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Fine-Tuning TinyLlama on WhatsApp Chats: Build Your Own Personal AI Chatbot! 🚀
Introduction
Ever wondered what it would be like to have an AI that talks just like you and your friends? What if you could train an AI chatbot on your WhatsApp conversations and make it understand your slang, emotions, and inside jokes? Well, now you can!
In this guide, we’ll fine-tune TinyLlama (1.1B Chat model) on WhatsApp chat data to create a personalized AI assistant that mirrors real-life conversations. We’ll use QLoRA (Quantized Low-Rank Adaptation) to make fine-tuning memory efficient — even on consumer GPUs!
Why Fine-Tune TinyLlama?
- ✅ Lightweight yet powerful — Only 1.1B parameters, making it efficient.
- ✅ Supports conversational AI — Optimized for chat-based interactions.
- ✅ Memory-efficient fine-tuning — Uses QLoRA for better performance on low-resource GPUs.
- ✅ Customizable — Fine-tune on your chat data to make AI sound like you.
First, we will evaluate the output of the TinyLlama 1.1B Chat model when loaded without quantization and without any fine-tuning.
Next, we will assess its performance when loaded with quantization and without any fine-tuning.
Step 1: Download and Prepare Your WhatsApp Chat Data
First, you need to extract chat data from WhatsApp and format it properly.
Download the Dataset from Kaggle
You can use an existing WhatsApp-style conversation dataset from Kaggle:
- Dataset URL: Kaggle — Conversation Dataset