README.md

---
license: apache-2.0
datasets:
- dawveed/AmazonWebServicesAWS-dataset
language:
- en
metrics:
- accuracy
pipeline_tag: text-generation
tags:
- cloud
- AWS
- amazon web services
- amazon
- web
- services
library_name: peft

base_model: tiiuae/falcon-7b
---
<img src="https://huggingface.co/dawveed/AWS-Sage/resolve/main/logo.png">
# Model Card for AWS Sage

The AWS-Sage is a Language Model (LLM) designed to assist users with questions related to Amazon Web Services (AWS) support. Powered by advanced natural language processing, it can swiftly provide answers to inquiries regarding AWS support plans, billing, technical issues, service limitations, and best practices. Whether you're a seasoned AWS user or new to the platform, the SupportBot offers timely and accurate assistance, helping you navigate the complexities of AWS support with ease.

## Model Details
### Model Description

The AWS Sage is a sophisticated Language Model (LLM) meticulously trained on a vast corpus of data extracted from Amazon Web Services (AWS) customer support interactions. This cutting-edge AI system is tailored specifically to address the diverse needs of AWS users seeking assistance and guidance with their cloud computing endeavors.

Equipped with state-of-the-art natural language understanding capabilities, the AWS Sage comprehensively tackles a wide array of inquiries related to AWS support services. Whether users are grappling with billing discrepancies, troubleshooting technical issues, seeking advice on optimizing their AWS infrastructure, or navigating the intricacies of support plans, the AWS Sage is adept at swiftly delivering accurate and insightful responses.

Utilizing a combination of machine learning algorithms and deep neural networks, the AWS Sage continuously refines its knowledge base and understanding of user queries, ensuring that it remains up-to-date with the latest developments and best practices in AWS support. Its ability to comprehend nuanced questions and provide contextually relevant answers makes it an invaluable resource for both novice and seasoned AWS users alike.

Moreover, the AWS Sage is designed to enhance the overall customer support experience by offering timely assistance and empowering users to resolve issues autonomously whenever possible. By leveraging the vast reservoir of knowledge accumulated through interactions with AWS support specialists, the AWS Sage serves as a virtual assistant capable of efficiently guiding users through various support processes and procedures.

In essence, the AWS Sage represents a paradigm shift in customer support, leveraging the power of artificial intelligence to deliver personalized, responsive, and effective assistance to AWS users across the globe. Whether users are seeking quick solutions to technical queries or seeking strategic advice to optimize their AWS deployments, the AWS Sage stands ready to assist, ensuring a seamless and rewarding experience in the AWS ecosystem.

- **Developed by:** David Lopez Oñate https://www.kinqo.com
- **License:** Apache 2.0
- **Finetuned from model:** tiiuae/falcon-7b


## Bias, Risks, and Limitations

- Bias in Training Data: The AWS Sage model may exhibit biases present in the training data, which could result in skewed or unfair responses to user inquiries, particularly if the data is not sufficiently diverse or representative.

Technical Limitations: Despite its advanced capabilities, AWS Sage may face limitations in understanding complex or nuanced language, potentially leading to incomplete or inaccurate responses to user queries.

Dependency on Training Data Quality: The effectiveness of AWS Sage relies heavily on the quality and relevance of its training data. Inaccurate or outdated data may undermine the model's ability to provide accurate and helpful support.

Risk of Misinterpretation: AWS Sage may misinterpret the intent or context of user inquiries, especially in cases of ambiguous or colloquial language, which could result in incorrect or misleading responses.

Lack of Emotional Intelligence: Unlike human support agents, AWS Sage may lack the ability to empathize with users or understand subtle emotional cues, potentially leading to impersonal interactions or dissatisfaction among users seeking emotional support.

Privacy Concerns: User inquiries processed by AWS Sage may contain sensitive or confidential information, raising concerns about data privacy and security, especially if proper safeguards are not in place to protect user data.

Limited Domain Expertise: While knowledgeable about AWS support topics, AWS Sage may lack expertise in certain specialized areas or industries, which could limit its ability to provide comprehensive support in those domains.

Overreliance on Automation: Users may become overly reliant on AWS Sage for support, potentially overlooking the value of human interaction or alternative support channels, which could lead to a loss of human touch in customer service.

Inability to Handle Unforeseen Scenarios: AWS Sage may struggle to handle novel or unforeseen support scenarios not covered in its training data, potentially leading to inadequate or ineffective responses in rapidly evolving situations.

Technical Failures or Errors: Like any AI system, AWS Sage is susceptible to technical failures, errors, or malfunctions, which could disrupt service delivery or lead to unintended consequences for users relying on its support. Regular monitoring and maintenance are essential to mitigate these risks.