How to Train a Niche LLM on Historical or Personal Data

8/12/2025

So You Want to Train an LLM That Talks Like It's From Another Time? Here’s How.

Ever wanted to chat with a 19th-century poet? Or maybe create a chatbot that perfectly captures your unique way of texting? It sounds like something out of a sci-fi movie, but honestly, it's more achievable than you might think. We're talking about training a niche Large Language Model (LLM) on texts from a specific time period, whether that's your own personal message history or a trove of historical documents.

It’s a pretty cool project, & it’s one of those things that can feel SUPER intimidating at first. But once you break it down, it's really a step-by-step process. I've been digging into this, & I'm going to walk you through how it's done, from gathering the data to figuring out if your new "time-traveling" LLM is actually any good.

The Big Idea: Why Niche LLMs are a Game-Changer

First off, why would you even want to do this? Well, general-purpose LLMs are amazing, but they're trained on the vast, messy, & VERY modern internet. They're a jack-of-all-trades, but a master of none. If you want an AI that truly understands a specific domain, whether that's the language of Shakespeare or the inside jokes between you & your friends, you need to specialize.

Fine-tuning an LLM on a specific dataset is like giving a brilliant, well-read student a specialized course. They already have the foundational knowledge; you're just giving them the expert-level insights for a particular field. This is where things get really powerful for businesses too. Imagine a customer service bot that doesn't just give generic answers but communicates in the precise, expert language of your industry.

Here’s the thing, for many businesses, creating this kind of specialized AI is becoming a key differentiator. This is where platforms like Arsturn come into play. Arsturn helps businesses build no-code AI chatbots trained on their own data. This means you can create a chatbot that understands your products, your customers, & your brand's unique voice, providing personalized experiences that boost conversions & engagement. It's about moving beyond generic interactions & building meaningful connections, & a custom-trained LLM is the heart of that.

Path #1: The Personal Time Capsule - Training an LLM on Your Own Messages

This is a popular & fascinating starting point for many. The goal is to create a "clone" of yourself, or at least your digital persona. People have done this with their SMS, iMessage, & WhatsApp histories, & the results can be startlingly realistic.

Step 1: Getting Your Hands on the Data

This is often the first hurdle. If you're an iPhone user, a utility called iMazing is a common choice to download your entire message history. You can export everything into a manageable format like a CSV file. For WhatsApp, you can export chats directly from the app.

The key is to get as much data as possible. A few thousand messages is a good start, but the more you have, the better the model will capture your nuances. One person even used 240,000 of their own messages!

Step 2: The Not-So-Fun Part - Prepping the Dataset

Raw message exports are messy. You'll need to clean & structure them into a format the LLM can learn from. This is probably the most time-consuming part of the project.

Here's what the process generally looks like:

Cleaning: You'll want to filter out group chats (unless you want the model to learn multiple personalities), people you barely talk to, & any automated messages.
Structuring: You need to group the messages into conversational blocks. A common method is to treat messages sent within a short time frame (say, five minutes) as a single turn in a conversation & group conversations that are separated by a longer period (like an hour).
Formatting: The data needs to be formatted in a specific way, often called a prompt template. This usually involves clearly marking who said what. For example:
1[INST] Write a chat between Person A and Person B [/INST] ### Person A: Hey, you free for lunch? ### Person B: Totally! Where are you thinking?

This step requires some coding knowledge, usually in Python, to script the cleaning & formatting process.

Step 3: Choosing Your Base Model & Fine-Tuning

You're not going to train an LLM from scratch. That would cost millions. Instead, you'll fine-tune a pre-trained model. A popular choice for this is one of Meta's Llama models, which are open-source & come in various sizes (e.g., 7B or 13B parameters).

The fine-tuning itself is usually done using a technique called QLoRA (Quantized Low-Rank Adaptation). Without getting too technical, QLoRA is a memory-efficient way to fine-tune large models. It allows you to get great results without needing a supercomputer. In fact, many people do this using cloud-based services like Google Colab or Runpod, renting powerful GPUs for a relatively low cost. One project training a model on 1800s texts only cost around $25-$30 for the GPU time on Runpod.

You'll need a Hugging Face account to access the base models & a Weights and Biases account is super helpful for visualizing how your model is learning during the training process.

Step 4: Talking to Yourself - Evaluation

How do you know if it worked? Well, you talk to it! The evaluation for a personal LLM is pretty subjective. You can prime it with a message from a friend & see how it responds. Does it sound like you? Does it capture the dynamic of that specific relationship?

The creator of the 240k message model found that it was convincing more than half the time & could even mimic his friends if he had exchanged over 1,000 messages with them. That's pretty wild.

Path #2: The Historical Chronicler - Training an LLM on Texts from a Bygone Era

This is where things get even more interesting, in my opinion. What if you could train an LLM on books, letters, & newspapers from the 1800s? Or ancient Sumerian texts? Researchers & hobbyists are already doing this, & it opens up a whole new way to interact with history.

