This Python script creates a password-protected ZIP file using the pyzipper library. It allows you to specify the files to include in the ZIP and set a password for encryption. The resulting ZIP file requires the provided password to access its contents, providing an additional layer of security.
Criteria | Submission Requirements
-- | --
Deploy the Llama2 Model on AWS Sagemaker | The Llama2 model is successfully deployed on AWS Sagemaker. The output of the Model_Evaluation.ipynb file verifies deployment Screenshots in the report show the model deployed in the SageMaker environment
E**valuate the Pre-trained Llama2 Text Generation Large Language Model for Domain Knowled**ge | Model evaluation uses input to evaluate generation of domain-specific content, based on chosen domain for fine-tuning. The model_evaluation.ipynb notebook contains relevant examples and output cells. The response of the model to domain-specific inputs is documented in the project report.
Pre-trained Model Evaluation
Criteria | Submission Requirements
-- | --
Deploy the Llama2 Model on AWS Sagemaker | The Llama2 model is successfully deployed on AWS Sagemaker. The output of the Model_Evaluation.ipynb file verifies deployment Screenshots in the report show the model deployed in the SageMaker environment
Evaluate the Pre-trained Llama2 Text Generation Large Language Model for Domain Knowledge | Model evaluation uses input to evaluate generation of domain-specific content, based on chosen domain for fine-tuning. The model_evaluation.ipynb notebook contains relevant examples and output cells. The response of the model to domain-specific inputs is documented in the project report.
UDACITY Introduction to Generative AI with AWS Project Documentation Report.pdf
Pre-trained Model Evaluation
Criteria | Submission Requirements -- | -- Deploy the Llama2 Model on AWS Sagemaker | The Llama2 model is successfully deployed on AWS Sagemaker. The output of the Model_Evaluation.ipynb file verifies deployment Screenshots in the report show the model deployed in the SageMaker environment E**valuate the Pre-trained Llama2 Text Generation Large Language Model for Domain Knowled**ge | Model evaluation uses input to evaluate generation of domain-specific content, based on chosen domain for fine-tuning. The model_evaluation.ipynb notebook contains relevant examples and output cells. The response of the model to domain-specific inputs is documented in the project report.Pre-trained Model Evaluation
Criteria | Submission Requirements -- | -- Deploy the Llama2 Model on AWS Sagemaker | The Llama2 model is successfully deployed on AWS Sagemaker. The output of the Model_Evaluation.ipynb file verifies deployment Screenshots in the report show the model deployed in the SageMaker environment Evaluate the Pre-trained Llama2 Text Generation Large Language Model for Domain Knowledge | Model evaluation uses input to evaluate generation of domain-specific content, based on chosen domain for fine-tuning. The model_evaluation.ipynb notebook contains relevant examples and output cells. The response of the model to domain-specific inputs is documented in the project report.