_operator import PseudoMomentumOperator\n",
     "\n",
     "scale=[0.1,1]\n",
     "trajectories = [];losses = []\n",
diff --git a/docs/examples/pinn_burgers.ipynb b/docs/examples/pinn_burgers.ipynb
index f70b07a..6782dfe 100644
--- a/docs/examples/pinn_burgers.ipynb
+++ b/docs/examples/pinn_burgers.ipynb
@@ -207,7 +207,7 @@
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
-    "from ConFIG.utils import get_para_vector,apply_para_vector\n",
+    "from conflictfree.utils import get_para_vector,apply_para_vector\n",
     "\n",
     "\n",
     "class Tester():\n",
@@ -364,8 +364,8 @@
     }
    ],
    "source": [
-    "from ConFIG.grad_operator import ConFIG_update\n",
-    "from ConFIG.utils import get_gradient_vector,apply_gradient_vector\n",
+    "from conflictfree.grad_operator import ConFIG_update\n",
+    "from conflictfree.utils import get_gradient_vector,apply_gradient_vector\n",
     "\n",
     "torch.manual_seed(seed)\n",
     "np.random.seed(seed)\n",
@@ -434,8 +434,8 @@
     }
    ],
    "source": [
-    "from ConFIG.utils import OrderedSliceSelector\n",
-    "from ConFIG.momentum_operator import PseudoMomentumOperator\n",
+    "from conflictfree.utils import OrderedSliceSelector\n",
+    "from conflictfree.momentum_operator import PseudoMomentumOperator\n",
     "\n",
     "torch.manual_seed(seed)\n",
     "np.random.seed(seed)\n",
diff --git a/docs/start/start.md b/docs/start/start.md
index b52ac2f..967ea9a 100644
--- a/docs/start/start.md
+++ b/docs/start/start.md
@@ -2,8 +2,10 @@
 
 ## Installation
 
-* Install through `pip`: `pip install git+https://github.com/tum-pbs/ConFIG`
-* Install locally: Download and run `pip install .` or `install.sh` in terminal.
+* Install through `pip`: `pip install conflictfree`
+* Install from repository online: `pip install git+https://github.com/tum-pbs/ConFIG`
+* Install from repository offline: Download the repository and run `pip install .` or `install.sh` in terminal.
+* Install from released wheel: Download the wheel and run `pip install conflictfree-x.x.x-py3-none-any.whl` in terminal.
 
 ## Use ConFIG method
 
@@ -23,8 +25,8 @@ for input_i in dataset:
 To use our ConFIG method, you can simply modify the code as
 
 ```python
-from ConFIG.grad_operator import ConFIG_update
-from ConFIG.utils import get_gradient_vector,apply_gradient_vector
+from conflictfree.grad_operator import ConFIG_update
+from conflictfree.utils import get_gradient_vector,apply_gradient_vector
 optimizer=torch.Adam(network.parameters(),lr=1e-3)
 for input_i in dataset:
     grads=[] # we record gradients rather than losses
@@ -41,8 +43,8 @@ for input_i in dataset:
 Or, you can use our `ConFIGOperator` class:
 
 ```python
-from ConFIG.grad_operator import ConFIGOperator
-from ConFIG.utils import get_gradient_vector,apply_gradient_vector
+from conflictfree.grad_operator import ConFIGOperator
+from conflictfree.utils import get_gradient_vector,apply_gradient_vector
 optimizer=torch.Adam(network.parameters(),lr=1e-3)
 operator=ConFIGOperator() # initialize operator
 for input_i in dataset:
@@ -64,8 +66,8 @@ The `ConFIGOperator` class and `ConFIG_update` is basically the same, you can ch
 The basic usage of `M-ConFIG` method in our code is similar to `ConFIGOperator` :
 
 ```python
-from ConFIG.momentum_operator import PseudoMomentumOperator
-from ConFIG.utils import get_gradient_vector,apply_gradient_vector
+from conflictfree.momentum_operator import PseudoMomentumOperator
+from conflictfree.utils import get_gradient_vector,apply_gradient_vector
 optimizer=torch.Adam(network.parameters(),lr=1e-3)
 operator=PseudoMomentumOperator(num_vector=len(loss_fns)) # initialize operator, the only difference here is we need to specify the number of gradient vectors.
 for input_i in dataset:
diff --git a/experiments/MTL/methods/weight_methods.py b/experiments/MTL/methods/weight_methods.py
index 5a14b15..3a29d69 100644
--- a/experiments/MTL/methods/weight_methods.py
+++ b/experiments/MTL/methods/weight_methods.py
@@ -11,9 +11,9 @@
 
 from methods.min_norm_solvers import MinNormSolver, gradient_normalizers
 
-from ConFIG.grad_operator import *
-from ConFIG.momentum_operator import *
-from ConFIG.helpers import OrderedSliceSelector
+from conflictfree.grad_operator import *
+from conflictfree.momentum_operator import *
+from conflictfree.helpers import OrderedSliceSelector
 
 EPS = 1e-8 # for numerical stability
 
diff --git a/experiments/PINN/README.md b/experiments/PINN/README.md
index 4a532e4..5d90de9 100644
--- a/experiments/PINN/README.md
+++ b/experiments/PINN/README.md
@@ -12,7 +12,7 @@ The following code shows how to start a training:
 