Step 1: Unearthing Your Historical Dataset

You can't just download this data from your phone. You'll need to find digitized historical texts. Luckily, there are some amazing resources out there:

Common Corpus: This is a HUGE, public domain dataset with 500 billion words from a wide range of cultural heritage sources. It includes millions of books & is multilingual, making it perfect for this kind of work.
Project Gutenberg: A classic source for out-of-copyright books.
Early English Books Online (EEBO) & Gallica: These are massive digital libraries with texts spanning from the 1400s to the 1700s.
The Internet Archive: One project aiming to train a model on texts from 1800-1875 London found around 175,000 texts available here.

Step 2: The Unique Challenges of Historical Data

Working with historical texts brings its own set of problems that you don't really face with modern messages.

OCR Errors: Many of these texts are scanned from physical books. The Optical Character Recognition (OCR) process, which turns images of text into actual text, isn't perfect. You might have to deal with weird characters, formatting issues, or garbled words. Tools like Transkribus are specifically designed to help with transcribing historical documents, but manual proofreading is often still necessary.
Archaic Language & Spelling: Language evolves. An LLM trained on modern English might get confused by the long 's' (ſ), different spellings ("publick" instead of "public"), or words that have completely different meanings now. This is one of the main reasons to fine-tune on this data—to teach the model these historical nuances.
Multilingual Texts: Historical documents, especially from Europe, can be a mix of languages. A project analyzing old fairground newspapers had to deal with this, training the LLM to recognize time-specific vocabulary across different languages.

Step 3: The Training Process - Full Pre-training vs. Fine-tuning

With historical texts, you have a choice. You can fine-tune a modern model, like with the personal chatbot, or you can take a more ambitious approach: full pre-training.

One fascinating project is attempting to train a model only on books from the 1800s. The goal is to see if a model trained this way can actually reason based on the knowledge of that time period. This is a much bigger undertaking & requires a massive dataset, but it's a way to create a more "pure" historical LLM.

For most people, though, fine-tuning a model like Mistral or Llama on a curated historical dataset is the way to go. The process is similar to the personal chatbot: format the data, choose a base model, & use a method like QLoRA for efficient training.

Step 4: Evaluating Your Historical AI

This is more complex than just "does it sound right?". You need to test if the model has actually absorbed the context of the time period.

Factual Recall: Ask it about historical events, people, or scientific knowledge from that era. A model trained on 19th-century texts shouldn't know about quantum mechanics, for example.
Conceptual Understanding: You can probe its understanding of the world. One project, MonadGPT, trained on texts from 1400-1700, was asked "What causes a sore throat?". Its answer would reflect the medical knowledge of that time, not modern science. This is a great way to see if it's genuinely "thinking" from a historical perspective.
Reference-Free Evaluation: You can also use LLM-as-a-judge, where another powerful LLM scores the output based on a rubric you define. For example, you could have it check for anachronisms or whether the tone is appropriate for the period.

The Elephant in the Room: Costs & Practicalities

Let's be real, this isn't free. But it's not astronomically expensive either, especially with efficient methods like LoRA.

APIs vs. Self-Hosting: Using an API like OpenAI's is the easiest way to start. Fine-tuning GPT-3.5 can cost as little as $0.008 per 1,000 tokens for training. The downside is you pay for every single use, which can add up.
Cloud GPUs: Renting a GPU on a service like AWS SageMaker or Runpod is a common middle ground. You might pay around $1.32/hour for a capable instance. Fine-tuning a 7B model might take a few hours & cost under $50 in compute time.
The Hidden Costs: The biggest cost is often not the compute, but the human effort. Data collection, cleaning, & annotation is where most of the work lies.

For businesses looking to leverage this kind of technology without the massive R&D overhead, this is again where a platform like Arsturn becomes so valuable. It handles the complexity of building & deploying custom AI chatbots, allowing businesses to focus on creating the best possible customer experience. Arsturn lets you train a chatbot on your own website content, documents, & knowledge base, providing instant, accurate, & 24/7 support to your visitors. It’s the practical application of this niche LLM technology, made accessible.

Final Thoughts

Training a niche LLM, whether it's a digital twin of yourself or a historical scholar, is an incredible way to see the power of AI up close. It's a journey that takes you through data science, history, & a bit of creative experimentation.

The tools & open-source models available today have put this capability into the hands of anyone with a bit of technical know-how & a lot of patience. It’s a field that’s moving incredibly fast, & the line between a generalist AI & a specialist AI is becoming a new frontier for innovation.

Hope this was a helpful look into what it takes to bring an LLM from a specific time period to life. It's a challenging but deeply rewarding process. Let me know what you think, or if you've tried a project like this yourself