Create an AI Clone with Your WhatsApp Chat History

Create an AI clone with your WhatsApp chat history

Short answer: You can train an open LLM like Llama 3 on your exported WhatsApp .txt file to make a chatbot that writes like you. This guide walks you through export, cleaning, preparing data, a simple fine-tune route with PEFT/QLoRA, and testing. If you want a ready start, check the WhatsApp-Llama GitHub repo and a practical writeup like Edward Donner's guide.

Why do this?

You get a personal assistant that sounds like you. It is a fun project that teaches data cleaning, dataset formatting, and model fine-tuning. Think of it like teaching a parrot your favorite phrases so it responds in your tone.

What you need (prerequisites)

• Basic Python and comfort with the command line.
• A free Google account for Google Colab or a small GPU machine.
• An exported WhatsApp chat file (.txt). See how to export at Llama Hub's WhatsApp instructions.
• About 30–60 minutes for a small run; more time for larger datasets.

Step 1 — Export your chat

Open the chat in WhatsApp, Menu > More > Export chat, choose "Without media" and save the .txt file to your working folder. Avoid including media for this first run.

Step 2 — Clean and format your data

WhatsApp exports have timestamps, names, and system lines. Clean them into simple pairs or conversation turns. You can use a small Python script or a loader like LlamaIndex's WhatsappChatLoader to parse the file.

from llama_index.readers.whatsapp.base import WhatsappChatLoader
loader = WhatsappChatLoader(path="/content/mychat.txt")
docs = loader.load_data()
# docs now holds parsed messages ready for tokenization

If you prefer a tiny custom cleaner, remove lines like "Messages to this chat and calls are now secured with end-to-end encryption" and forward tags. Keep speaker name and message text. Split long messages into shorter turns.

Dataset format

Fine-tuning works well with simple JSONL lines like:

{"prompt": "User: Hey, what's up?\nAssistant:", "completion": "Not much, just finishing lunch."}

Or use a conversation array if your training script needs it.

Step 3 — Choose a method: full fine-tune vs PEFT

Full fine-tuning needs lots of GPU and time. For personal projects, use PEFT techniques like QLoRA. They save memory and run well on Colab. Read about chat/completions at the Llama API docs for model calling patterns.

Step 4 — Run a simple Colab flow

High level steps for Colab:

1. Install libraries: transformers, peft, bitsandbytes, llama-index if needed.
2. Upload cleaned JSONL or point to your GitHub repo like WhatsApp-Llama.
3. Load a Llama 3 or Llama 2 chat model variant locally or from a hub compatible checkpoint.
4. Train with PEFT/QLoRA for a few epochs on your data.
5. Save the adapter and test locally.

# Example shell steps (in a Colab cell)
!pip install -q transformers peft bitsandbytes llama-index
# Example python call (very short pseudocode)
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("your-llama-checkpoint", load_in_8bit=True)
# attach PEFT adapter and train on your JSONL dataset

Note: exact commands vary by checkpoint and tooling. Use the WhatsApp-Llama repo or Edward Donner's tutorial for tested Colab cells.

Step 5 — Test your AI clone

Load the saved adapter and prompt it with a real chat line. Try short prompts first. If the tone matches, great. If not, collect more examples and re-run with more epochs.

user_prompt = "Hey, how's your day?"
response = chat_model.generate(user_prompt)
print(response)

Common pitfalls and fixes

• Poor formatting: If the model repeats timestamps or system text, re-clean the dataset.
• Catastrophic forgetting: Overfitting to recent messages makes the bot odd. Use a small learning rate and mix in neutral examples.
• Memory errors: Use 8-bit loading or QLoRA to reduce GPU RAM needs.
• Legal/privacy: Do not share other people's private messages. Remove names or redact sensitive content.

Quick tips

• Start with a small subset of chats to validate your pipeline.
• Keep prompts short and consistent.
• Save checkpoints so you can roll back if a run goes wrong.

One tiny analogy and a checkpoint

Think of the model like a cookbook. Your chat history is the recipe collection. Clean recipes make better dishes. Quick check: did you export without media and remove forwarded or system lines?

Where to learn more

Explore these resources for deeper examples: WhatsApp-Llama, Edward Donner's step-by-step, and LlamaIndex's WhatsappChatLoader. For community experiments, see discussions like the Reddit thread and related GitHub conversations.

You've got what you need to start. Try a short run, test the voice, then iterate. Need a hand with a small snippet? Try the sample code above in a Colab cell and come back with the exact error and I'll help debug it.