Instruction-Tuned Models vs. Base Models
Base models predict text. Instruction-tuned models follow directions. Understanding the difference is fundamental to working with LLMs.
Key Takeaways
| Takeaway | Details |
|---|---|
| Base Models | Raw pretrained models that predict next tokens but don't follow instructions or have conversations. |
| Instruction Tuning | Fine-tunes base models on instruction-response pairs to teach question-answering and task-completion behavior. |
| Training Process | Modern instruction tuning combines supervised fine-tuning with reinforcement learning from human feedback. |
| Use Cases | Instruction-tuned models suit most applications while base models are better for custom fine-tuning research. |
| Fine-tuning Pipeline | Production systems typically follow base model → instruct model → fine-tuned instruct model progression. |
What Is a Base Model?
A base model (also called a pretrained or foundation model) is the raw output of pretraining: a model trained on massive text corpora to predict the next token. Base models have learned rich representations of language, facts, and reasoning patterns, but they aren't designed to follow instructions or have conversations.
If you prompt a base model with 'What is the capital of France?', it might continue the sentence in any direction: adding more questions, generating a quiz, or producing encyclopedia-style text. It's completing a document, not answering a query. Base models like GPT-3 before instruction tuning, Llama base weights, and Mistral base are useful starting points for fine-tuning but not for direct deployment.
How Instruction Tuning Works
Instruction tuning fine-tunes a base model on a dataset of (instruction, response) pairs, examples of humans asking for things and ideal assistant responses. This teaches the model to switch from document-completion mode to question-answering and task-completion mode. The resulting model can follow diverse instructions it has never seen before, generalizing from the instruction-following patterns it learned.
Modern instruction tuning typically combines supervised fine-tuning (SFT) with reinforcement learning from human feedback (RLHF) or similar preference-optimization techniques. SFT teaches basic instruction following; RLHF refines the model's outputs to be more helpful, harmless, and honest according to human preferences. This two-stage process is what produces the assistants you interact with today.
When to Use Each
Instruction-tuned models are right for most applications: chatbots, writing assistants, code tools, and anything where users give the model directions. Base models are useful when you want to fine-tune on your own data without the constraints of prior alignment, or when you're building research systems that need precise control over model behavior.
One common confusion: you can further fine-tune an instruction-tuned model. Companies like OpenAI and Anthropic offer fine-tuning APIs for their instruct models, allowing you to specialize behavior without starting from scratch. The base model → instruct model → fine-tuned instruct model pipeline is how most production AI systems are built.
Read next
RLHF: How AI Models Learn to Be Helpful
Reinforcement Learning from Human Feedback, the training technique behind ChatGPT and Claude that shaped modern AI assistants to be helpful, harmless, and honest.
Fine-Tuning LLMs: When and How to Specialize AI Models
A practical guide to fine-tuning large language models, what it achieves, when it's worth the effort, the most popular methods (LoRA, SFT, RLHF), and how to evaluate results.
What Is a Large Language Model?
A clear, jargon-free introduction to large language models, what they are, how they work at a high level, and why they're transforming software and society.