/capstone-LLM

Record my learning for LLM

Primary LanguageJupyter Notebook

Readme

RAG folder explain

  • embedding.py is for split the text and then convert it to embedding format, store it in vector database
  • CustomLLM.py is load the LLM for langchain use
  • RAG.py --> RAG framework (called the CustomLLM)
  • Frontend.py ---> frontend webside to load the model and interact

Data folder

check the data folder readme

load_model folder

  • you should download the llama.cpp
  • get the gguf model
  • check the load_model folder readme

Reading