 ```python
 from lib_pinns.burgers.trainer import *
-from ConFIG.grad_operator import *
+from conflictfree.grad_operator import *
 
 SAVE_PATH="./PINN_trained/burgers/"
 
diff --git a/experiments/PINN/lib_pinns/trainer_basis.py b/experiments/PINN/lib_pinns/trainer_basis.py
index 541eab6..c2636ca 100644
--- a/experiments/PINN/lib_pinns/trainer_basis.py
+++ b/experiments/PINN/lib_pinns/trainer_basis.py
@@ -5,8 +5,8 @@
 from .helpers import *
 from .loss_weighter import *
 
-from ConFIG.grad_operator import *
-from ConFIG.momentum_operator import *
+from conflictfree.grad_operator import *
+from conflictfree.momentum_operator import *
 
 def get_cosine_constant_lambda(initial_lr,final_lr,epochs,warmup_epoch,constant_start_epoch):
     """
diff --git a/install.sh b/install.sh
index 0b6289d..3e74779 100755
--- a/install.sh
+++ b/install.sh
@@ -1,5 +1,5 @@
-pip uninstall ConFIG
+pip uninstall conflictfree
 rm -rf build dist foxutils.egg-info
 python3 setup.py sdist bdist_wheel
 cd dist
-pip install ConFIG-*.whl
+pip install conflictfree-*.whl
diff --git a/pypi_desc.md b/pypi_desc.md
index 1f8d2a9..80fcc8a 100644
--- a/pypi_desc.md
+++ b/pypi_desc.md
@@ -67,8 +67,10 @@ url={https://arxiv.org/abs/2408.11104},
 
 ## Installation
 
-* Install through `pip`: `pip install git+https://github.com/tum-pbs/ConFIG`
-* Install locally: Download and run `pip install .` or `install.sh` in terminal.
+* Install through `pip`: `pip install conflictfree`
+* Install from repository online: `pip install git+https://github.com/tum-pbs/ConFIG`
+* Install from repository offline: Download the repository and run `pip install .` or `install.sh` in terminal.
+* Install from released wheel: Download the wheel and run `pip install conflictfree-x.x.x-py3-none-any.whl` in terminal.
 
 ## Usage
 
@@ -90,8 +92,8 @@ for input_i in dataset:
 With `ConFIG`:
 
 ```python
-from ConFIG.grad_operator import ConFIG_update
-from ConFIG.utils import get_gradient_vector,apply_gradient_vector
+from conflictfree.grad_operator import ConFIG_update
+from conflictfree.utils import get_gradient_vector,apply_gradient_vector
 optimizer=torch.Adam(network.parameters(),lr=1e-3)
 for input_i in dataset:
     grads=[]
diff --git a/setup.py b/setup.py
index f01687b..2c4bd58 100644
--- a/setup.py
+++ b/setup.py
@@ -14,9 +14,9 @@ def get_install_requires():
     return targets
 
 setuptools.setup(
-    name="ConFIG",
-    version="0.1.1",
-    author="Qiang Liu, Nils Thuerey",
+    name="conflictfree",
+    version="0.1.2",
+    author="Qiang Liu, Mengyu Chu, Nils Thuerey",
     author_email="qiangliu.7@outlook.com",
     description="Official implementation of Conflict-free Inverse Gradients method",
     long_description=long_description,