Telecom LoRA RAG

This repository hosts experimental scripts and configurations for a Retrieval-Augmented Generation (RAG) pipeline built on top of a LoRA fine-tuned Flan-T5 model for telecommunications research.

---

🧭 Project Overview

The project aims to explore how lightweight LoRA fine-tuning and retrieval-based augmentation can improve domain-specific language understanding in telecom-related datasets.

At this stage, only preliminary testing and environment setup scripts are included.
Future scripts will expand on retrieval, evaluation, and deployment components.

---

📁 Repository Structure

telecom-lora-rag/
├── test_lora_model.py      # Example script to load and evaluate a LoRA adapter
├── pyproject.toml          # Project dependencies for Python
├── environment.yml         # Conda environment configuration
├── README.md               # This file
└── (additional scripts to be added later)

---

⚙️ Setup Instructions

1. Create and activate the Conda environment:

   conda env create -f environment.yml
   conda activate telecom-lora-rag

2. Edit configuration parameters: Each script contains a configuration block under:

   if __name__ == "__main__":
       # Modify these paths to match your local setup
       MODEL_BASE = "google/flan-t5-large"
       MODEL_DIR = "~/Downloads/telecom_lora_model"
       CSV_PATH = "./3gpp_rel18_qa_3000.csv"

   ⚠️ Paths and datasets are not tracked in Git. Each collaborator should update them manually in their local copy.

3. Run the test:

   python test_lora_model.py

---

🧱 Development Notes

• The base model (google/flan-t5-large) is automatically downloaded from Hugging Face when first used.
• LoRA adapter weights must be provided locally by each collaborator.
• No datasets, checkpoints, or training outputs are stored in this repository.

---

🚫 Git Policy

This repository is intended for code only. The following assets must not be committed to Git:

*.pt
*.bin
*.safetensors
checkpoint-*
*.csv
*.zip
*.pdf
*_model/

---

📜 Contribution Guidelines

• Keep all scripts self-contained and well-documented.

• Use clear English docstrings and comments.

• Update configuration paths manually before running experiments.

• Future additions may include:

  • RAG prototype using FAISS/ChromaDB
  • Dataset parsing utilities
  • Evaluation metrics and benchmarking tools

---

🧠 Summary

Telecom LoRA RAG provides a minimal, extensible codebase for experimenting with LoRA fine-tuning and retrieval-augmented generation techniques in the telecom domain. All confidential data and model files remain local to each contributor’s environment.