I Analyzed My Finance With Local LLMs
TLDRThe video script describes a personal finance project where the creator uses an open-source language model to analyze and categorize bank transactions. They install and run the model locally for privacy and security, then classify expenses into categories like groceries, rent, and travel. The data is analyzed in Python and visualized to provide insights into income and expenses. The project also touches on the potential of running large language models locally on personal devices, highlighting the benefits of such technology for personal finance management.
Takeaways
- 💰 The importance of managing finances grows with age, highlighting the need to regularly review income and expenses.
- 📊 Individuals can utilize personal finance tools to categorize transactions and gain insights into their spending habits.
- 🚀 Open-source language models (LLMs) can be run locally on personal devices, offering a secure and free solution for personal data analysis.
- 🛠️ Frameworks like Lama CPP and GPT allow for the efficient and memory-friendly use of large language models on personal devices.
- 🔧 Customizing LLMs with model files can tailor the AI's responses to specific use cases, such as expense classification.
- 🔢 Despite the capabilities of LLMs, they may not always provide accurate basic arithmetic answers, indicating the need for verification.
- 📈 The script demonstrates a practical application of LLMs in personal finance, including expense categorization and the creation of a personal finance dashboard.
- 🌐 The use of Python and libraries like Plotly Express can aid in analyzing and visualizing financial data for better understanding and decision-making.
- 🔄 The process of categorizing expenses can be optimized by looping through transactions in manageable batches to avoid token limits.
- 🛑 The script mentions the ability to interrupt and stop processes when necessary, emphasizing the importance of control in data analysis workflows.
- 🎯 Personal finance dashboards can provide a comprehensive overview of income and expenses, but should also consider assets for a complete financial picture.
Q & A
What is the main purpose of the video?
-The main purpose of the video is to demonstrate how to use a large language model (LLM) to classify personal expenses from bank transactions and create a personal finance dashboard.
Why does the author feel inspired to classify expenses and review incomes?
-The author feels inspired to classify expenses and review incomes to better understand their financial situation and to plan for retirement.
What is the issue with uploading bank statements to a website like Chat B?
-Uploading bank statements to a website like Chat B poses a risk to personal privacy as it involves sharing sensitive information such as places visited, shops frequented, and personal spending habits.
How does the author decide to handle their bank transaction data securely?
-The author decides to handle their bank transaction data securely by downloading and running an open-source LLM locally on their laptop, which protects personal data and avoids the need for internet connection or third-party services.
What are some of the frameworks available for running an open-source language model locally?
-Some popular frameworks for running an open-source language model locally include Lama CPP, GPT For, and AMA.
What are the two main things that frameworks like AMA and Lama CPP try to achieve?
-Frameworks like AMA and Lama CPP try to achieve quantization, which reduces the memory footprint of the raw model weights, and make the models more efficient for consumers to use.
How does the author install and use a language model locally through AMA?
-The author installs a language model locally through AMA by running the command 'Ama pool' followed by the model name. To use the model, they run 'Ama run' followed by the model name and then type their message or prompt.
What is the significance of the temperature parameter in customizing a language model?
-The temperature parameter in customizing a language model affects the creativity and coherence of the model's responses. A higher temperature makes the model more creative, while a lower temperature makes it more coherent and less creative.
How does the author handle the token limit when inserting transactions into the LLM?
-The author handles the token limit by creating a for loop that processes the transactions in groups of 30, which is found to be the optimal number for receiving a complete and sensible response from the LLM.
What is the role of the pantic library in this project?
-The pantic library is used as a validator for the output from the language model. It ensures that the output is in the desired format and if the validation fails, the language model is rerun to get the correct output.
How does the author create an interactive personal finance dashboard?
-The author creates an interactive personal finance dashboard using Plotly Express for the visualizations and Panel for organizing the dashboard. The dashboard includes pie charts for income and expense breakdown, bar charts for monthly income and expenses, and is designed with a template from Panel.
What additional insight does the author mention about personal finance at the end of the video?
-The author mentions that the overview of personal finance provided by the dashboard might not be complete because it does not account for assets, such as money transferred to investment accounts or mortgage payments, which are also part of one's financial picture.
Outlines
📈 Personal Finance and Large Language Models
The speaker discusses their annual practice of reviewing bank transactions and the inspiration to classify expenses into categories. They mention the challenge of handling sensitive financial data and their decision to use an open-source language model (LLM) locally for privacy and cost-effectiveness. The speaker introduces the process of installing and running an LLM, using it to classify bank statement expenses, and analyzing the data in Python. They also mention a sponsorship by Core, offering discounts on data analytics courses, including Google's data analytics certificates.
🧠 Exploring Local Deployment of Large Language Models
The speaker explores different frameworks for running an open-source language model locally, such as Lama CPP and GPT. They discuss the benefits of local deployment for data security and the ability to use models without an internet connection. The speaker provides a step-by-step guide on installing and using the AMA framework, including downloading models and running them through the terminal. They also touch on the concept of model quantization and efficiency, and share their experience with the Mistro and Llama 2 models for expense classification.
💻 Automating Expense Classification with Python and LLMs
The speaker describes the process of setting up a project folder, installing necessary libraries, and accessing language models through Python. They discuss the challenges of handling a large number of transactions and the strategy of processing them in batches to avoid token limits. The speaker explains the creation of a custom function to handle and validate the language model's output, ensuring the correct format is obtained. They conclude by mentioning the successful categorization of transactions and the plan to create a personal finance dashboard.
📊 Creating a Personal Finance Dashboard with Plotly and Panel
The speaker shares their experience in creating a personal finance dashboard using Plotly Express and Panel libraries. They detail the process of reading transaction data, creating pie charts for income and expense breakdowns, and generating bar charts for monthly earnings and expenditures. The speaker emphasizes the importance of considering assets alongside expenses for a complete financial overview. They conclude by reflecting on the potential future of using large language models on personal devices and encourage viewers to experiment with open-source models for their projects.
Mindmap
Keywords
💡Personal Finance
💡Income and Expense Breakdown
💡Open-source Language Model
💡Data Privacy
💡Machine Learning Frameworks
💡Quantization
💡Python
💡Jupyter Notebook
💡Data Visualization
💡Dashboard
Highlights
The importance of reviewing financial transactions and the realization that money is almost everything.
The process of downloading and reviewing bank transactions for income and expense analysis.
The inspiration to create an income and expense breakdown by using large language models (LLMs).
The challenge of classifying expenses from buying transactions into appropriate categories.
The decision to use an open-source LLM locally to protect sensitive personal and financial data.
The installation and use of the open-source LLM, llama 2, for expense classification.
The creation of a custom model file for expense analysis using the llama 2 base model.
The use of Python to analyze and visualize financial data obtained from transaction classification.
The development of a personal finance dashboard to display income and expense breakdowns for two years.
The use of Plotly Express and Panel for creating interactive visualizations and dashboards.
The importance of considering assets in personal finance in addition to expenses.
The potential future trend of running large language models locally on personal devices.
The project's aim to inspire others to experiment with open-source language models for personal finance management.
The use of quantization by frameworks like Lama CPP and AMA to reduce memory footprint and improve model efficiency.
The availability of different frameworks to run open-source language models locally, such as Lama CPP, GPT for, and AMA.
The process of installing AMA and using it to run different language models through the terminal.
The method of handling large transaction data by looping through 30 transactions at a time to avoid token limit issues.