How To Train an LLM on Your Own Data in 6 Steps

Out-of-the-box large language models (LLM) like GPT-4 and LLaMA are trained on vast amounts of general information, but they know nothing about your company’s unique communication policies and rhythms.

To create a model that speaks your language and delivers far more relevant results, you need to train it on your own data. Thankfully, the process isn’t as complex as it sounds, but it does require careful planning. You’ll need clean, structured data, the right model for your specific use case, and a solid training strategy.

Here’s everything you need to know about transforming your LLM from a know-it-all into a specialist that understands your language and audience.

How LLMs Learn: The Building Blocks of Training

LLMs don’t “think” like humans (at least not yet). They don’t truly absorb knowledge or come to conclusions on their own. Instead, they crunch through mountains of text, spotting patterns, predicting words, and refining responses based on probability.

At the core, LLMs learn through three key phases:

1. Pretraining: The model digests massive amounts of general text, learning grammar, sentence structure, and common knowledge. Think of this as reading the entire internet without remembering who wrote what.
2. Fine-Tuning: The model is enriched with specific data to specialize in a particular domain. This is where it picks up your company’s style, product details, and other industry-specific language, aka jargon.
3. Reinforcement Learning (RLHF): Humans step in, ranking responses to improve quality and align the model with useful answers. This exercise drastically reduces the occurrence of irrelevant replies.

The more relevant the training data, the better the results. Clean, well-structured information gets you a model that speaks your language, while messy data is akin to training a chef on random recipes scribbled on napkins.

Getting Your Data Ready: What Goes In Comes Out

To paint a picture, training an LLM is like cooking. Your dish is only as good as the ingredients. So when you give your model messy, inconsistent, or biased data, you’ll get unpredictable (or downright useless) responses.

But with well-prepared training data, your LLM delivers reliable, context-aware answers that actually make sense. Let’s take a closer look.

What Kind of Data Works Best?

Think of training data as everything your model needs to “read” to understand your domain. Here are the most useful sources:

• Text-Based Data: Internal documents, knowledge bases, research papers, blog posts, product descriptions, and FAQs. These help the model understand formal and structured language.
• Conversational Data: Customer support chats, email interactions, call transcripts. These train the model to grasp natural conversation flow, tone, and user intent.
• Structured Data: Spreadsheets, logs, API responses. While LLMs thrive on text, structured data can help fine-tune them for specific workflows and automations.

How to Clean Your Data

If you’ve ever tried searching for a file on your computer and found five outdated versions, you already understand why data cleaning matters. Your LLM needs clean, relevant data to avoid generating misleading or outdated responses.

Here’s how to refine your dataset:

• Remove duplicates: Redundant data skews learning and increases training time. If an email thread exists in five places, keep only the most relevant version.
• Filter out irrelevant content: Junk data like (error logs, random forum posts, or out-of-context snippets) adds noise and dilutes model accuracy.
• Fix formatting inconsistencies: Convert text to lowercase where needed, remove unnecessary spaces, and ensure uniform punctuation. This prevents the model from treating minor variations as entirely different inputs.
• Scrub sensitive or biased data: If your dataset includes confidential information or biased perspectives, it’s highly recommended that you strip them out. Otherwise, your LLM might unknowingly reinforce bad practices.
• Annotate when necessary: For supervised learning, clearly label data points to guide the model’s behavior (e.g., tagging emails as “positive” or “negative” feedback).

Preparing data may not be glamorous, but it’s the difference between an AI assistant that understands your world and one that makes things up. With your data cleaned, it’s time to choose your model.

Picking the Right Model and Prepping Your Training Setup

LLMs are notably diverse, and so are the environments that train them. Some models are ready to use out of the box, while others need customization. Similarly, some training setups can work well on your everyday laptop, but most require serious hardware. Let’s take a closer look.

Which Model Should You Use?

Broadly speaking, you have two options when deciding on your LLM type: open-source models and proprietary models (also known as closed-source AI).

Open-source models give you control over fine-tuning and deployment, as they’re usually under permissive licenses like Apache 2.0 or MIT. Popular choices include LLaMA (Meta), Falcon (TII), and Mistral. While flexible and often cost-effective, open-source LLMs require some degree of technical expertise.

What’s more, the bigger the model, the smarter it could be, but you’ll pay for that in GPU costs. Even so, smaller models (7B or 13B) often surprise with how capable they are, especially with good data.

Proprietary models, on the other hand, offer powerful capabilities out of the box. You’re essentially renting their brains and tweaking the last few layers. Popular picks today include GPT (OpenAI) and Claude (Anthropic). They’re easy to use but often come with API costs and usage restrictions.

A few other factors to consider:

• Adaptability: Some models handle fine-tuning better than others. Consider models that support parameter-efficient tuning techniques like LoRA (Low-Rank Adaptation) to keep costs down.
• Framework Support: Ensure compatibility with Hugging Face, PyTorch, TensorFlow, or other relevant training libraries for your use case. These make handling datasets, fine-tuning, and AI inference much smoother.
• Licensing: Last but not least, open-source models may have restrictions on commercial use. Check the fine print before choosing.

Ultimately, your choice depends on what you need the LLM to do, what you can afford, and how much control you want.

Setting Up Your Training Environment

Your hardware and software choices dictate how smoothly training runs. Training LLMs requires powerful GPUs and a robust data pipeline.

AMD’s MI300X and MI325X GPU accelerators are great choices here, as they come with high memory capacity and processing power for AI workloads. For memory, a 7B model might need 24GB+ VRAM, while a 65B model can push 512GB+.

