Time and Expense Monitoring with Llama Model
Time and Expense Monitoring with Llama 2 | ChatGPT
01:14 demo
04:12 introduction
04:45 llama2 installation
08:43 time card monitoring
16:21 building dynamic prompts
20:50 expense monitoring
https://github.com/AmitXShukla/RPA/bl...
Author: Amit Shukla
https://github.com/AmitXShukla
/ ashuklax
/ @amit.shukla
Use cases
Efficient Time and Expense Monitoring with Llama 2
Streamlining 3-Way Receipt Match and Duplicate Voucher Invoices with Llama 2
Enhancing Fraud Detection: Utilizing Llama 2 as an Advanced Alert System for Monitoring Transactions
Maximizing Tax Savings, Ensuring Compliance, and Streamlining Audits with Llama 2
Meta has recently released Llama, a large language model trained with up to 70B parameters,
positioning it as the fastest and most advanced solution available.
This model is expected to outperform other tools in terms of both speed and accuracy.
In this blog post, I will demonstrate some automation use cases I have been working on.
It's important to note that these use cases/models will work best when trained on "in-house" data.
However, training such models is a rigorous task that requires significant computing hours and resources.
To make things more accessible and easier to utilize in production, using "off the shelf"
language models like ChatGPT and Llama 2 is a viable solution.
Below, I present some toy examples of use cases I've been working on.
While these examples are not meant for production, they still showcase the powerful capabilities of the language models.