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Model Types

Instruction-tuned Models

The chatbots you know, RLHF-trained to follow instructions reliably

What it is

Instruction-tuned models are base models that have been fine-tuned through RLHF (or related techniques) to reliably follow user instructions and behave like assistants. These are the models behind ChatGPT, Claude, and Gemini as most people experience them.

Post-training teaches the model to: interpret prompts as instructions rather than text to continue, format responses appropriately, apply safety training, and maintain a consistent assistant persona. The training data is relatively small compared to pre-training (often millions of examples rather than trillions of tokens) but it dramatically changes the model's behavior.

The "instruct" or "chat" suffix in model names (e.g., Llama-3-8B-Instruct) signals this post-training.

Why it matters

When choosing a model for a client project, you're almost always choosing between instruction-tuned models. Understanding what post-training does (and what it doesn't do) helps you set realistic expectations, understand failure modes, and make better prompting decisions.

Related concepts

Resources

Deep Dive into LLMs like ChatGPT (Post-training / SFT section)
youtube.com· Explains how conversation datasets are structured, how SFT transforms a base model into a chat assistant, and the ChatML format. Covers hallucinations, tool use, knowledge of self, "jagged intelligence," and why models need tokens to think. Includes live comparison of base vs instruction-tuned behavior.
20 min
How I Use LLMs
youtube.com· Practical guide showing the difference between model tiers and how instruction-tuned models behave in practice. Great complement to the theoretical SFT explanation.
131 min
What Is Instruction Tuning?
ibm.com· Authoritative overview from IBM. Covers the FLAN paper, how instruction datasets are structured, and why instruction tuning was the key breakthrough that enabled ChatGPT.
10 min
LLM Architectures: Base, Instruct, and Chat Models
medium.com· Concise breakdown of the three model types (base → instruct → chat) with clear analogies. Covers SFT and RLHF in the context of model progression.
8 min
Base LLM vs Instruction-Tuned LLM
analyticsvidhya.com· Side-by-side comparison with example prompts showing how the same question gets different responses from base vs instruct models. Includes the formula: Instruction Tuned LLM = Base LLM + SFT + RLHF. Quick reference.
10 min