benbel376/llm-finetuning-for-amharic

# Amharic RAG Ad Builder ## Project Overview The project is organized into several tasks to achieve the business goals: 1. **Literature Review & Huggingface Ecosystem (Task 1):** - Understand key concepts and methods related to LLMs. - Explore the Huggingface ecosystem for inference and fine-tuning. - Review relevant literature and resources. 2. **Load an LLM and Use It for Inference (Task 2):** - Set up the work environment. - Choose an open-source LLM and load it. - Test the model's inference capabilities for various scenarios. 3. **Data Preprocessing and Preparation (Task 3):** - Parse and clean raw Telegram message data. - Extract and remove unnecessary features. - Prepare the data for fine-tuning. 4. **Fine-Tuning the LLM (Task 4):** - Understand the key components of LLM training and fine-tuning. - Choose a base model and fine-tune it for Amharic text. - Explore Huggingface documentation for inference and fine-tuning. 5. **Build a RAG Pipeline to Generate Telegram Amharic Ad Posts (Task 5):** - Implement RAG techniques for Amharic text generation. - Retrieve relevant information from English and Amharic texts. - Evaluate and deploy the RAG pipeline with a simple frontend. ## Repository Structure - **assets:** Contains additional project assets. - **demo:** Includes any demonstration files or resources. - **modeling:** Holds scripts and code related to model training and fine-tuning. - **notebooks:** Jupyter notebooks for exploratory data analysis and documentation. - **scripts:** Contains utility scripts for various tasks. - **utils:** Utility functions and helper modules. - **backend:** FastAPI backend for serving the RAG model. - **frontend:** React frontend for a user-friendly interface. ## How to Use 1. Clone the repository: `git clone github.com` 2. Navigate to the `backend` directory: `cd backend` 3. Install dependencies: `pip install -r requirements.txt` 4. Run the FastAPI backend: `uvicor …