Quick Start

This page will help you quickly get started with the MiniMind project.

📋 Requirements

Python: 3.10+
PyTorch: 1.12+
CUDA: 12.2+ (optional, for GPU acceleration)
VRAM: At least 8GB (24GB recommended)

!!! tip "Hardware Configuration Reference" - CPU: Intel i9-10980XE @ 3.00GHz - RAM: 128 GB - GPU: NVIDIA GeForce RTX 3090 (24GB)

🚀 Testing Existing Models

1. Clone the Project

git clone https://github.com/jingyaogong/minimind.git
cd minimind

2. Install Dependencies

pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple

!!! warning "Torch CUDA Check" After installation, test if Torch can use CUDA: python import torch print(torch.cuda.is_available())

3. Download Model

Download pretrained models from HuggingFace or ModelScope:

# From HuggingFace
git clone https://huggingface.co/jingyaogong/MiniMind2

# Or from ModelScope
git clone https://www.modelscope.cn/models/gongjy/MiniMind2.git

4. Command Line Q&A

# load=0: load PyTorch model, load=1: load Transformers model
python eval_model.py --load 1 --model_mode 2

5. Start WebUI (Optional)

# Requires Python >= 3.10
pip install streamlit
cd scripts
streamlit run web_demo.py

Visit http://localhost:8501 to use the web interface.

🔧 Third-party Inference Frameworks

MiniMind supports multiple mainstream inference frameworks:

Ollama

ollama run jingyaogong/minimind2

vLLM

vllm serve ./MiniMind2/ --served-model-name "minimind"

llama.cpp

# Convert model
python convert_hf_to_gguf.py ./MiniMind2/

# Quantize model
./build/bin/llama-quantize ./MiniMind2/MiniMind2-109M-F16.gguf ./Q4-MiniMind2.gguf Q4_K_M

# Inference
./build/bin/llama-cli -m ./Q4-MiniMind2.gguf --chat-template chatml

📝 Effect Testing

👶: Hello, please introduce yourself.
🤖️: Hello! I'm MiniMind, an AI assistant developed by Jingyao Gong.
    I interact with users through natural language processing and algorithm training.

👶: What is the highest mountain in the world?
🤖️: Mount Everest is the highest mountain in the world, located in the Himalayas,
    with an elevation of 8,848.86 meters (29,031.7 feet).

🎯 Next Steps

Check Model Training to learn how to train your own model from scratch
Read the source code to understand LLM implementation principles

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