If you are an engineering leader exploring LLMs, you have likely encountered a confusing naming convention on HuggingFace. You see Llama-3-8b (the Base model) and Llama-3-8b-Instruct.
What is the difference? Is it important? When to use each?
This article answers those questions with examples that are familiar to senior developers and engineering leaders.
Note: this content is primarily generated by AI (Gemini) and edited by me after understanding and verification.
A metaphor: lib vs app
For engineers accustomed to deterministic systems, the difference between these two model types is like the difference between a raw, unlinked library and a compiled, executable binary.
Here is the technical breakdown of what is actually happening under the hood, minus the AI hype.
1. The Base Model: lib
A Base Model (or Foundation Model) is the result of the pre-training phase. It has consumed terabytes of text and learned a statistical probability distribution. Its only function is: Given a sequence of tokens, predict the next most likely token.
It has no concept of “questions,” “answers,” or “instructions.” It only understands patterns.
Suppose you prompt the model
What is the capital of France?
The model analyzes the pattern. In its training data (internet forums, datasets, books), a list of questions often follows a question. It tries to minimize entropy by generating more questions.
It may then respond
And what is the population of Paris? What is the currency?
We can think of a base model as libc or a massive generic utility library:
It contains all the raw knowledge (functions, symbols, logic).
It has no entry point (
main()function).It has no opinion on how it should be used.
Use cases for Base Models
Code Completion: If you feed it
function calculateTax(amount) {, it naturally predicts the next lines of code because that pattern exists in its training data.Few-Shot Learning: You can “program” it by providing examples in the prompt, effectively forcing a pattern it can complete.
Fine-Tuning: This is the most critical use. You don’t deploy
libc; you build on top of it. You take a Base Model to fine-tune it on your proprietary data format (e.g., specialized medical records or legacy COBOL translation).
2. The Instruct Model: app
An Instruction Tuned Model is a Foundation Model (Base Model) that has gone through Post-Training.
This typically involves two distinct steps that map neatly to standard software development practices:
SFT: Supervised Fine-Tuning (OpenAI docs)
RLHF: Reinforcement Learning from Human Feedback
Step 1. SFT
Think of it as “unit testing” but for training AI models.
The base model is fed a massive dataset of (Instruction, Response) pairs.
It is then punished (mathematically, via loss functions) whenever it deviates from the expected response.
Loss function is a mathematical formula used to quantify the difference between a model's predicted output and the desired "ground truth" response from a training dataset.
This teaches the model a new behavior: When you see a prompt, do not autocomplete it. Execute it.
Step 2: RLHF
Think of it as “user acceptance testing” (UAT) but for training AI models.
Reinforcement Learning from Human Feedback (RLHF) aligns the model with human preference.
The model generates three possible answers.
A human (or a strong teacher model) ranks them: A > B > C.
The model updates its weights to maximize the reward (producing “A” type answers).
Note: this is when human biases and sycophancy creep in: “You are absolutely right”! 😄
When you use ChatGPT, Claude, or Gemini, you are interacting with an Instruct Model that follows your (and the AI vendor’s) orders.
When to use which?
If you are building an internal AI tool, you face a trade-off:
The “Wrapper” Approach (95% of use cases): Use an Instruct Model. You want a conversational agent that follows system prompts like “You are a helpful SRE assistant.” A Base model will ignore that system prompt and just autocomplete it.
The “Domain Expert” Approach (5% of use cases): If you need a model to speak a language no one else speaks (e.g., a proprietary internal query language), an Instruct Model might actually fight you. It has been “brainwashed” to be a helpful assistant. In this case, you take a Base Model and fine-tune it specifically on your query language, bypassing the “chatty assistant” behaviors entirely.
Fine-tuning is the process of taking a generalist base model and further training it using a smaller, specialized dataset to create a specialist such as a legal assistant, medical diagnostic tool, or branded customer service bot.
Recap
Base Model: Raw pattern matcher. Good for autocompletion and fine-tuning. Acts like a library.
Instruct Model: Fine-tuned for Q&A. Good for chat, reasoning, agentic workflows, and following orders. Acts like an app.
As you build out your AI capabilities, default to Instruct models for applications, and reserve Base models for when you need to compile your own proprietary “binaries” from scratch.