diff --git a/DOCS.md b/DOCS.md
index 94d4dfb..f40faf3 100644
--- a/DOCS.md
+++ b/DOCS.md
@@ -1,13 +1,13 @@
-# mlModelSaver documentation
+# jrjModelRegistry documentation
 
 ## [Tutorial youtube](https://www.youtube.com/watch?v=fchTlNk2P8s)
 
 
-Introducing **[mlModelSaver](https://pypi.org/project/mlModelSaver/)** – a streamlined Python module designed for data scientists and developers who seek a straightforward solution for model saving and serving.
+Introducing **[jrjModelRegistry](https://pypi.org/project/jrjModelRegistry/)** – a streamlined Python module designed for data scientists and developers who seek a straightforward solution for model saving and serving.
 
 While numerous tools are available for training machine learning models, many lightweight statistical models lack simple, efficient saving mechanisms. Existing enterprise solutions like MLflow are robust but come with considerable complexity. Based on my experience, I saw the need for an abstract model registry concept that simplifies this process.
 
-**[mlModelSaver](https://github.com/smartdev-ca/mlModelSaver)** fills this gap, offering an intuitive way to save machine learning models and transformers. It facilitates seamless integration with frameworks like FastAPI ([Examples](https://github.com/jafarijason/ml_models_deployments)), Flask, and Django, enabling easy deployment and serving of models in production environments. Empower your machine learning workflow with **mlModelSaver** – the easy and efficient tool for model management.
+**[jrjModelRegistry](https://gitea.root.sarava.dev/JRJ/model-registry)** fills this gap, offering an intuitive way to save machine learning models and transformers. It facilitates seamless integration with frameworks like FastAPI ([Examples](https://github.com/jafarijason/ml_models_deployments)), Flask, and Django, enabling easy deployment and serving of models in production environments. Empower your machine learning workflow with **jrjModelRegistry** – the easy and efficient tool for model management.
 
 ## [Demo](https://ml.jasonjafari.com/docs)
 
@@ -205,20 +205,20 @@ curl --location 'https://ml.jasonjafari.com/model/predict' \
 
 ## Installation
 
-You can install **mlModelSaver** via pip:
+You can install **jrjModelRegistry** via pip:
 
 ```bash
-pip install mlModelSaver
+pip install jrjModelRegistry
 ```
 
 
-# mlModelSaver Example: Simple Linear Regression
+# jrjModelRegistry Example: Simple Linear Regression
 
-In this example, we demonstrate how to use **mlModelSaver** to export a simple linear regression model based on a notebook from [ml_models_deployments](https://github.com/jafarijason/ml_models_deployments).
+In this example, we demonstrate how to use **jrjModelRegistry** to export a simple linear regression model based on a notebook from [ml_models_deployments](https://github.com/jafarijason/ml_models_deployments).
 
 ### Example Description
 
-This example builds a simple linear regression model to predict sales based on temperature, advertising, and discount factors. Once the model is fitted and satisfactory, **mlModelSaver** allows you to easily save and deploy the model for use in production environments.
+This example builds a simple linear regression model to predict sales based on temperature, advertising, and discount factors. Once the model is fitted and satisfactory, **jrjModelRegistry** allows you to easily save and deploy the model for use in production environments.
 
 ### Example Code - notebook available [here](https://github.com/jafarijason/ml_models_deployments/blob/master/notebooks/001.ipynb)
 
@@ -230,14 +230,14 @@ def add_constant_columnTransformer(df):
     df_with_const.insert(0, 'const', 1)
     return df_with_const
 
-from mlModelSaver import MlModelSaver
-mlModelSaverInstance = MlModelSaver({
+from jrjModelRegistry import jrjModelRegistry
+jrjModelRegistryInstance = jrjModelRegistry({
     "baseRelativePath": "..",
     "modelsFolder": "models"
 })
 
-# Export the model using MlModelSaver
-loadedModel = mlModelSaverInstance.exportModel(
+# Export the model using jrjModelRegistry
+loadedModel = jrjModelRegistryInstance.exportModel(
      # the models is fitted and ready for usage
     simpleLinearRegressionFittedModel,
     {
@@ -283,7 +283,7 @@ loadedModel.mlModelSavePredict(testDf)
 
