Jupyter Agent#
Jupyter Agent is a practical tool that allows large language models (LLM) to achieve direct interaction and code execution capabilities within Jupyter notebooks. It has the following useful features:
- Quickly and efficiently load data
- Accurately run user-written Python code
- Clearly present the final processing results in chart form
- Strictly complete various operations according to user ideas and instructions
This way, our collaboration with LLM will be smooth and natural when handling data-driven tasks. The current functionality is just the beginning, and its future development potential is enormous; the current results are merely the tip of the iceberg.
Available Models#
Supports various cutting-edge models:
meta-llama/Llama-3.1–8B-Instructmeta-llama/Llama-3.2–3B-Instructmeta-llama/Llama-3.1–70B-Instruct
These models each have unique advantages, catering to different use case needs from basic exploratory data analysis (EDA) to more advanced computations.
Jupyter Agent User Guide#
Using this tool is extremely simple; just follow these steps:
-
Visit the Jupyter Agent page on the HuggingFace platform
Jupyter Agent Page -
Select an available model
Choose an available model from the dropdown menu. -
Enter prompt information
Input your desired prompt in the text box, such as “What’s in the data? -> Upload data file -> csv, text...”. -
Click the “Start!” button
The agent will generate Python code based on your query and display it in the user interface. -
Download or upload files
You can choose to download the generated Jupyter notebook file to run on your local computer; if you need to analyze a custom dataset, you can also upload files directly through the interface. Additionally, there are advanced settings available, including custom system prompts, increasing context limits, and switching between different models.
Application Example Display#
Taking the pre-set example of “Solve the Lotka-Volterra equation and plot the results,” the specific operations are as follows:
- Prompt: Accurately enter the prompt, typing “Solve the Lotka-Volterra equation and plot the results.”
- Execution: Then execute the operation; the agent will automatically generate Python code to solve the equation and plot the corresponding result graph.
- Subsequent Prompts: Based on this, if you want to further optimize the output content, you can enter additional prompts, such as asking the agent to modify the style of the plot, add labels, or conduct further calculations based on existing results.
- Output: You can choose to download the generated notebook file or view the code directly in the operation interface. This tool is both convenient and flexible, making it a powerful asset for data workers!
Unique Advantages#
The powerful features of this tool are reflected in many aspects, with the following being some typical use cases:
- Exploratory Data Analysis (EDA): Quickly analyze datasets, present data patterns visually, and also perform data cleaning tasks.
- Equation Solving: As illustrated in the previous example, it can solve the Lotka-Volterra equation and produce highly valuable charts.
- Custom Workflow Automation: With just a simple prompt, repetitive coding tasks can be automated.
- Collaborative Research: It can collaborate with large language models (LLM) to conduct research projects involving large amounts of data.