rLLM (relation LLM) focuses on LLM-based relational data mining, prioritizing: Accuracy, Efficiency, and Economy.

• Accuracy: MAE for regression; Micro-F1 and Macro-F1 for classification.
• Efficiency: Runtime, measured in seconds.
• Economy: Money, measured in dollars.

• pytorch 2.1.2
• scikit-learn 1.4.0
• llama_cpp_python 0.2.52
• langchain 0.1.8
• langchain-community 0.0.21
• langchain-experimental 0.0.52
• tiktoken 0.6.0
• sentence-transformers 2.3.1
• numpy 1.26.4
• pandas 2.1.4

• We recommmend 4-bit quantized Gemma 2b model, which can be Downloaded from the SJTU cloud storage or HuggingFace.

• We recommend a light BERT-like model all-MiniLM-L6-v2 to make sentence embedding, which can be obtained from the SJTU cloud storage, or directly from HuggingFace.