 ## Supported Models
 
-Current supported models by **mlModelSaver**:
+Current supported models by **jrjModelRegistry**:
 
 ```python
 supportedModels = {
@@ -310,30 +310,30 @@ supportedModels = {
 
 ## Next Steps
 
-- [-] **Support More Models** **WIP**: Extend **mlModelSaver** to support various types of models beyond simple linear regression, such as decision trees, neural networks, and ensemble methods.
+- [-] **Support More Models** **WIP**: Extend **jrjModelRegistry** to support various types of models beyond simple linear regression, such as decision trees, neural networks, and ensemble methods.
 
-- [-] **Additional Examples**: Provide diverse examples demonstrating the use of **mlModelSaver** with different machine learning models and data preprocessing techniques.
+- [-] **Additional Examples**: Provide diverse examples demonstrating the use of **jrjModelRegistry** with different machine learning models and data preprocessing techniques.
 
-- [] **Video Tutorial**: Create a comprehensive video tutorial demonstrating how to train models, use **mlModelSaver** for saving and deployment, and integrate with popular frameworks like Flask and FastAPI.
+- [] **Video Tutorial**: Create a comprehensive video tutorial demonstrating how to train models, use **jrjModelRegistry** for saving and deployment, and integrate with popular frameworks like Flask and FastAPI.
 
 - [] **Save Models to S3**: Implement functionality to save models directly to Amazon S3 for scalable and reliable storage, ensuring robust deployment in cloud environments.
 
 
 ### Support More Models
 
-Expand **mlModelSaver** to handle a variety of machine learning models beyond simple linear regression. Example models could include decision trees, support vector machines, and deep learning models. Ensure that each model type integrates seamlessly with **mlModelSaver**'s saving and deployment functionalities.
+Expand **jrjModelRegistry** to handle a variety of machine learning models beyond simple linear regression. Example models could include decision trees, support vector machines, and deep learning models. Ensure that each model type integrates seamlessly with **jrjModelRegistry**'s saving and deployment functionalities.
 
 ### Save Models to S3
 
-Enhance **mlModelSaver** to include options for saving models directly to Amazon S3. This feature ensures that models are stored securely and can be accessed from any location, facilitating deployment across distributed systems and cloud environments.
+Enhance **jrjModelRegistry** to include options for saving models directly to Amazon S3. This feature ensures that models are stored securely and can be accessed from any location, facilitating deployment across distributed systems and cloud environments.
 
 ### Additional Examples
 
-Include a range of examples demonstrating **mlModelSaver**'s capabilities across different use cases. Examples should cover various scenarios such as regression, classification, and time series forecasting. Provide clear instructions and code snippets for each example, showcasing how to prepare data, train models, and deploy them using **mlModelSaver**.
+Include a range of examples demonstrating **jrjModelRegistry**'s capabilities across different use cases. Examples should cover various scenarios such as regression, classification, and time series forecasting. Provide clear instructions and code snippets for each example, showcasing how to prepare data, train models, and deploy them using **jrjModelRegistry**.
 
 ### Video Tutorial
 
-Produce a video tutorial that guides users through the entire process of using **mlModelSaver**. The tutorial should include steps for training a model, integrating with **mlModelSaver** for saving and loading, and deploying the model using popular web frameworks like Flask or FastAPI. Emphasize best practices and common pitfalls to help users maximize efficiency and reliability in their machine learning projects.
