What are the use of a model weight and parameters?

A model weight is used to decide what matters more when the model makes a decision.

That’s the only job of a weight.

Before we go further, one important clarification.

When people say weights or parameters, they are talking about the same thing.

They are just numbers learned by the model. Different words, same idea.

Now let us see one everyday example

You are deciding:

Should I carry an umbrella?

You look at three things:

Is it raining?

Is it cloudy?

Is it windy?

But you don’t treat them equally.

Rain matters a lot, Clouds matter a little, Wind barely matters

So in your head, you automatically do this:

Rain → very important
Clouds→ somewhat important
Wind → not very important

Those “importance levels” are weights.

They help you reach the right decision.

Now the same idea in LLMs

An AI model constantly answers one question:

What should come next?

Example:

“I love ___”

Possible next words:

• you
• food
• rain
• bugs

The model assigns numbers to each option:

you → high number
food → medium number
rain → low number
bugs → very low number

The word with the highest number is chosen.

👉 That is the use of weights:

to score options and pick the most likely one.

A weight is not a rule or a sentence stored in memory.

It is just a number — a small nudge — that makes one word slightly more likely than another.

Why weights are necessary

Without weights:

• every word would be treated the same
• the model would guess randomly
• language would make no sense

With weights:

• common patterns matter more
• rare or wrong patterns matter less
• predictions improve over time

How weights get their correct values

At first, weights are random → wrong answers.

Each time the model is wrong:

• it compares its answer with the correct one
• slightly adjusts the weights
• tries again

After millions of examples:

useful connections get stronger weights and useless ones get weaker weights.

This preference to adjust weight didn’t come from reasoning or understanding.

It came from practice — seeing similar sentences millions of times before.

Here, learning doesn’t mean understanding like a human.

It simply means numbers being adjusted based on past patterns.

So, If you remember just one thing from this blog, let it be this:

A weight, also called a parameter, is a number the model learned because it helped reduce mistakes.

All the impressive behavior we see later emerges from those numbers.

With this, it becomes clear why people say a model has 7 billion parameters — it simply means there are 7 billion learned numbers shaping its behavior.

So at its core, an LLM is not storing answers.

It is carrying a vast set of learned numbers that gently push it toward one word instead of another.

I hope I was able to explain this in a clear and simple way. Happy Learning!.

In the booming era of artificial intelligence, large language models (LLMs) are transforming how we interact with data. Yet, a challenge remains—how do we efficiently send structured data to these models without inflating token usage and cost? Traditionally, JSON (JavaScript Object Notation) has been the universal data format for transmitting information. But a rising alternative called TOON (Token-Oriented Object Notation) is redefining the game with clever token efficiency designed specifically for AI.

The JSON Legacy

JSON’s simplicity and flexibility made it the staple format for data exchange. Its key-value pairs, arrays, and nested objects suit a wide variety of applications—from APIs to configuration files. However, JSON’s verbosity, with repeated keys, braces, quotes, and commas, leads to bloated token counts when used as input for LLMs. Each token sent to an AI model costs time and money, and JSON often inflates that cost unnecessarily.
Example: Sending User Data in JSON
Consider this simple list of users:

{
“users”: [
{ “id”: 1, “name”: “Alice”, “role”: “admin” },
{ “id”: 2, “name”: “Bob”, “role”: “user” },
{ “id”: 3, “name”: “Carol”, “role”: “user” }
]
}

Notice how the keys “id”, “name”, and “role” repeat three times, and punctuation characters add to the overall size. Each of these elements consumes tokens and adds to the cost of processing.

TOON: Designed for LLMs

TOON simplifies this by cutting down on redundancy and structural overhead. It adopts a lightweight, tabular format that declares headers once, followed by rows of values. This approach minimizes tokens while keeping the data structured and easy to understand.

The Same User Data in TOON

users[3]{id,name,role}:
1,Alice,admin
2,Bob,user
3,Carol,user

With TOON, the keys are declared a single time, followed by compact rows of comma-separated values. No quotes, no braces, and no repeated keys—just the essential data.

Why TOON Excels in LLM Data Transmission

• Token Efficiency: TOON reduces token usage by 30–60%, significantly lowering API costs and speeding up model response time.
• Better Model Parsing: The explicit header row helps LLMs understand and process data with improved accuracy over verbose JSON.
• Human Readability: The tabular style of TOON makes data easier to read and debug in context, especially for large datasets.
• Ideal Use Cases: TOON shines with bulk, flat datasets like user lists, logs, product inventories, and analytics—where repetitive structures make JSON costly.

When to Use JSON

• For deeply nested or complex data structures requiring broad compatibility.
• When existing toolchains and APIs demand standard JSON.
• For irregular data that doesn’t fit TOON’s tabular format.

As LLMs continue to shape the future, TOON offers a smart, specialized alternative to JSON—one that optimizes token usage, reduces operational costs, and improves clarity for AI-powered workflows. By adopting TOON where appropriate, developers and AI practitioners can unlock faster, cheaper, and more accurate interactions with language models.
If you’re working with large, structured datasets in AI, the choice between JSON and TOON can have a material impact. TOON’s elegant design ensures your data travels light—perfectly tailored for the token-conscious world of large language models.