Welcome to the documentation and setup guide for the MiniMax-M3 model demo video! This repository serves as a landing page for the demo, showing how MiniMax-M3 functions as an autonomous data scientist and frontend engineer to turn raw data into interactive web interfaces.
- 📰 Official Blog Announcement: MiniMax M3 Blog Post — Read about the model architecture, training, and capabilities.
- 🤗 Download GGUF Weights: Unsloth MiniMax-M3-GGUF on Hugging Face — Get optimized GGUF quantized models for local deployment.
- 🌐 Live Cloud Playground: agent.minimax.io — Try the model directly in the browser via the official MiniMax chat agent interface.
You can test this model in two primary ways: locally using Ollama and the GGUF weights, or in the cloud via the official web agent.
To run MiniMax-M3 locally on your machine, you can import the GGUF file into Ollama using a custom Modelfile.
Download your preferred quantization (e.g., MiniMax-M3-Q4_K_M.gguf) from the Hugging Face Repository.
In the directory where you downloaded the GGUF file, create a plain text file named Modelfile (no file extension) and add the following contents:
# Point to your downloaded GGUF file path
FROM ./MiniMax-M3-Q4_K_M.gguf
# Set the temperature and system prompt parameters (optional, adjusts creativity)
PARAMETER temperature 0.7
PARAMETER stop "<|im_end|>"
# Define a system prompt to guide the assistant
SYSTEM """
You are MiniMax-M3, a state-of-the-art coding and reasoning assistant. Help the user solve programming, data science, and analytical tasks with structured, clean, and highly performant output.
"""Open your terminal/command prompt and run the following command to create the model in Ollama:
ollama create minimax-m3 -f ModelfileOnce built, launch the model interactively:
ollama run minimax-m3If you don't want to run the model locally, you can test it directly:
- Navigate to agent.minimax.io.
- Create a free account or log in.
- Start prompting the agent. You can upload datasets (like CSV files) directly into the chat box to perform autonomous data analysis and code generation.
In our YouTube video, we showcase MiniMax-M3's advanced context understanding and UI design skills by feeding it a real-world dataset and letting it build a fully functioning dashboard from scratch.
- Name:
wc_all_matches.csv - Source: Kaggle FIFA World Cup Complete Dataset (1930 - 2026) (Download the
wc_all_matches.csvfile). - Description: Match-level statistics for historic World Cup games (1930 - 2022). It includes 11 columns:
year,stage,team1,score1,score2,team2,venue,city,country,date, andnotes(annotations for upsets, shootouts, etc.).
Copy and paste this prompt into the MiniMax-M3 agent along with your CSV file to replicate the demo:
Act as an autonomous data scientist and frontend engineer to showcase your advanced context reasoning and visual coding capabilities; read the attached wc_all_matches.csv file, extract the most compelling historical insights, and compile everything into a single, standalone HTML file. Build a highly creative, interactive, and visually stunning multi-panel dashboard using Tailwind CSS and Chart.js via CDN, embedding your analyzed data directly into the JavaScript to make it completely self-contained and ready to open instantly in any browser.
- Context Parsing: The model reads and parses custom schema CSV files without prior training on the specific file layout.
- Code Generation: It writes a single self-contained HTML page containing structured CSS styles (using Tailwind CSS) and charting libraries (like Chart.js).
- Data Aggregation: The generated JavaScript parses and structures the data internally to populate charts, selectors, and summary cards.
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