{"id":133998,"date":"2026-09-04T15:48:15","date_gmt":"2026-09-04T15:48:15","guid":{"rendered":"https:\/\/www.hostinger.com\/my\/tutorials\/how-to-train-an-llm\/"},"modified":"2026-09-04T15:48:15","modified_gmt":"2026-09-04T15:48:15","slug":"how-to-train-an-llm","status":"publish","type":"post","link":"\/my\/tutorials\/how-to-train-an-llm\/","title":{"rendered":"How to train an LLM"},"content":{"rendered":"

To train a large language model (LLM), you need to adjust its learned parameters by presenting it with tokenized text, measuring prediction error, and optimizing its weights. <\/p>

During training, the model predicts the next token, calculates a loss that measures prediction error, uses backpropagation to calculate gradients, and applies an optimizer to update its weights. That loop repeats across enormous numbers of token sequences.<\/p>

Reaching that point requires far more than running a training loop. Every stage of the process influences the final model, from preparing high-quality datasets and selecting a tokenizer to evaluating the model’s performance after training. <\/p>

There are two main routes. Training from scratch starts with randomly initialized weights and a very large pretraining corpus, while fine-tuning starts with an already pretrained model and adapts it to a narrower task, style, domain, or instruction set using much less data and compute.<\/p>

For most developers and small teams, fine-tuning a suitable open-source model is more practical than training a foundation model from scratch because it requires far less data, compute, time, and engineering effort. <\/p>

Training an LLM from scratch vs. fine-tuning<\/h2>

Training from scratch builds a language model from randomly initialized weights, while fine-tuning models modifies an already pretrained model for a specific task, behavior, or domain.<\/p>

Between these two approaches is continued pretraining<\/strong>, which extends a pretrained model’s knowledge before it is fine-tuned for a particular application.<\/p>

The table below compares the main differences between training an LLM from scratch and fine-tuning an existing model.<\/p>

Factor<\/strong><\/p><\/td>

Train from scratch<\/strong><\/p><\/td>

Fine-tune an existing LLM<\/strong><\/p><\/td><\/tr>

Starting point<\/strong><\/p><\/td>

Random model weights<\/span><\/p><\/td>

Pretrained model<\/span><\/p><\/td><\/tr>

Data required<\/strong><\/p><\/td>

Very large corpus<\/span><\/p><\/td>

Smaller task- or domain-specific dataset<\/span><\/p><\/td><\/tr>

Compute<\/strong><\/p><\/td>

Multi-GPU or distributed GPU training<\/span><\/p><\/td>

Often one or several GPUs<\/span><\/p><\/td><\/tr>

Cost<\/strong><\/p><\/td>

Very high<\/span><\/p><\/td>

Much lower<\/span><\/p><\/td><\/tr>

Time<\/strong><\/p><\/td>

Days to months<\/span><\/p><\/td>

Hours to days<\/span><\/p><\/td><\/tr>

Flexibility<\/strong><\/p><\/td>

Full control over architecture and training data<\/span><\/p><\/td>

Works within the existing architecture<\/span><\/p><\/td><\/tr>

Best for<\/strong><\/p><\/td>

Research labs, foundation model providers, AI companies building proprietary models<\/span><\/p><\/td>

Application developers, startups, enterprises, and AI teams customizing existing models<\/span><\/p><\/td><\/tr>

Main risk<\/strong><\/p><\/td>

High cost and training instability<\/span><\/p><\/td>

Overfitting or degrading general capabilities<\/span><\/p><\/td><\/tr><\/tbody><\/table><\/figure>

Training from scratch begins with an untrained model whose parameters are randomly initialized.<\/strong> <\/p>

At this stage, the large language model<\/a> has no learned language representation from data; it has no understanding of language, grammar, facts, or reasoning. <\/p>

It gradually learns statistical patterns by processing a massive text corpus, repeatedly predicting the next token in each sequence, measuring prediction errors, and adjusting its parameters over a very large number of training steps and token sequences. <\/p>

As the training progresses, the model learns statistical patterns in the data, enabling it to generate coherent text and perform a wide range of language tasks.<\/p>

Because every capability must be learned during this initial training stage, training from scratch requires enormous datasets, significant computing resources, and careful optimization. <\/p>

The result is a foundation model that can later be adapted to specific domains or tasks through continued pretraining or fine-tuning.<\/p>

Continued pretraining<\/strong> starts with an existing pretrained model and exposes it to additional text from a particular domain, such as medicine, law, finance, or software development. The objective is to expand the model’s knowledge before adapting it to a specific application.<\/p>

Fine-tuning<\/strong> also starts with a pretrained model, but the objective is different. Rather than teaching the model new general knowledge, fine-tuning adjusts its behavior for a particular task, output format, writing style, or set of instructions using a much smaller dataset.<\/p>

What happens when you train an LLM?<\/h2>

The LLM workflow starts with preparing text and training the model, then moves into evaluation, post-training, where needed, and deployment after the trained model has been produced. <\/p>

Between those stages, the model repeatedly learns from the training data by making predictions, measuring errors, and improving its internal parameters.<\/p>

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