Deep Dive: Fine-Tuning LLMs and Implementing Custom RAG Systems
Introduction
Artificial Intelligence is shifting rapidly towards localized, custom architectures. In this guide, we will inspect the steps required to prepare custom datasets with Pandas and NumPy, perform fine-tuning on open-source LLMs using PyTorch, and connect them to a Retrieval-Augmented Generation (RAG) vector database.
Dataset Preparation
To feed clean data into PyTorch models, clean structured matrices are critical:
"""python
import pandas as pd
import numpy as np
Prepare training corpus
df = pd.read_csv('qa_pairs.csv')
questions = df['question'].values
answers = df['answer'].values
"""
Fine-Tuning with PyTorch
Training deep learning pipelines from scratch or adjusting pre-trained weights requires robust loss configurations:
Implementing RAG
Connecting LLMs to vector store systems like Pinecone or Supabase vector extensions allows domain-specific context feeding on queries. This drastically lowers hallucination rates and optimizes response validity!
#AI#PyTorch#LLMs#Data Science
Deep Dive: Fine-Tuning LLMs and Implementing Custom RAG Systems | Suraj Khadka
