Instruct Model
A base language model that has been fine-tuned to follow instructions from users, as opposed to just completing text in the style of its training data.
A base language model trained only to predict the next token will complete text in the style of its training corpus — it won't answer questions or follow directions in the way a user expects. An instruct model has been additionally trained (typically via supervised fine-tuning on human-written instruction-response pairs and RLHF) to be helpful, harmless, and honest.
The transformation from base model to instruct model is what makes GPT-4, Claude, and Gemini useful in practice. The same underlying architecture that generates arbitrary text is shaped by instruction tuning into a model that answers questions, writes code, and follows safety guidelines.
Many open-weight model families release both base and instruct variants. The base model (e.g. Llama 3.3 70B) is preferred by researchers who want to apply their own fine-tuning. The instruct model (e.g. Llama 3.3 70B Instruct) is ready to use out of the box for most applications.
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.