Another key consideration is the cloud vs. on-premise debate. Running LLMs on physical hardware is expensive and complex but gives you full control. In contrast, specialized AI cloud solutions provide scalability, flexibility, and cost efficiency.

Training decent LLMs requires high-performance infrastructure, and TensorWave delivers just that. Powered by next-gen AMD accelerators, TensorWave offers memory-optimized, scalable AI training solutions.

Whether you’re fine-tuning a small model or handling massive workloads, TensorWave’s cloud platform guarantees stellar efficiency and cost savings without the headaches of managing hardware. Get in touch today.

Step-by-Step Guide to Training an LLM on Your Own Data

As mentioned, training an LLM on your data requires careful planning, clean data, and the right techniques to get meaningful results. Here’s a step-by-step breakdown to guide you through it:

Select and Load Your Base Model

Your model’s foundation matters more than you might think. Imagine picking a language translator who already speaks multiple languages but needs to learn your specific dialect. So while base models like LLaMA and GPT come with pre-existing knowledge, they need your specific instructions.

Open-source models come as massive files (a 7B model is about 15GB) that need specific software to unpack. When loading your model, you’ll use libraries like Hugging Face Transformers, which act like a universal adapter for AI models.

Key considerations at this stage include model size, pre-training domain, and computational requirements. A smaller model might train faster, but a larger one could capture more nuanced information.

Conduct Data Preprocessing and Tokenization

Tokenization is your model’s translation dictionary. It breaks down raw text into digestible chunks that machines can understand. Libraries like SentencePiece and Byte-Pair Encoding (BPE) don’t just split words—they create intelligent mappings that help your model recognize language patterns (like “New York” becoming one token instead of two).

Think of tokenization like creating a detailed map. Each token is a landmark, helping your model navigate through complex linguistic terrain. You’ll want to:

• Standardize text formatting
• Remove irrelevant characters
• Create consistent token representations
• Handle out-of-vocabulary words intelligently

Your preprocessing pipeline should handle variations in text, from technical documentation to conversational language. This means creating robust tokenization strategies that can adapt to different writing styles while maintaining semantic integrity.

Train the Model

Once your data is ready, it’s time to train the model. This involves:

• Setting hyperparameters: Learning rate, batch size, and number of training cycles (epochs) need careful tuning. Too high, and the model becomes unstable; too low, and learning is slow.
• Using mixed precision: This speeds up training and reduces memory usage without losing accuracy.
• Monitoring loss and accuracy: If loss doesn’t decrease, your model isn’t learning properly. If it drops too low, the model may be memorizing data instead of generalizing.

Training requires patience. You’ll need to continuously adjust settings based on early results to avoid overfitting (where the model performs well on training data but poorly on real-world inputs).

Choose Your Fine-Tuning Strategy

Your fine-tuning approach depends on three critical factors: your computational resources, dataset size, and specific performance goals.

Depending on these factors, you’ll need to decide how much control you want over the model’s behavior:

• Full fine-tuning: Best when adapting an LLM to a highly specialized domain, but it requires more data and computing power.
• LoRA/QLoRA (Quantized LoRA): Efficient and cost-effective. Instead of retraining the whole model, it adjusts only specific layers, keeping the rest intact.
• Instruction tuning: If you’re building a chatbot, this method teaches the model to respond in specific ways by providing structured examples. It’s like training a multilingual interpreter to understand not just words, but context and intent.
• Reinforcement learning with human feedback (RLHF): This is used for refining responses based on human preferences. While resource-intensive, RLHF improves model reliability.

Evaluate Your LLM Performance

Evaluating your trained LLM is like taking a prototype sports car through rigorous track testing. You're not just checking if it runs: you're measuring its precision, consistency, and real-world performance.

In practice, you’ll need to check benchmark scores like:

• Perplexity: Lower scores mean the model predicts words better.
• BLEU & ROUGE scores: These are typically used in translation and summarization tasks. They gauge the model’s ability to generate coherent, contextually relevant text.

Remember that automated metrics don’t capture everything. Manually testing responses helps ensure clarity and relevance. If the model produces incorrect or biased responses, further fine-tuning or data adjustments may be required.

A few common performance issues to watch for include:

• Hallucinations (generating false or imaginary information)
• Inconsistent response quality
• Domain-specific knowledge gaps
• Unexpected behavioral quirks

Deploy and Continuously Improve

Training isn’t the end. Once your LLM is deployed, it’s time to monitor:

• User feedback: Are responses accurate and useful?
• Drift detection: Over time, models can degrade if data patterns change.
• Retraining schedules: Regular updates keep the model relevant.

The best LLMs today thrive on continuous refinement. The more you test and adjust, the better it performs in real-world scenarios.

Key Takeaways

Large language models know a lot; just not the things you care about. GPT-4 may explain quantum physics easily, but can it summarize your team’s meeting notes in your CEO’s signature blunt style? Probably not. That’s where LLM training comes in.

To recap:

• Training an LLM on your own data is like teaching a brilliant but clueless intern: they’ve got the brains, but they need your files, emails, and docs to actually be useful.
• A well-structured dataset helps a ton, but so does hardware that can handle the load. GPUs with high VRAM, efficient tokenization, and fine-tuning techniques like LoRA help make training feasible without breaking the bank.
• LLMs decay without care. Budget and plan for monitoring, updates, and the occasional full retrain to keep your models effective.

Successfully training LLMs at scale starts with having the right foundation. TensorWave simplifies the process with scalable, memory-optimized AI infrastructure powered by next-gen AMD accelerators.

Whether you’re training a small model from scratch or fine-tuning a massive one, TensorWave gives you the computational muscle to do it justice. Scale smarter with TensorWave.