+Produce a video tutorial that guides users through the entire process of using **jrjModelRegistry**. The tutorial should include steps for training a model, integrating with **jrjModelRegistry** for saving and loading, and deploying the model using popular web frameworks like Flask or FastAPI. Emphasize best practices and common pitfalls to help users maximize efficiency and reliability in their machine learning projects.
 
-These next steps will enhance **mlModelSaver**'s usability and scalability, enabling users to leverage advanced machine learning models effectively in production environments.
+These next steps will enhance **jrjModelRegistry**'s usability and scalability, enabling users to leverage advanced machine learning models effectively in production environments.
 
diff --git a/bun.lockb b/bun.lockb
new file mode 100755
index 0000000..4929885
Binary files /dev/null and b/bun.lockb differ
diff --git a/package.json b/package.json
index 3b55871..5965169 100644
--- a/package.json
+++ b/package.json
@@ -1,6 +1,6 @@
 {
   "name": "mlModelSaver",
-  "version": "1.0.33",
+  "version": "1.0.35",
   "description": "Make life easier for save and serving ml models",
   "main": "index.js",
   "repository": "git@github.com:smartdev-ca/mlModelSaver.git",
diff --git a/setup.py b/setup.py
index 494a731..7068167 100644
--- a/setup.py
+++ b/setup.py
@@ -1,15 +1,15 @@
 from setuptools import setup, find_packages
 
 setup(
-    name='mlModelSaver',
-    version='1.0.33',
+    name='jrjModelRegistry',
+    version='1.0.35',
     packages=find_packages(),
     description='Make life easier for saving and serving ML models',
     long_description=open('DOCS.md').read(),  # Assumes you have a README.md file
     long_description_content_type='text/markdown',  # Specify the format of the long description
     author='Jason Jafari',
     author_email='me@jasonjafari.com',
-    url='https://github.com/smartdev-ca/mlModelSaver',  # URL to your package repository
+    url='https://gitea.root.sarava.dev/JRJ/model-registry',  # URL to your package repository
     classifiers=[
        'Development Status :: 5 - Production/Stable',
         'Intended Audience :: Developers',
@@ -21,16 +21,12 @@ setup(
     ],
     keywords='machine learning model saving serving',  # Keywords for your package
     install_requires=[
-        'numpy>=1.26.4',
-        'pandas>=2.2.2',
-        'scikit-learn>=1.5.0',
-        'statsmodels>=0.14.2',
-        'matplotlib>=3.9.0',
         'dill>=0.3.8',
+        'jaraco.functools>=4.0.1'
     ],
     project_urls={  # Optional
-        'Documentation': 'https://github.com/smartdev-ca/mlModelSaver/blob/main/DOCS.md',
-        'Source': 'https://github.com/smartdev-ca/mlModelSaver',
-        'Tracker': 'https://github.com/smartdev-ca/mlModelSaver/issues',
+        'Documentation': 'https://gitea.root.sarava.dev/JRJ/model-registry/blob/main/DOCS.md',
+        'Source': 'https://gitea.root.sarava.dev/JRJ/model-registry',
+        'Tracker': 'https://gitea.root.sarava.dev/JRJ/model-registry/issues',
     },
 )
diff --git a/yarn.lock b/yarn.lock
index 99f190b..5b16c37 100644
--- a/yarn.lock
+++ b/yarn.lock
@@ -1,8 +1,9 @@
 # THIS IS AN AUTOGENERATED FILE. DO NOT EDIT THIS FILE DIRECTLY.
 # yarn lockfile v1
+# bun ./bun.lockb --hash: C761591D1FEC250A-7b9032bf9bced832-B040692D3DBC6CFD-d4dd2bf021bd4e3f
 
 
 husky@^9.0.11:
-  version "9.0.11"
-  resolved "https://registry.yarnpkg.com/husky/-/husky-9.0.11.tgz#fc91df4c756050de41b3e478b2158b87c1e79af9"
-  integrity sha512-AB6lFlbwwyIqMdHYhwPe+kjOC3Oc5P3nThEoW/AaO2BX3vJDjWPFxYLxokUZOo6RNX20He3AaT8sESs9NJcmEw==
+  version "9.1.7"
+  resolved "https://registry.npmjs.org/husky/-/husky-9.1.7.tgz"
+  integrity sha512-5gs5ytaNjBrh5Ow3zrvdUUY+0VxIuWVL4i9irt6friV+BqdCfmV11CQTWMiBYWHbXhco+J1kHfTOUkePhCDvMA==