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.